diff --git a/README.MD b/README.MD
index 3d2db34..1000367 100644
--- a/README.MD
+++ b/README.MD
@@ -1,6 +1,7 @@
-# Assignment 
+# <img src="docs/sources/assets/images/logo.png" style="height:2em; vertical-align: middle;">
+Customer churn predictor for an assignment below.
 
-**DISCLAIMER**: please see the [online documentation](https://5uperpalo.github.io/ecovadis_assignment/)
+Documentation: https://5uperpalo.github.io/ecovadis_assignment/
 
 ```
 In this assignment you're tasked with developing a machine learning solution for churn prediction to identify which customers are likely to leave a service (column "Exited" in the attached dataset). This assignment is meant to assess
@@ -11,8 +12,6 @@ In this assignment you're tasked with developing a machine learning solution for
 
 Please note that multiple solutions may exist and we do not expect a production ready solution, though any reflections on how you may wish to productionalise your solution are welcome. You are free to choose the medium (e.g., notebooks, python scripts). 
 
-Please return your solution by May 2 (9am CET). Once returned, our colleagues with the HR department will schedule the final interview where you'll be asked to walk us through your solution and reflect on any decisions made.
-
 Additional explanation of independent variables:
 
 NumberOfProducts - the number of accounts and bank-affiliated products 
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index c5180ed..9c6200b 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -1,6 +1,6 @@
-site_name: ecovadis_assignment
-repo_name: ecovadis_assignment
-repo_url: https://github.com/5uperpalo/ecovadis_assignment
+site_name: churnpred
+repo_name: churnpred
+repo_url: https://github.com/5uperpalo/churnpred
 copyright: Pavol Mulinka
 docs_dir: sources
 site_url: 
@@ -30,7 +30,7 @@ nav:
     - contributing: contributing.md
 
 theme:
-    logo: assets/images/ecovadis_green_logo.png
+    logo: assets/images/logo.png
     name: material
     custom_dir: sources/overrides
 
@@ -43,14 +43,14 @@ theme:
 
     palette:
     - scheme: default
-      primary: green
-      accent: teal
+      primary: purple
+      accent: pink
       toggle:
         icon: material/brightness-7
         name: Switch to dark mode
     - scheme: slate
-      primary: green
-      accent: teal
+      primary: purple
+      accent: pink
       toggle:
         icon: material/brightness-4
         name: Switch to light mode
diff --git a/docs/site/404.html b/docs/site/404.html
index 1f50680..a876852 100644
--- a/docs/site/404.html
+++ b/docs/site/404.html
@@ -8,29 +8,23 @@
       
       
       
-      
-      
-      
       <link rel="icon" href="/assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>ecovadis_assignment</title>
+        <title>churnpred</title>
       
     
     
-      <link rel="stylesheet" href="/assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="/assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="/assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="/assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
-
-
-    
     
-      
     
     
       
@@ -46,13 +40,12 @@
     
       <link rel="stylesheet" href="/stylesheets/extra.css">
     
-    <script>__md_scope=new URL("/",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("/",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
-    
   </head>
   
   
@@ -63,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -81,22 +77,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="/index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="/index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="/assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="/assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -109,39 +104,31 @@
       </div>
     </div>
     
-      
-        <form class="md-header__option" data-md-component="palette">
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
-    
-      <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
-      </label>
-    
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
-    
-      <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
-      </label>
-    
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-</form>
-      
-    
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-      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -150,15 +137,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -170,7 +154,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -179,13 +163,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -195,95 +179,60 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="/index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="/index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="/installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="/installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="/churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="/churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
         
   
   
-  
-    
-    
-      <li class="md-tabs__item">
-        <a href="/notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="/cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="/notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
       </a>
     </li>
   
@@ -292,35 +241,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="/code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="/cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="/contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="/code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="/contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -345,7 +296,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -353,22 +303,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="/index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="/index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="/assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="/assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
     </div>
@@ -377,19 +327,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="/index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
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+      <script src="/assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
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index 85449ec..a65078d 100644
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Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let s=i.nextNode();s;s=i.nextNode())if((a=s.parentElement)!=null&&a.offsetHeight){let c=s.textContent,f=n(c);f.length>c.length&&o.set(s,f)}for(let[s,c]of o){let{childNodes:f}=L("span",null,c);s.replaceWith(...Array.from(f))}return{ref:e,nodes:o}}))}function Ls(e,{viewport$:t,main$:r}){let n=e.parentElement,o=n.offsetTop-n.parentElement.offsetTop;return Y([r,t]).pipe(m(([{offset:i,height:a},{offset:{y:s}}])=>(a=a+Math.min(o,Math.max(0,s-i))-o,{height:a,locked:s>=i+o})),K((i,a)=>i.height===a.height&&i.locked===a.locked))}function tn(e,n){var o=n,{header$:t}=o,r=cn(o,["header$"]);let i=Q(".md-sidebar__scrollwrap",e),{y:a}=ze(i);return j(()=>{let s=new _;return s.pipe($e(0,Te),ge(t)).subscribe({next([{height:c},{height:f}]){i.style.height=`${c-2*a}px`,e.style.top=`${f}px`},complete(){i.style.height="",e.style.top=""}}),Ls(e,r).pipe(O(c=>s.next(c)),C(()=>s.complete()),m(c=>U({ref:e},c)))})}function Ri(e,t){if(typeof t!="undefined"){let r=`https://api.github.com/repos/${e}/${t}`;return Ot(ke(`${r}/releases/latest`).pipe(ce(()=>k),m(n=>({version:n.tag_name})),je({})),ke(r).pipe(ce(()=>k),m(n=>({stars:n.stargazers_count,forks:n.forks_count})),je({}))).pipe(m(([n,o])=>U(U({},n),o)))}else{let r=`https://api.github.com/users/${e}`;return ke(r).pipe(m(n=>({repositories:n.public_repos})),je({}))}}function ki(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return ke(r).pipe(ce(()=>k),m(({star_count:n,forks_count:o})=>({stars:n,forks:o})),je({}))}function Hi(e){let[t]=e.match(/(git(?:hub|lab))/i)||[];switch(t.toLowerCase()){case"github":let[,r,n]=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);return Ri(r,n);case"gitlab":let[,o,i]=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i);return ki(o,i);default:return k}}var As;function Cs(e){return As||(As=j(()=>{let t=__md_get("__source",sessionStorage);return 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s.hash="",history.replaceState({},"",`${s}`)}),ks(e,{viewport$:t,header$:r}).pipe(O(a=>o.next(a)),C(()=>o.complete()),m(a=>U({ref:e},a)))})}function Hs(e,{viewport$:t,main$:r,target$:n}){let o=t.pipe(m(({offset:{y:a}})=>a),Me(2,1),m(([a,s])=>a>s&&s>0),K()),i=r.pipe(m(({active:a})=>a));return Y([i,o]).pipe(m(([a,s])=>!(a&&s)),K(),Z(n.pipe(Le(1))),jr(!0),_t({delay:250}),m(a=>({hidden:a})))}function ji(e,{viewport$:t,header$:r,main$:n,target$:o}){let i=new _,a=i.pipe(fe(1));return i.subscribe({next({hidden:s}){e.hidden=s,s?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(Z(a),J("height")).subscribe(({height:s})=>{e.style.top=`${s+16}px`}),Hs(e,{viewport$:t,main$:n,target$:o}).pipe(O(s=>i.next(s)),C(()=>i.complete()),m(s=>U({ref:e},s)))}function Fi({document$:e,tablet$:t}){e.pipe(x(()=>B(".md-toggle--indeterminate, 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n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?t.insertBefore(this.previousSibling,n):t.replaceChild(n,this)}}}));document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var et=bo(),br=Co(),At=Io(),rn=Ao(),Ee=No(),vr=qr("(min-width: 960px)"),Vi=qr("(min-width: 1220px)"),Ni=$o(),zi=he(),qi=document.forms.namedItem("search")?(__search==null?void 0:__search.index)||ke(new URL("search/search_index.json",zi.base)):xe,nn=new _;vi({alert$:nn});oe("navigation.instant")&&gi({document$:et,location$:br,viewport$:Ee});var Wi;((Wi=zi.version)==null?void 0:Wi.provider)==="mike"&&Oi({document$:et});A(br,At).pipe(Fe(125)).subscribe(()=>{qe("drawer",!1),qe("search",!1)});rn.pipe(M(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=pe("[href][rel=prev]");typeof t!="undefined"&&t.click();break;case"n":case".":let r=pe("[href][rel=next]");typeof r!="undefined"&&r.click();break}});Fi({document$:et,tablet$:vr});Ui({document$:et});Di({viewport$:Ee,tablet$:vr});var Qe=li(we("header"),{viewport$:Ee}),hr=et.pipe(m(()=>we("main")),x(e=>hi(e,{viewport$:Ee,header$:Qe})),X(1)),Is=A(...ae("dialog").map(e=>pi(e,{alert$:nn})),...ae("header").map(e=>mi(e,{viewport$:Ee,header$:Qe,main$:hr})),...ae("palette").map(e=>bi(e)),...ae("search").map(e=>Ai(e,{index$:qi,keyboard$:rn})),...ae("source").map(e=>Pi(e))),$s=j(()=>A(...ae("content").map(e=>ui(e,{target$:At,print$:Ni})),...ae("content").map(e=>oe("search.highlight")?Ci(e,{index$:qi,location$:br}):k),...ae("header-title").map(e=>di(e,{viewport$:Ee,header$:Qe})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Qr(Vi,()=>tn(e,{viewport$:Ee,header$:Qe,main$:hr})):Qr(vr,()=>tn(e,{viewport$:Ee,header$:Qe,main$:hr}))),...ae("tabs").map(e=>Ii(e,{viewport$:Ee,header$:Qe})),...ae("toc").map(e=>$i(e,{viewport$:Ee,header$:Qe,target$:At})),...ae("top").map(e=>ji(e,{viewport$:Ee,header$:Qe,main$:hr,target$:At})))),Qi=et.pipe(x(()=>$s),Ze(Is),X(1));Qi.subscribe();window.document$=et;window.location$=br;window.target$=At;window.keyboard$=rn;window.viewport$=Ee;window.tablet$=vr;window.screen$=Vi;window.print$=Ni;window.alert$=nn;window.component$=Qi;})();
+//# sourceMappingURL=bundle.6c7ad80a.min.js.map
+
diff --git a/docs/site/assets/javascripts/bundle.6c7ad80a.min.js.map b/docs/site/assets/javascripts/bundle.6c7ad80a.min.js.map
new file mode 100644
index 0000000..1a973ec
--- /dev/null
+++ b/docs/site/assets/javascripts/bundle.6c7ad80a.min.js.map
@@ -0,0 +1,8 @@
+{
+  "version": 3,
+  "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/url-polyfill/url-polyfill.js", "node_modules/rxjs/node_modules/tslib/tslib.js", "node_modules/clipboard/dist/clipboard.js", "node_modules/escape-html/index.js", "node_modules/array-flat-polyfill/index.mjs", "src/assets/javascripts/bundle.ts", "node_modules/unfetch/polyfill/index.js", "node_modules/rxjs/node_modules/tslib/modules/index.js", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", "node_modules/rxjs/src/internal/util/errorContext.ts", "node_modules/rxjs/src/internal/Subscriber.ts", "node_modules/rxjs/src/internal/symbol/observable.ts", "node_modules/rxjs/src/internal/util/identity.ts", "node_modules/rxjs/src/internal/util/pipe.ts", "node_modules/rxjs/src/internal/Observable.ts", "node_modules/rxjs/src/internal/util/lift.ts", "node_modules/rxjs/src/internal/operators/OperatorSubscriber.ts", "node_modules/rxjs/src/internal/scheduler/animationFrameProvider.ts", "node_modules/rxjs/src/internal/util/ObjectUnsubscribedError.ts", "node_modules/rxjs/src/internal/Subject.ts", "node_modules/rxjs/src/internal/scheduler/dateTimestampProvider.ts", "node_modules/rxjs/src/internal/ReplaySubject.ts", "node_modules/rxjs/src/internal/scheduler/Action.ts", "node_modules/rxjs/src/internal/scheduler/intervalProvider.ts", "node_modules/rxjs/src/internal/scheduler/AsyncAction.ts", "node_modules/rxjs/src/internal/Scheduler.ts", "node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", "node_modules/rxjs/src/internal/observable/of.ts", "node_modules/rxjs/src/internal/observable/throwError.ts", "node_modules/rxjs/src/internal/util/isDate.ts", "node_modules/rxjs/src/internal/operators/map.ts", "node_modules/rxjs/src/internal/util/mapOneOrManyArgs.ts", "node_modules/rxjs/src/internal/util/argsArgArrayOrObject.ts", "node_modules/rxjs/src/internal/util/createObject.ts", "node_modules/rxjs/src/internal/observable/combineLatest.ts", "node_modules/rxjs/src/internal/operators/mergeInternals.ts", "node_modules/rxjs/src/internal/operators/mergeMap.ts", "node_modules/rxjs/src/internal/operators/mergeAll.ts", "node_modules/rxjs/src/internal/operators/concatAll.ts", "node_modules/rxjs/src/internal/observable/concat.ts", "node_modules/rxjs/src/internal/observable/defer.ts", "node_modules/rxjs/src/internal/observable/fromEvent.ts", "node_modules/rxjs/src/internal/observable/fromEventPattern.ts", "node_modules/rxjs/src/internal/observable/timer.ts", "node_modules/rxjs/src/internal/observable/merge.ts", "node_modules/rxjs/src/internal/observable/never.ts", "node_modules/rxjs/src/internal/util/argsOrArgArray.ts", "node_modules/rxjs/src/internal/operators/filter.ts", "node_modules/rxjs/src/internal/observable/zip.ts", "node_modules/rxjs/src/internal/operators/audit.ts", "node_modules/rxjs/src/internal/operators/auditTime.ts", "node_modules/rxjs/src/internal/operators/bufferCount.ts", "node_modules/rxjs/src/internal/operators/catchError.ts", "node_modules/rxjs/src/internal/operators/scanInternals.ts", "node_modules/rxjs/src/internal/operators/combineLatest.ts", "node_modules/rxjs/src/internal/operators/combineLatestWith.ts", "node_modules/rxjs/src/internal/operators/concatMap.ts", "node_modules/rxjs/src/internal/operators/debounceTime.ts", "node_modules/rxjs/src/internal/operators/defaultIfEmpty.ts", "node_modules/rxjs/src/internal/operators/take.ts", "node_modules/rxjs/src/internal/operators/ignoreElements.ts", "node_modules/rxjs/src/internal/operators/mapTo.ts", "node_modules/rxjs/src/internal/operators/delayWhen.ts", "node_modules/rxjs/src/internal/operators/delay.ts", "node_modules/rxjs/src/internal/operators/distinctUntilChanged.ts", "node_modules/rxjs/src/internal/operators/distinctUntilKeyChanged.ts", "node_modules/rxjs/src/internal/operators/endWith.ts", "node_modules/rxjs/src/internal/operators/finalize.ts", "node_modules/rxjs/src/internal/operators/takeLast.ts", "node_modules/rxjs/src/internal/operators/merge.ts", "node_modules/rxjs/src/internal/operators/mergeWith.ts", "node_modules/rxjs/src/internal/operators/repeat.ts", "node_modules/rxjs/src/internal/operators/sample.ts", "node_modules/rxjs/src/internal/operators/scan.ts", "node_modules/rxjs/src/internal/operators/share.ts", "node_modules/rxjs/src/internal/operators/shareReplay.ts", "node_modules/rxjs/src/internal/operators/skip.ts", "node_modules/rxjs/src/internal/operators/skipUntil.ts", "node_modules/rxjs/src/internal/operators/startWith.ts", "node_modules/rxjs/src/internal/operators/switchMap.ts", "node_modules/rxjs/src/internal/operators/takeUntil.ts", "node_modules/rxjs/src/internal/operators/takeWhile.ts", "node_modules/rxjs/src/internal/operators/tap.ts", "node_modules/rxjs/src/internal/operators/throttle.ts", "node_modules/rxjs/src/internal/operators/throttleTime.ts", "node_modules/rxjs/src/internal/operators/withLatestFrom.ts", "node_modules/rxjs/src/internal/operators/zip.ts", "node_modules/rxjs/src/internal/operators/zipWith.ts", "src/assets/javascripts/browser/document/index.ts", "src/assets/javascripts/browser/element/_/index.ts", "src/assets/javascripts/browser/element/focus/index.ts", "src/assets/javascripts/browser/element/offset/_/index.ts", "src/assets/javascripts/browser/element/offset/content/index.ts", "node_modules/resize-observer-polyfill/dist/ResizeObserver.es.js", "src/assets/javascripts/browser/element/size/_/index.ts", "src/assets/javascripts/browser/element/size/content/index.ts", "src/assets/javascripts/browser/element/visibility/index.ts", "src/assets/javascripts/browser/toggle/index.ts", "src/assets/javascripts/browser/keyboard/index.ts", "src/assets/javascripts/browser/location/_/index.ts", "src/assets/javascripts/utilities/h/index.ts", "src/assets/javascripts/utilities/string/index.ts", "src/assets/javascripts/browser/location/hash/index.ts", "src/assets/javascripts/browser/media/index.ts", "src/assets/javascripts/browser/request/index.ts", "src/assets/javascripts/browser/script/index.ts", "src/assets/javascripts/browser/viewport/offset/index.ts", "src/assets/javascripts/browser/viewport/size/index.ts", "src/assets/javascripts/browser/viewport/_/index.ts", "src/assets/javascripts/browser/viewport/at/index.ts", "src/assets/javascripts/browser/worker/index.ts", "src/assets/javascripts/_/index.ts", "src/assets/javascripts/components/_/index.ts", "src/assets/javascripts/components/content/code/_/index.ts", "src/assets/javascripts/templates/annotation/index.tsx", "src/assets/javascripts/templates/clipboard/index.tsx", "src/assets/javascripts/templates/search/index.tsx", "src/assets/javascripts/templates/source/index.tsx", "src/assets/javascripts/templates/tabbed/index.tsx", "src/assets/javascripts/templates/table/index.tsx", "src/assets/javascripts/templates/version/index.tsx", "src/assets/javascripts/components/content/annotation/_/index.ts", "src/assets/javascripts/components/content/annotation/list/index.ts", "src/assets/javascripts/components/content/code/mermaid/index.ts", "src/assets/javascripts/components/content/details/index.ts", "src/assets/javascripts/components/content/table/index.ts", "src/assets/javascripts/components/content/tabs/index.ts", "src/assets/javascripts/components/content/_/index.ts", "src/assets/javascripts/components/dialog/index.ts", "src/assets/javascripts/components/header/_/index.ts", "src/assets/javascripts/components/header/title/index.ts", "src/assets/javascripts/components/main/index.ts", "src/assets/javascripts/components/palette/index.ts", "src/assets/javascripts/integrations/clipboard/index.ts", "src/assets/javascripts/integrations/sitemap/index.ts", "src/assets/javascripts/integrations/instant/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/transform/index.ts", "src/assets/javascripts/integrations/search/worker/message/index.ts", "src/assets/javascripts/integrations/search/worker/_/index.ts", "src/assets/javascripts/integrations/version/index.ts", "src/assets/javascripts/components/search/query/index.ts", "src/assets/javascripts/components/search/result/index.ts", "src/assets/javascripts/components/search/share/index.ts", "src/assets/javascripts/components/search/suggest/index.ts", "src/assets/javascripts/components/search/_/index.ts", "src/assets/javascripts/components/search/highlight/index.ts", "src/assets/javascripts/components/sidebar/index.ts", "src/assets/javascripts/components/source/facts/github/index.ts", "src/assets/javascripts/components/source/facts/gitlab/index.ts", "src/assets/javascripts/components/source/facts/_/index.ts", "src/assets/javascripts/components/source/_/index.ts", "src/assets/javascripts/components/tabs/index.ts", "src/assets/javascripts/components/toc/index.ts", "src/assets/javascripts/components/top/index.ts", "src/assets/javascripts/patches/indeterminate/index.ts", "src/assets/javascripts/patches/scrollfix/index.ts", "src/assets/javascripts/patches/scrolllock/index.ts", "src/assets/javascripts/polyfills/index.ts"],
+  "sourceRoot": "../../../..",
+  "sourcesContent": ["(function (global, factory) {\n  typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n  typeof define === 'function' && define.amd ? define(factory) :\n  (factory());\n}(this, (function () { 'use strict';\n\n  /**\n   * Applies the :focus-visible polyfill at the given scope.\n   * A scope in this case is either the top-level Document or a Shadow Root.\n   *\n   * @param {(Document|ShadowRoot)} scope\n   * @see https://github.com/WICG/focus-visible\n   */\n  function applyFocusVisiblePolyfill(scope) {\n    var hadKeyboardEvent = true;\n    var hadFocusVisibleRecently = false;\n    var hadFocusVisibleRecentlyTimeout = null;\n\n    var inputTypesAllowlist = {\n      text: true,\n      search: true,\n      url: true,\n      tel: true,\n      email: true,\n      password: true,\n      number: true,\n      date: true,\n      month: true,\n      week: true,\n      time: true,\n      datetime: true,\n      'datetime-local': true\n    };\n\n    /**\n     * Helper function for legacy browsers and iframes which sometimes focus\n     * elements like document, body, and non-interactive SVG.\n     * @param {Element} el\n     */\n    function isValidFocusTarget(el) {\n      if (\n        el &&\n        el !== document &&\n        el.nodeName !== 'HTML' &&\n        el.nodeName !== 'BODY' &&\n        'classList' in el &&\n        'contains' in el.classList\n      ) {\n        return true;\n      }\n      return false;\n    }\n\n    /**\n     * Computes whether the given element should automatically trigger the\n     * `focus-visible` class being added, i.e. whether it should always match\n     * `:focus-visible` when focused.\n     * @param {Element} el\n     * @return {boolean}\n     */\n    function focusTriggersKeyboardModality(el) {\n      var type = el.type;\n      var tagName = el.tagName;\n\n      if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n        return true;\n      }\n\n      if (tagName === 'TEXTAREA' && !el.readOnly) {\n        return true;\n      }\n\n      if (el.isContentEditable) {\n        return true;\n      }\n\n      return false;\n    }\n\n    /**\n     * Add the `focus-visible` class to the given element if it was not added by\n     * the author.\n     * @param {Element} el\n     */\n    function addFocusVisibleClass(el) {\n      if (el.classList.contains('focus-visible')) {\n        return;\n      }\n      el.classList.add('focus-visible');\n      el.setAttribute('data-focus-visible-added', '');\n    }\n\n    /**\n     * Remove the `focus-visible` class from the given element if it was not\n     * originally added by the author.\n     * @param {Element} el\n     */\n    function removeFocusVisibleClass(el) {\n      if (!el.hasAttribute('data-focus-visible-added')) {\n        return;\n      }\n      el.classList.remove('focus-visible');\n      el.removeAttribute('data-focus-visible-added');\n    }\n\n    /**\n     * If the most recent user interaction was via the keyboard;\n     * and the key press did not include a meta, alt/option, or control key;\n     * then the modality is keyboard. Otherwise, the modality is not keyboard.\n     * Apply `focus-visible` to any current active element and keep track\n     * of our keyboard modality state with `hadKeyboardEvent`.\n     * @param {KeyboardEvent} e\n     */\n    function onKeyDown(e) {\n      if (e.metaKey || e.altKey || e.ctrlKey) {\n        return;\n      }\n\n      if (isValidFocusTarget(scope.activeElement)) {\n        addFocusVisibleClass(scope.activeElement);\n      }\n\n      hadKeyboardEvent = true;\n    }\n\n    /**\n     * If at any point a user clicks with a pointing device, ensure that we change\n     * the modality away from keyboard.\n     * This avoids the situation where a user presses a key on an already focused\n     * element, and then clicks on a different element, focusing it with a\n     * pointing device, while we still think we're in keyboard modality.\n     * @param {Event} e\n     */\n    function onPointerDown(e) {\n      hadKeyboardEvent = false;\n    }\n\n    /**\n     * On `focus`, add the `focus-visible` class to the target if:\n     * - the target received focus as a result of keyboard navigation, or\n     * - the event target is an element that will likely require interaction\n     *   via the keyboard (e.g. a text box)\n     * @param {Event} e\n     */\n    function onFocus(e) {\n      // Prevent IE from focusing the document or HTML element.\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n        addFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * On `blur`, remove the `focus-visible` class from the target.\n     * @param {Event} e\n     */\n    function onBlur(e) {\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (\n        e.target.classList.contains('focus-visible') ||\n        e.target.hasAttribute('data-focus-visible-added')\n      ) {\n        // To detect a tab/window switch, we look for a blur event followed\n        // rapidly by a visibility change.\n        // If we don't see a visibility change within 100ms, it's probably a\n        // regular focus change.\n        hadFocusVisibleRecently = true;\n        window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n        hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n          hadFocusVisibleRecently = false;\n        }, 100);\n        removeFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * If the user changes tabs, keep track of whether or not the previously\n     * focused element had .focus-visible.\n     * @param {Event} e\n     */\n    function onVisibilityChange(e) {\n      if (document.visibilityState === 'hidden') {\n        // If the tab becomes active again, the browser will handle calling focus\n        // on the element (Safari actually calls it twice).\n        // If this tab change caused a blur on an element with focus-visible,\n        // re-apply the class when the user switches back to the tab.\n        if (hadFocusVisibleRecently) {\n          hadKeyboardEvent = true;\n        }\n        addInitialPointerMoveListeners();\n      }\n    }\n\n    /**\n     * Add a group of listeners to detect usage of any pointing devices.\n     * These listeners will be added when the polyfill first loads, and anytime\n     * the window is blurred, so that they are active when the window regains\n     * focus.\n     */\n    function addInitialPointerMoveListeners() {\n      document.addEventListener('mousemove', onInitialPointerMove);\n      document.addEventListener('mousedown', onInitialPointerMove);\n      document.addEventListener('mouseup', onInitialPointerMove);\n      document.addEventListener('pointermove', onInitialPointerMove);\n      document.addEventListener('pointerdown', onInitialPointerMove);\n      document.addEventListener('pointerup', onInitialPointerMove);\n      document.addEventListener('touchmove', onInitialPointerMove);\n      document.addEventListener('touchstart', onInitialPointerMove);\n      document.addEventListener('touchend', onInitialPointerMove);\n    }\n\n    function removeInitialPointerMoveListeners() {\n      document.removeEventListener('mousemove', onInitialPointerMove);\n      document.removeEventListener('mousedown', onInitialPointerMove);\n      document.removeEventListener('mouseup', onInitialPointerMove);\n      document.removeEventListener('pointermove', onInitialPointerMove);\n      document.removeEventListener('pointerdown', onInitialPointerMove);\n      document.removeEventListener('pointerup', onInitialPointerMove);\n      document.removeEventListener('touchmove', onInitialPointerMove);\n      document.removeEventListener('touchstart', onInitialPointerMove);\n      document.removeEventListener('touchend', onInitialPointerMove);\n    }\n\n    /**\n     * When the polfyill first loads, assume the user is in keyboard modality.\n     * If any event is received from a pointing device (e.g. mouse, pointer,\n     * touch), turn off keyboard modality.\n     * This accounts for situations where focus enters the page from the URL bar.\n     * @param {Event} e\n     */\n    function onInitialPointerMove(e) {\n      // Work around a Safari quirk that fires a mousemove on <html> whenever the\n      // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n      if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n        return;\n      }\n\n      hadKeyboardEvent = false;\n      removeInitialPointerMoveListeners();\n    }\n\n    // For some kinds of state, we are interested in changes at the global scope\n    // only. For example, global pointer input, global key presses and global\n    // visibility change should affect the state at every scope:\n    document.addEventListener('keydown', onKeyDown, true);\n    document.addEventListener('mousedown', onPointerDown, true);\n    document.addEventListener('pointerdown', onPointerDown, true);\n    document.addEventListener('touchstart', onPointerDown, true);\n    document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n    addInitialPointerMoveListeners();\n\n    // For focus and blur, we specifically care about state changes in the local\n    // scope. This is because focus / blur events that originate from within a\n    // shadow root are not re-dispatched from the host element if it was already\n    // the active element in its own scope:\n    scope.addEventListener('focus', onFocus, true);\n    scope.addEventListener('blur', onBlur, true);\n\n    // We detect that a node is a ShadowRoot by ensuring that it is a\n    // DocumentFragment and also has a host property. This check covers native\n    // implementation and polyfill implementation transparently. If we only cared\n    // about the native implementation, we could just check if the scope was\n    // an instance of a ShadowRoot.\n    if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n      // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n      // have a root element to add a class to. So, we add this attribute to the\n      // host element instead:\n      scope.host.setAttribute('data-js-focus-visible', '');\n    } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n      document.documentElement.classList.add('js-focus-visible');\n      document.documentElement.setAttribute('data-js-focus-visible', '');\n    }\n  }\n\n  // It is important to wrap all references to global window and document in\n  // these checks to support server-side rendering use cases\n  // @see https://github.com/WICG/focus-visible/issues/199\n  if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n    // Make the polyfill helper globally available. This can be used as a signal\n    // to interested libraries that wish to coordinate with the polyfill for e.g.,\n    // applying the polyfill to a shadow root:\n    window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n    // Notify interested libraries of the polyfill's presence, in case the\n    // polyfill was loaded lazily:\n    var event;\n\n    try {\n      event = new CustomEvent('focus-visible-polyfill-ready');\n    } catch (error) {\n      // IE11 does not support using CustomEvent as a constructor directly:\n      event = document.createEvent('CustomEvent');\n      event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n    }\n\n    window.dispatchEvent(event);\n  }\n\n  if (typeof document !== 'undefined') {\n    // Apply the polyfill to the global document, so that no JavaScript\n    // coordination is required to use the polyfill in the top-level document:\n    applyFocusVisiblePolyfill(document);\n  }\n\n})));\n", "(function(global) {\r\n  /**\r\n   * Polyfill URLSearchParams\r\n   *\r\n   * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n   */\r\n\r\n  var checkIfIteratorIsSupported = function() {\r\n    try {\r\n      return !!Symbol.iterator;\r\n    } catch (error) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n  var createIterator = function(items) {\r\n    var iterator = {\r\n      next: function() {\r\n        var value = items.shift();\r\n        return { done: value === void 0, value: value };\r\n      }\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      iterator[Symbol.iterator] = function() {\r\n        return iterator;\r\n      };\r\n    }\r\n\r\n    return iterator;\r\n  };\r\n\r\n  /**\r\n   * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n   * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n   */\r\n  var serializeParam = function(value) {\r\n    return encodeURIComponent(value).replace(/%20/g, '+');\r\n  };\r\n\r\n  var deserializeParam = function(value) {\r\n    return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n  };\r\n\r\n  var polyfillURLSearchParams = function() {\r\n\r\n    var URLSearchParams = function(searchString) {\r\n      Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n      var typeofSearchString = typeof searchString;\r\n\r\n      if (typeofSearchString === 'undefined') {\r\n        // do nothing\r\n      } else if (typeofSearchString === 'string') {\r\n        if (searchString !== '') {\r\n          this._fromString(searchString);\r\n        }\r\n      } else if (searchString instanceof URLSearchParams) {\r\n        var _this = this;\r\n        searchString.forEach(function(value, name) {\r\n          _this.append(name, value);\r\n        });\r\n      } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n        if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n          for (var i = 0; i < searchString.length; i++) {\r\n            var entry = searchString[i];\r\n            if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n              this.append(entry[0], entry[1]);\r\n            } else {\r\n              throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n            }\r\n          }\r\n        } else {\r\n          for (var key in searchString) {\r\n            if (searchString.hasOwnProperty(key)) {\r\n              this.append(key, searchString[key]);\r\n            }\r\n          }\r\n        }\r\n      } else {\r\n        throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n      }\r\n    };\r\n\r\n    var proto = URLSearchParams.prototype;\r\n\r\n    proto.append = function(name, value) {\r\n      if (name in this._entries) {\r\n        this._entries[name].push(String(value));\r\n      } else {\r\n        this._entries[name] = [String(value)];\r\n      }\r\n    };\r\n\r\n    proto.delete = function(name) {\r\n      delete this._entries[name];\r\n    };\r\n\r\n    proto.get = function(name) {\r\n      return (name in this._entries) ? this._entries[name][0] : null;\r\n    };\r\n\r\n    proto.getAll = function(name) {\r\n      return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n    };\r\n\r\n    proto.has = function(name) {\r\n      return (name in this._entries);\r\n    };\r\n\r\n    proto.set = function(name, value) {\r\n      this._entries[name] = [String(value)];\r\n    };\r\n\r\n    proto.forEach = function(callback, thisArg) {\r\n      var entries;\r\n      for (var name in this._entries) {\r\n        if (this._entries.hasOwnProperty(name)) {\r\n          entries = this._entries[name];\r\n          for (var i = 0; i < entries.length; i++) {\r\n            callback.call(thisArg, entries[i], name, this);\r\n          }\r\n        }\r\n      }\r\n    };\r\n\r\n    proto.keys = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push(name);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.values = function() {\r\n      var items = [];\r\n      this.forEach(function(value) {\r\n        items.push(value);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.entries = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      proto[Symbol.iterator] = proto.entries;\r\n    }\r\n\r\n    proto.toString = function() {\r\n      var searchArray = [];\r\n      this.forEach(function(value, name) {\r\n        searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n      });\r\n      return searchArray.join('&');\r\n    };\r\n\r\n\r\n    global.URLSearchParams = URLSearchParams;\r\n  };\r\n\r\n  var checkIfURLSearchParamsSupported = function() {\r\n    try {\r\n      var URLSearchParams = global.URLSearchParams;\r\n\r\n      return (\r\n        (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n        (typeof URLSearchParams.prototype.set === 'function') &&\r\n        (typeof URLSearchParams.prototype.entries === 'function')\r\n      );\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n  if (!checkIfURLSearchParamsSupported()) {\r\n    polyfillURLSearchParams();\r\n  }\r\n\r\n  var proto = global.URLSearchParams.prototype;\r\n\r\n  if (typeof proto.sort !== 'function') {\r\n    proto.sort = function() {\r\n      var _this = this;\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n        if (!_this._entries) {\r\n          _this.delete(name);\r\n        }\r\n      });\r\n      items.sort(function(a, b) {\r\n        if (a[0] < b[0]) {\r\n          return -1;\r\n        } else if (a[0] > b[0]) {\r\n          return +1;\r\n        } else {\r\n          return 0;\r\n        }\r\n      });\r\n      if (_this._entries) { // force reset because IE keeps keys index\r\n        _this._entries = {};\r\n      }\r\n      for (var i = 0; i < items.length; i++) {\r\n        this.append(items[i][0], items[i][1]);\r\n      }\r\n    };\r\n  }\r\n\r\n  if (typeof proto._fromString !== 'function') {\r\n    Object.defineProperty(proto, '_fromString', {\r\n      enumerable: false,\r\n      configurable: false,\r\n      writable: false,\r\n      value: function(searchString) {\r\n        if (this._entries) {\r\n          this._entries = {};\r\n        } else {\r\n          var keys = [];\r\n          this.forEach(function(value, name) {\r\n            keys.push(name);\r\n          });\r\n          for (var i = 0; i < keys.length; i++) {\r\n            this.delete(keys[i]);\r\n          }\r\n        }\r\n\r\n        searchString = searchString.replace(/^\\?/, '');\r\n        var attributes = searchString.split('&');\r\n        var attribute;\r\n        for (var i = 0; i < attributes.length; i++) {\r\n          attribute = attributes[i].split('=');\r\n          this.append(\r\n            deserializeParam(attribute[0]),\r\n            (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n          );\r\n        }\r\n      }\r\n    });\r\n  }\r\n\r\n  // HTMLAnchorElement\r\n\r\n})(\r\n  (typeof global !== 'undefined') ? global\r\n    : ((typeof window !== 'undefined') ? window\r\n    : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n  /**\r\n   * Polyfill URL\r\n   *\r\n   * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n   */\r\n\r\n  var checkIfURLIsSupported = function() {\r\n    try {\r\n      var u = new global.URL('b', 'http://a');\r\n      u.pathname = 'c d';\r\n      return (u.href === 'http://a/c%20d') && u.searchParams;\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var polyfillURL = function() {\r\n    var _URL = global.URL;\r\n\r\n    var URL = function(url, base) {\r\n      if (typeof url !== 'string') url = String(url);\r\n      if (base && typeof base !== 'string') base = String(base);\r\n\r\n      // Only create another document if the base is different from current location.\r\n      var doc = document, baseElement;\r\n      if (base && (global.location === void 0 || base !== global.location.href)) {\r\n        base = base.toLowerCase();\r\n        doc = document.implementation.createHTMLDocument('');\r\n        baseElement = doc.createElement('base');\r\n        baseElement.href = base;\r\n        doc.head.appendChild(baseElement);\r\n        try {\r\n          if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n        } catch (err) {\r\n          throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n        }\r\n      }\r\n\r\n      var anchorElement = doc.createElement('a');\r\n      anchorElement.href = url;\r\n      if (baseElement) {\r\n        doc.body.appendChild(anchorElement);\r\n        anchorElement.href = anchorElement.href; // force href to refresh\r\n      }\r\n\r\n      var inputElement = doc.createElement('input');\r\n      inputElement.type = 'url';\r\n      inputElement.value = url;\r\n\r\n      if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n        throw new TypeError('Invalid URL');\r\n      }\r\n\r\n      Object.defineProperty(this, '_anchorElement', {\r\n        value: anchorElement\r\n      });\r\n\r\n\r\n      // create a linked searchParams which reflect its changes on URL\r\n      var searchParams = new global.URLSearchParams(this.search);\r\n      var enableSearchUpdate = true;\r\n      var enableSearchParamsUpdate = true;\r\n      var _this = this;\r\n      ['append', 'delete', 'set'].forEach(function(methodName) {\r\n        var method = searchParams[methodName];\r\n        searchParams[methodName] = function() {\r\n          method.apply(searchParams, arguments);\r\n          if (enableSearchUpdate) {\r\n            enableSearchParamsUpdate = false;\r\n            _this.search = searchParams.toString();\r\n            enableSearchParamsUpdate = true;\r\n          }\r\n        };\r\n      });\r\n\r\n      Object.defineProperty(this, 'searchParams', {\r\n        value: searchParams,\r\n        enumerable: true\r\n      });\r\n\r\n      var search = void 0;\r\n      Object.defineProperty(this, '_updateSearchParams', {\r\n        enumerable: false,\r\n        configurable: false,\r\n        writable: false,\r\n        value: function() {\r\n          if (this.search !== search) {\r\n            search = this.search;\r\n            if (enableSearchParamsUpdate) {\r\n              enableSearchUpdate = false;\r\n              this.searchParams._fromString(this.search);\r\n              enableSearchUpdate = true;\r\n            }\r\n          }\r\n        }\r\n      });\r\n    };\r\n\r\n    var proto = URL.prototype;\r\n\r\n    var linkURLWithAnchorAttribute = function(attributeName) {\r\n      Object.defineProperty(proto, attributeName, {\r\n        get: function() {\r\n          return this._anchorElement[attributeName];\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement[attributeName] = value;\r\n        },\r\n        enumerable: true\r\n      });\r\n    };\r\n\r\n    ['hash', 'host', 'hostname', 'port', 'protocol']\r\n      .forEach(function(attributeName) {\r\n        linkURLWithAnchorAttribute(attributeName);\r\n      });\r\n\r\n    Object.defineProperty(proto, 'search', {\r\n      get: function() {\r\n        return this._anchorElement['search'];\r\n      },\r\n      set: function(value) {\r\n        this._anchorElement['search'] = value;\r\n        this._updateSearchParams();\r\n      },\r\n      enumerable: true\r\n    });\r\n\r\n    Object.defineProperties(proto, {\r\n\r\n      'toString': {\r\n        get: function() {\r\n          var _this = this;\r\n          return function() {\r\n            return _this.href;\r\n          };\r\n        }\r\n      },\r\n\r\n      'href': {\r\n        get: function() {\r\n          return this._anchorElement.href.replace(/\\?$/, '');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.href = value;\r\n          this._updateSearchParams();\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'pathname': {\r\n        get: function() {\r\n          return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.pathname = value;\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'origin': {\r\n        get: function() {\r\n          // get expected port from protocol\r\n          var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n          // add port to origin if, expected port is different than actual port\r\n          // and it is not empty f.e http://foo:8080\r\n          // 8080 != 80 && 8080 != ''\r\n          var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n            this._anchorElement.port !== '';\r\n\r\n          return this._anchorElement.protocol +\r\n            '//' +\r\n            this._anchorElement.hostname +\r\n            (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'password': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'username': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n    });\r\n\r\n    URL.createObjectURL = function(blob) {\r\n      return _URL.createObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    URL.revokeObjectURL = function(url) {\r\n      return _URL.revokeObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    global.URL = URL;\r\n\r\n  };\r\n\r\n  if (!checkIfURLIsSupported()) {\r\n    polyfillURL();\r\n  }\r\n\r\n  if ((global.location !== void 0) && !('origin' in global.location)) {\r\n    var getOrigin = function() {\r\n      return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n  var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n  fakeElement.style.top = \"\".concat(yPosition, \"px\");\n  fakeElement.setAttribute('readonly', '');\n  fakeElement.value = value;\n  return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n  var fakeElement = createFakeElement(value);\n  options.container.appendChild(fakeElement);\n  var selectedText = select_default()(fakeElement);\n  command('copy');\n  fakeElement.remove();\n  return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n    container: document.body\n  };\n  var selectedText = '';\n\n  if (typeof target === 'string') {\n    selectedText = fakeCopyAction(target, options);\n  } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n    // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n    selectedText = fakeCopyAction(target.value, options);\n  } else {\n    selectedText = select_default()(target);\n    command('copy');\n  }\n\n  return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n  var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n  // Defines base properties passed from constructor.\n  var _options$action = options.action,\n      action = _options$action === void 0 ? 'copy' : _options$action,\n      container = options.container,\n      target = options.target,\n      text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n  if (action !== 'copy' && action !== 'cut') {\n    throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n  } // Sets the `target` property using an element that will be have its content copied.\n\n\n  if (target !== undefined) {\n    if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n      if (action === 'copy' && target.hasAttribute('disabled')) {\n        throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n      }\n\n      if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n        throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n      }\n    } else {\n      throw new Error('Invalid \"target\" value, use a valid Element');\n    }\n  } // Define selection strategy based on `text` property.\n\n\n  if (text) {\n    return actions_copy(text, {\n      container: container\n    });\n  } // Defines which selection strategy based on `target` property.\n\n\n  if (target) {\n    return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n      container: container\n    });\n  }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n  var attribute = \"data-clipboard-\".concat(suffix);\n\n  if (!element.hasAttribute(attribute)) {\n    return;\n  }\n\n  return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n  _inherits(Clipboard, _Emitter);\n\n  var _super = _createSuper(Clipboard);\n\n  /**\n   * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n   * @param {Object} options\n   */\n  function Clipboard(trigger, options) {\n    var _this;\n\n    _classCallCheck(this, Clipboard);\n\n    _this = _super.call(this);\n\n    _this.resolveOptions(options);\n\n    _this.listenClick(trigger);\n\n    return _this;\n  }\n  /**\n   * Defines if attributes would be resolved using internal setter functions\n   * or custom functions that were passed in the constructor.\n   * @param {Object} options\n   */\n\n\n  _createClass(Clipboard, [{\n    key: \"resolveOptions\",\n    value: function resolveOptions() {\n      var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n      this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n      this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n      this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n      this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n    }\n    /**\n     * Adds a click event listener to the passed trigger.\n     * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n     */\n\n  }, {\n    key: \"listenClick\",\n    value: function listenClick(trigger) {\n      var _this2 = this;\n\n      this.listener = listen_default()(trigger, 'click', function (e) {\n        return _this2.onClick(e);\n      });\n    }\n    /**\n     * Defines a new `ClipboardAction` on each click event.\n     * @param {Event} e\n     */\n\n  }, {\n    key: \"onClick\",\n    value: function onClick(e) {\n      var trigger = e.delegateTarget || e.currentTarget;\n      var action = this.action(trigger) || 'copy';\n      var text = actions_default({\n        action: action,\n        container: this.container,\n        target: this.target(trigger),\n        text: this.text(trigger)\n      }); // Fires an event based on the copy operation result.\n\n      this.emit(text ? 'success' : 'error', {\n        action: action,\n        text: text,\n        trigger: trigger,\n        clearSelection: function clearSelection() {\n          if (trigger) {\n            trigger.focus();\n          }\n\n          window.getSelection().removeAllRanges();\n        }\n      });\n    }\n    /**\n     * Default `action` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultAction\",\n    value: function defaultAction(trigger) {\n      return getAttributeValue('action', trigger);\n    }\n    /**\n     * Default `target` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultTarget\",\n    value: function defaultTarget(trigger) {\n      var selector = getAttributeValue('target', trigger);\n\n      if (selector) {\n        return document.querySelector(selector);\n      }\n    }\n    /**\n     * Allow fire programmatically a copy action\n     * @param {String|HTMLElement} target\n     * @param {Object} options\n     * @returns Text copied.\n     */\n\n  }, {\n    key: \"defaultText\",\n\n    /**\n     * Default `text` lookup function.\n     * @param {Element} trigger\n     */\n    value: function defaultText(trigger) {\n      return getAttributeValue('text', trigger);\n    }\n    /**\n     * Destroy lifecycle.\n     */\n\n  }, {\n    key: \"destroy\",\n    value: function destroy() {\n      this.listener.destroy();\n    }\n  }], [{\n    key: \"copy\",\n    value: function copy(target) {\n      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n        container: document.body\n      };\n      return actions_copy(target, options);\n    }\n    /**\n     * Allow fire programmatically a cut action\n     * @param {String|HTMLElement} target\n     * @returns Text cutted.\n     */\n\n  }, {\n    key: \"cut\",\n    value: function cut(target) {\n      return actions_cut(target);\n    }\n    /**\n     * Returns the support of the given action, or all actions if no action is\n     * given.\n     * @param {String} [action]\n     */\n\n  }, {\n    key: \"isSupported\",\n    value: function isSupported() {\n      var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n      var actions = typeof action === 'string' ? [action] : action;\n      var support = !!document.queryCommandSupported;\n      actions.forEach(function (action) {\n        support = support && !!document.queryCommandSupported(action);\n      });\n      return support;\n    }\n  }]);\n\n  return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n    var proto = Element.prototype;\n\n    proto.matches = proto.matchesSelector ||\n                    proto.mozMatchesSelector ||\n                    proto.msMatchesSelector ||\n                    proto.oMatchesSelector ||\n                    proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n    while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n        if (typeof element.matches === 'function' &&\n            element.matches(selector)) {\n          return element;\n        }\n        element = element.parentNode;\n    }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n    var listenerFn = listener.apply(this, arguments);\n\n    element.addEventListener(type, listenerFn, useCapture);\n\n    return {\n        destroy: function() {\n            element.removeEventListener(type, listenerFn, useCapture);\n        }\n    }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n    // Handle the regular Element usage\n    if (typeof elements.addEventListener === 'function') {\n        return _delegate.apply(null, arguments);\n    }\n\n    // Handle Element-less usage, it defaults to global delegation\n    if (typeof type === 'function') {\n        // Use `document` as the first parameter, then apply arguments\n        // This is a short way to .unshift `arguments` without running into deoptimizations\n        return _delegate.bind(null, document).apply(null, arguments);\n    }\n\n    // Handle Selector-based usage\n    if (typeof elements === 'string') {\n        elements = document.querySelectorAll(elements);\n    }\n\n    // Handle Array-like based usage\n    return Array.prototype.map.call(elements, function (element) {\n        return _delegate(element, selector, type, callback, useCapture);\n    });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n    return function(e) {\n        e.delegateTarget = closest(e.target, selector);\n\n        if (e.delegateTarget) {\n            callback.call(element, e);\n        }\n    }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n    return value !== undefined\n        && value instanceof HTMLElement\n        && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return value !== undefined\n        && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n        && ('length' in value)\n        && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n    return typeof value === 'string'\n        || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n    if (!target && !type && !callback) {\n        throw new Error('Missing required arguments');\n    }\n\n    if (!is.string(type)) {\n        throw new TypeError('Second argument must be a String');\n    }\n\n    if (!is.fn(callback)) {\n        throw new TypeError('Third argument must be a Function');\n    }\n\n    if (is.node(target)) {\n        return listenNode(target, type, callback);\n    }\n    else if (is.nodeList(target)) {\n        return listenNodeList(target, type, callback);\n    }\n    else if (is.string(target)) {\n        return listenSelector(target, type, callback);\n    }\n    else {\n        throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n    }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n    node.addEventListener(type, callback);\n\n    return {\n        destroy: function() {\n            node.removeEventListener(type, callback);\n        }\n    }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n    Array.prototype.forEach.call(nodeList, function(node) {\n        node.addEventListener(type, callback);\n    });\n\n    return {\n        destroy: function() {\n            Array.prototype.forEach.call(nodeList, function(node) {\n                node.removeEventListener(type, callback);\n            });\n        }\n    }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n    return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n    var selectedText;\n\n    if (element.nodeName === 'SELECT') {\n        element.focus();\n\n        selectedText = element.value;\n    }\n    else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n        var isReadOnly = element.hasAttribute('readonly');\n\n        if (!isReadOnly) {\n            element.setAttribute('readonly', '');\n        }\n\n        element.select();\n        element.setSelectionRange(0, element.value.length);\n\n        if (!isReadOnly) {\n            element.removeAttribute('readonly');\n        }\n\n        selectedText = element.value;\n    }\n    else {\n        if (element.hasAttribute('contenteditable')) {\n            element.focus();\n        }\n\n        var selection = window.getSelection();\n        var range = document.createRange();\n\n        range.selectNodeContents(element);\n        selection.removeAllRanges();\n        selection.addRange(range);\n\n        selectedText = selection.toString();\n    }\n\n    return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n  // Keep this empty so it's easier to inherit from\n  // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n  on: function (name, callback, ctx) {\n    var e = this.e || (this.e = {});\n\n    (e[name] || (e[name] = [])).push({\n      fn: callback,\n      ctx: ctx\n    });\n\n    return this;\n  },\n\n  once: function (name, callback, ctx) {\n    var self = this;\n    function listener () {\n      self.off(name, listener);\n      callback.apply(ctx, arguments);\n    };\n\n    listener._ = callback\n    return this.on(name, listener, ctx);\n  },\n\n  emit: function (name) {\n    var data = [].slice.call(arguments, 1);\n    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n    var i = 0;\n    var len = evtArr.length;\n\n    for (i; i < len; i++) {\n      evtArr[i].fn.apply(evtArr[i].ctx, data);\n    }\n\n    return this;\n  },\n\n  off: function (name, callback) {\n    var e = this.e || (this.e = {});\n    var evts = e[name];\n    var liveEvents = [];\n\n    if (evts && callback) {\n      for (var i = 0, len = evts.length; i < len; i++) {\n        if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n          liveEvents.push(evts[i]);\n      }\n    }\n\n    // Remove event from queue to prevent memory leak\n    // Suggested by https://github.com/lazd\n    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n    (liveEvents.length)\n      ? e[name] = liveEvents\n      : delete e[name];\n\n    return this;\n  }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n  EMPTY,\n  NEVER,\n  Subject,\n  defer,\n  delay,\n  filter,\n  map,\n  merge,\n  mergeWith,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n  at,\n  getOptionalElement,\n  requestJSON,\n  setToggle,\n  watchDocument,\n  watchKeyboard,\n  watchLocation,\n  watchLocationTarget,\n  watchMedia,\n  watchPrint,\n  watchViewport\n} from \"./browser\"\nimport {\n  getComponentElement,\n  getComponentElements,\n  mountBackToTop,\n  mountContent,\n  mountDialog,\n  mountHeader,\n  mountHeaderTitle,\n  mountPalette,\n  mountSearch,\n  mountSearchHiglight,\n  mountSidebar,\n  mountSource,\n  mountTableOfContents,\n  mountTabs,\n  watchHeader,\n  watchMain\n} from \"./components\"\nimport {\n  SearchIndex,\n  setupClipboardJS,\n  setupInstantLoading,\n  setupVersionSelector\n} from \"./integrations\"\nimport {\n  patchIndeterminate,\n  patchScrollfix,\n  patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$   = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$   = watchMedia(\"(min-width: 960px)\")\nconst screen$   = watchMedia(\"(min-width: 1220px)\")\nconst print$    = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n  ? __search?.index || requestJSON<SearchIndex>(\n    new URL(\"search/search_index.json\", config.base)\n  )\n  : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject<string>()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n  setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n  setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n  .pipe(\n    delay(125)\n  )\n    .subscribe(() => {\n      setToggle(\"drawer\", false)\n      setToggle(\"search\", false)\n    })\n\n/* Set up global keyboard handlers */\nkeyboard$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement(\"[href][rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            prev.click()\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement(\"[href][rel=next]\")\n          if (typeof next !== \"undefined\")\n            next.click()\n          break\n      }\n    })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n  .pipe(\n    map(() => getComponentElement(\"main\")),\n    switchMap(el => watchMain(el, { viewport$, header$ })),\n    shareReplay(1)\n  )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n  /* Dialog */\n  ...getComponentElements(\"dialog\")\n    .map(el => mountDialog(el, { alert$ })),\n\n  /* Header */\n  ...getComponentElements(\"header\")\n    .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n  /* Color palette */\n  ...getComponentElements(\"palette\")\n    .map(el => mountPalette(el)),\n\n  /* Search */\n  ...getComponentElements(\"search\")\n    .map(el => mountSearch(el, { index$, keyboard$ })),\n\n  /* Repository information */\n  ...getComponentElements(\"source\")\n    .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n  /* Content */\n  ...getComponentElements(\"content\")\n    .map(el => mountContent(el, { target$, print$ })),\n\n  /* Search highlighting */\n  ...getComponentElements(\"content\")\n    .map(el => feature(\"search.highlight\")\n      ? mountSearchHiglight(el, { index$, location$ })\n      : EMPTY\n    ),\n\n  /* Header title */\n  ...getComponentElements(\"header-title\")\n    .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n  /* Sidebar */\n  ...getComponentElements(\"sidebar\")\n    .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n      ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n      : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n    ),\n\n  /* Navigation tabs */\n  ...getComponentElements(\"tabs\")\n    .map(el => mountTabs(el, { viewport$, header$ })),\n\n  /* Table of contents */\n  ...getComponentElements(\"toc\")\n    .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n  /* Back-to-top button */\n  ...getComponentElements(\"top\")\n    .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n  .pipe(\n    switchMap(() => content$),\n    mergeWith(control$),\n    shareReplay(1)\n  )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$  = document$          /* Document observable */\nwindow.location$  = location$          /* Location subject */\nwindow.target$    = target$            /* Location target observable */\nwindow.keyboard$  = keyboard$          /* Keyboard observable */\nwindow.viewport$  = viewport$          /* Viewport observable */\nwindow.tablet$    = tablet$            /* Media tablet observable */\nwindow.screen$    = screen$            /* Media screen observable */\nwindow.print$     = print$             /* Media print observable */\nwindow.alert$     = alert$             /* Alert subject */\nwindow.component$ = component$         /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n    __extends,\r\n    __assign,\r\n    __rest,\r\n    __decorate,\r\n    __param,\r\n    __metadata,\r\n    __awaiter,\r\n    __generator,\r\n    __exportStar,\r\n    __createBinding,\r\n    __values,\r\n    __read,\r\n    __spread,\r\n    __spreadArrays,\r\n    __spreadArray,\r\n    __await,\r\n    __asyncGenerator,\r\n    __asyncDelegator,\r\n    __asyncValues,\r\n    __makeTemplateObject,\r\n    __importStar,\r\n    __importDefault,\r\n    __classPrivateFieldGet,\r\n    __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n    __extends,\r\n    __assign,\r\n    __rest,\r\n    __decorate,\r\n    __param,\r\n    __metadata,\r\n    __awaiter,\r\n    __generator,\r\n    __exportStar,\r\n    __createBinding,\r\n    __values,\r\n    __read,\r\n    __spread,\r\n    __spreadArrays,\r\n    __spreadArray,\r\n    __await,\r\n    __asyncGenerator,\r\n    __asyncDelegator,\r\n    __asyncValues,\r\n    __makeTemplateObject,\r\n    __importStar,\r\n    __importDefault,\r\n    __classPrivateFieldGet,\r\n    __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ReplaySubject,\n  Subject,\n  fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject<Document> {\n  const document$ = new ReplaySubject<Document>(1)\n  fromEvent(document, \"DOMContentLoaded\", { once: true })\n    .subscribe(() => document$.next(document))\n\n  /* Return document */\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounceTime,\n  distinctUntilChanged,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n  el: HTMLElement\n): Observable<boolean> {\n  return merge(\n    fromEvent(document.body, \"focusin\"),\n    fromEvent(document.body, \"focusout\")\n  )\n    .pipe(\n      debounceTime(1),\n      map(() => {\n        const active = getActiveElement()\n        return typeof active !== \"undefined\"\n          ? el.contains(active)\n          : false\n      }),\n      startWith(el === getActiveElement()),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.offsetLeft,\n    y: el.offsetTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(window, \"load\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementOffset(el)),\n      startWith(getElementOffset(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.scrollLeft,\n    y: el.scrollTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(el, \"scroll\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n    if (typeof Map !== 'undefined') {\r\n        return Map;\r\n    }\r\n    /**\r\n     * Returns index in provided array that matches the specified key.\r\n     *\r\n     * @param {Array<Array>} arr\r\n     * @param {*} key\r\n     * @returns {number}\r\n     */\r\n    function getIndex(arr, key) {\r\n        var result = -1;\r\n        arr.some(function (entry, index) {\r\n            if (entry[0] === key) {\r\n                result = index;\r\n                return true;\r\n            }\r\n            return false;\r\n        });\r\n        return result;\r\n    }\r\n    return /** @class */ (function () {\r\n        function class_1() {\r\n            this.__entries__ = [];\r\n        }\r\n        Object.defineProperty(class_1.prototype, \"size\", {\r\n            /**\r\n             * @returns {boolean}\r\n             */\r\n            get: function () {\r\n                return this.__entries__.length;\r\n            },\r\n            enumerable: true,\r\n            configurable: true\r\n        });\r\n        /**\r\n         * @param {*} key\r\n         * @returns {*}\r\n         */\r\n        class_1.prototype.get = function (key) {\r\n            var index = getIndex(this.__entries__, key);\r\n            var entry = this.__entries__[index];\r\n            return entry && entry[1];\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @param {*} value\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.set = function (key, value) {\r\n            var index = getIndex(this.__entries__, key);\r\n            if (~index) {\r\n                this.__entries__[index][1] = value;\r\n            }\r\n            else {\r\n                this.__entries__.push([key, value]);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.delete = function (key) {\r\n            var entries = this.__entries__;\r\n            var index = getIndex(entries, key);\r\n            if (~index) {\r\n                entries.splice(index, 1);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.has = function (key) {\r\n            return !!~getIndex(this.__entries__, key);\r\n        };\r\n        /**\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.clear = function () {\r\n            this.__entries__.splice(0);\r\n        };\r\n        /**\r\n         * @param {Function} callback\r\n         * @param {*} [ctx=null]\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.forEach = function (callback, ctx) {\r\n            if (ctx === void 0) { ctx = null; }\r\n            for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n                var entry = _a[_i];\r\n                callback.call(ctx, entry[1], entry[0]);\r\n            }\r\n        };\r\n        return class_1;\r\n    }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n    if (typeof global !== 'undefined' && global.Math === Math) {\r\n        return global;\r\n    }\r\n    if (typeof self !== 'undefined' && self.Math === Math) {\r\n        return self;\r\n    }\r\n    if (typeof window !== 'undefined' && window.Math === Math) {\r\n        return window;\r\n    }\r\n    // eslint-disable-next-line no-new-func\r\n    return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n    if (typeof requestAnimationFrame === 'function') {\r\n        // It's required to use a bounded function because IE sometimes throws\r\n        // an \"Invalid calling object\" error if rAF is invoked without the global\r\n        // object on the left hand side.\r\n        return requestAnimationFrame.bind(global$1);\r\n    }\r\n    return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n    var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n    /**\r\n     * Invokes the original callback function and schedules new invocation if\r\n     * the \"proxy\" was called during current request.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function resolvePending() {\r\n        if (leadingCall) {\r\n            leadingCall = false;\r\n            callback();\r\n        }\r\n        if (trailingCall) {\r\n            proxy();\r\n        }\r\n    }\r\n    /**\r\n     * Callback invoked after the specified delay. It will further postpone\r\n     * invocation of the original function delegating it to the\r\n     * requestAnimationFrame.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function timeoutCallback() {\r\n        requestAnimationFrame$1(resolvePending);\r\n    }\r\n    /**\r\n     * Schedules invocation of the original function.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function proxy() {\r\n        var timeStamp = Date.now();\r\n        if (leadingCall) {\r\n            // Reject immediately following calls.\r\n            if (timeStamp - lastCallTime < trailingTimeout) {\r\n                return;\r\n            }\r\n            // Schedule new call to be in invoked when the pending one is resolved.\r\n            // This is important for \"transitions\" which never actually start\r\n            // immediately so there is a chance that we might miss one if change\r\n            // happens amids the pending invocation.\r\n            trailingCall = true;\r\n        }\r\n        else {\r\n            leadingCall = true;\r\n            trailingCall = false;\r\n            setTimeout(timeoutCallback, delay);\r\n        }\r\n        lastCallTime = timeStamp;\r\n    }\r\n    return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserverController.\r\n     *\r\n     * @private\r\n     */\r\n    function ResizeObserverController() {\r\n        /**\r\n         * Indicates whether DOM listeners have been added.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.connected_ = false;\r\n        /**\r\n         * Tells that controller has subscribed for Mutation Events.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.mutationEventsAdded_ = false;\r\n        /**\r\n         * Keeps reference to the instance of MutationObserver.\r\n         *\r\n         * @private {MutationObserver}\r\n         */\r\n        this.mutationsObserver_ = null;\r\n        /**\r\n         * A list of connected observers.\r\n         *\r\n         * @private {Array<ResizeObserverSPI>}\r\n         */\r\n        this.observers_ = [];\r\n        this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n        this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n    }\r\n    /**\r\n     * Adds observer to observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be added.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.addObserver = function (observer) {\r\n        if (!~this.observers_.indexOf(observer)) {\r\n            this.observers_.push(observer);\r\n        }\r\n        // Add listeners if they haven't been added yet.\r\n        if (!this.connected_) {\r\n            this.connect_();\r\n        }\r\n    };\r\n    /**\r\n     * Removes observer from observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.removeObserver = function (observer) {\r\n        var observers = this.observers_;\r\n        var index = observers.indexOf(observer);\r\n        // Remove observer if it's present in registry.\r\n        if (~index) {\r\n            observers.splice(index, 1);\r\n        }\r\n        // Remove listeners if controller has no connected observers.\r\n        if (!observers.length && this.connected_) {\r\n            this.disconnect_();\r\n        }\r\n    };\r\n    /**\r\n     * Invokes the update of observers. It will continue running updates insofar\r\n     * it detects changes.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.refresh = function () {\r\n        var changesDetected = this.updateObservers_();\r\n        // Continue running updates if changes have been detected as there might\r\n        // be future ones caused by CSS transitions.\r\n        if (changesDetected) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Updates every observer from observers list and notifies them of queued\r\n     * entries.\r\n     *\r\n     * @private\r\n     * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n     *      dimensions of it's elements.\r\n     */\r\n    ResizeObserverController.prototype.updateObservers_ = function () {\r\n        // Collect observers that have active observations.\r\n        var activeObservers = this.observers_.filter(function (observer) {\r\n            return observer.gatherActive(), observer.hasActive();\r\n        });\r\n        // Deliver notifications in a separate cycle in order to avoid any\r\n        // collisions between observers, e.g. when multiple instances of\r\n        // ResizeObserver are tracking the same element and the callback of one\r\n        // of them changes content dimensions of the observed target. Sometimes\r\n        // this may result in notifications being blocked for the rest of observers.\r\n        activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n        return activeObservers.length > 0;\r\n    };\r\n    /**\r\n     * Initializes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.connect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already added.\r\n        if (!isBrowser || this.connected_) {\r\n            return;\r\n        }\r\n        // Subscription to the \"Transitionend\" event is used as a workaround for\r\n        // delayed transitions. This way it's possible to capture at least the\r\n        // final state of an element.\r\n        document.addEventListener('transitionend', this.onTransitionEnd_);\r\n        window.addEventListener('resize', this.refresh);\r\n        if (mutationObserverSupported) {\r\n            this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n            this.mutationsObserver_.observe(document, {\r\n                attributes: true,\r\n                childList: true,\r\n                characterData: true,\r\n                subtree: true\r\n            });\r\n        }\r\n        else {\r\n            document.addEventListener('DOMSubtreeModified', this.refresh);\r\n            this.mutationEventsAdded_ = true;\r\n        }\r\n        this.connected_ = true;\r\n    };\r\n    /**\r\n     * Removes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.disconnect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already removed.\r\n        if (!isBrowser || !this.connected_) {\r\n            return;\r\n        }\r\n        document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n        window.removeEventListener('resize', this.refresh);\r\n        if (this.mutationsObserver_) {\r\n            this.mutationsObserver_.disconnect();\r\n        }\r\n        if (this.mutationEventsAdded_) {\r\n            document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n        }\r\n        this.mutationsObserver_ = null;\r\n        this.mutationEventsAdded_ = false;\r\n        this.connected_ = false;\r\n    };\r\n    /**\r\n     * \"Transitionend\" event handler.\r\n     *\r\n     * @private\r\n     * @param {TransitionEvent} event\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n        var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n        // Detect whether transition may affect dimensions of an element.\r\n        var isReflowProperty = transitionKeys.some(function (key) {\r\n            return !!~propertyName.indexOf(key);\r\n        });\r\n        if (isReflowProperty) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Returns instance of the ResizeObserverController.\r\n     *\r\n     * @returns {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.getInstance = function () {\r\n        if (!this.instance_) {\r\n            this.instance_ = new ResizeObserverController();\r\n        }\r\n        return this.instance_;\r\n    };\r\n    /**\r\n     * Holds reference to the controller's instance.\r\n     *\r\n     * @private {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.instance_ = null;\r\n    return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n    for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n        var key = _a[_i];\r\n        Object.defineProperty(target, key, {\r\n            value: props[key],\r\n            enumerable: false,\r\n            writable: false,\r\n            configurable: true\r\n        });\r\n    }\r\n    return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n    // Assume that the element is an instance of Node, which means that it\r\n    // has the \"ownerDocument\" property from which we can retrieve a\r\n    // corresponding global object.\r\n    var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n    // Return the local global object if it's not possible extract one from\r\n    // provided element.\r\n    return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n    return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n    var positions = [];\r\n    for (var _i = 1; _i < arguments.length; _i++) {\r\n        positions[_i - 1] = arguments[_i];\r\n    }\r\n    return positions.reduce(function (size, position) {\r\n        var value = styles['border-' + position + '-width'];\r\n        return size + toFloat(value);\r\n    }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n    var positions = ['top', 'right', 'bottom', 'left'];\r\n    var paddings = {};\r\n    for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n        var position = positions_1[_i];\r\n        var value = styles['padding-' + position];\r\n        paddings[position] = toFloat(value);\r\n    }\r\n    return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n *      to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n    var bbox = target.getBBox();\r\n    return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n    // Client width & height properties can't be\r\n    // used exclusively as they provide rounded values.\r\n    var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n    // By this condition we can catch all non-replaced inline, hidden and\r\n    // detached elements. Though elements with width & height properties less\r\n    // than 0.5 will be discarded as well.\r\n    //\r\n    // Without it we would need to implement separate methods for each of\r\n    // those cases and it's not possible to perform a precise and performance\r\n    // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n    // gives wrong results for elements with width & height less than 0.5.\r\n    if (!clientWidth && !clientHeight) {\r\n        return emptyRect;\r\n    }\r\n    var styles = getWindowOf(target).getComputedStyle(target);\r\n    var paddings = getPaddings(styles);\r\n    var horizPad = paddings.left + paddings.right;\r\n    var vertPad = paddings.top + paddings.bottom;\r\n    // Computed styles of width & height are being used because they are the\r\n    // only dimensions available to JS that contain non-rounded values. It could\r\n    // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n    // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n    var width = toFloat(styles.width), height = toFloat(styles.height);\r\n    // Width & height include paddings and borders when the 'border-box' box\r\n    // model is applied (except for IE).\r\n    if (styles.boxSizing === 'border-box') {\r\n        // Following conditions are required to handle Internet Explorer which\r\n        // doesn't include paddings and borders to computed CSS dimensions.\r\n        //\r\n        // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n        // properties then it's either IE, and thus we don't need to subtract\r\n        // anything, or an element merely doesn't have paddings/borders styles.\r\n        if (Math.round(width + horizPad) !== clientWidth) {\r\n            width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n        }\r\n        if (Math.round(height + vertPad) !== clientHeight) {\r\n            height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n        }\r\n    }\r\n    // Following steps can't be applied to the document's root element as its\r\n    // client[Width/Height] properties represent viewport area of the window.\r\n    // Besides, it's as well not necessary as the <html> itself neither has\r\n    // rendered scroll bars nor it can be clipped.\r\n    if (!isDocumentElement(target)) {\r\n        // In some browsers (only in Firefox, actually) CSS width & height\r\n        // include scroll bars size which can be removed at this step as scroll\r\n        // bars are the only difference between rounded dimensions + paddings\r\n        // and \"client\" properties, though that is not always true in Chrome.\r\n        var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n        var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n        // Chrome has a rather weird rounding of \"client\" properties.\r\n        // E.g. for an element with content width of 314.2px it sometimes gives\r\n        // the client width of 315px and for the width of 314.7px it may give\r\n        // 314px. And it doesn't happen all the time. So just ignore this delta\r\n        // as a non-relevant.\r\n        if (Math.abs(vertScrollbar) !== 1) {\r\n            width -= vertScrollbar;\r\n        }\r\n        if (Math.abs(horizScrollbar) !== 1) {\r\n            height -= horizScrollbar;\r\n        }\r\n    }\r\n    return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n    // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n    // interface.\r\n    if (typeof SVGGraphicsElement !== 'undefined') {\r\n        return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n    }\r\n    // If it's so, then check that element is at least an instance of the\r\n    // SVGElement and that it has the \"getBBox\" method.\r\n    // eslint-disable-next-line no-extra-parens\r\n    return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n        typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element (<html>).\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n    return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n    if (!isBrowser) {\r\n        return emptyRect;\r\n    }\r\n    if (isSVGGraphicsElement(target)) {\r\n        return getSVGContentRect(target);\r\n    }\r\n    return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n    var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n    // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n    var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n    var rect = Object.create(Constr.prototype);\r\n    // Rectangle's properties are not writable and non-enumerable.\r\n    defineConfigurable(rect, {\r\n        x: x, y: y, width: width, height: height,\r\n        top: y,\r\n        right: x + width,\r\n        bottom: height + y,\r\n        left: x\r\n    });\r\n    return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n    return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObservation.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     */\r\n    function ResizeObservation(target) {\r\n        /**\r\n         * Broadcasted width of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastWidth = 0;\r\n        /**\r\n         * Broadcasted height of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastHeight = 0;\r\n        /**\r\n         * Reference to the last observed content rectangle.\r\n         *\r\n         * @private {DOMRectInit}\r\n         */\r\n        this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n        this.target = target;\r\n    }\r\n    /**\r\n     * Updates content rectangle and tells whether it's width or height properties\r\n     * have changed since the last broadcast.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObservation.prototype.isActive = function () {\r\n        var rect = getContentRect(this.target);\r\n        this.contentRect_ = rect;\r\n        return (rect.width !== this.broadcastWidth ||\r\n            rect.height !== this.broadcastHeight);\r\n    };\r\n    /**\r\n     * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n     * from the corresponding properties of the last observed content rectangle.\r\n     *\r\n     * @returns {DOMRectInit} Last observed content rectangle.\r\n     */\r\n    ResizeObservation.prototype.broadcastRect = function () {\r\n        var rect = this.contentRect_;\r\n        this.broadcastWidth = rect.width;\r\n        this.broadcastHeight = rect.height;\r\n        return rect;\r\n    };\r\n    return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObserverEntry.\r\n     *\r\n     * @param {Element} target - Element that is being observed.\r\n     * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n     */\r\n    function ResizeObserverEntry(target, rectInit) {\r\n        var contentRect = createReadOnlyRect(rectInit);\r\n        // According to the specification following properties are not writable\r\n        // and are also not enumerable in the native implementation.\r\n        //\r\n        // Property accessors are not being used as they'd require to define a\r\n        // private WeakMap storage which may cause memory leaks in browsers that\r\n        // don't support this type of collections.\r\n        defineConfigurable(this, { target: target, contentRect: contentRect });\r\n    }\r\n    return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n     *      when one of the observed elements changes it's content dimensions.\r\n     * @param {ResizeObserverController} controller - Controller instance which\r\n     *      is responsible for the updates of observer.\r\n     * @param {ResizeObserver} callbackCtx - Reference to the public\r\n     *      ResizeObserver instance which will be passed to callback function.\r\n     */\r\n    function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n        /**\r\n         * Collection of resize observations that have detected changes in dimensions\r\n         * of elements.\r\n         *\r\n         * @private {Array<ResizeObservation>}\r\n         */\r\n        this.activeObservations_ = [];\r\n        /**\r\n         * Registry of the ResizeObservation instances.\r\n         *\r\n         * @private {Map<Element, ResizeObservation>}\r\n         */\r\n        this.observations_ = new MapShim();\r\n        if (typeof callback !== 'function') {\r\n            throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n        }\r\n        this.callback_ = callback;\r\n        this.controller_ = controller;\r\n        this.callbackCtx_ = callbackCtx;\r\n    }\r\n    /**\r\n     * Starts observing provided element.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.observe = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is already being observed.\r\n        if (observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.set(target, new ResizeObservation(target));\r\n        this.controller_.addObserver(this);\r\n        // Force the update of observations.\r\n        this.controller_.refresh();\r\n    };\r\n    /**\r\n     * Stops observing provided element.\r\n     *\r\n     * @param {Element} target - Element to stop observing.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.unobserve = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is not being observed.\r\n        if (!observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.delete(target);\r\n        if (!observations.size) {\r\n            this.controller_.removeObserver(this);\r\n        }\r\n    };\r\n    /**\r\n     * Stops observing all elements.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.disconnect = function () {\r\n        this.clearActive();\r\n        this.observations_.clear();\r\n        this.controller_.removeObserver(this);\r\n    };\r\n    /**\r\n     * Collects observation instances the associated element of which has changed\r\n     * it's content rectangle.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.gatherActive = function () {\r\n        var _this = this;\r\n        this.clearActive();\r\n        this.observations_.forEach(function (observation) {\r\n            if (observation.isActive()) {\r\n                _this.activeObservations_.push(observation);\r\n            }\r\n        });\r\n    };\r\n    /**\r\n     * Invokes initial callback function with a list of ResizeObserverEntry\r\n     * instances collected from active resize observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.broadcastActive = function () {\r\n        // Do nothing if observer doesn't have active observations.\r\n        if (!this.hasActive()) {\r\n            return;\r\n        }\r\n        var ctx = this.callbackCtx_;\r\n        // Create ResizeObserverEntry instance for every active observation.\r\n        var entries = this.activeObservations_.map(function (observation) {\r\n            return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n        });\r\n        this.callback_.call(ctx, entries, ctx);\r\n        this.clearActive();\r\n    };\r\n    /**\r\n     * Clears the collection of active observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.clearActive = function () {\r\n        this.activeObservations_.splice(0);\r\n    };\r\n    /**\r\n     * Tells whether observer has active observations.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObserverSPI.prototype.hasActive = function () {\r\n        return this.activeObservations_.length > 0;\r\n    };\r\n    return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n     *      dimensions of the observed elements change.\r\n     */\r\n    function ResizeObserver(callback) {\r\n        if (!(this instanceof ResizeObserver)) {\r\n            throw new TypeError('Cannot call a class as a function.');\r\n        }\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        var controller = ResizeObserverController.getInstance();\r\n        var observer = new ResizeObserverSPI(callback, controller, this);\r\n        observers.set(this, observer);\r\n    }\r\n    return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n    'observe',\r\n    'unobserve',\r\n    'disconnect'\r\n].forEach(function (method) {\r\n    ResizeObserver.prototype[method] = function () {\r\n        var _a;\r\n        return (_a = observers.get(this))[method].apply(_a, arguments);\r\n    };\r\n});\n\nvar index = (function () {\r\n    // Export existing implementation if available.\r\n    if (typeof global$1.ResizeObserver !== 'undefined') {\r\n        return global$1.ResizeObserver;\r\n    }\r\n    return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n  width: number                        /* Element width */\n  height: number                       /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject<ResizeObserverEntry>()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new ResizeObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(() => getElementSize(el))\n        )\n      ),\n      startWith(getElementSize(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.scrollWidth,\n    height: el.scrollHeight\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElementContentSize,\n  getElementSize,\n  watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject<IntersectionObserverEntry>()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new IntersectionObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  }, {\n    threshold: 0\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(({ isIntersecting }) => isIntersecting)\n        )\n      )\n    )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n  el: HTMLElement, threshold = 16\n): Observable<boolean> {\n  return watchElementContentOffset(el)\n    .pipe(\n      map(({ y }) => {\n        const visible = getElementSize(el)\n        const content = getElementContentSize(el)\n        return y >= (\n          content.height - visible.height - threshold\n        )\n      }),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n  | \"drawer\"                           /* Toggle for drawer */\n  | \"search\"                           /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record<Toggle, HTMLInputElement> = {\n  drawer: getElement(\"[data-md-toggle=drawer]\"),\n  search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n  return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n  if (toggles[name].checked !== value)\n    toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable<boolean> {\n  const el = toggles[name]\n  return fromEvent(el, \"change\")\n    .pipe(\n      map(() => el.checked),\n      startWith(el.checked)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n  | \"global\"                           /* Global */\n  | \"search\"                           /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n  mode: KeyboardMode                   /* Keyboard mode */\n  type: string                         /* Key type */\n  claim(): void                        /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n  el: HTMLElement, type: string\n): boolean {\n  switch (el.constructor) {\n\n    /* Input elements */\n    case HTMLInputElement:\n      /* @ts-expect-error - omit unnecessary type cast */\n      if (el.type === \"radio\")\n        return /^Arrow/.test(type)\n      else\n        return true\n\n    /* Select element and textarea */\n    case HTMLSelectElement:\n    case HTMLTextAreaElement:\n      return true\n\n    /* Everything else */\n    default:\n      return el.isContentEditable\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable<Keyboard> {\n  return fromEvent<KeyboardEvent>(window, \"keydown\")\n    .pipe(\n      filter(ev => !(ev.metaKey || ev.ctrlKey)),\n      map(ev => ({\n        mode: getToggle(\"search\") ? \"search\" : \"global\",\n        type: ev.key,\n        claim() {\n          ev.preventDefault()\n          ev.stopPropagation()\n        }\n      } as Keyboard)),\n      filter(({ mode, type }) => {\n        if (mode === \"global\") {\n          const active = getActiveElement()\n          if (typeof active !== \"undefined\")\n            return !isSusceptibleToKeyboard(active, type)\n        }\n        return true\n      }),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n  return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n  location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject<URL> {\n  return new Subject<URL>()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n  & JSXInternal.HTMLAttributes\n  & JSXInternal.SVGAttributes\n  & Record<string, any>\n\n/**\n * Child element\n */\ntype Child =\n  | HTMLElement\n  | Text\n  | string\n  | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n  /* Handle primitive types (including raw HTML) */\n  if (typeof child === \"string\" || typeof child === \"number\") {\n    el.innerHTML += child.toString()\n\n  /* Handle nodes */\n  } else if (child instanceof Node) {\n    el.appendChild(child)\n\n  /* Handle nested children */\n  } else if (Array.isArray(child)) {\n    for (const node of child)\n      appendChild(el, node)\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h<T extends keyof HTMLElementTagNameMap>(\n  tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n  const el = document.createElement(tag)\n\n  /* Set attributes, if any */\n  if (attributes)\n    for (const attr of Object.keys(attributes)) {\n      if (typeof attributes[attr] === \"undefined\")\n        continue\n\n      /* Set default attribute or boolean */\n      if (typeof attributes[attr] !== \"boolean\")\n        el.setAttribute(attr, attributes[attr])\n      else\n        el.setAttribute(attr, \"\")\n    }\n\n  /* Append child nodes */\n  for (const child of children)\n    appendChild(el, child)\n\n  /* Return element */\n  return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n  namespace JSX {\n    type Element = HTMLElement\n    type IntrinsicElements = JSXInternal.IntrinsicElements\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n  let i = n\n  if (value.length > i) {\n    while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n    return `${value.substring(0, i)}...`\n  }\n  return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n  if (value > 999) {\n    const digits = +((value - 950) % 1000 > 99)\n    return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n  } else {\n    return value.toString()\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n  return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n  const el = h(\"a\", { href: hash })\n  el.addEventListener(\"click\", ev => ev.stopPropagation())\n  el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable<string> {\n  return fromEvent<HashChangeEvent>(window, \"hashchange\")\n    .pipe(\n      map(getLocationHash),\n      startWith(getLocationHash()),\n      filter(hash => hash.length > 0),\n      shareReplay(1)\n    )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable<HTMLElement> {\n  return watchLocationHash()\n    .pipe(\n      map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n      filter(el => typeof el !== \"undefined\")\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  fromEvent,\n  fromEventPattern,\n  map,\n  merge,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable<boolean> {\n  const media = matchMedia(query)\n  return fromEventPattern<boolean>(next => (\n    media.addListener(() => next(media.matches))\n  ))\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable<boolean> {\n  const media = matchMedia(\"print\")\n  return merge(\n    fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n    fromEvent(window, \"afterprint\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at<T>(\n  query$: Observable<boolean>, factory: () => Observable<T>\n): Observable<T> {\n  return query$\n    .pipe(\n      switchMap(active => active ? factory() : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  from,\n  map,\n  of,\n  shareReplay,\n  switchMap,\n  throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n  url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable<Response> {\n  return from(fetch(`${url}`, options))\n    .pipe(\n      catchError(() => EMPTY),\n      switchMap(res => res.status !== 200\n        ? throwError(() => new Error(res.statusText))\n        : of(res)\n      )\n    )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON<T>(\n  url: URL | string, options?: RequestInit\n): Observable<T> {\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.json()),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n  url: URL | string, options?: RequestInit\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/xml\")),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  switchMap,\n  take,\n  throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable<void> {\n  const script = h(\"script\", { src })\n  return defer(() => {\n    document.head.appendChild(script)\n    return merge(\n      fromEvent(script, \"load\"),\n      fromEvent(script, \"error\")\n        .pipe(\n          switchMap(() => (\n            throwError(() => new ReferenceError(`Invalid script: ${src}`))\n          ))\n        )\n    )\n      .pipe(\n        map(() => undefined),\n        finalize(() => document.head.removeChild(script)),\n        take(1)\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n  return {\n    x: Math.max(0, scrollX),\n    y: Math.max(0, scrollY)\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable<ViewportOffset> {\n  return merge(\n    fromEvent(window, \"scroll\", { passive: true }),\n    fromEvent(window, \"resize\", { passive: true })\n  )\n    .pipe(\n      map(getViewportOffset),\n      startWith(getViewportOffset())\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n  width: number                        /* Viewport width */\n  height: number                       /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n  return {\n    width:  innerWidth,\n    height: innerHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable<ViewportSize> {\n  return fromEvent(window, \"resize\", { passive: true })\n    .pipe(\n      map(getViewportSize),\n      startWith(getViewportSize())\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  map,\n  shareReplay\n} from \"rxjs\"\n\nimport {\n  ViewportOffset,\n  watchViewportOffset\n} from \"../offset\"\nimport {\n  ViewportSize,\n  watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n  offset: ViewportOffset               /* Viewport offset */\n  size: ViewportSize                   /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable<Viewport> {\n  return combineLatest([\n    watchViewportOffset(),\n    watchViewportSize()\n  ])\n    .pipe(\n      map(([offset, size]) => ({ offset, size })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilKeyChanged,\n  map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Viewport> {\n  const size$ = viewport$\n    .pipe(\n      distinctUntilKeyChanged(\"size\")\n    )\n\n  /* Compute element offset */\n  const offset$ = combineLatest([size$, header$])\n    .pipe(\n      map(() => getElementOffset(el))\n    )\n\n  /* Compute relative viewport, return hot observable */\n  return combineLatest([header$, viewport$, offset$])\n    .pipe(\n      map(([{ height }, { offset, size }, { x, y }]) => ({\n        offset: {\n          x: offset.x - x,\n          y: offset.y - y + height\n        },\n        size\n      }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  fromEvent,\n  map,\n  share,\n  switchMap,\n  tap,\n  throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n  type: unknown                        /* Message type */\n  data?: unknown                       /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n  T extends WorkerMessage\n> {\n  tx$: Subject<T>                      /* Message transmission subject */\n  rx$: Observable<T>                   /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions<T extends WorkerMessage> {\n  tx$: Observable<T>                   /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker<T extends WorkerMessage>(\n  worker: Worker, { tx$ }: WatchOptions<T>\n): Observable<T> {\n\n  /* Intercept messages from worker-like objects */\n  const rx$ = fromEvent<MessageEvent>(worker, \"message\")\n    .pipe(\n      map(({ data }) => data as T)\n    )\n\n  /* Send and receive messages, return hot observable */\n  return tx$\n    .pipe(\n      throttle(() => rx$, { leading: true, trailing: true }),\n      tap(message => worker.postMessage(message)),\n      switchMap(() => rx$),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n  | \"content.code.annotate\"            /* Code annotations */\n  | \"content.tabs.link\"                /* Link content tabs */\n  | \"header.autohide\"                  /* Hide header */\n  | \"navigation.expand\"                /* Automatic expansion */\n  | \"navigation.indexes\"               /* Section pages */\n  | \"navigation.instant\"               /* Instant loading */\n  | \"navigation.sections\"              /* Section navigation */\n  | \"navigation.tabs\"                  /* Tabs navigation */\n  | \"navigation.tabs.sticky\"           /* Tabs navigation (sticky) */\n  | \"navigation.top\"                   /* Back-to-top button */\n  | \"navigation.tracking\"              /* Anchor tracking */\n  | \"search.highlight\"                 /* Search highlighting */\n  | \"search.share\"                     /* Search sharing */\n  | \"search.suggest\"                   /* Search suggestions */\n  | \"toc.integrate\"                    /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n  | \"clipboard.copy\"                   /* Copy to clipboard */\n  | \"clipboard.copied\"                 /* Copied to clipboard */\n  | \"search.config.lang\"               /* Search language */\n  | \"search.config.pipeline\"           /* Search pipeline */\n  | \"search.config.separator\"          /* Search separator */\n  | \"search.placeholder\"               /* Search */\n  | \"search.result.placeholder\"        /* Type to start searching */\n  | \"search.result.none\"               /* No matching documents */\n  | \"search.result.one\"                /* 1 matching document */\n  | \"search.result.other\"              /* # matching documents */\n  | \"search.result.more.one\"           /* 1 more on this page */\n  | \"search.result.more.other\"         /* # more on this page */\n  | \"search.result.term.missing\"       /* Missing */\n  | \"select.version.title\"             /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record<Translation, string>\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n  provider: \"mike\"                     /* Version provider */\n  default?: string                     /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n  base: string                         /* Base URL */\n  features: Flag[]                     /* Feature flags */\n  translations: Translations           /* Translations */\n  search: string                       /* Search worker URL */\n  version?: Versioning                 /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n  return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n  return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n  key: Translation, value?: string | number\n): string {\n  return typeof value !== \"undefined\"\n    ? config.translations[key].replace(\"#\", value.toString())\n    : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n  | \"announce\"                         /* Announcement bar */\n  | \"container\"                        /* Container */\n  | \"content\"                          /* Content */\n  | \"dialog\"                           /* Dialog */\n  | \"header\"                           /* Header */\n  | \"header-title\"                     /* Header title */\n  | \"header-topic\"                     /* Header topic */\n  | \"main\"                             /* Main area */\n  | \"outdated\"                         /* Version warning */\n  | \"palette\"                          /* Color palette */\n  | \"search\"                           /* Search */\n  | \"search-query\"                     /* Search input */\n  | \"search-result\"                    /* Search results */\n  | \"search-share\"                     /* Search sharing */\n  | \"search-suggest\"                   /* Search suggestions */\n  | \"sidebar\"                          /* Sidebar */\n  | \"skip\"                             /* Skip link */\n  | \"source\"                           /* Repository information */\n  | \"tabs\"                             /* Navigation tabs */\n  | \"toc\"                              /* Table of contents */\n  | \"top\"                              /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n  T extends {} = {},\n  U extends HTMLElement = HTMLElement\n> =\n  T & {\n    ref: U                             /* Component reference */\n  }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n  \"announce\": HTMLElement              /* Announcement bar */\n  \"container\": HTMLElement             /* Container */\n  \"content\": HTMLElement               /* Content */\n  \"dialog\": HTMLElement                /* Dialog */\n  \"header\": HTMLElement                /* Header */\n  \"header-title\": HTMLElement          /* Header title */\n  \"header-topic\": HTMLElement          /* Header topic */\n  \"main\": HTMLElement                  /* Main area */\n  \"outdated\": HTMLElement              /* Version warning */\n  \"palette\": HTMLElement               /* Color palette */\n  \"search\": HTMLElement                /* Search */\n  \"search-query\": HTMLInputElement     /* Search input */\n  \"search-result\": HTMLElement         /* Search results */\n  \"search-share\": HTMLAnchorElement    /* Search sharing */\n  \"search-suggest\": HTMLElement        /* Search suggestions */\n  \"sidebar\": HTMLElement               /* Sidebar */\n  \"skip\": HTMLAnchorElement            /* Skip link */\n  \"source\": HTMLAnchorElement          /* Repository information */\n  \"tabs\": HTMLElement                  /* Navigation tabs */\n  \"toc\": HTMLElement                   /* Table of contents */\n  \"top\": HTMLAnchorElement             /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n  return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n  return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  map,\n  mergeWith,\n  switchMap,\n  take,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n  scrollable: boolean                  /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for Clipboard.js integration\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findCandidateList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n  el: HTMLElement\n): Observable<CodeBlock> {\n  return watchElementSize(el)\n    .pipe(\n      map(({ width }) => {\n        const content = getElementContentSize(el)\n        return {\n          scrollable: content.width > width\n        }\n      }),\n      distinctUntilKeyChanged(\"scrollable\")\n    )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<CodeBlock | Annotation>> {\n  const { matches: hover } = matchMedia(\"(hover)\")\n\n  /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n  const factory$ = defer(() => {\n    const push$ = new Subject<CodeBlock>()\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    if (ClipboardJS.isSupported()) {\n      const parent = el.closest(\"pre\")!\n      parent.id = `__code_${++sequence}`\n      parent.insertBefore(\n        renderClipboardButton(parent.id),\n        el\n      )\n    }\n\n    /* Handle code annotations */\n    const container = el.closest(\".highlight\")\n    if (container instanceof HTMLElement) {\n      const list = findCandidateList(container)\n\n      /* Mount code annotations, if enabled */\n      if (typeof list !== \"undefined\" && (\n        container.classList.contains(\"annotate\") ||\n        feature(\"content.code.annotate\")\n      )) {\n        const annotations$ = mountAnnotationList(list, el, options)\n\n        /* Create and return component */\n        return watchCodeBlock(el)\n          .pipe(\n            tap(state => push$.next(state)),\n            finalize(() => push$.complete()),\n            map(state => ({ ref: el, ...state })),\n            mergeWith(\n              watchElementSize(container)\n                .pipe(\n                  takeUntil(push$.pipe(takeLast(1))),\n                  map(({ width, height }) => width && height),\n                  distinctUntilChanged(),\n                  switchMap(active => active ? annotations$ : EMPTY)\n                )\n            )\n          )\n      }\n    }\n\n    /* Create and return component */\n    return watchCodeBlock(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n\n  /* Mount code block on first sight */\n  return watchElementVisibility(el)\n    .pipe(\n      filter(visible => visible),\n      take(1),\n      switchMap(() => factory$)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an empty annotation\n *\n * @param id - Annotation identifier\n *\n * @returns Element\n */\nexport function renderAnnotation(id: number): HTMLElement {\n  return (\n    <aside class=\"md-annotation\" tabIndex={0}>\n      <div class=\"md-annotation__inner md-tooltip\">\n        <div class=\"md-tooltip__inner md-typeset\"></div>\n      </div>\n      <span class=\"md-annotation__index\">\n        <span data-md-annotation-id={id}></span>\n      </span>\n    </aside>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n  return (\n    <button\n      class=\"md-clipboard md-icon\"\n      title={translation(\"clipboard.copy\")}\n      data-clipboard-target={`#${id} > code`}\n    ></button>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { feature, translation } from \"~/_\"\nimport {\n  SearchDocument,\n  SearchMetadata,\n  SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n  TEASER = 1,                          /* Render teaser */\n  PARENT = 2                           /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n  document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n  const parent = flag & Flag.PARENT\n  const teaser = flag & Flag.TEASER\n\n  /* Render missing query terms */\n  const missing = Object.keys(document.terms)\n    .filter(key => !document.terms[key])\n    .reduce<ComponentChild[]>((list, key) => [\n      ...list, <del>{key}</del>, \" \"\n    ], [])\n    .slice(0, -1)\n\n  /* Assemble query string for highlighting */\n  const url = new URL(document.location)\n  if (feature(\"search.highlight\"))\n    url.searchParams.set(\"h\", Object.entries(document.terms)\n      .filter(([, match]) => match)\n      .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n    )\n\n  /* Render article or section, depending on flags */\n  return (\n    <a href={`${url}`} class=\"md-search-result__link\" tabIndex={-1}>\n      <article\n        class={[\"md-search-result__article\", ...parent\n          ? [\"md-search-result__article--document\"]\n          : []\n        ].join(\" \")}\n        data-md-score={document.score.toFixed(2)}\n      >\n        {parent > 0 && <div class=\"md-search-result__icon md-icon\"></div>}\n        <h1 class=\"md-search-result__title\">{document.title}</h1>\n        {teaser > 0 && document.text.length > 0 &&\n          <p class=\"md-search-result__teaser\">\n            {truncate(document.text, 320)}\n          </p>\n        }\n        {document.tags && document.tags.map(tag => (\n          <span class=\"md-tag\">{tag}</span>\n        ))}\n        {teaser > 0 && missing.length > 0 &&\n          <p class=\"md-search-result__terms\">\n            {translation(\"search.result.term.missing\")}: {...missing}\n          </p>\n        }\n      </article>\n    </a>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n  result: SearchResultItem\n): HTMLElement {\n  const threshold = result[0].score\n  const docs = [...result]\n\n  /* Find and extract parent article */\n  const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n  const [article] = docs.splice(parent, 1)\n\n  /* Determine last index above threshold */\n  let index = docs.findIndex(doc => doc.score < threshold)\n  if (index === -1)\n    index = docs.length\n\n  /* Partition sections */\n  const best = docs.slice(0, index)\n  const more = docs.slice(index)\n\n  /* Render children */\n  const children = [\n    renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n    ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n    ...more.length ? [\n      <details class=\"md-search-result__more\">\n        <summary tabIndex={-1}>\n          {more.length > 0 && more.length === 1\n            ? translation(\"search.result.more.one\")\n            : translation(\"search.result.more.other\", more.length)\n          }\n        </summary>\n        {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n      </details>\n    ] : []\n  ]\n\n  /* Render search result */\n  return (\n    <li class=\"md-search-result__item\">\n      {children}\n    </li>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n  return (\n    <ul class=\"md-source__facts\">\n      {Object.entries(facts).map(([key, value]) => (\n        <li class={`md-source__fact md-source__fact--${key}`}>\n          {typeof value === \"number\" ? round(value) : value}\n        </li>\n      ))}\n    </ul>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n  | \"prev\"\n  | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n  type: TabbedControlType\n): HTMLElement {\n  const classes = `tabbed-control tabbed-control--${type}`\n  return (\n    <div class={classes} hidden>\n      <button class=\"tabbed-button\" tabIndex={-1}></button>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n  return (\n    <div class=\"md-typeset__scrollwrap\">\n      <div class=\"md-typeset__table\">\n        {table}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n  const config = configuration()\n\n  /* Ensure trailing slash, see https://bit.ly/3rL5u3f */\n  const url = new URL(`../${version.version}/`, config.base)\n  return (\n    <li class=\"md-version__item\">\n      <a href={`${url}`} class=\"md-version__link\">\n        {version.title}\n      </a>\n    </li>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n  versions: Version[], active: Version\n): HTMLElement {\n  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version.title\")}\n      >\n        {active.title}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  combineLatest,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  switchMap,\n  take,\n  takeLast,\n  takeUntil,\n  tap,\n  throttleTime\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getElement,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementOffset,\n  watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n  active: boolean                      /* Annotation is active */\n  offset: ElementOffset                /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Annotation> {\n  const offset$ = defer(() => combineLatest([\n    watchElementOffset(el),\n    watchElementContentOffset(container)\n  ]))\n    .pipe(\n      map(([{ x, y }, scroll]) => {\n        const { width } = getElementSize(el)\n        return ({\n          x: x - scroll.x + width / 2,\n          y: y - scroll.y\n        })\n      })\n    )\n\n  /* Actively watch annotation on focus */\n  return watchElementFocus(el)\n    .pipe(\n      switchMap(active => offset$\n        .pipe(\n          map(offset => ({ active, offset })),\n          take(+!active || Infinity)\n        )\n      )\n    )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Component<Annotation>> {\n  return defer(() => {\n    const push$ = new Subject<Annotation>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.style.removeProperty(\"--md-tooltip-x\")\n        el.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Start animation only when annotation is visible */\n    const done$ = push$.pipe(takeLast(1))\n    watchElementVisibility(el)\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(visible => {\n          el.toggleAttribute(\"data-md-visible\", visible)\n        })\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(500, animationFrameScheduler),\n        map(() => container.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next(origin) {\n            if (origin)\n              el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n            else\n              el.style.removeProperty(\"--md-tooltip-0\")\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-tooltip-0\")\n          }\n        })\n\n    /* Close open annotation on click */\n    const index = getElement(\":scope > :last-child\", el)\n    const blur$ = fromEvent(index, \"mousedown\", { once: true })\n    push$\n      .pipe(\n        switchMap(({ active }) => active ? blur$ : EMPTY),\n        tap(ev => ev.preventDefault())\n      )\n        .subscribe(() => el.blur())\n\n    /* Create and return component */\n    return watchAnnotation(el, container)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  merge,\n  share,\n  takeLast,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n  const markers: Text[] = []\n  for (const comment of getElements(\".c, .c1, .cm\", container)) {\n    let match: RegExpExecArray | null\n\n    /* Split text at marker and add to list */\n    let text = comment.firstChild as Text\n    if (text instanceof Text)\n      while ((match = /\\((\\d+)\\)/.exec(text.textContent!))) {\n        const marker = text.splitText(match.index)\n        text = marker.splitText(match[0].length)\n        markers.push(marker)\n      }\n  }\n  return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n  target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n  el: HTMLElement, container: HTMLElement, { print$ }: MountOptions\n): Observable<Component<Annotation>> {\n\n  /* Find and replace all markers with empty annotations */\n  const annotations = new Map<number, HTMLElement>()\n  for (const marker of findAnnotationMarkers(container)) {\n    const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n    if (getOptionalElement(`li:nth-child(${id})`, el)) {\n      annotations.set(+id, renderAnnotation(+id))\n      marker.replaceWith(annotations.get(+id)!)\n    }\n  }\n\n  /* Keep list if there are no annotations to render */\n  if (annotations.size === 0)\n    return EMPTY\n\n  /* Create and return component */\n  return defer(() => {\n    const done$ = new Subject<void>()\n\n    /* Handle print mode - see https://bit.ly/3rgPdpt */\n    print$\n      .pipe(\n        takeUntil(done$.pipe(takeLast(1)))\n      )\n        .subscribe(active => {\n          el.hidden = !active\n\n          /* Show annotations in code block or list (print) */\n          for (const [id, annotation] of annotations) {\n            const inner = getElement(\".md-typeset\", annotation)\n            const child = getElement(`li:nth-child(${id})`, el)\n            if (!active)\n              swap(child, inner)\n            else\n              swap(inner, child)\n          }\n        })\n\n    /* Create and return component */\n    return merge(...[...annotations]\n      .map(([, annotation]) => (\n        mountAnnotation(annotation, container)\n      ))\n    )\n      .pipe(\n        finalize(() => done$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable<void>\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable<void> {\n  return typeof mermaid === \"undefined\" || mermaid instanceof Element\n    ? watchScript(\"https://unpkg.com/mermaid@9.0.1/dist/mermaid.min.js\")\n    : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n  el: HTMLElement\n): Observable<Component<Mermaid>> {\n  el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n  mermaid$ ||= fetchScripts()\n    .pipe(\n      tap(() => mermaid.initialize({\n        startOnLoad: false,\n        themeCSS\n      })),\n      map(() => undefined),\n      shareReplay(1)\n    )\n\n  /* Render diagram */\n  mermaid$.subscribe(() => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${sequence++}`\n    const host = h(\"div\", { class: \"mermaid\" })\n    mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n      /* Create a shadow root and inject diagram */\n      const shadow = host.attachShadow({ mode: \"closed\" })\n      shadow.innerHTML = svg\n\n      /* Replace code block with diagram */\n      el.replaceWith(host)\n    })\n  })\n\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n  action: \"open\" | \"close\"             /* Details state */\n  reveal?: boolean                     /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n  el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable<Details> {\n  let open = true\n  return merge(\n\n    /* Open and focus details on location target */\n    target$\n      .pipe(\n        map(target => target.closest(\"details:not([open])\")!),\n        filter(details => el === details),\n        map(() => ({\n          action: \"open\", reveal: true\n        }) as Details)\n      ),\n\n    /* Open details on print and close afterwards */\n    print$\n      .pipe(\n        filter(active => active || !open),\n        tap(() => open = el.open),\n        map(active => ({\n          action: active ? \"open\" : \"close\"\n        }) as Details)\n      )\n  )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n  el: HTMLDetailsElement, options: MountOptions\n): Observable<Component<Details>> {\n  return defer(() => {\n    const push$ = new Subject<Details>()\n    push$.subscribe(({ action, reveal }) => {\n      if (action === \"open\")\n        el.setAttribute(\"open\", \"\")\n      else\n        el.removeAttribute(\"open\")\n      if (reveal)\n        el.scrollIntoView()\n    })\n\n    /* Create and return component */\n    return watchDetails(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n  el: HTMLElement\n): Observable<Component<DataTable>> {\n  el.replaceWith(sentinel)\n  sentinel.replaceWith(renderTable(el))\n\n  /* Create and return component */\n  return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  skip,\n  startWith,\n  subscribeOn,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElement,\n  getElementContentOffset,\n  getElementContentSize,\n  getElementOffset,\n  getElementSize,\n  getElements,\n  watchElementContentOffset,\n  watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n  active: HTMLLabelElement             /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  el: HTMLElement\n): Observable<ContentTabs> {\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\n  const initial = inputs.find(input => input.checked) || inputs[0]\n  return merge(...inputs.map(input => fromEvent(input, \"change\")\n    .pipe(\n      map(() => getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`))\n    )\n  ))\n    .pipe(\n      startWith(getElement<HTMLLabelElement>(`label[for=\"${initial.id}\"]`)),\n      map(active => ({ active }))\n    )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n  el: HTMLElement\n): Observable<Component<ContentTabs>> {\n\n  /* Render content tab previous button for pagination */\n  const prev = renderTabbedControl(\"prev\")\n  el.append(prev)\n\n  /* Render content tab next button for pagination */\n  const next = renderTabbedControl(\"next\")\n  el.append(next)\n\n  /* Mount component on subscription */\n  const container = getElement(\".tabbed-labels\", el)\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(takeLast(1))\n    combineLatest([push$, watchElementSize(el)])\n      .pipe(\n        auditTime(1, animationFrameScheduler),\n        takeUntil(done$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n            el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n            /* Scroll container to active content tab */\n            const content = getElementContentOffset(container)\n            if (\n              offset.x         < content.x              ||\n              offset.x + width > content.x + size.width\n            )\n              container.scrollTo({\n                left: Math.max(0, offset.x - 16),\n                behavior: \"smooth\"\n              })\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-indicator-x\")\n            el.style.removeProperty(\"--md-indicator-width\")\n          }\n        })\n\n    /* Hide content tab buttons on borders */\n    combineLatest([\n      watchElementContentOffset(container),\n      watchElementSize(container)\n    ])\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(([offset, size]) => {\n          const content = getElementContentSize(container)\n          prev.hidden = offset.x < 16\n          next.hidden = offset.x > content.width - size.width - 16\n        })\n\n    /* Paginate content tab container on click */\n    merge(\n      fromEvent(prev, \"click\").pipe(map(() => -1)),\n      fromEvent(next, \"click\").pipe(map(() => +1))\n    )\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.pipe(skip(1))\n        .subscribe(({ active }) => {\n          const tab = active.innerText.trim()\n          for (const set of getElements(\"[data-tabs]\"))\n            for (const input of getElements<HTMLInputElement>(\n              \":scope > input\", set\n            )) {\n              const label = getElement(`label[for=\"${input.id}\"]`)\n              if (label.innerText.trim() === tab) {\n                input.click()\n                break\n              }\n            }\n\n          /* Persist active tabs in local storage */\n          const tabs = __md_get<string[]>(\"__tabs\") || []\n          __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n        })\n\n    /* Create and return component */\n    return watchContentTabs(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n  CodeBlock,\n  Mermaid,\n  mountCodeBlock,\n  mountMermaid\n} from \"../code\"\nimport {\n  Details,\n  mountDetails\n} from \"../details\"\nimport {\n  DataTable,\n  mountDataTable\n} from \"../table\"\nimport {\n  ContentTabs,\n  mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n  | Annotation\n  | ContentTabs\n  | CodeBlock\n  | Mermaid\n  | DataTable\n  | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n  el: HTMLElement, { target$, print$ }: MountOptions\n): Observable<Component<Content>> {\n  return merge(\n\n    /* Code blocks */\n    ...getElements(\"pre:not(.mermaid) > code\", el)\n      .map(child => mountCodeBlock(child, { print$ })),\n\n    /* Mermaid diagrams */\n    ...getElements(\"pre.mermaid\", el)\n      .map(child => mountMermaid(child)),\n\n    /* Data tables */\n    ...getElements(\"table:not([class])\", el)\n      .map(child => mountDataTable(child)),\n\n    /* Details */\n    ...getElements(\"details\", el)\n      .map(child => mountDetails(child, { target$, print$ })),\n\n    /* Content tabs */\n    ...getElements(\"[data-tabs]\", el)\n      .map(child => mountContentTabs(child))\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  delay,\n  finalize,\n  map,\n  merge,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n  message: string                      /* Dialog message */\n  active: boolean                      /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n  _el: HTMLElement, { alert$ }: WatchOptions\n): Observable<Dialog> {\n  return alert$\n    .pipe(\n      switchMap(message => merge(\n        of(true),\n        of(false).pipe(delay(2000))\n      )\n        .pipe(\n          map(active => ({ message, active }))\n        )\n      )\n    )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Dialog>> {\n  const inner = getElement(\".md-typeset\", el)\n  return defer(() => {\n    const push$ = new Subject<Dialog>()\n    push$.subscribe(({ message, active }) => {\n      el.classList.toggle(\"md-dialog--active\", active)\n      inner.textContent = message\n    })\n\n    /* Create and return component */\n    return watchDialog(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  combineLatestWith,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  map,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeLast,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n  height: number                       /* Header visible height */\n  hidden: boolean                      /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable<boolean> {\n  if (!feature(\"header.autohide\"))\n    return of(false)\n\n  /* Compute direction and turning point */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => [a < b, b] as const),\n      distinctUntilKeyChanged(0)\n    )\n\n  /* Compute whether header should be hidden */\n  const hidden$ = combineLatest([viewport$, direction$])\n    .pipe(\n      filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n      map(([, [direction]]) => direction),\n      distinctUntilChanged()\n    )\n\n  /* Compute threshold for hiding */\n  const search$ = watchToggle(\"search\")\n  return combineLatest([viewport$, search$])\n    .pipe(\n      map(([{ offset }, search]) => offset.y > 400 && !search),\n      distinctUntilChanged(),\n      switchMap(active => active ? hidden$ : of(false)),\n      startWith(false)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n  el: HTMLElement, options: WatchOptions\n): Observable<Header> {\n  return defer(() => combineLatest([\n    watchElementSize(el),\n    isHidden(options)\n  ]))\n    .pipe(\n      map(([{ height }, hidden]) => ({\n        height,\n        hidden\n      })),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.hidden === b.hidden\n      )),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n  el: HTMLElement, { header$, main$ }: MountOptions\n): Observable<Component<Header>> {\n  return defer(() => {\n    const push$ = new Subject<Main>()\n    const done$ = push$.pipe(takeLast(1))\n    push$\n      .pipe(\n        distinctUntilKeyChanged(\"active\"),\n        combineLatestWith(header$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElementSize,\n  getOptionalElement,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n  active: boolean                      /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<HeaderTitle> {\n  return watchViewportAt(el, { viewport$, header$ })\n    .pipe(\n      map(({ offset: { y } }) => {\n        const { height } = getElementSize(el)\n        return {\n          active: y >= height\n        }\n      }),\n      distinctUntilKeyChanged(\"active\")\n    )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<HeaderTitle>> {\n  return defer(() => {\n    const push$ = new Subject<HeaderTitle>()\n    push$.subscribe(({ active }) => {\n      el.classList.toggle(\"md-header__title--active\", active)\n    })\n\n    /* Obtain headline, if any */\n    const heading = getOptionalElement(\"article h1\")\n    if (typeof heading === \"undefined\")\n      return EMPTY\n\n    /* Create and return component */\n    return watchHeaderTitle(heading, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  map,\n  switchMap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n  offset: number                       /* Main area top offset */\n  height: number                       /* Main area visible height */\n  active: boolean                      /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Main> {\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      map(({ height }) => height),\n      distinctUntilChanged()\n    )\n\n  /* Compute the main area's top and bottom borders */\n  const border$ = adjust$\n    .pipe(\n      switchMap(() => watchElementSize(el)\n        .pipe(\n          map(({ height }) => ({\n            top:    el.offsetTop,\n            bottom: el.offsetTop + height\n          })),\n          distinctUntilKeyChanged(\"bottom\")\n        )\n      )\n    )\n\n  /* Compute the main area's offset, visible height and if we scrolled past */\n  return combineLatest([adjust$, border$, viewport$])\n    .pipe(\n      map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n        height = Math.max(0, height\n          - Math.max(0, top    - y,  header)\n          - Math.max(0, height + y - bottom)\n        )\n        return {\n          offset: top - header,\n          height,\n          active: top - header <= y\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.offset === b.offset &&\n        a.height === b.height &&\n        a.active === b.active\n      ))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  shareReplay,\n  startWith,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n  scheme?: string                      /* Color scheme */\n  primary?: string                     /* Primary color */\n  accent?: string                      /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n  index: number                        /* Palette index */\n  color: PaletteColor                  /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n  inputs: HTMLInputElement[]\n): Observable<Palette> {\n  const current = __md_get<Palette>(\"__palette\") || {\n    index: inputs.findIndex(input => matchMedia(\n      input.getAttribute(\"data-md-color-media\")!\n    ).matches)\n  }\n\n  /* Emit changes in color palette */\n  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\")\n        .pipe(\n          map(() => input)\n        )\n      ),\n      startWith(inputs[Math.max(0, current.index)]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\n          scheme:  input.getAttribute(\"data-md-color-scheme\"),\n          primary: input.getAttribute(\"data-md-color-primary\"),\n          accent:  input.getAttribute(\"data-md-color-accent\")\n        }\n      } as Palette)),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n  el: HTMLElement\n): Observable<Component<Palette>> {\n  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* Toggle visibility */\n      for (let index = 0; index < inputs.length; index++) {\n        const label = inputs[index].nextElementSibling\n        if (label instanceof HTMLElement)\n          label.hidden = palette.index !== index\n      }\n\n      /* Persist preference in local storage */\n      __md_set(\"__palette\", palette)\n    })\n\n    /* Revert transition durations after color switch */\n    push$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    const inputs = getElements<HTMLInputElement>(\"input\", el)\n    return watchPalette(inputs)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n  el.setAttribute(\"data-md-copying\", \"\")\n  const text = el.innerText\n  el.removeAttribute(\"data-md-copying\")\n  return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n  { alert$ }: SetupOptions\n): void {\n  if (ClipboardJS.isSupported()) {\n    new Observable<ClipboardJS.Event>(subscriber => {\n      new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n        text: el => (\n          el.getAttribute(\"data-clipboard-text\")! ||\n          extract(getElement(\n            el.getAttribute(\"data-clipboard-target\")!\n          ))\n        )\n      })\n        .on(\"success\", ev => subscriber.next(ev))\n    })\n      .pipe(\n        tap(ev => {\n          const trigger = ev.trigger as HTMLElement\n          trigger.focus()\n        }),\n        map(() => translation(\"clipboard.copied\"))\n      )\n        .subscribe(alert$)\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  of,\n  tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n  if (urls.length < 2)\n    return [\"\"]\n\n  /* Take the first two URLs and remove everything after the last slash */\n  const [root, next] = [...urls]\n    .sort((a, b) => a.length - b.length)\n    .map(url => url.replace(/[^/]+$/, \"\"))\n\n  /* Compute common prefix */\n  let index = 0\n  if (root === next)\n    index = root.length\n  else\n    while (root.charCodeAt(index) === next.charCodeAt(index))\n      index++\n\n  /* Remove common prefix and return in original order */\n  return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable<Sitemap> {\n  const cached = __md_get<Sitemap>(\"__sitemap\", sessionStorage, base)\n  if (cached) {\n    return of(cached)\n  } else {\n    const config = configuration()\n    return requestXML(new URL(\"sitemap.xml\", base || config.base))\n      .pipe(\n        map(sitemap => preprocess(getElements(\"loc\", sitemap)\n          .map(node => node.textContent!)\n        )),\n        catchError(() => EMPTY), // @todo refactor instant loading\n        defaultIfEmpty([]),\n        tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  NEVER,\n  Observable,\n  Subject,\n  bufferCount,\n  catchError,\n  concatMap,\n  debounceTime,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  of,\n  sample,\n  share,\n  skip,\n  skipUntil,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  ViewportOffset,\n  getElements,\n  getOptionalElement,\n  request,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n  url: URL                             /* State URL */\n  offset?: ViewportOffset              /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n  location$: Subject<URL>              /* Location subject */\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n  { document$, location$, viewport$ }: SetupOptions\n): void {\n  const config = configuration()\n  if (location.protocol === \"file:\")\n    return\n\n  /* Disable automatic scroll restoration */\n  if (\"scrollRestoration\" in history) {\n    history.scrollRestoration = \"manual\"\n\n    /* Hack: ensure that reloads restore viewport offset */\n    fromEvent(window, \"beforeunload\")\n      .subscribe(() => {\n        history.scrollRestoration = \"auto\"\n      })\n  }\n\n  /* Hack: ensure absolute favicon link to omit 404s when switching */\n  const favicon = getOptionalElement<HTMLLinkElement>(\"link[rel=icon]\")\n  if (typeof favicon !== \"undefined\")\n    favicon.href = favicon.href\n\n  /* Intercept internal navigation */\n  const push$ = fetchSitemap()\n    .pipe(\n      map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          switchMap(ev => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target) {\n                const url = new URL(el.href)\n\n                /* Canonicalize URL */\n                url.search = \"\"\n                url.hash = \"\"\n\n                /* Check if URL should be intercepted */\n                if (\n                  url.pathname !== location.pathname &&\n                  urls.includes(url.toString())\n                ) {\n                  ev.preventDefault()\n                  return of({\n                    url: new URL(el.href)\n                  })\n                }\n              }\n            }\n            return NEVER\n          })\n        )\n      ),\n      share<HistoryState>()\n    )\n\n  /* Intercept history back and forward */\n  const pop$ = fromEvent<PopStateEvent>(window, \"popstate\")\n    .pipe(\n      filter(ev => ev.state !== null),\n      map(ev => ({\n        url: new URL(location.href),\n        offset: ev.state\n      })),\n      share<HistoryState>()\n    )\n\n  /* Emit location change */\n  merge(push$, pop$)\n    .pipe(\n      distinctUntilChanged((a, b) => a.url.href === b.url.href),\n      map(({ url }) => url)\n    )\n      .subscribe(location$)\n\n  /* Fetch document via `XMLHTTPRequest` */\n  const response$ = location$\n    .pipe(\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(url => request(url.href)\n        .pipe(\n          catchError(() => {\n            setLocation(url)\n            return NEVER\n          })\n        )\n      ),\n      share()\n    )\n\n  /* Set new location via `history.pushState` */\n  push$\n    .pipe(\n      sample(response$)\n    )\n      .subscribe(({ url }) => {\n        history.pushState({}, \"\", `${url}`)\n      })\n\n  /* Parse and emit fetched document */\n  const dom = new DOMParser()\n  response$\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/html\"))\n    )\n      .subscribe(document$)\n\n  /* Replace meta tags and components */\n  document$\n    .pipe(\n      skip(1)\n    )\n      .subscribe(replacement => {\n        for (const selector of [\n\n          /* Meta tags */\n          \"title\",\n          \"link[rel=canonical]\",\n          \"meta[name=author]\",\n          \"meta[name=description]\",\n\n          /* Components */\n          \"[data-md-component=announce]\",\n          \"[data-md-component=container]\",\n          \"[data-md-component=header-topic]\",\n          \"[data-md-component=outdated]\",\n          \"[data-md-component=logo]\",\n          \"[data-md-component=skip]\",\n          ...feature(\"navigation.tabs.sticky\")\n            ? [\"[data-md-component=tabs]\"]\n            : []\n        ]) {\n          const source = getOptionalElement(selector)\n          const target = getOptionalElement(selector, replacement)\n          if (\n            typeof source !== \"undefined\" &&\n            typeof target !== \"undefined\"\n          ) {\n            source.replaceWith(target)\n          }\n        }\n      })\n\n  /* Re-evaluate scripts */\n  document$\n    .pipe(\n      skip(1),\n      map(() => getComponentElement(\"container\")),\n      switchMap(el => getElements(\"script\", el)),\n      concatMap(el => {\n        const script = h(\"script\")\n        if (el.src) {\n          for (const name of el.getAttributeNames())\n            script.setAttribute(name, el.getAttribute(name)!)\n          el.replaceWith(script)\n\n          /* Complete when script is loaded */\n          return new Observable(observer => {\n            script.onload = () => observer.complete()\n          })\n\n        /* Complete immediately */\n        } else {\n          script.textContent = el.textContent\n          el.replaceWith(script)\n          return EMPTY\n        }\n      })\n    )\n      .subscribe()\n\n  /* Emit history state change */\n  merge(push$, pop$)\n    .pipe(\n      sample(document$)\n    )\n      .subscribe(({ url, offset }) => {\n        if (url.hash && !offset) {\n          setLocationHash(url.hash)\n        } else {\n          window.scrollTo(0, offset?.y || 0)\n        }\n      })\n\n  /* Debounce update of viewport offset */\n  viewport$\n    .pipe(\n      skipUntil(push$),\n      debounceTime(250),\n      distinctUntilKeyChanged(\"offset\")\n    )\n      .subscribe(({ offset }) => {\n        history.replaceState(offset, \"\")\n      })\n\n  /* Set viewport offset from history */\n  merge(push$, pop$)\n    .pipe(\n      bufferCount(2, 1),\n      filter(([a, b]) => a.url.pathname === b.url.pathname),\n      map(([, state]) => state)\n    )\n      .subscribe(({ offset }) => {\n        window.scrollTo(0, offset?.y || 0)\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n  parent?: SearchIndexDocument         /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map<string, SearchDocument>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchIndexDocument[]\n): SearchDocumentMap {\n  const documents = new Map<string, SearchDocument>()\n  const parents   = new Set<SearchDocument>()\n  for (const doc of docs) {\n    const [path, hash] = doc.location.split(\"#\")\n\n    /* Extract location, title and tags */\n    const location = doc.location\n    const title    = doc.title\n    const tags     = doc.tags\n\n    /* Escape and cleanup text */\n    const text = escapeHTML(doc.text)\n      .replace(/\\s+(?=[,.:;!?])/g, \"\")\n      .replace(/\\s+/g, \" \")\n\n    /* Handle section */\n    if (hash) {\n      const parent = documents.get(path)!\n\n      /* Ignore first section, override article */\n      if (!parents.has(parent)) {\n        parent.title = doc.title\n        parent.text  = text\n\n        /* Remember that we processed the article */\n        parents.add(parent)\n\n      /* Add subsequent section */\n      } else {\n        documents.set(location, {\n          location,\n          title,\n          text,\n          parent\n        })\n      }\n\n    /* Add article */\n    } else {\n      documents.set(location, {\n        location,\n        title,\n        text,\n        ...tags && { tags }\n      })\n    }\n  }\n  return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n  const separator = new RegExp(config.separator, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator})(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => (\n      escape\n        ? escapeHTML(value)\n        : value\n      )\n        .replace(match, highlight)\n        .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n *    that the resulting document must contain all terms, converting the query\n *    to an `AND` query (as opposed to the default `OR` behavior). While users\n *    may expect terms enclosed in quotation marks to map to span queries, i.e.\n *    for which order is important, Lunr.js doesn't support them, so the best\n *    we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n *    query or preceded by white space, or are not followed by a non-whitespace\n *    character or are at the end of the query string. Furthermore, filter\n *    unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n  return query\n    .split(/\"([^\"]+)\"/g)                            /* => 1 */\n      .map((terms, index) => index & 1\n        ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n        : terms\n      )\n      .join(\"\")\n    .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n    .trim()                                         /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n  SETUP,                               /* Search index setup */\n  READY,                               /* Search index ready */\n  QUERY,                               /* Search query */\n  RESULT                               /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n  type: SearchMessageType.SETUP        /* Message type */\n  data: SearchIndex                    /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n  type: SearchMessageType.READY        /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n  type: SearchMessageType.QUERY        /* Message type */\n  data: string                         /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n  type: SearchMessageType.RESULT       /* Message type */\n  data: SearchResult                   /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n  | SearchSetupMessage\n  | SearchReadyMessage\n  | SearchQueryMessage\n  | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n  message: SearchMessage\n): message is SearchSetupMessage {\n  return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n  message: SearchMessage\n): message is SearchReadyMessage {\n  return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n  message: SearchMessage\n): message is SearchQueryMessage {\n  return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n  message: SearchMessage\n): message is SearchResultMessage {\n  return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ObservableInput,\n  Subject,\n  from,\n  map,\n  share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n  SearchOptions,\n  SearchPipeline\n} from \"../../options\"\nimport {\n  SearchMessage,\n  SearchMessageType,\n  SearchSetupMessage,\n  isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler<SearchMessage>\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n  /* Override default language with value from translation */\n  if (config.lang.length === 1 && config.lang[0] === \"en\")\n    config.lang = [\n      translation(\"search.config.lang\")\n    ]\n\n  /* Override default separator with value from translation */\n  if (config.separator === \"[\\\\s\\\\-]+\")\n    config.separator = translation(\"search.config.separator\")\n\n  /* Set pipeline from translation */\n  const pipeline = translation(\"search.config.pipeline\")\n    .split(/\\s*,\\s*/)\n    .filter(Boolean) as SearchPipeline\n\n  /* Determine search options */\n  const options: SearchOptions = {\n    pipeline,\n    suggestions: feature(\"search.suggest\")\n  }\n\n  /* Return search index after defaulting */\n  return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n  url: string, index: ObservableInput<SearchIndex>\n): SearchWorker {\n  const config = configuration()\n  const worker = new Worker(url)\n\n  /* Create communication channels and resolve relative links */\n  const tx$ = new Subject<SearchMessage>()\n  const rx$ = watchWorker(worker, { tx$ })\n    .pipe(\n      map(message => {\n        if (isSearchResultMessage(message)) {\n          for (const result of message.data.items)\n            for (const document of result)\n              document.location = `${new URL(document.location, config.base)}`\n        }\n        return message\n      }),\n      share()\n    )\n\n  /* Set up search index */\n  from(index)\n    .pipe(\n      map(data => ({\n        type: SearchMessageType.SETUP,\n        data: setupSearchIndex(data)\n      } as SearchSetupMessage))\n    )\n      .subscribe(tx$.next.bind(tx$))\n\n  /* Return search worker */\n  return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n  { document$ }: SetupOptions\n): void {\n  const config = configuration()\n  const versions$ = requestJSON<Version[]>(\n    new URL(\"../versions.json\", config.base)\n  )\n    .pipe(\n      catchError(() => EMPTY) // @todo refactor instant loading\n    )\n\n  /* Determine current version */\n  const current$ = versions$\n    .pipe(\n      map(versions => {\n        const [, current] = config.base.match(/([^/]+)\\/?$/)!\n        return versions.find(({ version, aliases }) => (\n          version === current || aliases.includes(current)\n        )) || versions[0]\n      })\n    )\n\n  /* Intercept inter-version navigation */\n  versions$\n    .pipe(\n      map(versions => new Map(versions.map(version => [\n        `${new URL(`../${version.version}/`, config.base)}`,\n        version\n      ]))),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          withLatestFrom(current$),\n          switchMap(([ev, current]) => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target && urls.has(el.href)) {\n                const url = el.href\n                // This is a temporary hack to detect if a version inside the\n                // version selector or on another part of the site was clicked.\n                // If we're inside the version selector, we definitely want to\n                // find the same page, as we might have different deployments\n                // due to aliases. However, if we're outside the version\n                // selector, we must abort here, because we might otherwise\n                // interfere with instant loading. We need to refactor this\n                // at some point together with instant loading.\n                //\n                // See https://github.com/squidfunk/mkdocs-material/issues/4012\n                if (!ev.target.closest(\".md-version\")) {\n                  const version = urls.get(url)!\n                  if (version === current)\n                    return EMPTY\n                }\n                ev.preventDefault()\n                return of(url)\n              }\n            }\n            return EMPTY\n          }),\n          switchMap(url => {\n            const { version } = urls.get(url)!\n            return fetchSitemap(new URL(url))\n              .pipe(\n                map(sitemap => {\n                  const location = getLocation()\n                  const path = location.href.replace(config.base, \"\")\n                  return sitemap.includes(path)\n                    ? new URL(`../${version}/${path}`, config.base)\n                    : new URL(url)\n                })\n              )\n          })\n        )\n      )\n    )\n      .subscribe(url => setLocation(url))\n\n  /* Render version selector and warning */\n  combineLatest([versions$, current$])\n    .subscribe(([versions, current]) => {\n      const topic = getElement(\".md-header__topic\")\n      topic.appendChild(renderVersionSelector(versions, current))\n    })\n\n  /* Integrate outdated version banner with instant loading */\n  document$.pipe(switchMap(() => current$))\n    .subscribe(current => {\n\n      /* Check if version state was already determined */\n      let outdated = __md_get(\"__outdated\", sessionStorage)\n      if (outdated === null) {\n        const latest = config.version?.default || \"latest\"\n        outdated = !current.aliases.includes(latest)\n\n        /* Persist version state in session storage */\n        __md_set(\"__outdated\", outdated, sessionStorage)\n      }\n\n      /* Unhide outdated version banner */\n      if (outdated)\n        for (const warning of getComponentElements(\"outdated\"))\n          warning.hidden = false\n    })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  combineLatest,\n  delay,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  share,\n  shareReplay,\n  startWith,\n  take,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getLocation,\n  setToggle,\n  watchElementFocus,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessageType,\n  SearchQueryMessage,\n  SearchWorker,\n  defaultTransform,\n  isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n  value: string                        /* Query value */\n  focus: boolean                       /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n  el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable<SearchQuery> {\n  const fn = __search?.transform || defaultTransform\n\n  /* Immediately show search dialog */\n  const { searchParams } = getLocation()\n  if (searchParams.has(\"q\"))\n    setToggle(\"search\", true)\n\n  /* Intercept query parameter (deep link) */\n  const param$ = rx$\n    .pipe(\n      filter(isSearchReadyMessage),\n      take(1),\n      map(() => searchParams.get(\"q\") || \"\")\n    )\n\n  /* Remove query parameter when search is closed */\n  watchToggle(\"search\")\n    .pipe(\n      filter(active => !active),\n      take(1)\n    )\n      .subscribe(() => {\n        const url = new URL(location.href)\n        url.searchParams.delete(\"q\")\n        history.replaceState({}, \"\", `${url}`)\n      })\n\n  /* Set query from parameter */\n  param$.subscribe(value => { // TODO: not ideal - find a better way\n    if (value) {\n      el.value = value\n      el.focus()\n    }\n  })\n\n  /* Intercept focus and input events */\n  const focus$ = watchElementFocus(el)\n  const value$ = merge(\n    fromEvent(el, \"keyup\"),\n    fromEvent(el, \"focus\").pipe(delay(1)),\n    param$\n  )\n    .pipe(\n      map(() => fn(el.value)),\n      startWith(\"\"),\n      distinctUntilChanged(),\n    )\n\n  /* Combine into single observable */\n  return combineLatest([value$, focus$])\n    .pipe(\n      map(([value, focus]) => ({ value, focus })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n  el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable<Component<SearchQuery, HTMLInputElement>> {\n  const push$ = new Subject<SearchQuery>()\n  const done$ = push$.pipe(takeLast(1))\n\n  /* Handle value changes */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"value\"),\n      map(({ value }): SearchQueryMessage => ({\n        type: SearchMessageType.QUERY,\n        data: value\n      }))\n    )\n      .subscribe(tx$.next.bind(tx$))\n\n  /* Handle focus changes */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"focus\")\n    )\n      .subscribe(({ focus }) => {\n        if (focus) {\n          setToggle(\"search\", focus)\n          el.placeholder = \"\"\n        } else {\n          el.placeholder = translation(\"search.placeholder\")\n        }\n      })\n\n  /* Handle reset */\n  fromEvent(el.form!, \"reset\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(() => el.focus())\n\n  /* Create and return component */\n  return watchSearchQuery(el, { tx$, rx$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state })),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  skipUntil,\n  switchMap,\n  take,\n  tap,\n  withLatestFrom,\n  zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getElement,\n  watchElementBoundary\n} from \"~/browser\"\nimport {\n  SearchResult,\n  SearchWorker,\n  isSearchReadyMessage,\n  isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n  el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable<Component<SearchResult>> {\n  const push$ = new Subject<SearchResult>()\n  const boundary$ = watchElementBoundary(el.parentElement!)\n    .pipe(\n      filter(Boolean)\n    )\n\n  /* Retrieve nested components */\n  const meta = getElement(\":scope > :first-child\", el)\n  const list = getElement(\":scope > :last-child\", el)\n\n  /* Wait until search is ready */\n  const ready$ = rx$\n    .pipe(\n      filter(isSearchReadyMessage),\n      take(1)\n    )\n\n  /* Update search result metadata */\n  push$\n    .pipe(\n      withLatestFrom(query$),\n      skipUntil(ready$)\n    )\n      .subscribe(([{ items }, { value }]) => {\n        if (value) {\n          switch (items.length) {\n\n            /* No results */\n            case 0:\n              meta.textContent = translation(\"search.result.none\")\n              break\n\n            /* One result */\n            case 1:\n              meta.textContent = translation(\"search.result.one\")\n              break\n\n            /* Multiple result */\n            default:\n              meta.textContent = translation(\n                \"search.result.other\",\n                round(items.length)\n              )\n          }\n        } else {\n          meta.textContent = translation(\"search.result.placeholder\")\n        }\n      })\n\n  /* Update search result list */\n  push$\n    .pipe(\n      tap(() => list.innerHTML = \"\"),\n      switchMap(({ items }) => merge(\n        of(...items.slice(0, 10)),\n        of(...items.slice(10))\n          .pipe(\n            bufferCount(4),\n            zipWith(boundary$),\n            switchMap(([chunk]) => chunk)\n          )\n      ))\n    )\n      .subscribe(result => list.appendChild(\n        renderSearchResultItem(result)\n      ))\n\n  /* Filter search result message */\n  const result$ = rx$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  fromEvent,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n  url: URL                             /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n  _el: HTMLElement, { query$ }: WatchOptions\n): Observable<SearchShare> {\n  return query$\n    .pipe(\n      map(({ value }) => {\n        const url = getLocation()\n        url.hash = \"\"\n        url.searchParams.delete(\"h\")\n        url.searchParams.set(\"q\", value)\n        return { url }\n      })\n    )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n  el: HTMLAnchorElement, options: MountOptions\n): Observable<Component<SearchShare>> {\n  const push$ = new Subject<SearchShare>()\n  push$.subscribe(({ url }) => {\n    el.setAttribute(\"data-clipboard-text\", el.href)\n    el.href = `${url}`\n  })\n\n  /* Prevent following of link */\n  fromEvent(el, \"click\")\n    .subscribe(ev => ev.preventDefault())\n\n  /* Create and return component */\n  return watchSearchShare(el, options)\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  combineLatestWith,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  observeOn,\n  tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n  SearchResult,\n  SearchWorker,\n  isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n  el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable<Component<SearchSuggest>> {\n  const push$ = new Subject<SearchResult>()\n\n  /* Retrieve query component and track all changes */\n  const query  = getComponentElement(\"search-query\")\n  const query$ = merge(\n    fromEvent(query, \"keydown\"),\n    fromEvent(query, \"focus\")\n  )\n    .pipe(\n      observeOn(asyncScheduler),\n      map(() => query.value),\n      distinctUntilChanged(),\n    )\n\n  /* Update search suggestions */\n  push$\n    .pipe(\n      combineLatestWith(query$),\n      map(([{ suggestions }, value]) => {\n        const words = value.split(/([\\s-]+)/)\n        if (suggestions?.length && words[words.length - 1]) {\n          const last = suggestions[suggestions.length - 1]\n          if (last.startsWith(words[words.length - 1]))\n            words[words.length - 1] = last\n        } else {\n          words.length = 0\n        }\n        return words\n      })\n    )\n      .subscribe(words => el.innerHTML = words\n        .join(\"\")\n        .replace(/\\s/g, \"&nbsp;\")\n      )\n\n  /* Set up search keyboard handlers */\n  keyboard$\n    .pipe(\n      filter(({ mode }) => mode === \"search\")\n    )\n      .subscribe(key => {\n        switch (key.type) {\n\n          /* Right arrow: accept current suggestion */\n          case \"ArrowRight\":\n            if (\n              el.innerText.length &&\n              query.selectionStart === query.value.length\n            )\n              query.value = el.innerText\n            break\n        }\n      })\n\n  /* Filter search result message */\n  const result$ = rx$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  ObservableInput,\n  filter,\n  merge,\n  mergeWith,\n  sample,\n  take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  Keyboard,\n  getActiveElement,\n  getElements,\n  setToggle\n} from \"~/browser\"\nimport {\n  SearchIndex,\n  SearchResult,\n  isSearchQueryMessage,\n  isSearchReadyMessage,\n  setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n  Component,\n  getComponentElement,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SearchQuery,\n  mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n  SearchShare,\n  mountSearchShare\n} from \"../share\"\nimport {\n  SearchSuggest,\n  mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n  | SearchQuery\n  | SearchResult\n  | SearchShare\n  | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n  el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable<Component<Search>> {\n  const config = configuration()\n  try {\n    const url = __search?.worker || config.search\n    const worker = setupSearchWorker(url, index$)\n\n    /* Retrieve query and result components */\n    const query  = getComponentElement(\"search-query\", el)\n    const result = getComponentElement(\"search-result\", el)\n\n    /* Re-emit query when search is ready */\n    const { tx$, rx$ } = worker\n    tx$\n      .pipe(\n        filter(isSearchQueryMessage),\n        sample(rx$.pipe(filter(isSearchReadyMessage))),\n        take(1)\n      )\n        .subscribe(tx$.next.bind(tx$))\n\n    /* Set up search keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"search\")\n      )\n        .subscribe(key => {\n          const active = getActiveElement()\n          switch (key.type) {\n\n            /* Enter: go to first (best) result */\n            case \"Enter\":\n              if (active === query) {\n                const anchors = new Map<HTMLAnchorElement, number>()\n                for (const anchor of getElements<HTMLAnchorElement>(\n                  \":first-child [href]\", result\n                )) {\n                  const article = anchor.firstElementChild!\n                  anchors.set(anchor, parseFloat(\n                    article.getAttribute(\"data-md-score\")!\n                  ))\n                }\n\n                /* Go to result with highest score, if any */\n                if (anchors.size) {\n                  const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n                  best.click()\n                }\n\n                /* Otherwise omit form submission */\n                key.claim()\n              }\n              break\n\n            /* Escape or Tab: close search */\n            case \"Escape\":\n            case \"Tab\":\n              setToggle(\"search\", false)\n              query.blur()\n              break\n\n            /* Vertical arrows: select previous or next search result */\n            case \"ArrowUp\":\n            case \"ArrowDown\":\n              if (typeof active === \"undefined\") {\n                query.focus()\n              } else {\n                const els = [query, ...getElements(\n                  \":not(details) > [href], summary, details[open] [href]\",\n                  result\n                )]\n                const i = Math.max(0, (\n                  Math.max(0, els.indexOf(active)) + els.length + (\n                    key.type === \"ArrowUp\" ? -1 : +1\n                  )\n                ) % els.length)\n                els[i].focus()\n              }\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n\n            /* All other keys: hand to search query */\n            default:\n              if (query !== getActiveElement())\n                query.focus()\n          }\n        })\n\n    /* Set up global keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"global\"),\n      )\n        .subscribe(key => {\n          switch (key.type) {\n\n            /* Open search and select query */\n            case \"f\":\n            case \"s\":\n            case \"/\":\n              query.focus()\n              query.select()\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n          }\n        })\n\n    /* Create and return component */\n    const query$  = mountSearchQuery(query, worker)\n    const result$ = mountSearchResult(result, worker, { query$ })\n    return merge(query$, result$)\n      .pipe(\n        mergeWith(\n\n          /* Search sharing */\n          ...getComponentElements(\"search-share\", el)\n            .map(child => mountSearchShare(child, { query$ })),\n\n          /* Search suggestions */\n          ...getComponentElements(\"search-suggest\", el)\n            .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n        )\n      )\n\n  /* Gracefully handle broken search */\n  } catch (err) {\n    el.hidden = true\n    return NEVER\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  ObservableInput,\n  combineLatest,\n  filter,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n  SearchIndex,\n  setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n  nodes: Map<ChildNode, string>        /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  location$: Observable<URL>           /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n  el: HTMLElement, { index$, location$ }: MountOptions\n): Observable<Component<SearchHighlight>> {\n  return combineLatest([\n    index$,\n    location$\n      .pipe(\n        startWith(getLocation()),\n        filter(url => !!url.searchParams.get(\"h\"))\n      )\n  ])\n    .pipe(\n      map(([index, url]) => setupSearchHighlighter(index.config, true)(\n        url.searchParams.get(\"h\")!\n      )),\n      map(fn => {\n        const nodes = new Map<ChildNode, string>()\n\n        /* Traverse text nodes and collect matches */\n        const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n        for (let node = it.nextNode(); node; node = it.nextNode()) {\n          if (node.parentElement?.offsetHeight) {\n            const original = node.textContent!\n            const replaced = fn(original)\n            if (replaced.length > original.length)\n              nodes.set(node as ChildNode, replaced)\n          }\n        }\n\n        /* Replace original nodes with matches */\n        for (const [node, text] of nodes) {\n          const { childNodes } = h(\"span\", null, text)\n          node.replaceWith(...Array.from(childNodes))\n        }\n\n        /* Return component */\n        return { ref: el, nodes }\n      })\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  distinctUntilChanged,\n  finalize,\n  map,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElement,\n  getElementOffset\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n  height: number                       /* Sidebar height */\n  locked: boolean                      /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n  el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable<Sidebar> {\n  const parent = el.parentElement!\n  const adjust =\n    parent.offsetTop -\n    parent.parentElement!.offsetTop\n\n  /* Compute the sidebar's available height and if it should be locked */\n  return combineLatest([main$, viewport$])\n    .pipe(\n      map(([{ offset, height }, { offset: { y } }]) => {\n        height = height\n          + Math.min(adjust, Math.max(0, y - offset))\n          - adjust\n        return {\n          height,\n          locked: y >= offset + adjust\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.locked === b.locked\n      ))\n    )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n  el: HTMLElement, { header$, ...options }: MountOptions\n): Observable<Component<Sidebar>> {\n  const inner = getElement(\".md-sidebar__scrollwrap\", el)\n  const { y } = getElementOffset(inner)\n  return defer(() => {\n    const push$ = new Subject<Sidebar>()\n    push$\n      .pipe(\n        auditTime(0, animationFrameScheduler),\n        withLatestFrom(header$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ height }, { height: offset }]) {\n            inner.style.height = `${height - 2 * y}px`\n            el.style.top       = `${offset}px`\n          },\n\n          /* Handle complete */\n          complete() {\n            inner.style.height = \"\"\n            el.style.top       = \"\"\n          }\n        })\n\n    /* Create and return component */\n    return watchSidebar(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n  tag_name: string                     /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n  user: string, repo?: string\n): Observable<SourceFacts> {\n  if (typeof repo !== \"undefined\") {\n    const url = `https://api.github.com/repos/${user}/${repo}`\n    return zip(\n\n      /* Fetch version */\n      requestJSON<Release>(`${url}/releases/latest`)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(release => ({\n            version: release.tag_name\n          })),\n          defaultIfEmpty({})\n        ),\n\n      /* Fetch stars and forks */\n      requestJSON<Repo>(url)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(info => ({\n            stars: info.stargazers_count,\n            forks: info.forks_count\n          })),\n          defaultIfEmpty({})\n        )\n    )\n      .pipe(\n        map(([release, info]) => ({ ...release, ...info }))\n      )\n\n  /* User or organization */\n  } else {\n    const url = `https://api.github.com/users/${user}`\n    return requestJSON<User>(url)\n      .pipe(\n        map(info => ({\n          repositories: info.public_repos\n        })),\n        defaultIfEmpty({})\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n  base: string, project: string\n): Observable<SourceFacts> {\n  const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n  return requestJSON<ProjectSchema>(url)\n    .pipe(\n      catchError(() => EMPTY), // @todo refactor instant loading\n      map(({ star_count, forks_count }) => ({\n        stars: star_count,\n        forks: forks_count\n      })),\n      defaultIfEmpty({})\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n  stars?: number                       /* Number of stars */\n  forks?: number                       /* Number of forks */\n  version?: string                     /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n  repositories?: number                /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n  | RepositoryFacts\n  | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n  url: string\n): Observable<SourceFacts> {\n  const [type] = url.match(/(git(?:hub|lab))/i) || []\n  switch (type.toLowerCase()) {\n\n    /* GitHub repository */\n    case \"github\":\n      const [, user, repo] = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)!\n      return fetchSourceFactsFromGitHub(user, repo)\n\n    /* GitLab repository */\n    case \"gitlab\":\n      const [, base, slug] = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)!\n      return fetchSourceFactsFromGitLab(base, slug)\n\n    /* Everything else */\n    default:\n      return EMPTY\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  defer,\n  filter,\n  finalize,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport { Component } from \"../../_\"\nimport {\n  SourceFacts,\n  fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n  facts: SourceFacts                   /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable<Source>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n  el: HTMLAnchorElement\n): Observable<Source> {\n  return fetch$ ||= defer(() => {\n    const cached = __md_get<SourceFacts>(\"__source\", sessionStorage)\n    if (cached)\n      return of(cached)\n    else\n      return fetchSourceFacts(el.href)\n        .pipe(\n          tap(facts => __md_set(\"__source\", facts, sessionStorage))\n        )\n  })\n    .pipe(\n      catchError(() => EMPTY),\n      filter(facts => Object.keys(facts).length > 0),\n      map(facts => ({ facts })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n  el: HTMLAnchorElement\n): Observable<Component<Source>> {\n  const inner = getElement(\":scope > :last-child\", el)\n  return defer(() => {\n    const push$ = new Subject<Source>()\n    push$.subscribe(({ facts }) => {\n      inner.appendChild(renderSourceFacts(facts))\n      inner.classList.add(\"md-source__repository--active\")\n    })\n\n    /* Create and return component */\n    return watchSource(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n  hidden: boolean                      /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Tabs> {\n  return watchElementSize(document.body)\n    .pipe(\n      switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n      map(({ offset: { y } }) => {\n        return {\n          hidden: y >= 10\n        }\n      }),\n      distinctUntilKeyChanged(\"hidden\")\n    )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Tabs>> {\n  return defer(() => {\n    const push$ = new Subject<Tabs>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ hidden }) {\n        el.hidden = hidden\n      },\n\n      /* Handle complete */\n      complete() {\n        el.hidden = false\n      }\n    })\n\n    /* Create and return component */\n    return (\n      feature(\"navigation.tabs.sticky\")\n        ? of({ hidden: false })\n        : watchTabs(el, options)\n    )\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatestWith,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  repeat,\n  scan,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  takeLast,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  watchElementSize\n} from \"~/browser\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n  prev: HTMLAnchorElement[][]          /* Anchors (previous) */\n  next: HTMLAnchorElement[][]          /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<TableOfContents> {\n  const table = new Map<HTMLAnchorElement, HTMLElement>()\n\n  /* Compute anchor-to-target mapping */\n  const anchors = getElements<HTMLAnchorElement>(\"[href^=\\\\#]\", el)\n  for (const anchor of anchors) {\n    const id = decodeURIComponent(anchor.hash.substring(1))\n    const target = getOptionalElement(`[id=\"${id}\"]`)\n    if (typeof target !== \"undefined\")\n      table.set(anchor, target)\n  }\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n      map(({ height }) => {\n        const main = getComponentElement(\"main\")\n        const grid = getElement(\":scope > :first-child\", main)\n        return height + 0.8 * (\n          grid.offsetTop -\n          main.offsetTop\n        )\n      }),\n      share()\n    )\n\n  /* Compute partition of previous and next anchors */\n  const partition$ = watchElementSize(document.body)\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n\n      /* Build index to map anchor paths to vertical offsets */\n      switchMap(body => defer(() => {\n        let path: HTMLAnchorElement[] = []\n        return of([...table].reduce((index, [anchor, target]) => {\n          while (path.length) {\n            const last = table.get(path[path.length - 1])!\n            if (last.tagName >= target.tagName) {\n              path.pop()\n            } else {\n              break\n            }\n          }\n\n          /* If the current anchor is hidden, continue with its parent */\n          let offset = target.offsetTop\n          while (!offset && target.parentElement) {\n            target = target.parentElement\n            offset = target.offsetTop\n          }\n\n          /* Map reversed anchor path to vertical offset */\n          return index.set(\n            [...path = [...path, anchor]].reverse(),\n            offset\n          )\n        }, new Map<HTMLAnchorElement[], number>()))\n      })\n        .pipe(\n\n          /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n          map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n          combineLatestWith(adjust$),\n\n          /* Re-compute partition when viewport offset changes */\n          switchMap(([index, adjust]) => viewport$\n            .pipe(\n              scan(([prev, next], { offset: { y }, size }) => {\n                const last = y + size.height >= Math.floor(body.height)\n\n                /* Look forward */\n                while (next.length) {\n                  const [, offset] = next[0]\n                  if (offset - adjust < y || last) {\n                    prev = [...prev, next.shift()!]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Look backward */\n                while (prev.length) {\n                  const [, offset] = prev[prev.length - 1]\n                  if (offset - adjust >= y && !last) {\n                    next = [prev.pop()!, ...next]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Return partition */\n                return [prev, next]\n              }, [[], [...index]]),\n              distinctUntilChanged((a, b) => (\n                a[0] === b[0] &&\n                a[1] === b[1]\n              ))\n            )\n          )\n        )\n      )\n    )\n\n  /* Compute and return anchor list migrations */\n  return partition$\n    .pipe(\n      map(([prev, next]) => ({\n        prev: prev.map(([path]) => path),\n        next: next.map(([path]) => path)\n      })),\n\n      /* Extract anchor list migrations */\n      startWith({ prev: [], next: [] }),\n      bufferCount(2, 1),\n      map(([a, b]) => {\n\n        /* Moving down */\n        if (a.prev.length < b.prev.length) {\n          return {\n            prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n            next: []\n          }\n\n        /* Moving up */\n        } else {\n          return {\n            prev: b.prev.slice(-1),\n            next: b.next.slice(0, b.next.length - a.next.length)\n          }\n        }\n      })\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n  el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable<Component<TableOfContents>> {\n  return defer(() => {\n    const push$ = new Subject<TableOfContents>()\n    const done$ = push$.pipe(takeLast(1))\n    push$.subscribe(({ prev, next }) => {\n\n      /* Look forward */\n      for (const [anchor] of next) {\n        anchor.classList.remove(\"md-nav__link--passed\")\n        anchor.classList.remove(\"md-nav__link--active\")\n      }\n\n      /* Look backward */\n      for (const [index, [anchor]] of prev.entries()) {\n        anchor.classList.add(\"md-nav__link--passed\")\n        anchor.classList.toggle(\n          \"md-nav__link--active\",\n          index === prev.length - 1\n        )\n      }\n    })\n\n    /* Set up anchor tracking, if enabled */\n    if (feature(\"navigation.tracking\"))\n      viewport$\n        .pipe(\n          takeUntil(done$),\n          distinctUntilKeyChanged(\"offset\"),\n          debounceTime(250),\n          skip(1),\n          takeUntil(target$.pipe(skip(1))),\n          repeat({ delay: 250 }),\n          withLatestFrom(push$)\n        )\n          .subscribe(([, { prev }]) => {\n            const url = getLocation()\n\n            /* Set hash fragment to active anchor */\n            const anchor = prev[prev.length - 1]\n            if (anchor && anchor.length) {\n              const [active] = anchor\n              const { hash } = new URL(active.href)\n              if (url.hash !== hash) {\n                url.hash = hash\n                history.replaceState({}, \"\", `${url}`)\n              }\n\n            /* Reset anchor when at the top */\n            } else {\n              url.hash = \"\"\n              history.replaceState({}, \"\", `${url}`)\n            }\n          })\n\n    /* Create and return component */\n    return watchTableOfContents(el, { viewport$, header$ })\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  map,\n  repeat,\n  skip,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n  hidden: boolean                      /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n  _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable<BackToTop> {\n\n  /* Compute direction */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => a > b && b > 0),\n      distinctUntilChanged()\n    )\n\n  /* Compute whether main area is active */\n  const active$ = main$\n    .pipe(\n      map(({ active }) => active)\n    )\n\n  /* Compute threshold for hiding */\n  return combineLatest([active$, direction$])\n    .pipe(\n      map(([active, direction]) => !(active && direction)),\n      distinctUntilChanged(),\n      takeUntil(target$.pipe(skip(1))),\n      endWith(true),\n      repeat({ delay: 250 }),\n      map(hidden => ({ hidden }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<BackToTop>> {\n  const push$ = new Subject<BackToTop>()\n  const done$ = push$.pipe(takeLast(1))\n  push$.subscribe({\n\n    /* Handle emission */\n    next({ hidden }) {\n      el.hidden = hidden\n      if (hidden) {\n        el.setAttribute(\"tabindex\", \"-1\")\n        el.blur()\n      } else {\n        el.removeAttribute(\"tabindex\")\n      }\n    },\n\n    /* Handle complete */\n    complete() {\n      el.style.top = \"\"\n      el.hidden = true\n      el.removeAttribute(\"tabindex\")\n    }\n  })\n\n  /* Watch header height */\n  header$\n    .pipe(\n      takeUntil(done$),\n      distinctUntilKeyChanged(\"height\")\n    )\n      .subscribe(({ height }) => {\n        el.style.top = `${height + 16}px`\n      })\n\n  /* Create and return component */\n  return watchBackToTop(el, { viewport$, main$, target$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  takeWhile,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n  { document$, tablet$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements<HTMLInputElement>(\n        // @todo `data-md-state` is deprecated and removed in v9\n        \".md-toggle--indeterminate, [data-md-state=indeterminate]\"\n      )),\n      tap(el => {\n        el.indeterminate = true\n        el.checked = false\n      }),\n      mergeMap(el => fromEvent(el, \"change\")\n        .pipe(\n          takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n          map(() => el)\n        )\n      ),\n      withLatestFrom(tablet$)\n    )\n      .subscribe(([el, tablet]) => {\n        el.classList.remove(\"md-toggle--indeterminate\")\n        if (tablet)\n          el.checked = false\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n  return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n  { document$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\"[data-md-scrollfix]\")),\n      tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n      filter(isAppleDevice),\n      mergeMap(el => fromEvent(el, \"touchstart\")\n        .pipe(\n          map(() => el)\n        )\n      )\n    )\n      .subscribe(el => {\n        const top = el.scrollTop\n\n        /* We're at the top of the container */\n        if (top === 0) {\n          el.scrollTop = 1\n\n        /* We're at the bottom of the container */\n        } else if (top + el.offsetHeight === el.scrollHeight) {\n          el.scrollTop = top - 1\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  delay,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n  { viewport$, tablet$ }: PatchOptions\n): void {\n  combineLatest([watchToggle(\"search\"), tablet$])\n    .pipe(\n      map(([active, tablet]) => active && !tablet),\n      switchMap(active => of(active)\n        .pipe(\n          delay(active ? 400 : 100)\n        )\n      ),\n      withLatestFrom(viewport$)\n    )\n      .subscribe(([active, { offset: { y }}]) => {\n        if (active) {\n          document.body.setAttribute(\"data-md-scrolllock\", \"\")\n          document.body.style.top = `-${y}px`\n        } else {\n          const value = -1 * parseInt(document.body.style.top, 10)\n          document.body.removeAttribute(\"data-md-scrolllock\")\n          document.body.style.top = \"\"\n          if (value)\n            window.scrollTo(0, value)\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node !== \"object\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n"],
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+  "names": []
+}
diff --git a/docs/site/assets/javascripts/bundle.dd8806f2.min.js b/docs/site/assets/javascripts/bundle.dd8806f2.min.js
deleted file mode 100644
index e22d189..0000000
--- a/docs/site/assets/javascripts/bundle.dd8806f2.min.js
+++ /dev/null
@@ -1,29 +0,0 @@
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circle{fill:var(--md-mermaid-label-bg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead 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l=P(`label[for="${c.id}"]`);l.replaceChildren(E("a",{href:`#${l.htmlFor}`,tabIndex:-1},...Array.from(l.childNodes))),d(l.firstElementChild,"click").pipe(U(p),b(f=>!(f.metaKey||f.ctrlKey)),y(f=>{f.preventDefault(),f.stopPropagation()})).subscribe(()=>{history.replaceState({},"",`#${l.htmlFor}`),l.click()})}return G("content.tabs.link")&&s.pipe(Ce(1),ee(t)).subscribe(([{active:c},{offset:l}])=>{let f=c.innerText.trim();if(c.hasAttribute("data-md-switching"))c.removeAttribute("data-md-switching");else{let u=e.offsetTop-l.y;for(let w of $("[data-tabs]"))for(let A of $(":scope > input",w)){let te=P(`label[for="${A.id}"]`);if(te!==c&&te.innerText.trim()===f){te.setAttribute("data-md-switching",""),A.click();break}}window.scrollTo({top:e.offsetTop-u});let h=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([f,...h])])}}),s.pipe(U(p)).subscribe(()=>{for(let c of $("audio, video",e))c.pause()}),tt(e).pipe(v(()=>Na(n)),y(c=>s.next(c)),L(()=>s.complete()),m(c=>R({ref:e},c)))}).pipe(Qe(se))}function Un(e,{viewport$:t,target$:r,print$:o}){return S(...$(".annotate:not(.highlight)",e).map(n=>Cn(n,{target$:r,print$:o})),...$("pre:not(.mermaid) > code",e).map(n=>$n(n,{target$:r,print$:o})),...$("pre.mermaid",e).map(n=>In(n)),...$("table:not([class])",e).map(n=>jn(n)),...$("details",e).map(n=>Pn(n,{target$:r,print$:o})),...$("[data-tabs]",e).map(n=>Wn(n,{viewport$:t,target$:r})),...$("[title]",e).filter(()=>G("content.tooltips")).map(n=>lt(n,{viewport$:t})))}function za(e,{alert$:t}){return t.pipe(v(r=>S(I(!0),I(!1).pipe(Ge(2e3))).pipe(m(o=>({message:r,active:o})))))}function Dn(e,t){let r=P(".md-typeset",e);return C(()=>{let o=new g;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),za(e,t).pipe(y(n=>o.next(n)),L(()=>o.complete()),m(n=>R({ref:e},n)))})}var qa=0;function Qa(e,t){document.body.append(e);let{width:r}=ce(e);e.style.setProperty("--md-tooltip-width",`${r}px`),e.remove();let o=cr(t),n=typeof o!="undefined"?De(o):I({x:0,y:0}),i=S(et(t),kt(t)).pipe(K());return z([i,n]).pipe(m(([a,s])=>{let{x:p,y:c}=Ue(t),l=ce(t),f=t.closest("table");return f&&t.parentElement&&(p+=f.offsetLeft+t.parentElement.offsetLeft,c+=f.offsetTop+t.parentElement.offsetTop),{active:a,offset:{x:p-s.x+l.width/2-r/2,y:c-s.y+l.height+8}}}))}function Vn(e){let t=e.title;if(!t.length)return M;let r=`__tooltip_${qa++}`,o=Pt(r,"inline"),n=P(".md-typeset",o);return n.innerHTML=t,C(()=>{let i=new g;return i.subscribe({next({offset:a}){o.style.setProperty("--md-tooltip-x",`${a.x}px`),o.style.setProperty("--md-tooltip-y",`${a.y}px`)},complete(){o.style.removeProperty("--md-tooltip-x"),o.style.removeProperty("--md-tooltip-y")}}),S(i.pipe(b(({active:a})=>a)),i.pipe(_e(250),b(({active:a})=>!a))).subscribe({next({active:a}){a?(e.insertAdjacentElement("afterend",o),e.setAttribute("aria-describedby",r),e.removeAttribute("title")):(o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t))},complete(){o.remove(),e.removeAttribute("aria-describedby"),e.setAttribute("title",t)}}),i.pipe(Le(16,me)).subscribe(({active:a})=>{o.classList.toggle("md-tooltip--active",a)}),i.pipe(ct(125,me),b(()=>!!e.offsetParent),m(()=>e.offsetParent.getBoundingClientRect()),m(({x:a})=>a)).subscribe({next(a){a?o.style.setProperty("--md-tooltip-0",`${-a}px`):o.style.removeProperty("--md-tooltip-0")},complete(){o.style.removeProperty("--md-tooltip-0")}}),Qa(o,e).pipe(y(a=>i.next(a)),L(()=>i.complete()),m(a=>R({ref:e},a)))}).pipe(Qe(se))}function Ka({viewport$:e}){if(!G("header.autohide"))return I(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Ye(2,1),m(([n,i])=>[n<i,i]),Z(0)),r=z([e,t]).pipe(b(([{offset:n},[,i]])=>Math.abs(i-n.y)>100),m(([,[n]])=>n),K()),o=Ve("search");return z([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),K(),v(n=>n?r:I(!1)),Q(!1))}function Nn(e,t){return C(()=>z([ge(e),Ka(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),K((r,o)=>r.height===o.height&&r.hidden===o.hidden),B(1))}function zn(e,{header$:t,main$:r}){return C(()=>{let o=new g,n=o.pipe(X(),ne(!0));o.pipe(Z("active"),We(t)).subscribe(([{active:a},{hidden:s}])=>{e.classList.toggle("md-header--shadow",a&&!s),e.hidden=s});let i=ue($("[title]",e)).pipe(b(()=>G("content.tooltips")),oe(a=>Vn(a)));return r.subscribe(o),t.pipe(U(n),m(a=>R({ref:e},a)),Pe(i.pipe(U(n))))})}function Ya(e,{viewport$:t,header$:r}){return mr(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=ce(e);return{active:o>=n}}),Z("active"))}function qn(e,t){return C(()=>{let r=new g;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=fe(".md-content h1");return typeof o=="undefined"?M:Ya(o,t).pipe(y(n=>r.next(n)),L(()=>r.complete()),m(n=>R({ref:e},n)))})}function Qn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),K()),n=o.pipe(v(()=>ge(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),Z("bottom"))));return z([o,n,t]).pipe(m(([i,{top:a,bottom:s},{offset:{y:p},size:{height:c}}])=>(c=Math.max(0,c-Math.max(0,a-p,i)-Math.max(0,c+p-s)),{offset:a-i,height:c,active:a-i<=p})),K((i,a)=>i.offset===a.offset&&i.height===a.height&&i.active===a.active))}function Ba(e){let t=__md_get("__palette")||{index:e.findIndex(o=>matchMedia(o.getAttribute("data-md-color-media")).matches)},r=Math.max(0,Math.min(t.index,e.length-1));return I(...e).pipe(oe(o=>d(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),B(1))}function Kn(e){let t=$("input",e),r=E("meta",{name:"theme-color"});document.head.appendChild(r);let o=E("meta",{name:"color-scheme"});document.head.appendChild(o);let n=$t("(prefers-color-scheme: light)");return C(()=>{let i=new g;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),p=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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g,o=r.pipe(X(),ne(!0));z([t.pipe(Ae(It)),r],(i,a)=>a).pipe(Z("value")).subscribe(({value:i})=>t.next({type:2,data:i})),r.pipe(Z("focus")).subscribe(({focus:i})=>{i&&Je("search",i)}),d(e.form,"reset").pipe(U(o)).subscribe(()=>e.focus());let n=P("header [for=__search]");return d(n,"click").subscribe(()=>e.focus()),ns(e,{worker$:t}).pipe(y(i=>r.next(i)),L(()=>r.complete()),m(i=>R({ref:e},i)),B(1))}function si(e,{worker$:t,query$:r}){let o=new g,n=tn(e.parentElement).pipe(b(Boolean)),i=e.parentElement,a=P(":scope > :first-child",e),s=P(":scope > :last-child",e);Ve("search").subscribe(l=>s.setAttribute("role",l?"list":"presentation")),o.pipe(ee(r),Ur(t.pipe(Ae(It)))).subscribe(([{items:l},{value:f}])=>{switch(l.length){case 0:a.textContent=f.length?ye("search.result.none"):ye("search.result.placeholder");break;case 1:a.textContent=ye("search.result.one");break;default:let u=sr(l.length);a.textContent=ye("search.result.other",u)}});let 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g,n=Se("search-query"),i=S(d(n,"keydown"),d(n,"focus")).pipe(be(se),m(()=>n.value),K());return o.pipe(We(i),m(([{suggest:s},p])=>{let c=p.split(/([\s-]+)/);if(s!=null&&s.length&&c[c.length-1]){let l=s[s.length-1];l.startsWith(c[c.length-1])&&(c[c.length-1]=l)}else c.length=0;return c})).subscribe(s=>e.innerHTML=s.join("").replace(/\s/g,"&nbsp;")),r.pipe(b(({mode:s})=>s==="search")).subscribe(s=>{switch(s.type){case"ArrowRight":e.innerText.length&&n.selectionStart===n.value.length&&(n.value=e.innerText);break}}),t.pipe(b(dr),m(({data:s})=>s)).pipe(y(s=>o.next(s)),L(()=>o.complete()),m(()=>({ref:e})))}function li(e,{index$:t,keyboard$:r}){let o=Te();try{let n=ni(o.search,t),i=Se("search-query",e),a=Se("search-result",e);d(e,"click").pipe(b(({target:p})=>p instanceof Element&&!!p.closest("a"))).subscribe(()=>Je("search",!1)),r.pipe(b(({mode:p})=>p==="search")).subscribe(p=>{let c=Re();switch(p.type){case"Enter":if(c===i){let l=new Map;for(let f of $(":first-child [href]",a)){let 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z([t,r.pipe(Q(xe()),b(o=>!!o.searchParams.get("h")))]).pipe(m(([o,n])=>oi(o.config)(n.searchParams.get("h"))),m(o=>{var a;let n=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let s=i.nextNode();s;s=i.nextNode())if((a=s.parentElement)!=null&&a.offsetHeight){let p=s.textContent,c=o(p);c.length>p.length&&n.set(s,c)}for(let[s,p]of n){let{childNodes:c}=E("span",null,p);s.replaceWith(...Array.from(c))}return{ref:e,nodes:n}}))}function as(e,{viewport$:t,main$:r}){let o=e.closest(".md-grid"),n=o.offsetTop-o.parentElement.offsetTop;return z([r,t]).pipe(m(([{offset:i,height:a},{offset:{y:s}}])=>(a=a+Math.min(n,Math.max(0,s-i))-n,{height:a,locked:s>=i+n})),K((i,a)=>i.height===a.height&&i.locked===a.locked))}function Jr(e,o){var n=o,{header$:t}=n,r=io(n,["header$"]);let i=P(".md-sidebar__scrollwrap",e),{y:a}=Ue(i);return C(()=>{let s=new g,p=s.pipe(X(),ne(!0)),c=s.pipe(Le(0,me));return c.pipe(ee(t)).subscribe({next([{height:l},{height:f}]){i.style.height=`${l-2*a}px`,e.style.top=`${f}px`},complete(){i.style.height="",e.style.top=""}}),c.pipe(Ae()).subscribe(()=>{for(let l of $(".md-nav__link--active[href]",e)){if(!l.clientHeight)continue;let f=l.closest(".md-sidebar__scrollwrap");if(typeof f!="undefined"){let u=l.offsetTop-f.offsetTop,{height:h}=ce(f);f.scrollTo({top:u-h/2})}}}),ue($("label[tabindex]",e)).pipe(oe(l=>d(l,"click").pipe(be(se),m(()=>l),U(p)))).subscribe(l=>{let f=P(`[id="${l.htmlFor}"]`);P(`[aria-labelledby="${l.id}"]`).setAttribute("aria-expanded",`${f.checked}`)}),as(e,r).pipe(y(l=>s.next(l)),L(()=>s.complete()),m(l=>R({ref:e},l)))})}function fi(e,t){if(typeof t!="undefined"){let r=`https://api.github.com/repos/${e}/${t}`;return Ct(Ne(`${r}/releases/latest`).pipe(ve(()=>M),m(o=>({version:o.tag_name})),Be({})),Ne(r).pipe(ve(()=>M),m(o=>({stars:o.stargazers_count,forks:o.forks_count})),Be({}))).pipe(m(([o,n])=>R(R({},o),n)))}else{let r=`https://api.github.com/users/${e}`;return Ne(r).pipe(m(o=>({repositories:o.public_repos})),Be({}))}}function ui(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return Ne(r).pipe(ve(()=>M),m(({star_count:o,forks_count:n})=>({stars:o,forks:n})),Be({}))}function di(e){let t=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);if(t){let[,r,o]=t;return fi(r,o)}if(t=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i),t){let[,r,o]=t;return ui(r,o)}return M}var ss;function cs(e){return ss||(ss=C(()=>{let t=__md_get("__source",sessionStorage);if(t)return I(t);if(ae("consent").length){let o=__md_get("__consent");if(!(o&&o.github))return M}return di(e.href).pipe(y(o=>__md_set("__source",o,sessionStorage)))}).pipe(ve(()=>M),b(t=>Object.keys(t).length>0),m(t=>({facts:t})),B(1)))}function hi(e){let t=P(":scope > :last-child",e);return C(()=>{let r=new g;return r.subscribe(({facts:o})=>{t.appendChild(Sn(o)),t.classList.add("md-source__repository--active")}),cs(e).pipe(y(o=>r.next(o)),L(()=>r.complete()),m(o=>R({ref:e},o)))})}function ps(e,{viewport$:t,header$:r}){return ge(document.body).pipe(v(()=>mr(e,{header$:r,viewport$:t})),m(({offset:{y:o}})=>({hidden:o>=10})),Z("hidden"))}function bi(e,t){return C(()=>{let r=new g;return r.subscribe({next({hidden:o}){e.hidden=o},complete(){e.hidden=!1}}),(G("navigation.tabs.sticky")?I({hidden:!1}):ps(e,t)).pipe(y(o=>r.next(o)),L(()=>r.complete()),m(o=>R({ref:e},o)))})}function ls(e,{viewport$:t,header$:r}){let o=new Map,n=$(".md-nav__link",e);for(let s of n){let p=decodeURIComponent(s.hash.substring(1)),c=fe(`[id="${p}"]`);typeof c!="undefined"&&o.set(s,c)}let i=r.pipe(Z("height"),m(({height:s})=>{let p=Se("main"),c=P(":scope > :first-child",p);return s+.8*(c.offsetTop-p.offsetTop)}),pe());return ge(document.body).pipe(Z("height"),v(s=>C(()=>{let p=[];return I([...o].reduce((c,[l,f])=>{for(;p.length&&o.get(p[p.length-1]).tagName>=f.tagName;)p.pop();let u=f.offsetTop;for(;!u&&f.parentElement;)f=f.parentElement,u=f.offsetTop;let h=f.offsetParent;for(;h;h=h.offsetParent)u+=h.offsetTop;return c.set([...p=[...p,l]].reverse(),u)},new Map))}).pipe(m(p=>new Map([...p].sort(([,c],[,l])=>c-l))),We(i),v(([p,c])=>t.pipe(jr(([l,f],{offset:{y:u},size:h})=>{let w=u+h.height>=Math.floor(s.height);for(;f.length;){let[,A]=f[0];if(A-c<u||w)l=[...l,f.shift()];else break}for(;l.length;){let[,A]=l[l.length-1];if(A-c>=u&&!w)f=[l.pop(),...f];else break}return[l,f]},[[],[...p]]),K((l,f)=>l[0]===f[0]&&l[1]===f[1])))))).pipe(m(([s,p])=>({prev:s.map(([c])=>c),next:p.map(([c])=>c)})),Q({prev:[],next:[]}),Ye(2,1),m(([s,p])=>s.prev.length<p.prev.length?{prev:p.prev.slice(Math.max(0,s.prev.length-1),p.prev.length),next:[]}:{prev:p.prev.slice(-1),next:p.next.slice(0,p.next.length-s.next.length)}))}function vi(e,{viewport$:t,header$:r,main$:o,target$:n}){return C(()=>{let i=new g,a=i.pipe(X(),ne(!0));if(i.subscribe(({prev:s,next:p})=>{for(let[c]of p)c.classList.remove("md-nav__link--passed"),c.classList.remove("md-nav__link--active");for(let[c,[l]]of s.entries())l.classList.add("md-nav__link--passed"),l.classList.toggle("md-nav__link--active",c===s.length-1)}),G("toc.follow")){let s=S(t.pipe(_e(1),m(()=>{})),t.pipe(_e(250),m(()=>"smooth")));i.pipe(b(({prev:p})=>p.length>0),We(o.pipe(be(se))),ee(s)).subscribe(([[{prev:p}],c])=>{let[l]=p[p.length-1];if(l.offsetHeight){let f=cr(l);if(typeof f!="undefined"){let u=l.offsetTop-f.offsetTop,{height:h}=ce(f);f.scrollTo({top:u-h/2,behavior:c})}}})}return G("navigation.tracking")&&t.pipe(U(a),Z("offset"),_e(250),Ce(1),U(n.pipe(Ce(1))),st({delay:250}),ee(i)).subscribe(([,{prev:s}])=>{let p=xe(),c=s[s.length-1];if(c&&c.length){let[l]=c,{hash:f}=new URL(l.href);p.hash!==f&&(p.hash=f,history.replaceState({},"",`${p}`))}else p.hash="",history.replaceState({},"",`${p}`)}),ls(e,{viewport$:t,header$:r}).pipe(y(s=>i.next(s)),L(()=>i.complete()),m(s=>R({ref:e},s)))})}function ms(e,{viewport$:t,main$:r,target$:o}){let n=t.pipe(m(({offset:{y:a}})=>a),Ye(2,1),m(([a,s])=>a>s&&s>0),K()),i=r.pipe(m(({active:a})=>a));return z([i,n]).pipe(m(([a,s])=>!(a&&s)),K(),U(o.pipe(Ce(1))),ne(!0),st({delay:250}),m(a=>({hidden:a})))}function gi(e,{viewport$:t,header$:r,main$:o,target$:n}){let i=new g,a=i.pipe(X(),ne(!0));return i.subscribe({next({hidden:s}){e.hidden=s,s?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(U(a),Z("height")).subscribe(({height:s})=>{e.style.top=`${s+16}px`}),d(e,"click").subscribe(s=>{s.preventDefault(),window.scrollTo({top:0})}),ms(e,{viewport$:t,main$:o,target$:n}).pipe(y(s=>i.next(s)),L(()=>i.complete()),m(s=>R({ref:e},s)))}function xi({document$:e,viewport$:t}){e.pipe(v(()=>$(".md-ellipsis")),oe(r=>tt(r).pipe(U(e.pipe(Ce(1))),b(o=>o),m(()=>r),we(1))),b(r=>r.offsetWidth<r.scrollWidth),oe(r=>{let o=r.innerText,n=r.closest("a")||r;return n.title=o,lt(n,{viewport$:t}).pipe(U(e.pipe(Ce(1))),L(()=>n.removeAttribute("title")))})).subscribe(),e.pipe(v(()=>$(".md-status")),oe(r=>lt(r,{viewport$:t}))).subscribe()}function yi({document$:e,tablet$:t}){e.pipe(v(()=>$(".md-toggle--indeterminate")),y(r=>{r.indeterminate=!0,r.checked=!1}),oe(r=>d(r,"change").pipe(Dr(()=>r.classList.contains("md-toggle--indeterminate")),m(()=>r))),ee(t)).subscribe(([r,o])=>{r.classList.remove("md-toggle--indeterminate"),o&&(r.checked=!1)})}function fs(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Ei({document$:e}){e.pipe(v(()=>$("[data-md-scrollfix]")),y(t=>t.removeAttribute("data-md-scrollfix")),b(fs),oe(t=>d(t,"touchstart").pipe(m(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function wi({viewport$:e,tablet$:t}){z([Ve("search"),t]).pipe(m(([r,o])=>r&&!o),v(r=>I(r).pipe(Ge(r?400:100))),ee(e)).subscribe(([r,{offset:{y:o}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${o}px`;else{let n=-1*parseInt(document.body.style.top,10);document.body.removeAttribute("data-md-scrolllock"),document.body.style.top="",n&&window.scrollTo(0,n)}})}Object.entries||(Object.entries=function(e){let t=[];for(let r of Object.keys(e))t.push([r,e[r]]);return t});Object.values||(Object.values=function(e){let t=[];for(let r of Object.keys(e))t.push(e[r]);return t});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(e,t){typeof e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let o=e[r];typeof o=="string"?o=document.createTextNode(o):o.parentNode&&o.parentNode.removeChild(o),r?t.insertBefore(this.previousSibling,o):t.replaceChild(o,this)}}}));function us(){return location.protocol==="file:"?wt(`${new URL("search/search_index.js",Xr.base)}`).pipe(m(()=>__index),B(1)):Ne(new URL("search/search_index.json",Xr.base))}document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var ot=Yo(),jt=nn(),Ot=cn(jt),Zr=on(),Oe=bn(),hr=$t("(min-width: 960px)"),Si=$t("(min-width: 1220px)"),Oi=pn(),Xr=Te(),Mi=document.forms.namedItem("search")?us():Ke,eo=new g;Bn({alert$:eo});var to=new g;G("navigation.instant")&&Zn({location$:jt,viewport$:Oe,progress$:to}).subscribe(ot);var Ti;((Ti=Xr.version)==null?void 0:Ti.provider)==="mike"&&ii({document$:ot});S(jt,Ot).pipe(Ge(125)).subscribe(()=>{Je("drawer",!1),Je("search",!1)});Zr.pipe(b(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=fe("link[rel=prev]");typeof t!="undefined"&&pt(t);break;case"n":case".":let r=fe("link[rel=next]");typeof r!="undefined"&&pt(r);break;case"Enter":let o=Re();o instanceof HTMLLabelElement&&o.click()}});xi({viewport$:Oe,document$:ot});yi({document$:ot,tablet$:hr});Ei({document$:ot});wi({viewport$:Oe,tablet$:hr});var rt=Nn(Se("header"),{viewport$:Oe}),Ft=ot.pipe(m(()=>Se("main")),v(e=>Qn(e,{viewport$:Oe,header$:rt})),B(1)),ds=S(...ae("consent").map(e=>xn(e,{target$:Ot})),...ae("dialog").map(e=>Dn(e,{alert$:eo})),...ae("header").map(e=>zn(e,{viewport$:Oe,header$:rt,main$:Ft})),...ae("palette").map(e=>Kn(e)),...ae("progress").map(e=>Yn(e,{progress$:to})),...ae("search").map(e=>li(e,{index$:Mi,keyboard$:Zr})),...ae("source").map(e=>hi(e))),hs=C(()=>S(...ae("announce").map(e=>gn(e)),...ae("content").map(e=>Un(e,{viewport$:Oe,target$:Ot,print$:Oi})),...ae("content").map(e=>G("search.highlight")?mi(e,{index$:Mi,location$:jt}):M),...ae("header-title").map(e=>qn(e,{viewport$:Oe,header$:rt})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Nr(Si,()=>Jr(e,{viewport$:Oe,header$:rt,main$:Ft})):Nr(hr,()=>Jr(e,{viewport$:Oe,header$:rt,main$:Ft}))),...ae("tabs").map(e=>bi(e,{viewport$:Oe,header$:rt})),...ae("toc").map(e=>vi(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Ot})),...ae("top").map(e=>gi(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Ot})))),Li=ot.pipe(v(()=>hs),Pe(ds),B(1));Li.subscribe();window.document$=ot;window.location$=jt;window.target$=Ot;window.keyboard$=Zr;window.viewport$=Oe;window.tablet$=hr;window.screen$=Si;window.print$=Oi;window.alert$=eo;window.progress$=to;window.component$=Li;})();
-//# sourceMappingURL=bundle.dd8806f2.min.js.map
-
diff --git a/docs/site/assets/javascripts/bundle.dd8806f2.min.js.map b/docs/site/assets/javascripts/bundle.dd8806f2.min.js.map
deleted file mode 100644
index 17bf025..0000000
--- a/docs/site/assets/javascripts/bundle.dd8806f2.min.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/clipboard/dist/clipboard.js", "node_modules/escape-html/index.js", "src/templates/assets/javascripts/bundle.ts", "node_modules/rxjs/node_modules/tslib/tslib.es6.js", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", "node_modules/rxjs/src/internal/util/errorContext.ts", "node_modules/rxjs/src/internal/Subscriber.ts", "node_modules/rxjs/src/internal/symbol/observable.ts", "node_modules/rxjs/src/internal/util/identity.ts", "node_modules/rxjs/src/internal/util/pipe.ts", "node_modules/rxjs/src/internal/Observable.ts", "node_modules/rxjs/src/internal/util/lift.ts", "node_modules/rxjs/src/internal/operators/OperatorSubscriber.ts", "node_modules/rxjs/src/internal/scheduler/animationFrameProvider.ts", "node_modules/rxjs/src/internal/util/ObjectUnsubscribedError.ts", "node_modules/rxjs/src/internal/Subject.ts", "node_modules/rxjs/src/internal/BehaviorSubject.ts", "node_modules/rxjs/src/internal/scheduler/dateTimestampProvider.ts", "node_modules/rxjs/src/internal/ReplaySubject.ts", "node_modules/rxjs/src/internal/scheduler/Action.ts", "node_modules/rxjs/src/internal/scheduler/intervalProvider.ts", "node_modules/rxjs/src/internal/scheduler/AsyncAction.ts", "node_modules/rxjs/src/internal/Scheduler.ts", "node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/QueueAction.ts", "node_modules/rxjs/src/internal/scheduler/QueueScheduler.ts", "node_modules/rxjs/src/internal/scheduler/queue.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", "node_modules/rxjs/src/internal/observable/of.ts", "node_modules/rxjs/src/internal/observable/throwError.ts", "node_modules/rxjs/src/internal/util/EmptyError.ts", "node_modules/rxjs/src/internal/util/isDate.ts", "node_modules/rxjs/src/internal/operators/map.ts", "node_modules/rxjs/src/internal/util/mapOneOrManyArgs.ts", "node_modules/rxjs/src/internal/util/argsArgArrayOrObject.ts", "node_modules/rxjs/src/internal/util/createObject.ts", "node_modules/rxjs/src/internal/observable/combineLatest.ts", "node_modules/rxjs/src/internal/operators/mergeInternals.ts", "node_modules/rxjs/src/internal/operators/mergeMap.ts", "node_modules/rxjs/src/internal/operators/mergeAll.ts", "node_modules/rxjs/src/internal/operators/concatAll.ts", "node_modules/rxjs/src/internal/observable/concat.ts", "node_modules/rxjs/src/internal/observable/defer.ts", "node_modules/rxjs/src/internal/observable/fromEvent.ts", "node_modules/rxjs/src/internal/observable/fromEventPattern.ts", "node_modules/rxjs/src/internal/observable/timer.ts", "node_modules/rxjs/src/internal/observable/merge.ts", "node_modules/rxjs/src/internal/observable/never.ts", "node_modules/rxjs/src/internal/util/argsOrArgArray.ts", "node_modules/rxjs/src/internal/operators/filter.ts", "node_modules/rxjs/src/internal/observable/zip.ts", "node_modules/rxjs/src/internal/operators/audit.ts", "node_modules/rxjs/src/internal/operators/auditTime.ts", "node_modules/rxjs/src/internal/operators/bufferCount.ts", "node_modules/rxjs/src/internal/operators/catchError.ts", "node_modules/rxjs/src/internal/operators/scanInternals.ts", "node_modules/rxjs/src/internal/operators/combineLatest.ts", "node_modules/rxjs/src/internal/operators/combineLatestWith.ts", "node_modules/rxjs/src/internal/operators/debounce.ts", "node_modules/rxjs/src/internal/operators/debounceTime.ts", "node_modules/rxjs/src/internal/operators/defaultIfEmpty.ts", "node_modules/rxjs/src/internal/operators/take.ts", "node_modules/rxjs/src/internal/operators/ignoreElements.ts", "node_modules/rxjs/src/internal/operators/mapTo.ts", "node_modules/rxjs/src/internal/operators/delayWhen.ts", "node_modules/rxjs/src/internal/operators/delay.ts", "node_modules/rxjs/src/internal/operators/distinctUntilChanged.ts", "node_modules/rxjs/src/internal/operators/distinctUntilKeyChanged.ts", "node_modules/rxjs/src/internal/operators/throwIfEmpty.ts", "node_modules/rxjs/src/internal/operators/endWith.ts", "node_modules/rxjs/src/internal/operators/finalize.ts", "node_modules/rxjs/src/internal/operators/first.ts", "node_modules/rxjs/src/internal/operators/takeLast.ts", "node_modules/rxjs/src/internal/operators/merge.ts", "node_modules/rxjs/src/internal/operators/mergeWith.ts", "node_modules/rxjs/src/internal/operators/repeat.ts", "node_modules/rxjs/src/internal/operators/scan.ts", "node_modules/rxjs/src/internal/operators/share.ts", "node_modules/rxjs/src/internal/operators/shareReplay.ts", "node_modules/rxjs/src/internal/operators/skip.ts", "node_modules/rxjs/src/internal/operators/skipUntil.ts", "node_modules/rxjs/src/internal/operators/startWith.ts", "node_modules/rxjs/src/internal/operators/switchMap.ts", "node_modules/rxjs/src/internal/operators/takeUntil.ts", "node_modules/rxjs/src/internal/operators/takeWhile.ts", "node_modules/rxjs/src/internal/operators/tap.ts", "node_modules/rxjs/src/internal/operators/throttle.ts", "node_modules/rxjs/src/internal/operators/throttleTime.ts", "node_modules/rxjs/src/internal/operators/withLatestFrom.ts", "node_modules/rxjs/src/internal/operators/zip.ts", "node_modules/rxjs/src/internal/operators/zipWith.ts", "src/templates/assets/javascripts/browser/document/index.ts", "src/templates/assets/javascripts/browser/element/_/index.ts", "src/templates/assets/javascripts/browser/element/focus/index.ts", "src/templates/assets/javascripts/browser/element/hover/index.ts", "src/templates/assets/javascripts/utilities/h/index.ts", "src/templates/assets/javascripts/utilities/round/index.ts", "src/templates/assets/javascripts/browser/script/index.ts", "src/templates/assets/javascripts/browser/element/size/_/index.ts", "src/templates/assets/javascripts/browser/element/size/content/index.ts", "src/templates/assets/javascripts/browser/element/offset/_/index.ts", "src/templates/assets/javascripts/browser/element/offset/content/index.ts", "src/templates/assets/javascripts/browser/element/visibility/index.ts", "src/templates/assets/javascripts/browser/toggle/index.ts", "src/templates/assets/javascripts/browser/keyboard/index.ts", "src/templates/assets/javascripts/browser/location/_/index.ts", "src/templates/assets/javascripts/browser/location/hash/index.ts", "src/templates/assets/javascripts/browser/media/index.ts", "src/templates/assets/javascripts/browser/request/index.ts", "src/templates/assets/javascripts/browser/viewport/offset/index.ts", "src/templates/assets/javascripts/browser/viewport/size/index.ts", "src/templates/assets/javascripts/browser/viewport/_/index.ts", "src/templates/assets/javascripts/browser/viewport/at/index.ts", "src/templates/assets/javascripts/browser/worker/index.ts", "src/templates/assets/javascripts/_/index.ts", "src/templates/assets/javascripts/components/_/index.ts", "src/templates/assets/javascripts/components/announce/index.ts", "src/templates/assets/javascripts/components/consent/index.ts", "src/templates/assets/javascripts/templates/tooltip/index.tsx", "src/templates/assets/javascripts/templates/annotation/index.tsx", "src/templates/assets/javascripts/templates/clipboard/index.tsx", "src/templates/assets/javascripts/templates/search/index.tsx", "src/templates/assets/javascripts/templates/source/index.tsx", "src/templates/assets/javascripts/templates/tabbed/index.tsx", "src/templates/assets/javascripts/templates/table/index.tsx", "src/templates/assets/javascripts/templates/version/index.tsx", "src/templates/assets/javascripts/components/tooltip2/index.ts", "src/templates/assets/javascripts/components/content/annotation/_/index.ts", "src/templates/assets/javascripts/components/content/annotation/list/index.ts", "src/templates/assets/javascripts/components/content/annotation/block/index.ts", "src/templates/assets/javascripts/components/content/code/_/index.ts", "src/templates/assets/javascripts/components/content/details/index.ts", "src/templates/assets/javascripts/components/content/mermaid/index.css", "src/templates/assets/javascripts/components/content/mermaid/index.ts", "src/templates/assets/javascripts/components/content/table/index.ts", "src/templates/assets/javascripts/components/content/tabs/index.ts", "src/templates/assets/javascripts/components/content/_/index.ts", "src/templates/assets/javascripts/components/dialog/index.ts", "src/templates/assets/javascripts/components/tooltip/index.ts", "src/templates/assets/javascripts/components/header/_/index.ts", "src/templates/assets/javascripts/components/header/title/index.ts", "src/templates/assets/javascripts/components/main/index.ts", "src/templates/assets/javascripts/components/palette/index.ts", "src/templates/assets/javascripts/components/progress/index.ts", "src/templates/assets/javascripts/integrations/clipboard/index.ts", "src/templates/assets/javascripts/integrations/sitemap/index.ts", "src/templates/assets/javascripts/integrations/instant/index.ts", "src/templates/assets/javascripts/integrations/search/highlighter/index.ts", "src/templates/assets/javascripts/integrations/search/worker/message/index.ts", "src/templates/assets/javascripts/integrations/search/worker/_/index.ts", "src/templates/assets/javascripts/integrations/version/index.ts", "src/templates/assets/javascripts/components/search/query/index.ts", "src/templates/assets/javascripts/components/search/result/index.ts", "src/templates/assets/javascripts/components/search/share/index.ts", "src/templates/assets/javascripts/components/search/suggest/index.ts", "src/templates/assets/javascripts/components/search/_/index.ts", "src/templates/assets/javascripts/components/search/highlight/index.ts", "src/templates/assets/javascripts/components/sidebar/index.ts", "src/templates/assets/javascripts/components/source/facts/github/index.ts", "src/templates/assets/javascripts/components/source/facts/gitlab/index.ts", "src/templates/assets/javascripts/components/source/facts/_/index.ts", "src/templates/assets/javascripts/components/source/_/index.ts", "src/templates/assets/javascripts/components/tabs/index.ts", "src/templates/assets/javascripts/components/toc/index.ts", "src/templates/assets/javascripts/components/top/index.ts", "src/templates/assets/javascripts/patches/ellipsis/index.ts", "src/templates/assets/javascripts/patches/indeterminate/index.ts", "src/templates/assets/javascripts/patches/scrollfix/index.ts", "src/templates/assets/javascripts/patches/scrolllock/index.ts", "src/templates/assets/javascripts/polyfills/index.ts"],
-  "sourcesContent": ["(function (global, factory) {\n  typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n  typeof define === 'function' && define.amd ? define(factory) :\n  (factory());\n}(this, (function () { 'use strict';\n\n  /**\n   * Applies the :focus-visible polyfill at the given scope.\n   * A scope in this case is either the top-level Document or a Shadow Root.\n   *\n   * @param {(Document|ShadowRoot)} scope\n   * @see https://github.com/WICG/focus-visible\n   */\n  function applyFocusVisiblePolyfill(scope) {\n    var hadKeyboardEvent = true;\n    var hadFocusVisibleRecently = false;\n    var hadFocusVisibleRecentlyTimeout = null;\n\n    var inputTypesAllowlist = {\n      text: true,\n      search: true,\n      url: true,\n      tel: true,\n      email: true,\n      password: true,\n      number: true,\n      date: true,\n      month: true,\n      week: true,\n      time: true,\n      datetime: true,\n      'datetime-local': true\n    };\n\n    /**\n     * Helper function for legacy browsers and iframes which sometimes focus\n     * elements like document, body, and non-interactive SVG.\n     * @param {Element} el\n     */\n    function isValidFocusTarget(el) {\n      if (\n        el &&\n        el !== document &&\n        el.nodeName !== 'HTML' &&\n        el.nodeName !== 'BODY' &&\n        'classList' in el &&\n        'contains' in el.classList\n      ) {\n        return true;\n      }\n      return false;\n    }\n\n    /**\n     * Computes whether the given element should automatically trigger the\n     * `focus-visible` class being added, i.e. whether it should always match\n     * `:focus-visible` when focused.\n     * @param {Element} el\n     * @return {boolean}\n     */\n    function focusTriggersKeyboardModality(el) {\n      var type = el.type;\n      var tagName = el.tagName;\n\n      if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n        return true;\n      }\n\n      if (tagName === 'TEXTAREA' && !el.readOnly) {\n        return true;\n      }\n\n      if (el.isContentEditable) {\n        return true;\n      }\n\n      return false;\n    }\n\n    /**\n     * Add the `focus-visible` class to the given element if it was not added by\n     * the author.\n     * @param {Element} el\n     */\n    function addFocusVisibleClass(el) {\n      if (el.classList.contains('focus-visible')) {\n        return;\n      }\n      el.classList.add('focus-visible');\n      el.setAttribute('data-focus-visible-added', '');\n    }\n\n    /**\n     * Remove the `focus-visible` class from the given element if it was not\n     * originally added by the author.\n     * @param {Element} el\n     */\n    function removeFocusVisibleClass(el) {\n      if (!el.hasAttribute('data-focus-visible-added')) {\n        return;\n      }\n      el.classList.remove('focus-visible');\n      el.removeAttribute('data-focus-visible-added');\n    }\n\n    /**\n     * If the most recent user interaction was via the keyboard;\n     * and the key press did not include a meta, alt/option, or control key;\n     * then the modality is keyboard. Otherwise, the modality is not keyboard.\n     * Apply `focus-visible` to any current active element and keep track\n     * of our keyboard modality state with `hadKeyboardEvent`.\n     * @param {KeyboardEvent} e\n     */\n    function onKeyDown(e) {\n      if (e.metaKey || e.altKey || e.ctrlKey) {\n        return;\n      }\n\n      if (isValidFocusTarget(scope.activeElement)) {\n        addFocusVisibleClass(scope.activeElement);\n      }\n\n      hadKeyboardEvent = true;\n    }\n\n    /**\n     * If at any point a user clicks with a pointing device, ensure that we change\n     * the modality away from keyboard.\n     * This avoids the situation where a user presses a key on an already focused\n     * element, and then clicks on a different element, focusing it with a\n     * pointing device, while we still think we're in keyboard modality.\n     * @param {Event} e\n     */\n    function onPointerDown(e) {\n      hadKeyboardEvent = false;\n    }\n\n    /**\n     * On `focus`, add the `focus-visible` class to the target if:\n     * - the target received focus as a result of keyboard navigation, or\n     * - the event target is an element that will likely require interaction\n     *   via the keyboard (e.g. a text box)\n     * @param {Event} e\n     */\n    function onFocus(e) {\n      // Prevent IE from focusing the document or HTML element.\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n        addFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * On `blur`, remove the `focus-visible` class from the target.\n     * @param {Event} e\n     */\n    function onBlur(e) {\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (\n        e.target.classList.contains('focus-visible') ||\n        e.target.hasAttribute('data-focus-visible-added')\n      ) {\n        // To detect a tab/window switch, we look for a blur event followed\n        // rapidly by a visibility change.\n        // If we don't see a visibility change within 100ms, it's probably a\n        // regular focus change.\n        hadFocusVisibleRecently = true;\n        window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n        hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n          hadFocusVisibleRecently = false;\n        }, 100);\n        removeFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * If the user changes tabs, keep track of whether or not the previously\n     * focused element had .focus-visible.\n     * @param {Event} e\n     */\n    function onVisibilityChange(e) {\n      if (document.visibilityState === 'hidden') {\n        // If the tab becomes active again, the browser will handle calling focus\n        // on the element (Safari actually calls it twice).\n        // If this tab change caused a blur on an element with focus-visible,\n        // re-apply the class when the user switches back to the tab.\n        if (hadFocusVisibleRecently) {\n          hadKeyboardEvent = true;\n        }\n        addInitialPointerMoveListeners();\n      }\n    }\n\n    /**\n     * Add a group of listeners to detect usage of any pointing devices.\n     * These listeners will be added when the polyfill first loads, and anytime\n     * the window is blurred, so that they are active when the window regains\n     * focus.\n     */\n    function addInitialPointerMoveListeners() {\n      document.addEventListener('mousemove', onInitialPointerMove);\n      document.addEventListener('mousedown', onInitialPointerMove);\n      document.addEventListener('mouseup', onInitialPointerMove);\n      document.addEventListener('pointermove', onInitialPointerMove);\n      document.addEventListener('pointerdown', onInitialPointerMove);\n      document.addEventListener('pointerup', onInitialPointerMove);\n      document.addEventListener('touchmove', onInitialPointerMove);\n      document.addEventListener('touchstart', onInitialPointerMove);\n      document.addEventListener('touchend', onInitialPointerMove);\n    }\n\n    function removeInitialPointerMoveListeners() {\n      document.removeEventListener('mousemove', onInitialPointerMove);\n      document.removeEventListener('mousedown', onInitialPointerMove);\n      document.removeEventListener('mouseup', onInitialPointerMove);\n      document.removeEventListener('pointermove', onInitialPointerMove);\n      document.removeEventListener('pointerdown', onInitialPointerMove);\n      document.removeEventListener('pointerup', onInitialPointerMove);\n      document.removeEventListener('touchmove', onInitialPointerMove);\n      document.removeEventListener('touchstart', onInitialPointerMove);\n      document.removeEventListener('touchend', onInitialPointerMove);\n    }\n\n    /**\n     * When the polfyill first loads, assume the user is in keyboard modality.\n     * If any event is received from a pointing device (e.g. mouse, pointer,\n     * touch), turn off keyboard modality.\n     * This accounts for situations where focus enters the page from the URL bar.\n     * @param {Event} e\n     */\n    function onInitialPointerMove(e) {\n      // Work around a Safari quirk that fires a mousemove on <html> whenever the\n      // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n      if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n        return;\n      }\n\n      hadKeyboardEvent = false;\n      removeInitialPointerMoveListeners();\n    }\n\n    // For some kinds of state, we are interested in changes at the global scope\n    // only. For example, global pointer input, global key presses and global\n    // visibility change should affect the state at every scope:\n    document.addEventListener('keydown', onKeyDown, true);\n    document.addEventListener('mousedown', onPointerDown, true);\n    document.addEventListener('pointerdown', onPointerDown, true);\n    document.addEventListener('touchstart', onPointerDown, true);\n    document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n    addInitialPointerMoveListeners();\n\n    // For focus and blur, we specifically care about state changes in the local\n    // scope. This is because focus / blur events that originate from within a\n    // shadow root are not re-dispatched from the host element if it was already\n    // the active element in its own scope:\n    scope.addEventListener('focus', onFocus, true);\n    scope.addEventListener('blur', onBlur, true);\n\n    // We detect that a node is a ShadowRoot by ensuring that it is a\n    // DocumentFragment and also has a host property. This check covers native\n    // implementation and polyfill implementation transparently. If we only cared\n    // about the native implementation, we could just check if the scope was\n    // an instance of a ShadowRoot.\n    if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n      // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n      // have a root element to add a class to. So, we add this attribute to the\n      // host element instead:\n      scope.host.setAttribute('data-js-focus-visible', '');\n    } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n      document.documentElement.classList.add('js-focus-visible');\n      document.documentElement.setAttribute('data-js-focus-visible', '');\n    }\n  }\n\n  // It is important to wrap all references to global window and document in\n  // these checks to support server-side rendering use cases\n  // @see https://github.com/WICG/focus-visible/issues/199\n  if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n    // Make the polyfill helper globally available. This can be used as a signal\n    // to interested libraries that wish to coordinate with the polyfill for e.g.,\n    // applying the polyfill to a shadow root:\n    window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n    // Notify interested libraries of the polyfill's presence, in case the\n    // polyfill was loaded lazily:\n    var event;\n\n    try {\n      event = new CustomEvent('focus-visible-polyfill-ready');\n    } catch (error) {\n      // IE11 does not support using CustomEvent as a constructor directly:\n      event = document.createEvent('CustomEvent');\n      event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n    }\n\n    window.dispatchEvent(event);\n  }\n\n  if (typeof document !== 'undefined') {\n    // Apply the polyfill to the global document, so that no JavaScript\n    // coordination is required to use the polyfill in the top-level document:\n    applyFocusVisiblePolyfill(document);\n  }\n\n})));\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n  \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n  try {\n    return document.execCommand(type);\n  } catch (err) {\n    return false;\n  }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n  var selectedText = select_default()(target);\n  command('cut');\n  return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n  var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n  var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n  fakeElement.style.fontSize = '12pt'; // Reset box model\n\n  fakeElement.style.border = '0';\n  fakeElement.style.padding = '0';\n  fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n  fakeElement.style.position = 'absolute';\n  fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n  var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n  fakeElement.style.top = \"\".concat(yPosition, \"px\");\n  fakeElement.setAttribute('readonly', '');\n  fakeElement.value = value;\n  return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n  var fakeElement = createFakeElement(value);\n  options.container.appendChild(fakeElement);\n  var selectedText = select_default()(fakeElement);\n  command('copy');\n  fakeElement.remove();\n  return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n    container: document.body\n  };\n  var selectedText = '';\n\n  if (typeof target === 'string') {\n    selectedText = fakeCopyAction(target, options);\n  } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n    // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n    selectedText = fakeCopyAction(target.value, options);\n  } else {\n    selectedText = select_default()(target);\n    command('copy');\n  }\n\n  return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n  var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n  // Defines base properties passed from constructor.\n  var _options$action = options.action,\n      action = _options$action === void 0 ? 'copy' : _options$action,\n      container = options.container,\n      target = options.target,\n      text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n  if (action !== 'copy' && action !== 'cut') {\n    throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n  } // Sets the `target` property using an element that will be have its content copied.\n\n\n  if (target !== undefined) {\n    if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n      if (action === 'copy' && target.hasAttribute('disabled')) {\n        throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n      }\n\n      if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n        throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n      }\n    } else {\n      throw new Error('Invalid \"target\" value, use a valid Element');\n    }\n  } // Define selection strategy based on `text` property.\n\n\n  if (text) {\n    return actions_copy(text, {\n      container: container\n    });\n  } // Defines which selection strategy based on `target` property.\n\n\n  if (target) {\n    return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n      container: container\n    });\n  }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n  var attribute = \"data-clipboard-\".concat(suffix);\n\n  if (!element.hasAttribute(attribute)) {\n    return;\n  }\n\n  return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n  _inherits(Clipboard, _Emitter);\n\n  var _super = _createSuper(Clipboard);\n\n  /**\n   * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n   * @param {Object} options\n   */\n  function Clipboard(trigger, options) {\n    var _this;\n\n    _classCallCheck(this, Clipboard);\n\n    _this = _super.call(this);\n\n    _this.resolveOptions(options);\n\n    _this.listenClick(trigger);\n\n    return _this;\n  }\n  /**\n   * Defines if attributes would be resolved using internal setter functions\n   * or custom functions that were passed in the constructor.\n   * @param {Object} options\n   */\n\n\n  _createClass(Clipboard, [{\n    key: \"resolveOptions\",\n    value: function resolveOptions() {\n      var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n      this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n      this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n      this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n      this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n    }\n    /**\n     * Adds a click event listener to the passed trigger.\n     * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n     */\n\n  }, {\n    key: \"listenClick\",\n    value: function listenClick(trigger) {\n      var _this2 = this;\n\n      this.listener = listen_default()(trigger, 'click', function (e) {\n        return _this2.onClick(e);\n      });\n    }\n    /**\n     * Defines a new `ClipboardAction` on each click event.\n     * @param {Event} e\n     */\n\n  }, {\n    key: \"onClick\",\n    value: function onClick(e) {\n      var trigger = e.delegateTarget || e.currentTarget;\n      var action = this.action(trigger) || 'copy';\n      var text = actions_default({\n        action: action,\n        container: this.container,\n        target: this.target(trigger),\n        text: this.text(trigger)\n      }); // Fires an event based on the copy operation result.\n\n      this.emit(text ? 'success' : 'error', {\n        action: action,\n        text: text,\n        trigger: trigger,\n        clearSelection: function clearSelection() {\n          if (trigger) {\n            trigger.focus();\n          }\n\n          window.getSelection().removeAllRanges();\n        }\n      });\n    }\n    /**\n     * Default `action` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultAction\",\n    value: function defaultAction(trigger) {\n      return getAttributeValue('action', trigger);\n    }\n    /**\n     * Default `target` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultTarget\",\n    value: function defaultTarget(trigger) {\n      var selector = getAttributeValue('target', trigger);\n\n      if (selector) {\n        return document.querySelector(selector);\n      }\n    }\n    /**\n     * Allow fire programmatically a copy action\n     * @param {String|HTMLElement} target\n     * @param {Object} options\n     * @returns Text copied.\n     */\n\n  }, {\n    key: \"defaultText\",\n\n    /**\n     * Default `text` lookup function.\n     * @param {Element} trigger\n     */\n    value: function defaultText(trigger) {\n      return getAttributeValue('text', trigger);\n    }\n    /**\n     * Destroy lifecycle.\n     */\n\n  }, {\n    key: \"destroy\",\n    value: function destroy() {\n      this.listener.destroy();\n    }\n  }], [{\n    key: \"copy\",\n    value: function copy(target) {\n      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n        container: document.body\n      };\n      return actions_copy(target, options);\n    }\n    /**\n     * Allow fire programmatically a cut action\n     * @param {String|HTMLElement} target\n     * @returns Text cutted.\n     */\n\n  }, {\n    key: \"cut\",\n    value: function cut(target) {\n      return actions_cut(target);\n    }\n    /**\n     * Returns the support of the given action, or all actions if no action is\n     * given.\n     * @param {String} [action]\n     */\n\n  }, {\n    key: \"isSupported\",\n    value: function isSupported() {\n      var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n      var actions = typeof action === 'string' ? [action] : action;\n      var support = !!document.queryCommandSupported;\n      actions.forEach(function (action) {\n        support = support && !!document.queryCommandSupported(action);\n      });\n      return support;\n    }\n  }]);\n\n  return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n    var proto = Element.prototype;\n\n    proto.matches = proto.matchesSelector ||\n                    proto.mozMatchesSelector ||\n                    proto.msMatchesSelector ||\n                    proto.oMatchesSelector ||\n                    proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n    while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n        if (typeof element.matches === 'function' &&\n            element.matches(selector)) {\n          return element;\n        }\n        element = element.parentNode;\n    }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n    var listenerFn = listener.apply(this, arguments);\n\n    element.addEventListener(type, listenerFn, useCapture);\n\n    return {\n        destroy: function() {\n            element.removeEventListener(type, listenerFn, useCapture);\n        }\n    }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n    // Handle the regular Element usage\n    if (typeof elements.addEventListener === 'function') {\n        return _delegate.apply(null, arguments);\n    }\n\n    // Handle Element-less usage, it defaults to global delegation\n    if (typeof type === 'function') {\n        // Use `document` as the first parameter, then apply arguments\n        // This is a short way to .unshift `arguments` without running into deoptimizations\n        return _delegate.bind(null, document).apply(null, arguments);\n    }\n\n    // Handle Selector-based usage\n    if (typeof elements === 'string') {\n        elements = document.querySelectorAll(elements);\n    }\n\n    // Handle Array-like based usage\n    return Array.prototype.map.call(elements, function (element) {\n        return _delegate(element, selector, type, callback, useCapture);\n    });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n    return function(e) {\n        e.delegateTarget = closest(e.target, selector);\n\n        if (e.delegateTarget) {\n            callback.call(element, e);\n        }\n    }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n    return value !== undefined\n        && value instanceof HTMLElement\n        && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return value !== undefined\n        && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n        && ('length' in value)\n        && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n    return typeof value === 'string'\n        || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n    if (!target && !type && !callback) {\n        throw new Error('Missing required arguments');\n    }\n\n    if (!is.string(type)) {\n        throw new TypeError('Second argument must be a String');\n    }\n\n    if (!is.fn(callback)) {\n        throw new TypeError('Third argument must be a Function');\n    }\n\n    if (is.node(target)) {\n        return listenNode(target, type, callback);\n    }\n    else if (is.nodeList(target)) {\n        return listenNodeList(target, type, callback);\n    }\n    else if (is.string(target)) {\n        return listenSelector(target, type, callback);\n    }\n    else {\n        throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n    }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n    node.addEventListener(type, callback);\n\n    return {\n        destroy: function() {\n            node.removeEventListener(type, callback);\n        }\n    }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n    Array.prototype.forEach.call(nodeList, function(node) {\n        node.addEventListener(type, callback);\n    });\n\n    return {\n        destroy: function() {\n            Array.prototype.forEach.call(nodeList, function(node) {\n                node.removeEventListener(type, callback);\n            });\n        }\n    }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n    return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n    var selectedText;\n\n    if (element.nodeName === 'SELECT') {\n        element.focus();\n\n        selectedText = element.value;\n    }\n    else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n        var isReadOnly = element.hasAttribute('readonly');\n\n        if (!isReadOnly) {\n            element.setAttribute('readonly', '');\n        }\n\n        element.select();\n        element.setSelectionRange(0, element.value.length);\n\n        if (!isReadOnly) {\n            element.removeAttribute('readonly');\n        }\n\n        selectedText = element.value;\n    }\n    else {\n        if (element.hasAttribute('contenteditable')) {\n            element.focus();\n        }\n\n        var selection = window.getSelection();\n        var range = document.createRange();\n\n        range.selectNodeContents(element);\n        selection.removeAllRanges();\n        selection.addRange(range);\n\n        selectedText = selection.toString();\n    }\n\n    return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n  // Keep this empty so it's easier to inherit from\n  // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n  on: function (name, callback, ctx) {\n    var e = this.e || (this.e = {});\n\n    (e[name] || (e[name] = [])).push({\n      fn: callback,\n      ctx: ctx\n    });\n\n    return this;\n  },\n\n  once: function (name, callback, ctx) {\n    var self = this;\n    function listener () {\n      self.off(name, listener);\n      callback.apply(ctx, arguments);\n    };\n\n    listener._ = callback\n    return this.on(name, listener, ctx);\n  },\n\n  emit: function (name) {\n    var data = [].slice.call(arguments, 1);\n    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n    var i = 0;\n    var len = evtArr.length;\n\n    for (i; i < len; i++) {\n      evtArr[i].fn.apply(evtArr[i].ctx, data);\n    }\n\n    return this;\n  },\n\n  off: function (name, callback) {\n    var e = this.e || (this.e = {});\n    var evts = e[name];\n    var liveEvents = [];\n\n    if (evts && callback) {\n      for (var i = 0, len = evts.length; i < len; i++) {\n        if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n          liveEvents.push(evts[i]);\n      }\n    }\n\n    // Remove event from queue to prevent memory leak\n    // Suggested by https://github.com/lazd\n    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n    (liveEvents.length)\n      ? e[name] = liveEvents\n      : delete e[name];\n\n    return this;\n  }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n  EMPTY,\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  delay,\n  filter,\n  map,\n  merge,\n  mergeWith,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n  at,\n  getActiveElement,\n  getOptionalElement,\n  requestJSON,\n  setLocation,\n  setToggle,\n  watchDocument,\n  watchKeyboard,\n  watchLocation,\n  watchLocationTarget,\n  watchMedia,\n  watchPrint,\n  watchScript,\n  watchViewport\n} from \"./browser\"\nimport {\n  getComponentElement,\n  getComponentElements,\n  mountAnnounce,\n  mountBackToTop,\n  mountConsent,\n  mountContent,\n  mountDialog,\n  mountHeader,\n  mountHeaderTitle,\n  mountPalette,\n  mountProgress,\n  mountSearch,\n  mountSearchHiglight,\n  mountSidebar,\n  mountSource,\n  mountTableOfContents,\n  mountTabs,\n  watchHeader,\n  watchMain\n} from \"./components\"\nimport {\n  SearchIndex,\n  setupClipboardJS,\n  setupInstantNavigation,\n  setupVersionSelector\n} from \"./integrations\"\nimport {\n  patchEllipsis,\n  patchIndeterminate,\n  patchScrollfix,\n  patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable<SearchIndex> {\n  if (location.protocol === \"file:\") {\n    return watchScript(\n      `${new URL(\"search/search_index.js\", config.base)}`\n    )\n      .pipe(\n        // @ts-ignore - @todo fix typings\n        map(() => __index),\n        shareReplay(1)\n      )\n  } else {\n    return requestJSON<SearchIndex>(\n      new URL(\"search/search_index.json\", config.base)\n    )\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$   = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$   = watchMedia(\"(min-width: 960px)\")\nconst screen$   = watchMedia(\"(min-width: 1220px)\")\nconst print$    = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n  ? fetchSearchIndex()\n  : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject<string>()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject<number>()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n  setupInstantNavigation({ location$, viewport$, progress$ })\n    .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n  setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n  .pipe(\n    delay(125)\n  )\n    .subscribe(() => {\n      setToggle(\"drawer\", false)\n      setToggle(\"search\", false)\n    })\n\n/* Set up global keyboard handlers */\nkeyboard$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement<HTMLLinkElement>(\"link[rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            setLocation(prev)\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement<HTMLLinkElement>(\"link[rel=next]\")\n          if (typeof next !== \"undefined\")\n            setLocation(next)\n          break\n\n        /* Expand navigation, see https://bit.ly/3ZjG5io */\n        case \"Enter\":\n          const active = getActiveElement()\n          if (active instanceof HTMLLabelElement)\n            active.click()\n      }\n    })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n  .pipe(\n    map(() => getComponentElement(\"main\")),\n    switchMap(el => watchMain(el, { viewport$, header$ })),\n    shareReplay(1)\n  )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n  /* Consent */\n  ...getComponentElements(\"consent\")\n    .map(el => mountConsent(el, { target$ })),\n\n  /* Dialog */\n  ...getComponentElements(\"dialog\")\n    .map(el => mountDialog(el, { alert$ })),\n\n  /* Header */\n  ...getComponentElements(\"header\")\n    .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n  /* Color palette */\n  ...getComponentElements(\"palette\")\n    .map(el => mountPalette(el)),\n\n  /* Progress bar */\n  ...getComponentElements(\"progress\")\n    .map(el => mountProgress(el, { progress$ })),\n\n  /* Search */\n  ...getComponentElements(\"search\")\n    .map(el => mountSearch(el, { index$, keyboard$ })),\n\n  /* Repository information */\n  ...getComponentElements(\"source\")\n    .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n  /* Announcement bar */\n  ...getComponentElements(\"announce\")\n    .map(el => mountAnnounce(el)),\n\n  /* Content */\n  ...getComponentElements(\"content\")\n    .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n  /* Search highlighting */\n  ...getComponentElements(\"content\")\n    .map(el => feature(\"search.highlight\")\n      ? mountSearchHiglight(el, { index$, location$ })\n      : EMPTY\n    ),\n\n  /* Header title */\n  ...getComponentElements(\"header-title\")\n    .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n  /* Sidebar */\n  ...getComponentElements(\"sidebar\")\n    .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n      ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n      : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n    ),\n\n  /* Navigation tabs */\n  ...getComponentElements(\"tabs\")\n    .map(el => mountTabs(el, { viewport$, header$ })),\n\n  /* Table of contents */\n  ...getComponentElements(\"toc\")\n    .map(el => mountTableOfContents(el, {\n      viewport$, header$, main$, target$\n    })),\n\n  /* Back-to-top button */\n  ...getComponentElements(\"top\")\n    .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n  .pipe(\n    switchMap(() => content$),\n    mergeWith(control$),\n    shareReplay(1)\n  )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$  = document$          /* Document observable */\nwindow.location$  = location$          /* Location subject */\nwindow.target$    = target$            /* Location target observable */\nwindow.keyboard$  = keyboard$          /* Keyboard observable */\nwindow.viewport$  = viewport$          /* Viewport observable */\nwindow.tablet$    = tablet$            /* Media tablet observable */\nwindow.screen$    = screen$            /* Media screen observable */\nwindow.print$     = print$             /* Media print observable */\nwindow.alert$     = alert$             /* Alert subject */\nwindow.progress$  = progress$          /* Progress indicator subject */\nwindow.component$ = component$         /* Component observable */\n", "/*! *****************************************************************************\r\nCopyright (c) Microsoft Corporation.\r\n\r\nPermission to use, copy, modify, and/or distribute this software for any\r\npurpose with or without fee is hereby granted.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\r\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\r\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global Reflect, Promise */\r\n\r\nvar extendStatics = function(d, b) {\r\n    extendStatics = Object.setPrototypeOf ||\r\n        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n        function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n    return extendStatics(d, b);\r\n};\r\n\r\nexport function __extends(d, b) {\r\n    if (typeof b !== \"function\" && b !== null)\r\n        throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n    extendStatics(d, b);\r\n    function __() { this.constructor = d; }\r\n    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\r\n}\r\n\r\nexport var __assign = function() {\r\n    __assign = Object.assign || function __assign(t) {\r\n        for (var s, i = 1, n = arguments.length; i < n; i++) {\r\n            s = arguments[i];\r\n            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\r\n        }\r\n        return t;\r\n    }\r\n    return __assign.apply(this, arguments);\r\n}\r\n\r\nexport function __rest(s, e) {\r\n    var t = {};\r\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\r\n        t[p] = s[p];\r\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\r\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\r\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\r\n                t[p[i]] = s[p[i]];\r\n        }\r\n    return t;\r\n}\r\n\r\nexport function __decorate(decorators, target, key, desc) {\r\n    var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\r\n    if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\r\n    else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\r\n    return c > 3 && r && Object.defineProperty(target, key, r), r;\r\n}\r\n\r\nexport function __param(paramIndex, decorator) {\r\n    return function (target, key) { decorator(target, key, paramIndex); }\r\n}\r\n\r\nexport function __metadata(metadataKey, metadataValue) {\r\n    if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\r\n}\r\n\r\nexport function __awaiter(thisArg, _arguments, P, generator) {\r\n    function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\r\n    return new (P || (P = Promise))(function (resolve, reject) {\r\n        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\r\n        function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\r\n        function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\r\n        step((generator = generator.apply(thisArg, _arguments || [])).next());\r\n    });\r\n}\r\n\r\nexport function __generator(thisArg, body) {\r\n    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\r\n    return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\r\n    function verb(n) { return function (v) { return step([n, v]); }; }\r\n    function step(op) {\r\n        if (f) throw new TypeError(\"Generator is already executing.\");\r\n        while (_) try {\r\n            if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\r\n            if (y = 0, t) op = [op[0] & 2, t.value];\r\n            switch (op[0]) {\r\n                case 0: case 1: t = op; break;\r\n                case 4: _.label++; return { value: op[1], done: false };\r\n                case 5: _.label++; y = op[1]; op = [0]; continue;\r\n                case 7: op = _.ops.pop(); _.trys.pop(); continue;\r\n                default:\r\n                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\r\n                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\r\n                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\r\n                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\r\n                    if (t[2]) _.ops.pop();\r\n                    _.trys.pop(); continue;\r\n            }\r\n            op = body.call(thisArg, _);\r\n        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\r\n        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\r\n    }\r\n}\r\n\r\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\r\n    if (k2 === undefined) k2 = k;\r\n    Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });\r\n}) : (function(o, m, k, k2) {\r\n    if (k2 === undefined) k2 = k;\r\n    o[k2] = m[k];\r\n});\r\n\r\nexport function __exportStar(m, o) {\r\n    for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\r\n}\r\n\r\nexport function __values(o) {\r\n    var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\r\n    if (m) return m.call(o);\r\n    if (o && typeof o.length === \"number\") return {\r\n        next: function () {\r\n            if (o && i >= o.length) o = void 0;\r\n            return { value: o && o[i++], done: !o };\r\n        }\r\n    };\r\n    throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\r\n}\r\n\r\nexport function __read(o, n) {\r\n    var m = typeof Symbol === \"function\" && o[Symbol.iterator];\r\n    if (!m) return o;\r\n    var i = m.call(o), r, ar = [], e;\r\n    try {\r\n        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\r\n    }\r\n    catch (error) { e = { error: error }; }\r\n    finally {\r\n        try {\r\n            if (r && !r.done && (m = i[\"return\"])) m.call(i);\r\n        }\r\n        finally { if (e) throw e.error; }\r\n    }\r\n    return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spread() {\r\n    for (var ar = [], i = 0; i < arguments.length; i++)\r\n        ar = ar.concat(__read(arguments[i]));\r\n    return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spreadArrays() {\r\n    for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\r\n    for (var r = Array(s), k = 0, i = 0; i < il; i++)\r\n        for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\r\n            r[k] = a[j];\r\n    return r;\r\n}\r\n\r\nexport function __spreadArray(to, from, pack) {\r\n    if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\r\n        if (ar || !(i in from)) {\r\n            if (!ar) ar = Array.prototype.slice.call(from, 0, i);\r\n            ar[i] = from[i];\r\n        }\r\n    }\r\n    return to.concat(ar || Array.prototype.slice.call(from));\r\n}\r\n\r\nexport function __await(v) {\r\n    return this instanceof __await ? (this.v = v, this) : new __await(v);\r\n}\r\n\r\nexport function __asyncGenerator(thisArg, _arguments, generator) {\r\n    if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n    var g = generator.apply(thisArg, _arguments || []), i, q = [];\r\n    return i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i;\r\n    function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }\r\n    function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\r\n    function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\r\n    function fulfill(value) { resume(\"next\", value); }\r\n    function reject(value) { resume(\"throw\", value); }\r\n    function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\r\n}\r\n\r\nexport function __asyncDelegator(o) {\r\n    var i, p;\r\n    return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\r\n    function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === \"return\" } : f ? f(v) : v; } : f; }\r\n}\r\n\r\nexport function __asyncValues(o) {\r\n    if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n    var m = o[Symbol.asyncIterator], i;\r\n    return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\r\n    function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\r\n    function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\r\n}\r\n\r\nexport function __makeTemplateObject(cooked, raw) {\r\n    if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\r\n    return cooked;\r\n};\r\n\r\nvar __setModuleDefault = Object.create ? (function(o, v) {\r\n    Object.defineProperty(o, \"default\", { enumerable: true, value: v });\r\n}) : function(o, v) {\r\n    o[\"default\"] = v;\r\n};\r\n\r\nexport function __importStar(mod) {\r\n    if (mod && mod.__esModule) return mod;\r\n    var result = {};\r\n    if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\r\n    __setModuleDefault(result, mod);\r\n    return result;\r\n}\r\n\r\nexport function __importDefault(mod) {\r\n    return (mod && mod.__esModule) ? mod : { default: mod };\r\n}\r\n\r\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\r\n    if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\r\n    if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\r\n    return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\r\n}\r\n\r\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\r\n    if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\r\n    if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\r\n    if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\r\n    return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\r\n}\r\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n  return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass<T>(createImpl: (_super: any) => any): T {\n  const _super = (instance: any) => {\n    Error.call(instance);\n    instance.stack = new Error().stack;\n  };\n\n  const ctorFunc = createImpl(_super);\n  ctorFunc.prototype = Object.create(Error.prototype);\n  ctorFunc.prototype.constructor = ctorFunc;\n  return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n  readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n  (_super) =>\n    function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n      _super(this);\n      this.message = errors\n        ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n  ')}`\n        : '';\n      this.name = 'UnsubscriptionError';\n      this.errors = errors;\n    }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove<T>(arr: T[] | undefined | null, item: T) {\n  if (arr) {\n    const index = arr.indexOf(item);\n    0 <= index && arr.splice(index, 1);\n  }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n  /** @nocollapse */\n  public static EMPTY = (() => {\n    const empty = new Subscription();\n    empty.closed = true;\n    return empty;\n  })();\n\n  /**\n   * A flag to indicate whether this Subscription has already been unsubscribed.\n   */\n  public closed = false;\n\n  private _parentage: Subscription[] | Subscription | null = null;\n\n  /**\n   * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n   * list occurs in the {@link #add} and {@link #remove} methods.\n   */\n  private _finalizers: Exclude<TeardownLogic, void>[] | null = null;\n\n  /**\n   * @param initialTeardown A function executed first as part of the finalization\n   * process that is kicked off when {@link #unsubscribe} is called.\n   */\n  constructor(private initialTeardown?: () => void) {}\n\n  /**\n   * Disposes the resources held by the subscription. May, for instance, cancel\n   * an ongoing Observable execution or cancel any other type of work that\n   * started when the Subscription was created.\n   * @return {void}\n   */\n  unsubscribe(): void {\n    let errors: any[] | undefined;\n\n    if (!this.closed) {\n      this.closed = true;\n\n      // Remove this from it's parents.\n      const { _parentage } = this;\n      if (_parentage) {\n        this._parentage = null;\n        if (Array.isArray(_parentage)) {\n          for (const parent of _parentage) {\n            parent.remove(this);\n          }\n        } else {\n          _parentage.remove(this);\n        }\n      }\n\n      const { initialTeardown: initialFinalizer } = this;\n      if (isFunction(initialFinalizer)) {\n        try {\n          initialFinalizer();\n        } catch (e) {\n          errors = e instanceof UnsubscriptionError ? e.errors : [e];\n        }\n      }\n\n      const { _finalizers } = this;\n      if (_finalizers) {\n        this._finalizers = null;\n        for (const finalizer of _finalizers) {\n          try {\n            execFinalizer(finalizer);\n          } catch (err) {\n            errors = errors ?? [];\n            if (err instanceof UnsubscriptionError) {\n              errors = [...errors, ...err.errors];\n            } else {\n              errors.push(err);\n            }\n          }\n        }\n      }\n\n      if (errors) {\n        throw new UnsubscriptionError(errors);\n      }\n    }\n  }\n\n  /**\n   * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n   * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n   * because it has already been unsubscribed, then whatever finalizer is passed to it\n   * will automatically be executed (unless the finalizer itself is also a closed subscription).\n   *\n   * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n   * subscription to a any subscription will result in no operation. (A noop).\n   *\n   * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n   * operation at all. (A noop).\n   *\n   * `Subscription` instances that are added to this instance will automatically remove themselves\n   * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n   * will need to be removed manually with {@link #remove}\n   *\n   * @param teardown The finalization logic to add to this subscription.\n   */\n  add(teardown: TeardownLogic): void {\n    // Only add the finalizer if it's not undefined\n    // and don't add a subscription to itself.\n    if (teardown && teardown !== this) {\n      if (this.closed) {\n        // If this subscription is already closed,\n        // execute whatever finalizer is handed to it automatically.\n        execFinalizer(teardown);\n      } else {\n        if (teardown instanceof Subscription) {\n          // We don't add closed subscriptions, and we don't add the same subscription\n          // twice. Subscription unsubscribe is idempotent.\n          if (teardown.closed || teardown._hasParent(this)) {\n            return;\n          }\n          teardown._addParent(this);\n        }\n        (this._finalizers = this._finalizers ?? []).push(teardown);\n      }\n    }\n  }\n\n  /**\n   * Checks to see if a this subscription already has a particular parent.\n   * This will signal that this subscription has already been added to the parent in question.\n   * @param parent the parent to check for\n   */\n  private _hasParent(parent: Subscription) {\n    const { _parentage } = this;\n    return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n  }\n\n  /**\n   * Adds a parent to this subscription so it can be removed from the parent if it\n   * unsubscribes on it's own.\n   *\n   * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n   * @param parent The parent subscription to add\n   */\n  private _addParent(parent: Subscription) {\n    const { _parentage } = this;\n    this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n  }\n\n  /**\n   * Called on a child when it is removed via {@link #remove}.\n   * @param parent The parent to remove\n   */\n  private _removeParent(parent: Subscription) {\n    const { _parentage } = this;\n    if (_parentage === parent) {\n      this._parentage = null;\n    } else if (Array.isArray(_parentage)) {\n      arrRemove(_parentage, parent);\n    }\n  }\n\n  /**\n   * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n   *\n   * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n   * from every other `Subscription` they have been added to. This means that using the `remove` method\n   * is not a common thing and should be used thoughtfully.\n   *\n   * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n   * more than once, you will need to call `remove` the same number of times to remove all instances.\n   *\n   * All finalizer instances are removed to free up memory upon unsubscription.\n   *\n   * @param teardown The finalizer to remove from this subscription\n   */\n  remove(teardown: Exclude<TeardownLogic, void>): void {\n    const { _finalizers } = this;\n    _finalizers && arrRemove(_finalizers, teardown);\n\n    if (teardown instanceof Subscription) {\n      teardown._removeParent(this);\n    }\n  }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n  return (\n    value instanceof Subscription ||\n    (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n  );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n  if (isFunction(finalizer)) {\n    finalizer();\n  } else {\n    finalizer.unsubscribe();\n  }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n  onUnhandledError: null,\n  onStoppedNotification: null,\n  Promise: undefined,\n  useDeprecatedSynchronousErrorHandling: false,\n  useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n  /**\n   * A registration point for unhandled errors from RxJS. These are errors that\n   * cannot were not handled by consuming code in the usual subscription path. For\n   * example, if you have this configured, and you subscribe to an observable without\n   * providing an error handler, errors from that subscription will end up here. This\n   * will _always_ be called asynchronously on another job in the runtime. This is because\n   * we do not want errors thrown in this user-configured handler to interfere with the\n   * behavior of the library.\n   */\n  onUnhandledError: ((err: any) => void) | null;\n\n  /**\n   * A registration point for notifications that cannot be sent to subscribers because they\n   * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n   * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n   * might want a different behavior. For example, with sources that attempt to report errors\n   * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n   * This will _always_ be called asynchronously on another job in the runtime. This is because\n   * we do not want errors thrown in this user-configured handler to interfere with the\n   * behavior of the library.\n   */\n  onStoppedNotification: ((notification: ObservableNotification<any>, subscriber: Subscriber<any>) => void) | null;\n\n  /**\n   * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n   * methods.\n   *\n   * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n   * Promise constructor. If you need a Promise implementation other than native promises,\n   * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n   */\n  Promise?: PromiseConstructorLike;\n\n  /**\n   * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n   * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n   * call in a try/catch block. It also enables producer interference, a nasty bug\n   * where a multicast can be broken for all observers by a downstream consumer with\n   * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n   * FOR MIGRATION REASONS.\n   *\n   * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n   * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n   * behaviors described above. Will be removed in v8.\n   */\n  useDeprecatedSynchronousErrorHandling: boolean;\n\n  /**\n   * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n   * `unsubscribe()` via `this` context in `next` functions created in observers passed\n   * to `subscribe`.\n   *\n   * This is being removed because the performance was severely problematic, and it could also cause\n   * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n   * their `this` context overwritten.\n   *\n   * @deprecated As of version 8, RxJS will no longer support altering the\n   * context of next functions provided as part of an observer to Subscribe. Instead,\n   * you will have access to a subscription or a signal or token that will allow you to do things like\n   * unsubscribe and test closed status. Will be removed in v8.\n   */\n  useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n  setTimeout: SetTimeoutFunction;\n  clearTimeout: ClearTimeoutFunction;\n  delegate:\n    | {\n        setTimeout: SetTimeoutFunction;\n        clearTimeout: ClearTimeoutFunction;\n      }\n    | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  setTimeout(handler: () => void, timeout?: number, ...args) {\n    const { delegate } = timeoutProvider;\n    if (delegate?.setTimeout) {\n      return delegate.setTimeout(handler, timeout, ...args);\n    }\n    return setTimeout(handler, timeout, ...args);\n  },\n  clearTimeout(handle) {\n    const { delegate } = timeoutProvider;\n    return (delegate?.clearTimeout || clearTimeout)(handle as any);\n  },\n  delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n  timeoutProvider.setTimeout(() => {\n    const { onUnhandledError } = config;\n    if (onUnhandledError) {\n      // Execute the user-configured error handler.\n      onUnhandledError(err);\n    } else {\n      // Throw so it is picked up by the runtime's uncaught error mechanism.\n      throw err;\n    }\n  });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n  return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification<T>(value: T) {\n  return createNotification('N', value, undefined) as NextNotification<T>;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n  return {\n    kind,\n    value,\n    error,\n  };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n  if (config.useDeprecatedSynchronousErrorHandling) {\n    const isRoot = !context;\n    if (isRoot) {\n      context = { errorThrown: false, error: null };\n    }\n    cb();\n    if (isRoot) {\n      const { errorThrown, error } = context!;\n      context = null;\n      if (errorThrown) {\n        throw error;\n      }\n    }\n  } else {\n    // This is the general non-deprecated path for everyone that\n    // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n    cb();\n  }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n  if (config.useDeprecatedSynchronousErrorHandling && context) {\n    context.errorThrown = true;\n    context.error = err;\n  }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber<T>\n */\nexport class Subscriber<T> extends Subscription implements Observer<T> {\n  /**\n   * A static factory for a Subscriber, given a (potentially partial) definition\n   * of an Observer.\n   * @param next The `next` callback of an Observer.\n   * @param error The `error` callback of an\n   * Observer.\n   * @param complete The `complete` callback of an\n   * Observer.\n   * @return A Subscriber wrapping the (partially defined)\n   * Observer represented by the given arguments.\n   * @nocollapse\n   * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n   * method, and there is no reason to be creating instances of `Subscriber` directly.\n   * If you have a specific use case, please file an issue.\n   */\n  static create<T>(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber<T> {\n    return new SafeSubscriber(next, error, complete);\n  }\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  protected isStopped: boolean = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  protected destination: Subscriber<any> | Observer<any>; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n   */\n  constructor(destination?: Subscriber<any> | Observer<any>) {\n    super();\n    if (destination) {\n      this.destination = destination;\n      // Automatically chain subscriptions together here.\n      // if destination is a Subscription, then it is a Subscriber.\n      if (isSubscription(destination)) {\n        destination.add(this);\n      }\n    } else {\n      this.destination = EMPTY_OBSERVER;\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive notifications of type `next` from\n   * the Observable, with a value. The Observable may call this method 0 or more\n   * times.\n   * @param {T} [value] The `next` value.\n   * @return {void}\n   */\n  next(value?: T): void {\n    if (this.isStopped) {\n      handleStoppedNotification(nextNotification(value), this);\n    } else {\n      this._next(value!);\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive notifications of type `error` from\n   * the Observable, with an attached `Error`. Notifies the Observer that\n   * the Observable has experienced an error condition.\n   * @param {any} [err] The `error` exception.\n   * @return {void}\n   */\n  error(err?: any): void {\n    if (this.isStopped) {\n      handleStoppedNotification(errorNotification(err), this);\n    } else {\n      this.isStopped = true;\n      this._error(err);\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive a valueless notification of type\n   * `complete` from the Observable. Notifies the Observer that the Observable\n   * has finished sending push-based notifications.\n   * @return {void}\n   */\n  complete(): void {\n    if (this.isStopped) {\n      handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n    } else {\n      this.isStopped = true;\n      this._complete();\n    }\n  }\n\n  unsubscribe(): void {\n    if (!this.closed) {\n      this.isStopped = true;\n      super.unsubscribe();\n      this.destination = null!;\n    }\n  }\n\n  protected _next(value: T): void {\n    this.destination.next(value);\n  }\n\n  protected _error(err: any): void {\n    try {\n      this.destination.error(err);\n    } finally {\n      this.unsubscribe();\n    }\n  }\n\n  protected _complete(): void {\n    try {\n      this.destination.complete();\n    } finally {\n      this.unsubscribe();\n    }\n  }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind<Fn extends (...args: any[]) => any>(fn: Fn, thisArg: any): Fn {\n  return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver<T> implements Observer<T> {\n  constructor(private partialObserver: Partial<Observer<T>>) {}\n\n  next(value: T): void {\n    const { partialObserver } = this;\n    if (partialObserver.next) {\n      try {\n        partialObserver.next(value);\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    }\n  }\n\n  error(err: any): void {\n    const { partialObserver } = this;\n    if (partialObserver.error) {\n      try {\n        partialObserver.error(err);\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    } else {\n      handleUnhandledError(err);\n    }\n  }\n\n  complete(): void {\n    const { partialObserver } = this;\n    if (partialObserver.complete) {\n      try {\n        partialObserver.complete();\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    }\n  }\n}\n\nexport class SafeSubscriber<T> extends Subscriber<T> {\n  constructor(\n    observerOrNext?: Partial<Observer<T>> | ((value: T) => void) | null,\n    error?: ((e?: any) => void) | null,\n    complete?: (() => void) | null\n  ) {\n    super();\n\n    let partialObserver: Partial<Observer<T>>;\n    if (isFunction(observerOrNext) || !observerOrNext) {\n      // The first argument is a function, not an observer. The next\n      // two arguments *could* be observers, or they could be empty.\n      partialObserver = {\n        next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n        error: error ?? undefined,\n        complete: complete ?? undefined,\n      };\n    } else {\n      // The first argument is a partial observer.\n      let context: any;\n      if (this && config.useDeprecatedNextContext) {\n        // This is a deprecated path that made `this.unsubscribe()` available in\n        // next handler functions passed to subscribe. This only exists behind a flag\n        // now, as it is *very* slow.\n        context = Object.create(observerOrNext);\n        context.unsubscribe = () => this.unsubscribe();\n        partialObserver = {\n          next: observerOrNext.next && bind(observerOrNext.next, context),\n          error: observerOrNext.error && bind(observerOrNext.error, context),\n          complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n        };\n      } else {\n        // The \"normal\" path. Just use the partial observer directly.\n        partialObserver = observerOrNext;\n      }\n    }\n\n    // Wrap the partial observer to ensure it's a full observer, and\n    // make sure proper error handling is accounted for.\n    this.destination = new ConsumerObserver(partialObserver);\n  }\n}\n\nfunction handleUnhandledError(error: any) {\n  if (config.useDeprecatedSynchronousErrorHandling) {\n    captureError(error);\n  } else {\n    // Ideal path, we report this as an unhandled error,\n    // which is thrown on a new call stack.\n    reportUnhandledError(error);\n  }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n  throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification<any>, subscriber: Subscriber<any>) {\n  const { onStoppedNotification } = config;\n  onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly<Observer<any>> & { closed: true } = {\n  closed: true,\n  next: noop,\n  error: defaultErrorHandler,\n  complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `<T>(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n *   map(i => range(i)),\n *   mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n *   next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n *   next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity<T>(x: T): T {\n  return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe<T, A>(fn1: UnaryFunction<T, A>): UnaryFunction<T, A>;\nexport function pipe<T, A, B>(fn1: UnaryFunction<T, A>, fn2: UnaryFunction<A, B>): UnaryFunction<T, B>;\nexport function pipe<T, A, B, C>(fn1: UnaryFunction<T, A>, fn2: UnaryFunction<A, B>, fn3: UnaryFunction<B, C>): UnaryFunction<T, C>;\nexport function pipe<T, A, B, C, D>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>\n): UnaryFunction<T, D>;\nexport function pipe<T, A, B, C, D, E>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>\n): UnaryFunction<T, E>;\nexport function pipe<T, A, B, C, D, E, F>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>\n): UnaryFunction<T, F>;\nexport function pipe<T, A, B, C, D, E, F, G>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>\n): UnaryFunction<T, G>;\nexport function pipe<T, A, B, C, D, E, F, G, H>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>\n): UnaryFunction<T, H>;\nexport function pipe<T, A, B, C, D, E, F, G, H, I>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>,\n  fn9: UnaryFunction<H, I>\n): UnaryFunction<T, I>;\nexport function pipe<T, A, B, C, D, E, F, G, H, I>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>,\n  fn9: UnaryFunction<H, I>,\n  ...fns: UnaryFunction<any, any>[]\n): UnaryFunction<T, unknown>;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on.  \n */\nexport function pipe(...fns: Array<UnaryFunction<any, any>>): UnaryFunction<any, any> {\n  return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray<T, R>(fns: Array<UnaryFunction<T, R>>): UnaryFunction<T, R> {\n  if (fns.length === 0) {\n    return identity as UnaryFunction<any, any>;\n  }\n\n  if (fns.length === 1) {\n    return fns[0];\n  }\n\n  return function piped(input: T): R {\n    return fns.reduce((prev: any, fn: UnaryFunction<T, R>) => fn(prev), input as any);\n  };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable<T>\n */\nexport class Observable<T> implements Subscribable<T> {\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   */\n  source: Observable<any> | undefined;\n\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   */\n  operator: Operator<any, T> | undefined;\n\n  /**\n   * @constructor\n   * @param {Function} subscribe the function that is called when the Observable is\n   * initially subscribed to. This function is given a Subscriber, to which new values\n   * can be `next`ed, or an `error` method can be called to raise an error, or\n   * `complete` can be called to notify of a successful completion.\n   */\n  constructor(subscribe?: (this: Observable<T>, subscriber: Subscriber<T>) => TeardownLogic) {\n    if (subscribe) {\n      this._subscribe = subscribe;\n    }\n  }\n\n  // HACK: Since TypeScript inherits static properties too, we have to\n  // fight against TypeScript here so Subject can have a different static create signature\n  /**\n   * Creates a new Observable by calling the Observable constructor\n   * @owner Observable\n   * @method create\n   * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n   * @return {Observable} a new observable\n   * @nocollapse\n   * @deprecated Use `new Observable()` instead. Will be removed in v8.\n   */\n  static create: (...args: any[]) => any = <T>(subscribe?: (subscriber: Subscriber<T>) => TeardownLogic) => {\n    return new Observable<T>(subscribe);\n  };\n\n  /**\n   * Creates a new Observable, with this Observable instance as the source, and the passed\n   * operator defined as the new observable's operator.\n   * @method lift\n   * @param operator the operator defining the operation to take on the observable\n   * @return a new observable with the Operator applied\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   * If you have implemented an operator using `lift`, it is recommended that you create an\n   * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n   * scratch\" section here: https://rxjs.dev/guide/operators\n   */\n  lift<R>(operator?: Operator<T, R>): Observable<R> {\n    const observable = new Observable<R>();\n    observable.source = this;\n    observable.operator = operator;\n    return observable;\n  }\n\n  subscribe(observerOrNext?: Partial<Observer<T>> | ((value: T) => void)): Subscription;\n  /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n  subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n  /**\n   * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n   *\n   * <span class=\"informal\">Use it when you have all these Observables, but still nothing is happening.</span>\n   *\n   * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n   * might be for example a function that you passed to Observable's constructor, but most of the time it is\n   * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n   * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n   * the thought.\n   *\n   * Apart from starting the execution of an Observable, this method allows you to listen for values\n   * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n   * of the following ways.\n   *\n   * The first way is creating an object that implements {@link Observer} interface. It should have methods\n   * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n   * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n   * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n   * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n   * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n   * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n   * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n   * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n   * an `error` method to avoid missing thrown errors.\n   *\n   * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n   * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n   * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n   * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n   * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n   * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n   *\n   * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n   * and you also handled emissions internally by using operators (e.g. using `tap`).\n   *\n   * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n   * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n   * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n   * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n   *\n   * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n   * It is an Observable itself that decides when these functions will be called. For example {@link of}\n   * by default emits all its values synchronously. Always check documentation for how given Observable\n   * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n   *\n   * #### Examples\n   *\n   * Subscribe with an {@link guide/observer Observer}\n   *\n   * ```ts\n   * import { of } from 'rxjs';\n   *\n   * const sumObserver = {\n   *   sum: 0,\n   *   next(value) {\n   *     console.log('Adding: ' + value);\n   *     this.sum = this.sum + value;\n   *   },\n   *   error() {\n   *     // We actually could just remove this method,\n   *     // since we do not really care about errors right now.\n   *   },\n   *   complete() {\n   *     console.log('Sum equals: ' + this.sum);\n   *   }\n   * };\n   *\n   * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n   *   .subscribe(sumObserver);\n   *\n   * // Logs:\n   * // 'Adding: 1'\n   * // 'Adding: 2'\n   * // 'Adding: 3'\n   * // 'Sum equals: 6'\n   * ```\n   *\n   * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n   *\n   * ```ts\n   * import { of } from 'rxjs'\n   *\n   * let sum = 0;\n   *\n   * of(1, 2, 3).subscribe(\n   *   value => {\n   *     console.log('Adding: ' + value);\n   *     sum = sum + value;\n   *   },\n   *   undefined,\n   *   () => console.log('Sum equals: ' + sum)\n   * );\n   *\n   * // Logs:\n   * // 'Adding: 1'\n   * // 'Adding: 2'\n   * // 'Adding: 3'\n   * // 'Sum equals: 6'\n   * ```\n   *\n   * Cancel a subscription\n   *\n   * ```ts\n   * import { interval } from 'rxjs';\n   *\n   * const subscription = interval(1000).subscribe({\n   *   next(num) {\n   *     console.log(num)\n   *   },\n   *   complete() {\n   *     // Will not be called, even when cancelling subscription.\n   *     console.log('completed!');\n   *   }\n   * });\n   *\n   * setTimeout(() => {\n   *   subscription.unsubscribe();\n   *   console.log('unsubscribed!');\n   * }, 2500);\n   *\n   * // Logs:\n   * // 0 after 1s\n   * // 1 after 2s\n   * // 'unsubscribed!' after 2.5s\n   * ```\n   *\n   * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n   * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n   * Observable.\n   * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n   * the error will be thrown asynchronously as unhandled.\n   * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n   * @return {Subscription} a subscription reference to the registered handlers\n   * @method subscribe\n   */\n  subscribe(\n    observerOrNext?: Partial<Observer<T>> | ((value: T) => void) | null,\n    error?: ((error: any) => void) | null,\n    complete?: (() => void) | null\n  ): Subscription {\n    const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n    errorContext(() => {\n      const { operator, source } = this;\n      subscriber.add(\n        operator\n          ? // We're dealing with a subscription in the\n            // operator chain to one of our lifted operators.\n            operator.call(subscriber, source)\n          : source\n          ? // If `source` has a value, but `operator` does not, something that\n            // had intimate knowledge of our API, like our `Subject`, must have\n            // set it. We're going to just call `_subscribe` directly.\n            this._subscribe(subscriber)\n          : // In all other cases, we're likely wrapping a user-provided initializer\n            // function, so we need to catch errors and handle them appropriately.\n            this._trySubscribe(subscriber)\n      );\n    });\n\n    return subscriber;\n  }\n\n  /** @internal */\n  protected _trySubscribe(sink: Subscriber<T>): TeardownLogic {\n    try {\n      return this._subscribe(sink);\n    } catch (err) {\n      // We don't need to return anything in this case,\n      // because it's just going to try to `add()` to a subscription\n      // above.\n      sink.error(err);\n    }\n  }\n\n  /**\n   * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n   * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n   *\n   * **WARNING**: Only use this with observables you *know* will complete. If the source\n   * observable does not complete, you will end up with a promise that is hung up, and\n   * potentially all of the state of an async function hanging out in memory. To avoid\n   * this situation, look into adding something like {@link timeout}, {@link take},\n   * {@link takeWhile}, or {@link takeUntil} amongst others.\n   *\n   * #### Example\n   *\n   * ```ts\n   * import { interval, take } from 'rxjs';\n   *\n   * const source$ = interval(1000).pipe(take(4));\n   *\n   * async function getTotal() {\n   *   let total = 0;\n   *\n   *   await source$.forEach(value => {\n   *     total += value;\n   *     console.log('observable -> ' + value);\n   *   });\n   *\n   *   return total;\n   * }\n   *\n   * getTotal().then(\n   *   total => console.log('Total: ' + total)\n   * );\n   *\n   * // Expected:\n   * // 'observable -> 0'\n   * // 'observable -> 1'\n   * // 'observable -> 2'\n   * // 'observable -> 3'\n   * // 'Total: 6'\n   * ```\n   *\n   * @param next a handler for each value emitted by the observable\n   * @return a promise that either resolves on observable completion or\n   *  rejects with the handled error\n   */\n  forEach(next: (value: T) => void): Promise<void>;\n\n  /**\n   * @param next a handler for each value emitted by the observable\n   * @param promiseCtor a constructor function used to instantiate the Promise\n   * @return a promise that either resolves on observable completion or\n   *  rejects with the handled error\n   * @deprecated Passing a Promise constructor will no longer be available\n   * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n   * little benefit. If you need this functionality, it is recommended that you either\n   * polyfill Promise, or you create an adapter to convert the returned native promise\n   * to whatever promise implementation you wanted. Will be removed in v8.\n   */\n  forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise<void>;\n\n  forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise<void> {\n    promiseCtor = getPromiseCtor(promiseCtor);\n\n    return new promiseCtor<void>((resolve, reject) => {\n      const subscriber = new SafeSubscriber<T>({\n        next: (value) => {\n          try {\n            next(value);\n          } catch (err) {\n            reject(err);\n            subscriber.unsubscribe();\n          }\n        },\n        error: reject,\n        complete: resolve,\n      });\n      this.subscribe(subscriber);\n    }) as Promise<void>;\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<any>): TeardownLogic {\n    return this.source?.subscribe(subscriber);\n  }\n\n  /**\n   * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n   * @method Symbol.observable\n   * @return {Observable} this instance of the observable\n   */\n  [Symbol_observable]() {\n    return this;\n  }\n\n  /* tslint:disable:max-line-length */\n  pipe(): Observable<T>;\n  pipe<A>(op1: OperatorFunction<T, A>): Observable<A>;\n  pipe<A, B>(op1: OperatorFunction<T, A>, op2: OperatorFunction<A, B>): Observable<B>;\n  pipe<A, B, C>(op1: OperatorFunction<T, A>, op2: OperatorFunction<A, B>, op3: OperatorFunction<B, C>): Observable<C>;\n  pipe<A, B, C, D>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>\n  ): Observable<D>;\n  pipe<A, B, C, D, E>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>\n  ): Observable<E>;\n  pipe<A, B, C, D, E, F>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>\n  ): Observable<F>;\n  pipe<A, B, C, D, E, F, G>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>\n  ): Observable<G>;\n  pipe<A, B, C, D, E, F, G, H>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>\n  ): Observable<H>;\n  pipe<A, B, C, D, E, F, G, H, I>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>,\n    op9: OperatorFunction<H, I>\n  ): Observable<I>;\n  pipe<A, B, C, D, E, F, G, H, I>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>,\n    op9: OperatorFunction<H, I>,\n    ...operations: OperatorFunction<any, any>[]\n  ): Observable<unknown>;\n  /* tslint:enable:max-line-length */\n\n  /**\n   * Used to stitch together functional operators into a chain.\n   * @method pipe\n   * @return {Observable} the Observable result of all of the operators having\n   * been called in the order they were passed in.\n   *\n   * ## Example\n   *\n   * ```ts\n   * import { interval, filter, map, scan } from 'rxjs';\n   *\n   * interval(1000)\n   *   .pipe(\n   *     filter(x => x % 2 === 0),\n   *     map(x => x + x),\n   *     scan((acc, x) => acc + x)\n   *   )\n   *   .subscribe(x => console.log(x));\n   * ```\n   */\n  pipe(...operations: OperatorFunction<any, any>[]): Observable<any> {\n    return pipeFromArray(operations)(this);\n  }\n\n  /* tslint:disable:max-line-length */\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(): Promise<T | undefined>;\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(PromiseCtor: typeof Promise): Promise<T | undefined>;\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(PromiseCtor: PromiseConstructorLike): Promise<T | undefined>;\n  /* tslint:enable:max-line-length */\n\n  /**\n   * Subscribe to this Observable and get a Promise resolving on\n   * `complete` with the last emission (if any).\n   *\n   * **WARNING**: Only use this with observables you *know* will complete. If the source\n   * observable does not complete, you will end up with a promise that is hung up, and\n   * potentially all of the state of an async function hanging out in memory. To avoid\n   * this situation, look into adding something like {@link timeout}, {@link take},\n   * {@link takeWhile}, or {@link takeUntil} amongst others.\n   *\n   * @method toPromise\n   * @param [promiseCtor] a constructor function used to instantiate\n   * the Promise\n   * @return A Promise that resolves with the last value emit, or\n   * rejects on an error. If there were no emissions, Promise\n   * resolves with undefined.\n   * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n   */\n  toPromise(promiseCtor?: PromiseConstructorLike): Promise<T | undefined> {\n    promiseCtor = getPromiseCtor(promiseCtor);\n\n    return new promiseCtor((resolve, reject) => {\n      let value: T | undefined;\n      this.subscribe(\n        (x: T) => (value = x),\n        (err: any) => reject(err),\n        () => resolve(value)\n      );\n    }) as Promise<T | undefined>;\n  }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n  return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver<T>(value: any): value is Observer<T> {\n  return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber<T>(value: any): value is Subscriber<T> {\n  return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType<typeof Observable>['lift'] } {\n  return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate<T, R>(\n  init: (liftedSource: Observable<T>, subscriber: Subscriber<R>) => (() => void) | void\n): OperatorFunction<T, R> {\n  return (source: Observable<T>) => {\n    if (hasLift(source)) {\n      return source.lift(function (this: Subscriber<R>, liftedSource: Observable<T>) {\n        try {\n          return init(liftedSource, this);\n        } catch (err) {\n          this.error(err);\n        }\n      });\n    }\n    throw new TypeError('Unable to lift unknown Observable type');\n  };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber<T>(\n  destination: Subscriber<any>,\n  onNext?: (value: T) => void,\n  onComplete?: () => void,\n  onError?: (err: any) => void,\n  onFinalize?: () => void\n): Subscriber<T> {\n  return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber<T> extends Subscriber<T> {\n  /**\n   * Creates an instance of an `OperatorSubscriber`.\n   * @param destination The downstream subscriber.\n   * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n   * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n   * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n   * and send to the `destination` error handler.\n   * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n   * this handler are sent to the `destination` error handler.\n   * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n   * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n   * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n   * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n   * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n   * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n   */\n  constructor(\n    destination: Subscriber<any>,\n    onNext?: (value: T) => void,\n    onComplete?: () => void,\n    onError?: (err: any) => void,\n    private onFinalize?: () => void,\n    private shouldUnsubscribe?: () => boolean\n  ) {\n    // It's important - for performance reasons - that all of this class's\n    // members are initialized and that they are always initialized in the same\n    // order. This will ensure that all OperatorSubscriber instances have the\n    // same hidden class in V8. This, in turn, will help keep the number of\n    // hidden classes involved in property accesses within the base class as\n    // low as possible. If the number of hidden classes involved exceeds four,\n    // the property accesses will become megamorphic and performance penalties\n    // will be incurred - i.e. inline caches won't be used.\n    //\n    // The reasons for ensuring all instances have the same hidden class are\n    // further discussed in this blog post from Benedikt Meurer:\n    // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n    super(destination);\n    this._next = onNext\n      ? function (this: OperatorSubscriber<T>, value: T) {\n          try {\n            onNext(value);\n          } catch (err) {\n            destination.error(err);\n          }\n        }\n      : super._next;\n    this._error = onError\n      ? function (this: OperatorSubscriber<T>, err: any) {\n          try {\n            onError(err);\n          } catch (err) {\n            // Send any errors that occur down stream.\n            destination.error(err);\n          } finally {\n            // Ensure finalization.\n            this.unsubscribe();\n          }\n        }\n      : super._error;\n    this._complete = onComplete\n      ? function (this: OperatorSubscriber<T>) {\n          try {\n            onComplete();\n          } catch (err) {\n            // Send any errors that occur down stream.\n            destination.error(err);\n          } finally {\n            // Ensure finalization.\n            this.unsubscribe();\n          }\n        }\n      : super._complete;\n  }\n\n  unsubscribe() {\n    if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n      const { closed } = this;\n      super.unsubscribe();\n      // Execute additional teardown if we have any and we didn't already do so.\n      !closed && this.onFinalize?.();\n    }\n  }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n  schedule(callback: FrameRequestCallback): Subscription;\n  requestAnimationFrame: typeof requestAnimationFrame;\n  cancelAnimationFrame: typeof cancelAnimationFrame;\n  delegate:\n    | {\n        requestAnimationFrame: typeof requestAnimationFrame;\n        cancelAnimationFrame: typeof cancelAnimationFrame;\n      }\n    | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  schedule(callback) {\n    let request = requestAnimationFrame;\n    let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n    const { delegate } = animationFrameProvider;\n    if (delegate) {\n      request = delegate.requestAnimationFrame;\n      cancel = delegate.cancelAnimationFrame;\n    }\n    const handle = request((timestamp) => {\n      // Clear the cancel function. The request has been fulfilled, so\n      // attempting to cancel the request upon unsubscription would be\n      // pointless.\n      cancel = undefined;\n      callback(timestamp);\n    });\n    return new Subscription(() => cancel?.(handle));\n  },\n  requestAnimationFrame(...args) {\n    const { delegate } = animationFrameProvider;\n    return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n  },\n  cancelAnimationFrame(...args) {\n    const { delegate } = animationFrameProvider;\n    return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n  },\n  delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n  (_super) =>\n    function ObjectUnsubscribedErrorImpl(this: any) {\n      _super(this);\n      this.name = 'ObjectUnsubscribedError';\n      this.message = 'object unsubscribed';\n    }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject<T> extends Observable<T> implements SubscriptionLike {\n  closed = false;\n\n  private currentObservers: Observer<T>[] | null = null;\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  observers: Observer<T>[] = [];\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  isStopped = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  hasError = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  thrownError: any = null;\n\n  /**\n   * Creates a \"subject\" by basically gluing an observer to an observable.\n   *\n   * @nocollapse\n   * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n   */\n  static create: (...args: any[]) => any = <T>(destination: Observer<T>, source: Observable<T>): AnonymousSubject<T> => {\n    return new AnonymousSubject<T>(destination, source);\n  };\n\n  constructor() {\n    // NOTE: This must be here to obscure Observable's constructor.\n    super();\n  }\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  lift<R>(operator: Operator<T, R>): Observable<R> {\n    const subject = new AnonymousSubject(this, this);\n    subject.operator = operator as any;\n    return subject as any;\n  }\n\n  /** @internal */\n  protected _throwIfClosed() {\n    if (this.closed) {\n      throw new ObjectUnsubscribedError();\n    }\n  }\n\n  next(value: T) {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        if (!this.currentObservers) {\n          this.currentObservers = Array.from(this.observers);\n        }\n        for (const observer of this.currentObservers) {\n          observer.next(value);\n        }\n      }\n    });\n  }\n\n  error(err: any) {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        this.hasError = this.isStopped = true;\n        this.thrownError = err;\n        const { observers } = this;\n        while (observers.length) {\n          observers.shift()!.error(err);\n        }\n      }\n    });\n  }\n\n  complete() {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        this.isStopped = true;\n        const { observers } = this;\n        while (observers.length) {\n          observers.shift()!.complete();\n        }\n      }\n    });\n  }\n\n  unsubscribe() {\n    this.isStopped = this.closed = true;\n    this.observers = this.currentObservers = null!;\n  }\n\n  get observed() {\n    return this.observers?.length > 0;\n  }\n\n  /** @internal */\n  protected _trySubscribe(subscriber: Subscriber<T>): TeardownLogic {\n    this._throwIfClosed();\n    return super._trySubscribe(subscriber);\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    this._throwIfClosed();\n    this._checkFinalizedStatuses(subscriber);\n    return this._innerSubscribe(subscriber);\n  }\n\n  /** @internal */\n  protected _innerSubscribe(subscriber: Subscriber<any>) {\n    const { hasError, isStopped, observers } = this;\n    if (hasError || isStopped) {\n      return EMPTY_SUBSCRIPTION;\n    }\n    this.currentObservers = null;\n    observers.push(subscriber);\n    return new Subscription(() => {\n      this.currentObservers = null;\n      arrRemove(observers, subscriber);\n    });\n  }\n\n  /** @internal */\n  protected _checkFinalizedStatuses(subscriber: Subscriber<any>) {\n    const { hasError, thrownError, isStopped } = this;\n    if (hasError) {\n      subscriber.error(thrownError);\n    } else if (isStopped) {\n      subscriber.complete();\n    }\n  }\n\n  /**\n   * Creates a new Observable with this Subject as the source. You can do this\n   * to create custom Observer-side logic of the Subject and conceal it from\n   * code that uses the Observable.\n   * @return {Observable} Observable that the Subject casts to\n   */\n  asObservable(): Observable<T> {\n    const observable: any = new Observable<T>();\n    observable.source = this;\n    return observable;\n  }\n}\n\n/**\n * @class AnonymousSubject<T>\n */\nexport class AnonymousSubject<T> extends Subject<T> {\n  constructor(\n    /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n    public destination?: Observer<T>,\n    source?: Observable<T>\n  ) {\n    super();\n    this.source = source;\n  }\n\n  next(value: T) {\n    this.destination?.next?.(value);\n  }\n\n  error(err: any) {\n    this.destination?.error?.(err);\n  }\n\n  complete() {\n    this.destination?.complete?.();\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n  }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n *\n * @class BehaviorSubject<T>\n */\nexport class BehaviorSubject<T> extends Subject<T> {\n  constructor(private _value: T) {\n    super();\n  }\n\n  get value(): T {\n    return this.getValue();\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    const subscription = super._subscribe(subscriber);\n    !subscription.closed && subscriber.next(this._value);\n    return subscription;\n  }\n\n  getValue(): T {\n    const { hasError, thrownError, _value } = this;\n    if (hasError) {\n      throw thrownError;\n    }\n    this._throwIfClosed();\n    return _value;\n  }\n\n  next(value: T): void {\n    super.next((this._value = value));\n  }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n  delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n  now() {\n    // Use the variable rather than `this` so that the function can be called\n    // without being bound to the provider.\n    return (dateTimestampProvider.delegate || Date).now();\n  },\n  delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject<T> extends Subject<T> {\n  private _buffer: (T | number)[] = [];\n  private _infiniteTimeWindow = true;\n\n  /**\n   * @param bufferSize The size of the buffer to replay on subscription\n   * @param windowTime The amount of time the buffered items will stay buffered\n   * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n   * calculate the amount of time something has been buffered.\n   */\n  constructor(\n    private _bufferSize = Infinity,\n    private _windowTime = Infinity,\n    private _timestampProvider: TimestampProvider = dateTimestampProvider\n  ) {\n    super();\n    this._infiniteTimeWindow = _windowTime === Infinity;\n    this._bufferSize = Math.max(1, _bufferSize);\n    this._windowTime = Math.max(1, _windowTime);\n  }\n\n  next(value: T): void {\n    const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n    if (!isStopped) {\n      _buffer.push(value);\n      !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n    }\n    this._trimBuffer();\n    super.next(value);\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    this._throwIfClosed();\n    this._trimBuffer();\n\n    const subscription = this._innerSubscribe(subscriber);\n\n    const { _infiniteTimeWindow, _buffer } = this;\n    // We use a copy here, so reentrant code does not mutate our array while we're\n    // emitting it to a new subscriber.\n    const copy = _buffer.slice();\n    for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n      subscriber.next(copy[i] as T);\n    }\n\n    this._checkFinalizedStatuses(subscriber);\n\n    return subscription;\n  }\n\n  private _trimBuffer() {\n    const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n    // If we don't have an infinite buffer size, and we're over the length,\n    // use splice to truncate the old buffer values off. Note that we have to\n    // double the size for instances where we're not using an infinite time window\n    // because we're storing the values and the timestamps in the same array.\n    const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n    _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n    // Now, if we're not in an infinite time window, remove all values where the time is\n    // older than what is allowed.\n    if (!_infiniteTimeWindow) {\n      const now = _timestampProvider.now();\n      let last = 0;\n      // Search the array for the first timestamp that isn't expired and\n      // truncate the buffer up to that point.\n      for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n        last = i;\n      }\n      last && _buffer.splice(0, last + 1);\n    }\n  }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action<T> extends Subscription {\n *   new (scheduler: Scheduler, work: (state?: T) => void);\n *   schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action<T>\n */\nexport class Action<T> extends Subscription {\n  constructor(scheduler: Scheduler, work: (this: SchedulerAction<T>, state?: T) => void) {\n    super();\n  }\n  /**\n   * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n   * some context object, `state`. May happen at some point in the future,\n   * according to the `delay` parameter, if specified.\n   * @param {T} [state] Some contextual data that the `work` function uses when\n   * called by the Scheduler.\n   * @param {number} [delay] Time to wait before executing the work, where the\n   * time unit is implicit and defined by the Scheduler.\n   * @return {void}\n   */\n  public schedule(state?: T, delay: number = 0): Subscription {\n    return this;\n  }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n  setInterval: SetIntervalFunction;\n  clearInterval: ClearIntervalFunction;\n  delegate:\n    | {\n        setInterval: SetIntervalFunction;\n        clearInterval: ClearIntervalFunction;\n      }\n    | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  setInterval(handler: () => void, timeout?: number, ...args) {\n    const { delegate } = intervalProvider;\n    if (delegate?.setInterval) {\n      return delegate.setInterval(handler, timeout, ...args);\n    }\n    return setInterval(handler, timeout, ...args);\n  },\n  clearInterval(handle) {\n    const { delegate } = intervalProvider;\n    return (delegate?.clearInterval || clearInterval)(handle as any);\n  },\n  delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction<T> extends Action<T> {\n  public id: TimerHandle | undefined;\n  public state?: T;\n  // @ts-ignore: Property has no initializer and is not definitely assigned\n  public delay: number;\n  protected pending: boolean = false;\n\n  constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  public schedule(state?: T, delay: number = 0): Subscription {\n    if (this.closed) {\n      return this;\n    }\n\n    // Always replace the current state with the new state.\n    this.state = state;\n\n    const id = this.id;\n    const scheduler = this.scheduler;\n\n    //\n    // Important implementation note:\n    //\n    // Actions only execute once by default, unless rescheduled from within the\n    // scheduled callback. This allows us to implement single and repeat\n    // actions via the same code path, without adding API surface area, as well\n    // as mimic traditional recursion but across asynchronous boundaries.\n    //\n    // However, JS runtimes and timers distinguish between intervals achieved by\n    // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n    // serial `setTimeout` calls can be individually delayed, which delays\n    // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n    // guarantee the interval callback will be invoked more precisely to the\n    // interval period, regardless of load.\n    //\n    // Therefore, we use `setInterval` to schedule single and repeat actions.\n    // If the action reschedules itself with the same delay, the interval is not\n    // canceled. If the action doesn't reschedule, or reschedules with a\n    // different delay, the interval will be canceled after scheduled callback\n    // execution.\n    //\n    if (id != null) {\n      this.id = this.recycleAsyncId(scheduler, id, delay);\n    }\n\n    // Set the pending flag indicating that this action has been scheduled, or\n    // has recursively rescheduled itself.\n    this.pending = true;\n\n    this.delay = delay;\n    // If this action has already an async Id, don't request a new one.\n    this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n    return this;\n  }\n\n  protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n    return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n  }\n\n  protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n    // If this action is rescheduled with the same delay time, don't clear the interval id.\n    if (delay != null && this.delay === delay && this.pending === false) {\n      return id;\n    }\n    // Otherwise, if the action's delay time is different from the current delay,\n    // or the action has been rescheduled before it's executed, clear the interval id\n    if (id != null) {\n      intervalProvider.clearInterval(id);\n    }\n\n    return undefined;\n  }\n\n  /**\n   * Immediately executes this action and the `work` it contains.\n   * @return {any}\n   */\n  public execute(state: T, delay: number): any {\n    if (this.closed) {\n      return new Error('executing a cancelled action');\n    }\n\n    this.pending = false;\n    const error = this._execute(state, delay);\n    if (error) {\n      return error;\n    } else if (this.pending === false && this.id != null) {\n      // Dequeue if the action didn't reschedule itself. Don't call\n      // unsubscribe(), because the action could reschedule later.\n      // For example:\n      // ```\n      // scheduler.schedule(function doWork(counter) {\n      //   /* ... I'm a busy worker bee ... */\n      //   var originalAction = this;\n      //   /* wait 100ms before rescheduling the action */\n      //   setTimeout(function () {\n      //     originalAction.schedule(counter + 1);\n      //   }, 100);\n      // }, 1000);\n      // ```\n      this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n    }\n  }\n\n  protected _execute(state: T, _delay: number): any {\n    let errored: boolean = false;\n    let errorValue: any;\n    try {\n      this.work(state);\n    } catch (e) {\n      errored = true;\n      // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n      // return here, we can't have it return \"\" or 0 or false.\n      // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n      errorValue = e ? e : new Error('Scheduled action threw falsy error');\n    }\n    if (errored) {\n      this.unsubscribe();\n      return errorValue;\n    }\n  }\n\n  unsubscribe() {\n    if (!this.closed) {\n      const { id, scheduler } = this;\n      const { actions } = scheduler;\n\n      this.work = this.state = this.scheduler = null!;\n      this.pending = false;\n\n      arrRemove(actions, this);\n      if (id != null) {\n        this.id = this.recycleAsyncId(scheduler, id, null);\n      }\n\n      this.delay = null!;\n      super.unsubscribe();\n    }\n  }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n *   now(): number;\n *   schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n  public static now: () => number = dateTimestampProvider.now;\n\n  constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n    this.now = now;\n  }\n\n  /**\n   * A getter method that returns a number representing the current time\n   * (at the time this function was called) according to the scheduler's own\n   * internal clock.\n   * @return {number} A number that represents the current time. May or may not\n   * have a relation to wall-clock time. May or may not refer to a time unit\n   * (e.g. milliseconds).\n   */\n  public now: () => number;\n\n  /**\n   * Schedules a function, `work`, for execution. May happen at some point in\n   * the future, according to the `delay` parameter, if specified. May be passed\n   * some context object, `state`, which will be passed to the `work` function.\n   *\n   * The given arguments will be processed an stored as an Action object in a\n   * queue of actions.\n   *\n   * @param {function(state: ?T): ?Subscription} work A function representing a\n   * task, or some unit of work to be executed by the Scheduler.\n   * @param {number} [delay] Time to wait before executing the work, where the\n   * time unit is implicit and defined by the Scheduler itself.\n   * @param {T} [state] Some contextual data that the `work` function uses when\n   * called by the Scheduler.\n   * @return {Subscription} A subscription in order to be able to unsubscribe\n   * the scheduled work.\n   */\n  public schedule<T>(work: (this: SchedulerAction<T>, state?: T) => void, delay: number = 0, state?: T): Subscription {\n    return new this.schedulerActionCtor<T>(this, work).schedule(state, delay);\n  }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n  public actions: Array<AsyncAction<any>> = [];\n  /**\n   * A flag to indicate whether the Scheduler is currently executing a batch of\n   * queued actions.\n   * @type {boolean}\n   * @internal\n   */\n  public _active: boolean = false;\n  /**\n   * An internal ID used to track the latest asynchronous task such as those\n   * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n   * others.\n   * @type {any}\n   * @internal\n   */\n  public _scheduled: TimerHandle | undefined;\n\n  constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n    super(SchedulerAction, now);\n  }\n\n  public flush(action: AsyncAction<any>): void {\n    const { actions } = this;\n\n    if (this._active) {\n      actions.push(action);\n      return;\n    }\n\n    let error: any;\n    this._active = true;\n\n    do {\n      if ((error = action.execute(action.state, action.delay))) {\n        break;\n      }\n    } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n    this._active = false;\n\n    if (error) {\n      while ((action = actions.shift()!)) {\n        action.unsubscribe();\n      }\n      throw error;\n    }\n  }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * <span class=\"informal\">Schedule task as if you used setTimeout(task, duration)</span>\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n *   console.log(state);\n *   this.schedule(state + 1, 1000); // `this` references currently executing Action,\n *                                   // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction<T> extends AsyncAction<T> {\n  constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  public schedule(state?: T, delay: number = 0): Subscription {\n    if (delay > 0) {\n      return super.schedule(state, delay);\n    }\n    this.delay = delay;\n    this.state = state;\n    this.scheduler.flush(this);\n    return this;\n  }\n\n  public execute(state: T, delay: number): any {\n    return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n  }\n\n  protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n    // If delay exists and is greater than 0, or if the delay is null (the\n    // action wasn't rescheduled) but was originally scheduled as an async\n    // action, then recycle as an async action.\n\n    if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n      return super.requestAsyncId(scheduler, id, delay);\n    }\n\n    // Otherwise flush the scheduler starting with this action.\n    scheduler.flush(this);\n\n    // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n    // `TimerHandle`, and generally the return value here isn't really used. So the\n    // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n    // as opposed to refactoring every other instanceo of `requestAsyncId`.\n    return 0;\n  }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * <span class=\"informal\">Put every next task on a queue, instead of executing it immediately</span>\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n *   queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n *   console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n *   if (state !== 0) {\n *     console.log('before', state);\n *     this.schedule(state - 1); // `this` references currently executing Action,\n *                               // which we reschedule with new state\n *     console.log('after', state);\n *   }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction<T> extends AsyncAction<T> {\n  constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n    // If delay is greater than 0, request as an async action.\n    if (delay !== null && delay > 0) {\n      return super.requestAsyncId(scheduler, id, delay);\n    }\n    // Push the action to the end of the scheduler queue.\n    scheduler.actions.push(this);\n    // If an animation frame has already been requested, don't request another\n    // one. If an animation frame hasn't been requested yet, request one. Return\n    // the current animation frame request id.\n    return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n  }\n\n  protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n    // If delay exists and is greater than 0, or if the delay is null (the\n    // action wasn't rescheduled) but was originally scheduled as an async\n    // action, then recycle as an async action.\n    if (delay != null ? delay > 0 : this.delay > 0) {\n      return super.recycleAsyncId(scheduler, id, delay);\n    }\n    // If the scheduler queue has no remaining actions with the same async id,\n    // cancel the requested animation frame and set the scheduled flag to\n    // undefined so the next AnimationFrameAction will request its own.\n    const { actions } = scheduler;\n    if (id != null && actions[actions.length - 1]?.id !== id) {\n      animationFrameProvider.cancelAnimationFrame(id as number);\n      scheduler._scheduled = undefined;\n    }\n    // Return undefined so the action knows to request a new async id if it's rescheduled.\n    return undefined;\n  }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n  public flush(action?: AsyncAction<any>): void {\n    this._active = true;\n    // The async id that effects a call to flush is stored in _scheduled.\n    // Before executing an action, it's necessary to check the action's async\n    // id to determine whether it's supposed to be executed in the current\n    // flush.\n    // Previous implementations of this method used a count to determine this,\n    // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n    // are removed from the actions array and that can shift actions that are\n    // scheduled to be executed in a subsequent flush into positions at which\n    // they are executed within the current flush.\n    const flushId = this._scheduled;\n    this._scheduled = undefined;\n\n    const { actions } = this;\n    let error: any;\n    action = action || actions.shift()!;\n\n    do {\n      if ((error = action.execute(action.state, action.delay))) {\n        break;\n      }\n    } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n    this._active = false;\n\n    if (error) {\n      while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n        action.unsubscribe();\n      }\n      throw error;\n    }\n  }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * <span class=\"informal\">Perform task when `window.requestAnimationFrame` would fire</span>\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html: <div style=\"background: #0ff;\"></div>\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n *   div.style.height = height + \"px\";\n *\n *   this.schedule(height + 1);  // `this` references currently executing Action,\n *                               // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * <span class=\"informal\">Just emits 'complete', and nothing else.</span>\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n *   next: () => console.log('Next'),\n *   complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n *   mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable<never>((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n  return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n  return new Observable<never>((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n  return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last<T>(arr: T[]): T | undefined {\n  return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n  return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n  return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n  return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = (<T>(x: any): x is ArrayLike<T> => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike<any> {\n  return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable<any> {\n  return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable<T>(obj: any): obj is AsyncIterable<T> {\n  return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n  // TODO: We should create error codes that can be looked up, so this can be less verbose.\n  return new TypeError(\n    `You provided ${\n      input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n    } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n  );\n}\n", "export function getSymbolIterator(): symbol {\n  if (typeof Symbol !== 'function' || !Symbol.iterator) {\n    return '@@iterator' as any;\n  }\n\n  return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable<any> {\n  return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator<T>(readableStream: ReadableStreamLike<T>): AsyncGenerator<T> {\n  const reader = readableStream.getReader();\n  try {\n    while (true) {\n      const { value, done } = await reader.read();\n      if (done) {\n        return;\n      }\n      yield value!;\n    }\n  } finally {\n    reader.releaseLock();\n  }\n}\n\nexport function isReadableStreamLike<T>(obj: any): obj is ReadableStreamLike<T> {\n  // We don't want to use instanceof checks because they would return\n  // false for instances from another Realm, like an <iframe>.\n  return isFunction(obj?.getReader);\n}\n", "import { isArrayLike } from '../util/isArrayLike';\nimport { isPromise } from '../util/isPromise';\nimport { Observable } from '../Observable';\nimport { ObservableInput, ObservedValueOf, ReadableStreamLike } from '../types';\nimport { isInteropObservable } from '../util/isInteropObservable';\nimport { isAsyncIterable } from '../util/isAsyncIterable';\nimport { createInvalidObservableTypeError } from '../util/throwUnobservableError';\nimport { isIterable } from '../util/isIterable';\nimport { isReadableStreamLike, readableStreamLikeToAsyncGenerator } from '../util/isReadableStreamLike';\nimport { Subscriber } from '../Subscriber';\nimport { isFunction } from '../util/isFunction';\nimport { reportUnhandledError } from '../util/reportUnhandledError';\nimport { observable as Symbol_observable } from '../symbol/observable';\n\nexport function innerFrom<O extends ObservableInput<any>>(input: O): Observable<ObservedValueOf<O>>;\nexport function innerFrom<T>(input: ObservableInput<T>): Observable<T> {\n  if (input instanceof Observable) {\n    return input;\n  }\n  if (input != null) {\n    if (isInteropObservable(input)) {\n      return fromInteropObservable(input);\n    }\n    if (isArrayLike(input)) {\n      return fromArrayLike(input);\n    }\n    if (isPromise(input)) {\n      return fromPromise(input);\n    }\n    if (isAsyncIterable(input)) {\n      return fromAsyncIterable(input);\n    }\n    if (isIterable(input)) {\n      return fromIterable(input);\n    }\n    if (isReadableStreamLike(input)) {\n      return fromReadableStreamLike(input);\n    }\n  }\n\n  throw createInvalidObservableTypeError(input);\n}\n\n/**\n * Creates an RxJS Observable from an object that implements `Symbol.observable`.\n * @param obj An object that properly implements `Symbol.observable`.\n */\nexport function fromInteropObservable<T>(obj: any) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    const obs = obj[Symbol_observable]();\n    if (isFunction(obs.subscribe)) {\n      return obs.subscribe(subscriber);\n    }\n    // Should be caught by observable subscribe function error handling.\n    throw new TypeError('Provided object does not correctly implement Symbol.observable');\n  });\n}\n\n/**\n * Synchronously emits the values of an array like and completes.\n * This is exported because there are creation functions and operators that need to\n * make direct use of the same logic, and there's no reason to make them run through\n * `from` conditionals because we *know* they're dealing with an array.\n * @param array The array to emit values from\n */\nexport function fromArrayLike<T>(array: ArrayLike<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    // Loop over the array and emit each value. Note two things here:\n    // 1. We're making sure that the subscriber is not closed on each loop.\n    //    This is so we don't continue looping over a very large array after\n    //    something like a `take`, `takeWhile`, or other synchronous unsubscription\n    //    has already unsubscribed.\n    // 2. In this form, reentrant code can alter that array we're looping over.\n    //    This is a known issue, but considered an edge case. The alternative would\n    //    be to copy the array before executing the loop, but this has\n    //    performance implications.\n    for (let i = 0; i < array.length && !subscriber.closed; i++) {\n      subscriber.next(array[i]);\n    }\n    subscriber.complete();\n  });\n}\n\nexport function fromPromise<T>(promise: PromiseLike<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    promise\n      .then(\n        (value) => {\n          if (!subscriber.closed) {\n            subscriber.next(value);\n            subscriber.complete();\n          }\n        },\n        (err: any) => subscriber.error(err)\n      )\n      .then(null, reportUnhandledError);\n  });\n}\n\nexport function fromIterable<T>(iterable: Iterable<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    for (const value of iterable) {\n      subscriber.next(value);\n      if (subscriber.closed) {\n        return;\n      }\n    }\n    subscriber.complete();\n  });\n}\n\nexport function fromAsyncIterable<T>(asyncIterable: AsyncIterable<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    process(asyncIterable, subscriber).catch((err) => subscriber.error(err));\n  });\n}\n\nexport function fromReadableStreamLike<T>(readableStream: ReadableStreamLike<T>) {\n  return fromAsyncIterable(readableStreamLikeToAsyncGenerator(readableStream));\n}\n\nasync function process<T>(asyncIterable: AsyncIterable<T>, subscriber: Subscriber<T>) {\n  for await (const value of asyncIterable) {\n    subscriber.next(value);\n    // A side-effect may have closed our subscriber,\n    // check before the next iteration.\n    if (subscriber.closed) {\n      return;\n    }\n  }\n  subscriber.complete();\n}\n", "import { Subscription } from '../Subscription';\nimport { SchedulerAction, SchedulerLike } from '../types';\n\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay: number,\n  repeat: true\n): void;\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay?: number,\n  repeat?: false\n): Subscription;\n\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay = 0,\n  repeat = false\n): Subscription | void {\n  const scheduleSubscription = scheduler.schedule(function (this: SchedulerAction<any>) {\n    work();\n    if (repeat) {\n      parentSubscription.add(this.schedule(null, delay));\n    } else {\n      this.unsubscribe();\n    }\n  }, delay);\n\n  parentSubscription.add(scheduleSubscription);\n\n  if (!repeat) {\n    // Because user-land scheduler implementations are unlikely to properly reuse\n    // Actions for repeat scheduling, we can't trust that the returned subscription\n    // will control repeat subscription scenarios. So we're trying to avoid using them\n    // incorrectly within this library.\n    return scheduleSubscription;\n  }\n}\n", "/** @prettier */\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { executeSchedule } from '../util/executeSchedule';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Re-emits all notifications from source Observable with specified scheduler.\n *\n * <span class=\"informal\">Ensure a specific scheduler is used, from outside of an Observable.</span>\n *\n * `observeOn` is an operator that accepts a scheduler as a first parameter, which will be used to reschedule\n * notifications emitted by the source Observable. It might be useful, if you do not have control over\n * internal scheduler of a given Observable, but want to control when its values are emitted nevertheless.\n *\n * Returned Observable emits the same notifications (nexted values, complete and error events) as the source Observable,\n * but rescheduled with provided scheduler. Note that this doesn't mean that source Observables internal\n * scheduler will be replaced in any way. Original scheduler still will be used, but when the source Observable emits\n * notification, it will be immediately scheduled again - this time with scheduler passed to `observeOn`.\n * An anti-pattern would be calling `observeOn` on Observable that emits lots of values synchronously, to split\n * that emissions into asynchronous chunks. For this to happen, scheduler would have to be passed into the source\n * Observable directly (usually into the operator that creates it). `observeOn` simply delays notifications a\n * little bit more, to ensure that they are emitted at expected moments.\n *\n * As a matter of fact, `observeOn` accepts second parameter, which specifies in milliseconds with what delay notifications\n * will be emitted. The main difference between {@link delay} operator and `observeOn` is that `observeOn`\n * will delay all notifications - including error notifications - while `delay` will pass through error\n * from source Observable immediately when it is emitted. In general it is highly recommended to use `delay` operator\n * for any kind of delaying of values in the stream, while using `observeOn` to specify which scheduler should be used\n * for notification emissions in general.\n *\n * ## Example\n *\n * Ensure values in subscribe are called just before browser repaint\n *\n * ```ts\n * import { interval, observeOn, animationFrameScheduler } from 'rxjs';\n *\n * const someDiv = document.createElement('div');\n * someDiv.style.cssText = 'width: 200px;background: #09c';\n * document.body.appendChild(someDiv);\n * const intervals = interval(10);      // Intervals are scheduled\n *                                      // with async scheduler by default...\n * intervals.pipe(\n *   observeOn(animationFrameScheduler) // ...but we will observe on animationFrame\n * )                                    // scheduler to ensure smooth animation.\n * .subscribe(val => {\n *   someDiv.style.height = val + 'px';\n * });\n * ```\n *\n * @see {@link delay}\n *\n * @param scheduler Scheduler that will be used to reschedule notifications from source Observable.\n * @param delay Number of milliseconds that states with what delay every notification should be rescheduled.\n * @return A function that returns an Observable that emits the same\n * notifications as the source Observable, but with provided scheduler.\n */\nexport function observeOn<T>(scheduler: SchedulerLike, delay = 0): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => executeSchedule(subscriber, scheduler, () => subscriber.next(value), delay),\n        () => executeSchedule(subscriber, scheduler, () => subscriber.complete(), delay),\n        (err) => executeSchedule(subscriber, scheduler, () => subscriber.error(err), delay)\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\n\n/**\n * Asynchronously subscribes Observers to this Observable on the specified {@link SchedulerLike}.\n *\n * With `subscribeOn` you can decide what type of scheduler a specific Observable will be using when it is subscribed to.\n *\n * Schedulers control the speed and order of emissions to observers from an Observable stream.\n *\n * ![](subscribeOn.png)\n *\n * ## Example\n *\n * Given the following code:\n *\n * ```ts\n * import { of, merge } from 'rxjs';\n *\n * const a = of(1, 2, 3);\n * const b = of(4, 5, 6);\n *\n * merge(a, b).subscribe(console.log);\n *\n * // Outputs\n * // 1\n * // 2\n * // 3\n * // 4\n * // 5\n * // 6\n * ```\n *\n * Both Observable `a` and `b` will emit their values directly and synchronously once they are subscribed to.\n *\n * If we instead use the `subscribeOn` operator declaring that we want to use the {@link asyncScheduler} for values emitted by Observable `a`:\n *\n * ```ts\n * import { of, subscribeOn, asyncScheduler, merge } from 'rxjs';\n *\n * const a = of(1, 2, 3).pipe(subscribeOn(asyncScheduler));\n * const b = of(4, 5, 6);\n *\n * merge(a, b).subscribe(console.log);\n *\n * // Outputs\n * // 4\n * // 5\n * // 6\n * // 1\n * // 2\n * // 3\n * ```\n *\n * The reason for this is that Observable `b` emits its values directly and synchronously like before\n * but the emissions from `a` are scheduled on the event loop because we are now using the {@link asyncScheduler} for that specific Observable.\n *\n * @param scheduler The {@link SchedulerLike} to perform subscription actions on.\n * @param delay A delay to pass to the scheduler to delay subscriptions\n * @return A function that returns an Observable modified so that its\n * subscriptions happen on the specified {@link SchedulerLike}.\n */\nexport function subscribeOn<T>(scheduler: SchedulerLike, delay: number = 0): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    subscriber.add(scheduler.schedule(() => source.subscribe(subscriber), delay));\n  });\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { observeOn } from '../operators/observeOn';\nimport { subscribeOn } from '../operators/subscribeOn';\nimport { InteropObservable, SchedulerLike } from '../types';\n\nexport function scheduleObservable<T>(input: InteropObservable<T>, scheduler: SchedulerLike) {\n  return innerFrom(input).pipe(subscribeOn(scheduler), observeOn(scheduler));\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { observeOn } from '../operators/observeOn';\nimport { subscribeOn } from '../operators/subscribeOn';\nimport { SchedulerLike } from '../types';\n\nexport function schedulePromise<T>(input: PromiseLike<T>, scheduler: SchedulerLike) {\n  return innerFrom(input).pipe(subscribeOn(scheduler), observeOn(scheduler));\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\nexport function scheduleArray<T>(input: ArrayLike<T>, scheduler: SchedulerLike) {\n  return new Observable<T>((subscriber) => {\n    // The current array index.\n    let i = 0;\n    // Start iterating over the array like on a schedule.\n    return scheduler.schedule(function () {\n      if (i === input.length) {\n        // If we have hit the end of the array like in the\n        // previous job, we can complete.\n        subscriber.complete();\n      } else {\n        // Otherwise let's next the value at the current index,\n        // then increment our index.\n        subscriber.next(input[i++]);\n        // If the last emission didn't cause us to close the subscriber\n        // (via take or some side effect), reschedule the job and we'll\n        // make another pass.\n        if (!subscriber.closed) {\n          this.schedule();\n        }\n      }\n    });\n  });\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\nimport { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from '../util/isFunction';\nimport { executeSchedule } from '../util/executeSchedule';\n\n/**\n * Used in {@link scheduled} to create an observable from an Iterable.\n * @param input The iterable to create an observable from\n * @param scheduler The scheduler to use\n */\nexport function scheduleIterable<T>(input: Iterable<T>, scheduler: SchedulerLike) {\n  return new Observable<T>((subscriber) => {\n    let iterator: Iterator<T, T>;\n\n    // Schedule the initial creation of the iterator from\n    // the iterable. This is so the code in the iterable is\n    // not called until the scheduled job fires.\n    executeSchedule(subscriber, scheduler, () => {\n      // Create the iterator.\n      iterator = (input as any)[Symbol_iterator]();\n\n      executeSchedule(\n        subscriber,\n        scheduler,\n        () => {\n          let value: T;\n          let done: boolean | undefined;\n          try {\n            // Pull the value out of the iterator\n            ({ value, done } = iterator.next());\n          } catch (err) {\n            // We got an error while pulling from the iterator\n            subscriber.error(err);\n            return;\n          }\n\n          if (done) {\n            // If it is \"done\" we just complete. This mimics the\n            // behavior of JavaScript's `for..of` consumption of\n            // iterables, which will not emit the value from an iterator\n            // result of `{ done: true: value: 'here' }`.\n            subscriber.complete();\n          } else {\n            // The iterable is not done, emit the value.\n            subscriber.next(value);\n          }\n        },\n        0,\n        true\n      );\n    });\n\n    // During finalization, if we see this iterator has a `return` method,\n    // then we know it is a Generator, and not just an Iterator. So we call\n    // the `return()` function. This will ensure that any `finally { }` blocks\n    // inside of the generator we can hit will be hit properly.\n    return () => isFunction(iterator?.return) && iterator.return();\n  });\n}\n", "import { SchedulerLike } from '../types';\nimport { Observable } from '../Observable';\nimport { executeSchedule } from '../util/executeSchedule';\n\nexport function scheduleAsyncIterable<T>(input: AsyncIterable<T>, scheduler: SchedulerLike) {\n  if (!input) {\n    throw new Error('Iterable cannot be null');\n  }\n  return new Observable<T>((subscriber) => {\n    executeSchedule(subscriber, scheduler, () => {\n      const iterator = input[Symbol.asyncIterator]();\n      executeSchedule(\n        subscriber,\n        scheduler,\n        () => {\n          iterator.next().then((result) => {\n            if (result.done) {\n              // This will remove the subscriptions from\n              // the parent subscription.\n              subscriber.complete();\n            } else {\n              subscriber.next(result.value);\n            }\n          });\n        },\n        0,\n        true\n      );\n    });\n  });\n}\n", "import { SchedulerLike, ReadableStreamLike } from '../types';\nimport { Observable } from '../Observable';\nimport { scheduleAsyncIterable } from './scheduleAsyncIterable';\nimport { readableStreamLikeToAsyncGenerator } from '../util/isReadableStreamLike';\n\nexport function scheduleReadableStreamLike<T>(input: ReadableStreamLike<T>, scheduler: SchedulerLike): Observable<T> {\n  return scheduleAsyncIterable(readableStreamLikeToAsyncGenerator(input), scheduler);\n}\n", "import { scheduleObservable } from './scheduleObservable';\nimport { schedulePromise } from './schedulePromise';\nimport { scheduleArray } from './scheduleArray';\nimport { scheduleIterable } from './scheduleIterable';\nimport { scheduleAsyncIterable } from './scheduleAsyncIterable';\nimport { isInteropObservable } from '../util/isInteropObservable';\nimport { isPromise } from '../util/isPromise';\nimport { isArrayLike } from '../util/isArrayLike';\nimport { isIterable } from '../util/isIterable';\nimport { ObservableInput, SchedulerLike } from '../types';\nimport { Observable } from '../Observable';\nimport { isAsyncIterable } from '../util/isAsyncIterable';\nimport { createInvalidObservableTypeError } from '../util/throwUnobservableError';\nimport { isReadableStreamLike } from '../util/isReadableStreamLike';\nimport { scheduleReadableStreamLike } from './scheduleReadableStreamLike';\n\n/**\n * Converts from a common {@link ObservableInput} type to an observable where subscription and emissions\n * are scheduled on the provided scheduler.\n *\n * @see {@link from}\n * @see {@link of}\n *\n * @param input The observable, array, promise, iterable, etc you would like to schedule\n * @param scheduler The scheduler to use to schedule the subscription and emissions from\n * the returned observable.\n */\nexport function scheduled<T>(input: ObservableInput<T>, scheduler: SchedulerLike): Observable<T> {\n  if (input != null) {\n    if (isInteropObservable(input)) {\n      return scheduleObservable(input, scheduler);\n    }\n    if (isArrayLike(input)) {\n      return scheduleArray(input, scheduler);\n    }\n    if (isPromise(input)) {\n      return schedulePromise(input, scheduler);\n    }\n    if (isAsyncIterable(input)) {\n      return scheduleAsyncIterable(input, scheduler);\n    }\n    if (isIterable(input)) {\n      return scheduleIterable(input, scheduler);\n    }\n    if (isReadableStreamLike(input)) {\n      return scheduleReadableStreamLike(input, scheduler);\n    }\n  }\n  throw createInvalidObservableTypeError(input);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, SchedulerLike, ObservedValueOf } from '../types';\nimport { scheduled } from '../scheduled/scheduled';\nimport { innerFrom } from './innerFrom';\n\nexport function from<O extends ObservableInput<any>>(input: O): Observable<ObservedValueOf<O>>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function from<O extends ObservableInput<any>>(input: O, scheduler: SchedulerLike | undefined): Observable<ObservedValueOf<O>>;\n\n/**\n * Creates an Observable from an Array, an array-like object, a Promise, an iterable object, or an Observable-like object.\n *\n * <span class=\"informal\">Converts almost anything to an Observable.</span>\n *\n * ![](from.png)\n *\n * `from` converts various other objects and data types into Observables. It also converts a Promise, an array-like, or an\n * <a href=\"https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols#iterable\" target=\"_blank\">iterable</a>\n * object into an Observable that emits the items in that promise, array, or iterable. A String, in this context, is treated\n * as an array of characters. Observable-like objects (contains a function named with the ES2015 Symbol for Observable) can also be\n * converted through this operator.\n *\n * ## Examples\n *\n * Converts an array to an Observable\n *\n * ```ts\n * import { from } from 'rxjs';\n *\n * const array = [10, 20, 30];\n * const result = from(array);\n *\n * result.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 10\n * // 20\n * // 30\n * ```\n *\n * Convert an infinite iterable (from a generator) to an Observable\n *\n * ```ts\n * import { from, take } from 'rxjs';\n *\n * function* generateDoubles(seed) {\n *    let i = seed;\n *    while (true) {\n *      yield i;\n *      i = 2 * i; // double it\n *    }\n * }\n *\n * const iterator = generateDoubles(3);\n * const result = from(iterator).pipe(take(10));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 3\n * // 6\n * // 12\n * // 24\n * // 48\n * // 96\n * // 192\n * // 384\n * // 768\n * // 1536\n * ```\n *\n * With `asyncScheduler`\n *\n * ```ts\n * import { from, asyncScheduler } from 'rxjs';\n *\n * console.log('start');\n *\n * const array = [10, 20, 30];\n * const result = from(array, asyncScheduler);\n *\n * result.subscribe(x => console.log(x));\n *\n * console.log('end');\n *\n * // Logs:\n * // 'start'\n * // 'end'\n * // 10\n * // 20\n * // 30\n * ```\n *\n * @see {@link fromEvent}\n * @see {@link fromEventPattern}\n *\n * @param {ObservableInput<T>} A subscription object, a Promise, an Observable-like,\n * an Array, an iterable, or an array-like object to be converted.\n * @param {SchedulerLike} An optional {@link SchedulerLike} on which to schedule the emission of values.\n * @return {Observable<T>}\n */\nexport function from<T>(input: ObservableInput<T>, scheduler?: SchedulerLike): Observable<T> {\n  return scheduler ? scheduled(input, scheduler) : innerFrom(input);\n}\n", "import { SchedulerLike, ValueFromArray } from '../types';\nimport { Observable } from '../Observable';\nimport { popScheduler } from '../util/args';\nimport { from } from './from';\n\n// Devs are more likely to pass null or undefined than they are a scheduler\n// without accompanying values. To make things easier for (naughty) devs who\n// use the `strictNullChecks: false` TypeScript compiler option, these\n// overloads with explicit null and undefined values are included.\n\nexport function of(value: null): Observable<null>;\nexport function of(value: undefined): Observable<undefined>;\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function of(scheduler: SchedulerLike): Observable<never>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function of<A extends readonly unknown[]>(...valuesAndScheduler: [...A, SchedulerLike]): Observable<ValueFromArray<A>>;\n\nexport function of(): Observable<never>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function of<T>(): Observable<T>;\nexport function of<T>(value: T): Observable<T>;\nexport function of<A extends readonly unknown[]>(...values: A): Observable<ValueFromArray<A>>;\n\n/**\n * Converts the arguments to an observable sequence.\n *\n * <span class=\"informal\">Each argument becomes a `next` notification.</span>\n *\n * ![](of.png)\n *\n * Unlike {@link from}, it does not do any flattening and emits each argument in whole\n * as a separate `next` notification.\n *\n * ## Examples\n *\n * Emit the values `10, 20, 30`\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * of(10, 20, 30)\n *   .subscribe({\n *     next: value => console.log('next:', value),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // Outputs\n * // next: 10\n * // next: 20\n * // next: 30\n * // the end\n * ```\n *\n * Emit the array `[1, 2, 3]`\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * of([1, 2, 3])\n *   .subscribe({\n *     next: value => console.log('next:', value),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // Outputs\n * // next: [1, 2, 3]\n * // the end\n * ```\n *\n * @see {@link from}\n * @see {@link range}\n *\n * @param {...T} values A comma separated list of arguments you want to be emitted\n * @return {Observable} An Observable that emits the arguments\n * described above and then completes.\n */\nexport function of<T>(...args: Array<T | SchedulerLike>): Observable<T> {\n  const scheduler = popScheduler(args);\n  return from(args as T[], scheduler);\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { SchedulerLike } from '../types';\nimport { isFunction } from '../util/isFunction';\n\n/**\n * Creates an observable that will create an error instance and push it to the consumer as an error\n * immediately upon subscription.\n *\n * <span class=\"informal\">Just errors and does nothing else</span>\n *\n * ![](throw.png)\n *\n * This creation function is useful for creating an observable that will create an error and error every\n * time it is subscribed to. Generally, inside of most operators when you might want to return an errored\n * observable, this is unnecessary. In most cases, such as in the inner return of {@link concatMap},\n * {@link mergeMap}, {@link defer}, and many others, you can simply throw the error, and RxJS will pick\n * that up and notify the consumer of the error.\n *\n * ## Example\n *\n * Create a simple observable that will create a new error with a timestamp and log it\n * and the message every time you subscribe to it\n *\n * ```ts\n * import { throwError } from 'rxjs';\n *\n * let errorCount = 0;\n *\n * const errorWithTimestamp$ = throwError(() => {\n *   const error: any = new Error(`This is error number ${ ++errorCount }`);\n *   error.timestamp = Date.now();\n *   return error;\n * });\n *\n * errorWithTimestamp$.subscribe({\n *   error: err => console.log(err.timestamp, err.message)\n * });\n *\n * errorWithTimestamp$.subscribe({\n *   error: err => console.log(err.timestamp, err.message)\n * });\n *\n * // Logs the timestamp and a new error message for each subscription\n * ```\n *\n * ### Unnecessary usage\n *\n * Using `throwError` inside of an operator or creation function\n * with a callback, is usually not necessary\n *\n * ```ts\n * import { of, concatMap, timer, throwError } from 'rxjs';\n *\n * const delays$ = of(1000, 2000, Infinity, 3000);\n *\n * delays$.pipe(\n *   concatMap(ms => {\n *     if (ms < 10000) {\n *       return timer(ms);\n *     } else {\n *       // This is probably overkill.\n *       return throwError(() => new Error(`Invalid time ${ ms }`));\n *     }\n *   })\n * )\n * .subscribe({\n *   next: console.log,\n *   error: console.error\n * });\n * ```\n *\n * You can just throw the error instead\n *\n * ```ts\n * import { of, concatMap, timer } from 'rxjs';\n *\n * const delays$ = of(1000, 2000, Infinity, 3000);\n *\n * delays$.pipe(\n *   concatMap(ms => {\n *     if (ms < 10000) {\n *       return timer(ms);\n *     } else {\n *       // Cleaner and easier to read for most folks.\n *       throw new Error(`Invalid time ${ ms }`);\n *     }\n *   })\n * )\n * .subscribe({\n *   next: console.log,\n *   error: console.error\n * });\n * ```\n *\n * @param errorFactory A factory function that will create the error instance that is pushed.\n */\nexport function throwError(errorFactory: () => any): Observable<never>;\n\n/**\n * Returns an observable that will error with the specified error immediately upon subscription.\n *\n * @param error The error instance to emit\n * @deprecated Support for passing an error value will be removed in v8. Instead, pass a factory function to `throwError(() => new Error('test'))`. This is\n * because it will create the error at the moment it should be created and capture a more appropriate stack trace. If\n * for some reason you need to create the error ahead of time, you can still do that: `const err = new Error('test'); throwError(() => err);`.\n */\nexport function throwError(error: any): Observable<never>;\n\n/**\n * Notifies the consumer of an error using a given scheduler by scheduling it at delay `0` upon subscription.\n *\n * @param errorOrErrorFactory An error instance or error factory\n * @param scheduler A scheduler to use to schedule the error notification\n * @deprecated The `scheduler` parameter will be removed in v8.\n * Use `throwError` in combination with {@link observeOn}: `throwError(() => new Error('test')).pipe(observeOn(scheduler));`.\n * Details: https://rxjs.dev/deprecations/scheduler-argument\n */\nexport function throwError(errorOrErrorFactory: any, scheduler: SchedulerLike): Observable<never>;\n\nexport function throwError(errorOrErrorFactory: any, scheduler?: SchedulerLike): Observable<never> {\n  const errorFactory = isFunction(errorOrErrorFactory) ? errorOrErrorFactory : () => errorOrErrorFactory;\n  const init = (subscriber: Subscriber<never>) => subscriber.error(errorFactory());\n  return new Observable(scheduler ? (subscriber) => scheduler.schedule(init as any, 0, subscriber) : init);\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface EmptyError extends Error {}\n\nexport interface EmptyErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (): EmptyError;\n}\n\n/**\n * An error thrown when an Observable or a sequence was queried but has no\n * elements.\n *\n * @see {@link first}\n * @see {@link last}\n * @see {@link single}\n * @see {@link firstValueFrom}\n * @see {@link lastValueFrom}\n *\n * @class EmptyError\n */\nexport const EmptyError: EmptyErrorCtor = createErrorClass((_super) => function EmptyErrorImpl(this: any) {\n  _super(this);\n  this.name = 'EmptyError';\n  this.message = 'no elements in sequence';\n});\n", "/**\n * Checks to see if a value is not only a `Date` object,\n * but a *valid* `Date` object that can be converted to a\n * number. For example, `new Date('blah')` is indeed an\n * `instanceof Date`, however it cannot be converted to a\n * number.\n */\nexport function isValidDate(value: any): value is Date {\n  return value instanceof Date && !isNaN(value as any);\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function map<T, R>(project: (value: T, index: number) => R): OperatorFunction<T, R>;\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function map<T, R, A>(project: (this: A, value: T, index: number) => R, thisArg: A): OperatorFunction<T, R>;\n\n/**\n * Applies a given `project` function to each value emitted by the source\n * Observable, and emits the resulting values as an Observable.\n *\n * <span class=\"informal\">Like [Array.prototype.map()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/map),\n * it passes each source value through a transformation function to get\n * corresponding output values.</span>\n *\n * ![](map.png)\n *\n * Similar to the well known `Array.prototype.map` function, this operator\n * applies a projection to each value and emits that projection in the output\n * Observable.\n *\n * ## Example\n *\n * Map every click to the `clientX` position of that click\n *\n * ```ts\n * import { fromEvent, map } from 'rxjs';\n *\n * const clicks = fromEvent<PointerEvent>(document, 'click');\n * const positions = clicks.pipe(map(ev => ev.clientX));\n *\n * positions.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link mapTo}\n * @see {@link pluck}\n *\n * @param {function(value: T, index: number): R} project The function to apply\n * to each `value` emitted by the source Observable. The `index` parameter is\n * the number `i` for the i-th emission that has happened since the\n * subscription, starting from the number `0`.\n * @param {any} [thisArg] An optional argument to define what `this` is in the\n * `project` function.\n * @return A function that returns an Observable that emits the values from the\n * source Observable transformed by the given `project` function.\n */\nexport function map<T, R>(project: (value: T, index: number) => R, thisArg?: any): OperatorFunction<T, R> {\n  return operate((source, subscriber) => {\n    // The index of the value from the source. Used with projection.\n    let index = 0;\n    // Subscribe to the source, all errors and completions are sent along\n    // to the consumer.\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value: T) => {\n        // Call the projection function with the appropriate this context,\n        // and send the resulting value to the consumer.\n        subscriber.next(project.call(thisArg, value, index++));\n      })\n    );\n  });\n}\n", "import { OperatorFunction } from \"../types\";\nimport { map } from \"../operators/map\";\n\nconst { isArray } = Array;\n\nfunction callOrApply<T, R>(fn: ((...values: T[]) => R), args: T|T[]): R {\n    return isArray(args) ? fn(...args) : fn(args);\n}\n\n/**\n * Used in several -- mostly deprecated -- situations where we need to \n * apply a list of arguments or a single argument to a result selector.\n */\nexport function mapOneOrManyArgs<T, R>(fn: ((...values: T[]) => R)): OperatorFunction<T|T[], R> {\n    return map(args => callOrApply(fn, args))\n}", "const { isArray } = Array;\nconst { getPrototypeOf, prototype: objectProto, keys: getKeys } = Object;\n\n/**\n * Used in functions where either a list of arguments, a single array of arguments, or a\n * dictionary of arguments can be returned. Returns an object with an `args` property with\n * the arguments in an array, if it is a dictionary, it will also return the `keys` in another\n * property.\n */\nexport function argsArgArrayOrObject<T, O extends Record<string, T>>(args: T[] | [O] | [T[]]): { args: T[]; keys: string[] | null } {\n  if (args.length === 1) {\n    const first = args[0];\n    if (isArray(first)) {\n      return { args: first, keys: null };\n    }\n    if (isPOJO(first)) {\n      const keys = getKeys(first);\n      return {\n        args: keys.map((key) => first[key]),\n        keys,\n      };\n    }\n  }\n\n  return { args: args as T[], keys: null };\n}\n\nfunction isPOJO(obj: any): obj is object {\n  return obj && typeof obj === 'object' && getPrototypeOf(obj) === objectProto;\n}\n", "export function createObject(keys: string[], values: any[]) {\n  return keys.reduce((result, key, i) => ((result[key] = values[i]), result), {} as any);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, SchedulerLike, ObservedValueOf, ObservableInputTuple } from '../types';\nimport { argsArgArrayOrObject } from '../util/argsArgArrayOrObject';\nimport { Subscriber } from '../Subscriber';\nimport { from } from './from';\nimport { identity } from '../util/identity';\nimport { Subscription } from '../Subscription';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\nimport { popResultSelector, popScheduler } from '../util/args';\nimport { createObject } from '../util/createObject';\nimport { createOperatorSubscriber } from '../operators/OperatorSubscriber';\nimport { AnyCatcher } from '../AnyCatcher';\nimport { executeSchedule } from '../util/executeSchedule';\n\n// combineLatest(any)\n// We put this first because we need to catch cases where the user has supplied\n// _exactly `any`_ as the argument. Since `any` literally matches _anything_,\n// we don't want it to randomly hit one of the other type signatures below,\n// as we have no idea at build-time what type we should be returning when given an any.\n\n/**\n * You have passed `any` here, we can't figure out if it is\n * an array or an object, so you're getting `unknown`. Use better types.\n * @param arg Something typed as `any`\n */\nexport function combineLatest<T extends AnyCatcher>(arg: T): Observable<unknown>;\n\n// combineLatest([a, b, c])\nexport function combineLatest(sources: []): Observable<never>;\nexport function combineLatest<A extends readonly unknown[]>(sources: readonly [...ObservableInputTuple<A>]): Observable<A>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  sources: readonly [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R,\n  scheduler: SchedulerLike\n): Observable<R>;\nexport function combineLatest<A extends readonly unknown[], R>(\n  sources: readonly [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R\n): Observable<R>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[]>(\n  sources: readonly [...ObservableInputTuple<A>],\n  scheduler: SchedulerLike\n): Observable<A>;\n\n// combineLatest(a, b, c)\n/** @deprecated Pass an array of sources instead. The rest-parameters signature will be removed in v8. Details: https://rxjs.dev/deprecations/array-argument */\nexport function combineLatest<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelectorAndScheduler: [...ObservableInputTuple<A>, (...values: A) => R, SchedulerLike]\n): Observable<R>;\n/** @deprecated Pass an array of sources instead. The rest-parameters signature will be removed in v8. Details: https://rxjs.dev/deprecations/array-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelector: [...ObservableInputTuple<A>, (...values: A) => R]\n): Observable<R>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike]\n): Observable<A>;\n\n// combineLatest({a, b, c})\nexport function combineLatest(sourcesObject: { [K in any]: never }): Observable<never>;\nexport function combineLatest<T extends Record<string, ObservableInput<any>>>(\n  sourcesObject: T\n): Observable<{ [K in keyof T]: ObservedValueOf<T[K]> }>;\n\n/**\n * Combines multiple Observables to create an Observable whose values are\n * calculated from the latest values of each of its input Observables.\n *\n * <span class=\"informal\">Whenever any input Observable emits a value, it\n * computes a formula using the latest values from all the inputs, then emits\n * the output of that formula.</span>\n *\n * ![](combineLatest.png)\n *\n * `combineLatest` combines the values from all the Observables passed in the\n * observables array. This is done by subscribing to each Observable in order and,\n * whenever any Observable emits, collecting an array of the most recent\n * values from each Observable. So if you pass `n` Observables to this operator,\n * the returned Observable will always emit an array of `n` values, in an order\n * corresponding to the order of the passed Observables (the value from the first Observable\n * will be at index 0 of the array and so on).\n *\n * Static version of `combineLatest` accepts an array of Observables. Note that an array of\n * Observables is a good choice, if you don't know beforehand how many Observables\n * you will combine. Passing an empty array will result in an Observable that\n * completes immediately.\n *\n * To ensure the output array always has the same length, `combineLatest` will\n * actually wait for all input Observables to emit at least once,\n * before it starts emitting results. This means if some Observable emits\n * values before other Observables started emitting, all these values but the last\n * will be lost. On the other hand, if some Observable does not emit a value but\n * completes, resulting Observable will complete at the same moment without\n * emitting anything, since it will now be impossible to include a value from the\n * completed Observable in the resulting array. Also, if some input Observable does\n * not emit any value and never completes, `combineLatest` will also never emit\n * and never complete, since, again, it will wait for all streams to emit some\n * value.\n *\n * If at least one Observable was passed to `combineLatest` and all passed Observables\n * emitted something, the resulting Observable will complete when all combined\n * streams complete. So even if some Observable completes, the result of\n * `combineLatest` will still emit values when other Observables do. In case\n * of a completed Observable, its value from now on will always be the last\n * emitted value. On the other hand, if any Observable errors, `combineLatest`\n * will error immediately as well, and all other Observables will be unsubscribed.\n *\n * ## Examples\n *\n * Combine two timer Observables\n *\n * ```ts\n * import { timer, combineLatest } from 'rxjs';\n *\n * const firstTimer = timer(0, 1000); // emit 0, 1, 2... after every second, starting from now\n * const secondTimer = timer(500, 1000); // emit 0, 1, 2... after every second, starting 0,5s from now\n * const combinedTimers = combineLatest([firstTimer, secondTimer]);\n * combinedTimers.subscribe(value => console.log(value));\n * // Logs\n * // [0, 0] after 0.5s\n * // [1, 0] after 1s\n * // [1, 1] after 1.5s\n * // [2, 1] after 2s\n * ```\n *\n * Combine a dictionary of Observables\n *\n * ```ts\n * import { of, delay, startWith, combineLatest } from 'rxjs';\n *\n * const observables = {\n *   a: of(1).pipe(delay(1000), startWith(0)),\n *   b: of(5).pipe(delay(5000), startWith(0)),\n *   c: of(10).pipe(delay(10000), startWith(0))\n * };\n * const combined = combineLatest(observables);\n * combined.subscribe(value => console.log(value));\n * // Logs\n * // { a: 0, b: 0, c: 0 } immediately\n * // { a: 1, b: 0, c: 0 } after 1s\n * // { a: 1, b: 5, c: 0 } after 5s\n * // { a: 1, b: 5, c: 10 } after 10s\n * ```\n *\n * Combine an array of Observables\n *\n * ```ts\n * import { of, delay, startWith, combineLatest } from 'rxjs';\n *\n * const observables = [1, 5, 10].map(\n *   n => of(n).pipe(\n *     delay(n * 1000), // emit 0 and then emit n after n seconds\n *     startWith(0)\n *   )\n * );\n * const combined = combineLatest(observables);\n * combined.subscribe(value => console.log(value));\n * // Logs\n * // [0, 0, 0] immediately\n * // [1, 0, 0] after 1s\n * // [1, 5, 0] after 5s\n * // [1, 5, 10] after 10s\n * ```\n *\n * Use map operator to dynamically calculate the Body-Mass Index\n *\n * ```ts\n * import { of, combineLatest, map } from 'rxjs';\n *\n * const weight = of(70, 72, 76, 79, 75);\n * const height = of(1.76, 1.77, 1.78);\n * const bmi = combineLatest([weight, height]).pipe(\n *   map(([w, h]) => w / (h * h)),\n * );\n * bmi.subscribe(x => console.log('BMI is ' + x));\n *\n * // With output to console:\n * // BMI is 24.212293388429753\n * // BMI is 23.93948099205209\n * // BMI is 23.671253629592222\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link merge}\n * @see {@link withLatestFrom}\n *\n * @param {ObservableInput} [observables] An array of input Observables to combine with each other.\n * An array of Observables must be given as the first argument.\n * @param {function} [project] An optional function to project the values from\n * the combined latest values into a new value on the output Observable.\n * @param {SchedulerLike} [scheduler=null] The {@link SchedulerLike} to use for subscribing to\n * each input Observable.\n * @return {Observable} An Observable of projected values from the most recent\n * values from each input Observable, or an array of the most recent values from\n * each input Observable.\n */\nexport function combineLatest<O extends ObservableInput<any>, R>(...args: any[]): Observable<R> | Observable<ObservedValueOf<O>[]> {\n  const scheduler = popScheduler(args);\n  const resultSelector = popResultSelector(args);\n\n  const { args: observables, keys } = argsArgArrayOrObject(args);\n\n  if (observables.length === 0) {\n    // If no observables are passed, or someone has passed an empty array\n    // of observables, or even an empty object POJO, we need to just\n    // complete (EMPTY), but we have to honor the scheduler provided if any.\n    return from([], scheduler as any);\n  }\n\n  const result = new Observable<ObservedValueOf<O>[]>(\n    combineLatestInit(\n      observables as ObservableInput<ObservedValueOf<O>>[],\n      scheduler,\n      keys\n        ? // A handler for scrubbing the array of args into a dictionary.\n          (values) => createObject(keys, values)\n        : // A passthrough to just return the array\n          identity\n    )\n  );\n\n  return resultSelector ? (result.pipe(mapOneOrManyArgs(resultSelector)) as Observable<R>) : result;\n}\n\nexport function combineLatestInit(\n  observables: ObservableInput<any>[],\n  scheduler?: SchedulerLike,\n  valueTransform: (values: any[]) => any = identity\n) {\n  return (subscriber: Subscriber<any>) => {\n    // The outer subscription. We're capturing this in a function\n    // because we may have to schedule it.\n    maybeSchedule(\n      scheduler,\n      () => {\n        const { length } = observables;\n        // A store for the values each observable has emitted so far. We match observable to value on index.\n        const values = new Array(length);\n        // The number of currently active subscriptions, as they complete, we decrement this number to see if\n        // we are all done combining values, so we can complete the result.\n        let active = length;\n        // The number of inner sources that still haven't emitted the first value\n        // We need to track this because all sources need to emit one value in order\n        // to start emitting values.\n        let remainingFirstValues = length;\n        // The loop to kick off subscription. We're keying everything on index `i` to relate the observables passed\n        // in to the slot in the output array or the key in the array of keys in the output dictionary.\n        for (let i = 0; i < length; i++) {\n          maybeSchedule(\n            scheduler,\n            () => {\n              const source = from(observables[i], scheduler as any);\n              let hasFirstValue = false;\n              source.subscribe(\n                createOperatorSubscriber(\n                  subscriber,\n                  (value) => {\n                    // When we get a value, record it in our set of values.\n                    values[i] = value;\n                    if (!hasFirstValue) {\n                      // If this is our first value, record that.\n                      hasFirstValue = true;\n                      remainingFirstValues--;\n                    }\n                    if (!remainingFirstValues) {\n                      // We're not waiting for any more\n                      // first values, so we can emit!\n                      subscriber.next(valueTransform(values.slice()));\n                    }\n                  },\n                  () => {\n                    if (!--active) {\n                      // We only complete the result if we have no more active\n                      // inner observables.\n                      subscriber.complete();\n                    }\n                  }\n                )\n              );\n            },\n            subscriber\n          );\n        }\n      },\n      subscriber\n    );\n  };\n}\n\n/**\n * A small utility to handle the couple of locations where we want to schedule if a scheduler was provided,\n * but we don't if there was no scheduler.\n */\nfunction maybeSchedule(scheduler: SchedulerLike | undefined, execute: () => void, subscription: Subscription) {\n  if (scheduler) {\n    executeSchedule(subscription, scheduler, execute);\n  } else {\n    execute();\n  }\n}\n", "import { Observable } from '../Observable';\nimport { innerFrom } from '../observable/innerFrom';\nimport { Subscriber } from '../Subscriber';\nimport { ObservableInput, SchedulerLike } from '../types';\nimport { executeSchedule } from '../util/executeSchedule';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * A process embodying the general \"merge\" strategy. This is used in\n * `mergeMap` and `mergeScan` because the logic is otherwise nearly identical.\n * @param source The original source observable\n * @param subscriber The consumer subscriber\n * @param project The projection function to get our inner sources\n * @param concurrent The number of concurrent inner subscriptions\n * @param onBeforeNext Additional logic to apply before nexting to our consumer\n * @param expand If `true` this will perform an \"expand\" strategy, which differs only\n * in that it recurses, and the inner subscription must be schedule-able.\n * @param innerSubScheduler A scheduler to use to schedule inner subscriptions,\n * this is to support the expand strategy, mostly, and should be deprecated\n */\nexport function mergeInternals<T, R>(\n  source: Observable<T>,\n  subscriber: Subscriber<R>,\n  project: (value: T, index: number) => ObservableInput<R>,\n  concurrent: number,\n  onBeforeNext?: (innerValue: R) => void,\n  expand?: boolean,\n  innerSubScheduler?: SchedulerLike,\n  additionalFinalizer?: () => void\n) {\n  // Buffered values, in the event of going over our concurrency limit\n  const buffer: T[] = [];\n  // The number of active inner subscriptions.\n  let active = 0;\n  // An index to pass to our accumulator function\n  let index = 0;\n  // Whether or not the outer source has completed.\n  let isComplete = false;\n\n  /**\n   * Checks to see if we can complete our result or not.\n   */\n  const checkComplete = () => {\n    // If the outer has completed, and nothing is left in the buffer,\n    // and we don't have any active inner subscriptions, then we can\n    // Emit the state and complete.\n    if (isComplete && !buffer.length && !active) {\n      subscriber.complete();\n    }\n  };\n\n  // If we're under our concurrency limit, just start the inner subscription, otherwise buffer and wait.\n  const outerNext = (value: T) => (active < concurrent ? doInnerSub(value) : buffer.push(value));\n\n  const doInnerSub = (value: T) => {\n    // If we're expanding, we need to emit the outer values and the inner values\n    // as the inners will \"become outers\" in a way as they are recursively fed\n    // back to the projection mechanism.\n    expand && subscriber.next(value as any);\n\n    // Increment the number of active subscriptions so we can track it\n    // against our concurrency limit later.\n    active++;\n\n    // A flag used to show that the inner observable completed.\n    // This is checked during finalization to see if we should\n    // move to the next item in the buffer, if there is on.\n    let innerComplete = false;\n\n    // Start our inner subscription.\n    innerFrom(project(value, index++)).subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (innerValue) => {\n          // `mergeScan` has additional handling here. For example\n          // taking the inner value and updating state.\n          onBeforeNext?.(innerValue);\n\n          if (expand) {\n            // If we're expanding, then just recurse back to our outer\n            // handler. It will emit the value first thing.\n            outerNext(innerValue as any);\n          } else {\n            // Otherwise, emit the inner value.\n            subscriber.next(innerValue);\n          }\n        },\n        () => {\n          // Flag that we have completed, so we know to check the buffer\n          // during finalization.\n          innerComplete = true;\n        },\n        // Errors are passed to the destination.\n        undefined,\n        () => {\n          // During finalization, if the inner completed (it wasn't errored or\n          // cancelled), then we want to try the next item in the buffer if\n          // there is one.\n          if (innerComplete) {\n            // We have to wrap this in a try/catch because it happens during\n            // finalization, possibly asynchronously, and we want to pass\n            // any errors that happen (like in a projection function) to\n            // the outer Subscriber.\n            try {\n              // INNER SOURCE COMPLETE\n              // Decrement the active count to ensure that the next time\n              // we try to call `doInnerSub`, the number is accurate.\n              active--;\n              // If we have more values in the buffer, try to process those\n              // Note that this call will increment `active` ahead of the\n              // next conditional, if there were any more inner subscriptions\n              // to start.\n              while (buffer.length && active < concurrent) {\n                const bufferedValue = buffer.shift()!;\n                // Particularly for `expand`, we need to check to see if a scheduler was provided\n                // for when we want to start our inner subscription. Otherwise, we just start\n                // are next inner subscription.\n                if (innerSubScheduler) {\n                  executeSchedule(subscriber, innerSubScheduler, () => doInnerSub(bufferedValue));\n                } else {\n                  doInnerSub(bufferedValue);\n                }\n              }\n              // Check to see if we can complete, and complete if so.\n              checkComplete();\n            } catch (err) {\n              subscriber.error(err);\n            }\n          }\n        }\n      )\n    );\n  };\n\n  // Subscribe to our source observable.\n  source.subscribe(\n    createOperatorSubscriber(subscriber, outerNext, () => {\n      // Outer completed, make a note of it, and check to see if we can complete everything.\n      isComplete = true;\n      checkComplete();\n    })\n  );\n\n  // Additional finalization (for when the destination is torn down).\n  // Other finalization is added implicitly via subscription above.\n  return () => {\n    additionalFinalizer?.();\n  };\n}\n", "import { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { map } from './map';\nimport { innerFrom } from '../observable/innerFrom';\nimport { operate } from '../util/lift';\nimport { mergeInternals } from './mergeInternals';\nimport { isFunction } from '../util/isFunction';\n\n/* tslint:disable:max-line-length */\nexport function mergeMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  concurrent?: number\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function mergeMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: undefined,\n  concurrent?: number\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function mergeMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R,\n  concurrent?: number\n): OperatorFunction<T, R>;\n/* tslint:enable:max-line-length */\n\n/**\n * Projects each source value to an Observable which is merged in the output\n * Observable.\n *\n * <span class=\"informal\">Maps each value to an Observable, then flattens all of\n * these inner Observables using {@link mergeAll}.</span>\n *\n * ![](mergeMap.png)\n *\n * Returns an Observable that emits items based on applying a function that you\n * supply to each item emitted by the source Observable, where that function\n * returns an Observable, and then merging those resulting Observables and\n * emitting the results of this merger.\n *\n * ## Example\n *\n * Map and flatten each letter to an Observable ticking every 1 second\n *\n * ```ts\n * import { of, mergeMap, interval, map } from 'rxjs';\n *\n * const letters = of('a', 'b', 'c');\n * const result = letters.pipe(\n *   mergeMap(x => interval(1000).pipe(map(i => x + i)))\n * );\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // a0\n * // b0\n * // c0\n * // a1\n * // b1\n * // c1\n * // continues to list a, b, c every second with respective ascending integers\n * ```\n *\n * @see {@link concatMap}\n * @see {@link exhaustMap}\n * @see {@link merge}\n * @see {@link mergeAll}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n * @see {@link switchMap}\n *\n * @param {function(value: T, ?index: number): ObservableInput} project A function\n * that, when applied to an item emitted by the source Observable, returns an\n * Observable.\n * @param {number} [concurrent=Infinity] Maximum number of input\n * Observables being subscribed to concurrently.\n * @return A function that returns an Observable that emits the result of\n * applying the projection function (and the optional deprecated\n * `resultSelector`) to each item emitted by the source Observable and merging\n * the results of the Observables obtained from this transformation.\n */\nexport function mergeMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector?: ((outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R) | number,\n  concurrent: number = Infinity\n): OperatorFunction<T, ObservedValueOf<O> | R> {\n  if (isFunction(resultSelector)) {\n    // DEPRECATED PATH\n    return mergeMap((a, i) => map((b: any, ii: number) => resultSelector(a, b, i, ii))(innerFrom(project(a, i))), concurrent);\n  } else if (typeof resultSelector === 'number') {\n    concurrent = resultSelector;\n  }\n\n  return operate((source, subscriber) => mergeInternals(source, subscriber, project, concurrent));\n}\n", "import { mergeMap } from './mergeMap';\nimport { identity } from '../util/identity';\nimport { OperatorFunction, ObservableInput, ObservedValueOf } from '../types';\n\n/**\n * Converts a higher-order Observable into a first-order Observable which\n * concurrently delivers all values that are emitted on the inner Observables.\n *\n * <span class=\"informal\">Flattens an Observable-of-Observables.</span>\n *\n * ![](mergeAll.png)\n *\n * `mergeAll` subscribes to an Observable that emits Observables, also known as\n * a higher-order Observable. Each time it observes one of these emitted inner\n * Observables, it subscribes to that and delivers all the values from the\n * inner Observable on the output Observable. The output Observable only\n * completes once all inner Observables have completed. Any error delivered by\n * a inner Observable will be immediately emitted on the output Observable.\n *\n * ## Examples\n *\n * Spawn a new interval Observable for each click event, and blend their outputs as one Observable\n *\n * ```ts\n * import { fromEvent, map, interval, mergeAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(map(() => interval(1000)));\n * const firstOrder = higherOrder.pipe(mergeAll());\n *\n * firstOrder.subscribe(x => console.log(x));\n * ```\n *\n * Count from 0 to 9 every second for each click, but only allow 2 concurrent timers\n *\n * ```ts\n * import { fromEvent, map, interval, take, mergeAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(\n *   map(() => interval(1000).pipe(take(10)))\n * );\n * const firstOrder = higherOrder.pipe(mergeAll(2));\n *\n * firstOrder.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link concatAll}\n * @see {@link exhaustAll}\n * @see {@link merge}\n * @see {@link mergeMap}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n * @see {@link switchAll}\n * @see {@link switchMap}\n * @see {@link zipAll}\n *\n * @param {number} [concurrent=Infinity] Maximum number of inner\n * Observables being subscribed to concurrently.\n * @return A function that returns an Observable that emits values coming from\n * all the inner Observables emitted by the source Observable.\n */\nexport function mergeAll<O extends ObservableInput<any>>(concurrent: number = Infinity): OperatorFunction<O, ObservedValueOf<O>> {\n  return mergeMap(identity, concurrent);\n}\n", "import { mergeAll } from './mergeAll';\nimport { OperatorFunction, ObservableInput, ObservedValueOf } from '../types';\n\n/**\n * Converts a higher-order Observable into a first-order Observable by\n * concatenating the inner Observables in order.\n *\n * <span class=\"informal\">Flattens an Observable-of-Observables by putting one\n * inner Observable after the other.</span>\n *\n * ![](concatAll.svg)\n *\n * Joins every Observable emitted by the source (a higher-order Observable), in\n * a serial fashion. It subscribes to each inner Observable only after the\n * previous inner Observable has completed, and merges all of their values into\n * the returned observable.\n *\n * __Warning:__ If the source Observable emits Observables quickly and\n * endlessly, and the inner Observables it emits generally complete slower than\n * the source emits, you can run into memory issues as the incoming Observables\n * collect in an unbounded buffer.\n *\n * Note: `concatAll` is equivalent to `mergeAll` with concurrency parameter set\n * to `1`.\n *\n * ## Example\n *\n * For each click event, tick every second from 0 to 3, with no concurrency\n *\n * ```ts\n * import { fromEvent, map, interval, take, concatAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(\n *   map(() => interval(1000).pipe(take(4)))\n * );\n * const firstOrder = higherOrder.pipe(concatAll());\n * firstOrder.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // (results are not concurrent)\n * // For every click on the \"document\" it will emit values 0 to 3 spaced\n * // on a 1000ms interval\n * // one click = 1000ms-> 0 -1000ms-> 1 -1000ms-> 2 -1000ms-> 3\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link concat}\n * @see {@link concatMap}\n * @see {@link concatMapTo}\n * @see {@link exhaustAll}\n * @see {@link mergeAll}\n * @see {@link switchAll}\n * @see {@link switchMap}\n * @see {@link zipAll}\n *\n * @return A function that returns an Observable emitting values from all the\n * inner Observables concatenated.\n */\nexport function concatAll<O extends ObservableInput<any>>(): OperatorFunction<O, ObservedValueOf<O>> {\n  return mergeAll(1);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInputTuple, SchedulerLike } from '../types';\nimport { concatAll } from '../operators/concatAll';\nimport { popScheduler } from '../util/args';\nimport { from } from './from';\n\nexport function concat<T extends readonly unknown[]>(...inputs: [...ObservableInputTuple<T>]): Observable<T[number]>;\nexport function concat<T extends readonly unknown[]>(\n  ...inputsAndScheduler: [...ObservableInputTuple<T>, SchedulerLike]\n): Observable<T[number]>;\n\n/**\n * Creates an output Observable which sequentially emits all values from the first given\n * Observable and then moves on to the next.\n *\n * <span class=\"informal\">Concatenates multiple Observables together by\n * sequentially emitting their values, one Observable after the other.</span>\n *\n * ![](concat.png)\n *\n * `concat` joins multiple Observables together, by subscribing to them one at a time and\n * merging their results into the output Observable. You can pass either an array of\n * Observables, or put them directly as arguments. Passing an empty array will result\n * in Observable that completes immediately.\n *\n * `concat` will subscribe to first input Observable and emit all its values, without\n * changing or affecting them in any way. When that Observable completes, it will\n * subscribe to then next Observable passed and, again, emit its values. This will be\n * repeated, until the operator runs out of Observables. When last input Observable completes,\n * `concat` will complete as well. At any given moment only one Observable passed to operator\n * emits values. If you would like to emit values from passed Observables concurrently, check out\n * {@link merge} instead, especially with optional `concurrent` parameter. As a matter of fact,\n * `concat` is an equivalent of `merge` operator with `concurrent` parameter set to `1`.\n *\n * Note that if some input Observable never completes, `concat` will also never complete\n * and Observables following the one that did not complete will never be subscribed. On the other\n * hand, if some Observable simply completes immediately after it is subscribed, it will be\n * invisible for `concat`, which will just move on to the next Observable.\n *\n * If any Observable in chain errors, instead of passing control to the next Observable,\n * `concat` will error immediately as well. Observables that would be subscribed after\n * the one that emitted error, never will.\n *\n * If you pass to `concat` the same Observable many times, its stream of values\n * will be \"replayed\" on every subscription, which means you can repeat given Observable\n * as many times as you like. If passing the same Observable to `concat` 1000 times becomes tedious,\n * you can always use {@link repeat}.\n *\n * ## Examples\n *\n * Concatenate a timer counting from 0 to 3 with a synchronous sequence from 1 to 10\n *\n * ```ts\n * import { interval, take, range, concat } from 'rxjs';\n *\n * const timer = interval(1000).pipe(take(4));\n * const sequence = range(1, 10);\n * const result = concat(timer, sequence);\n * result.subscribe(x => console.log(x));\n *\n * // results in:\n * // 0 -1000ms-> 1 -1000ms-> 2 -1000ms-> 3 -immediate-> 1 ... 10\n * ```\n *\n * Concatenate 3 Observables\n *\n * ```ts\n * import { interval, take, concat } from 'rxjs';\n *\n * const timer1 = interval(1000).pipe(take(10));\n * const timer2 = interval(2000).pipe(take(6));\n * const timer3 = interval(500).pipe(take(10));\n *\n * const result = concat(timer1, timer2, timer3);\n * result.subscribe(x => console.log(x));\n *\n * // results in the following:\n * // (Prints to console sequentially)\n * // -1000ms-> 0 -1000ms-> 1 -1000ms-> ... 9\n * // -2000ms-> 0 -2000ms-> 1 -2000ms-> ... 5\n * // -500ms-> 0 -500ms-> 1 -500ms-> ... 9\n * ```\n *\n * Concatenate the same Observable to repeat it\n *\n * ```ts\n * import { interval, take, concat } from 'rxjs';\n *\n * const timer = interval(1000).pipe(take(2));\n *\n * concat(timer, timer) // concatenating the same Observable!\n *   .subscribe({\n *     next: value => console.log(value),\n *     complete: () => console.log('...and it is done!')\n *   });\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 0 after 3s\n * // 1 after 4s\n * // '...and it is done!' also after 4s\n * ```\n *\n * @see {@link concatAll}\n * @see {@link concatMap}\n * @see {@link concatMapTo}\n * @see {@link startWith}\n * @see {@link endWith}\n *\n * @param args Input Observables to concatenate.\n */\nexport function concat(...args: any[]): Observable<unknown> {\n  return concatAll()(from(args, popScheduler(args)));\n}\n", "import { Observable } from '../Observable';\nimport { ObservedValueOf, ObservableInput } from '../types';\nimport { innerFrom } from './innerFrom';\n\n/**\n * Creates an Observable that, on subscribe, calls an Observable factory to\n * make an Observable for each new Observer.\n *\n * <span class=\"informal\">Creates the Observable lazily, that is, only when it\n * is subscribed.\n * </span>\n *\n * ![](defer.png)\n *\n * `defer` allows you to create an Observable only when the Observer\n * subscribes. It waits until an Observer subscribes to it, calls the given\n * factory function to get an Observable -- where a factory function typically\n * generates a new Observable -- and subscribes the Observer to this Observable.\n * In case the factory function returns a falsy value, then EMPTY is used as\n * Observable instead. Last but not least, an exception during the factory\n * function call is transferred to the Observer by calling `error`.\n *\n * ## Example\n *\n * Subscribe to either an Observable of clicks or an Observable of interval, at random\n *\n * ```ts\n * import { defer, fromEvent, interval } from 'rxjs';\n *\n * const clicksOrInterval = defer(() => {\n *   return Math.random() > 0.5\n *     ? fromEvent(document, 'click')\n *     : interval(1000);\n * });\n * clicksOrInterval.subscribe(x => console.log(x));\n *\n * // Results in the following behavior:\n * // If the result of Math.random() is greater than 0.5 it will listen\n * // for clicks anywhere on the \"document\"; when document is clicked it\n * // will log a MouseEvent object to the console. If the result is less\n * // than 0.5 it will emit ascending numbers, one every second(1000ms).\n * ```\n *\n * @see {@link Observable}\n *\n * @param {function(): ObservableInput} observableFactory The Observable\n * factory function to invoke for each Observer that subscribes to the output\n * Observable. May also return a Promise, which will be converted on the fly\n * to an Observable.\n * @return {Observable} An Observable whose Observers' subscriptions trigger\n * an invocation of the given Observable factory function.\n */\nexport function defer<R extends ObservableInput<any>>(observableFactory: () => R): Observable<ObservedValueOf<R>> {\n  return new Observable<ObservedValueOf<R>>((subscriber) => {\n    innerFrom(observableFactory()).subscribe(subscriber);\n  });\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { Observable } from '../Observable';\nimport { mergeMap } from '../operators/mergeMap';\nimport { isArrayLike } from '../util/isArrayLike';\nimport { isFunction } from '../util/isFunction';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\n\n// These constants are used to create handler registry functions using array mapping below.\nconst nodeEventEmitterMethods = ['addListener', 'removeListener'] as const;\nconst eventTargetMethods = ['addEventListener', 'removeEventListener'] as const;\nconst jqueryMethods = ['on', 'off'] as const;\n\nexport interface NodeStyleEventEmitter {\n  addListener(eventName: string | symbol, handler: NodeEventHandler): this;\n  removeListener(eventName: string | symbol, handler: NodeEventHandler): this;\n}\n\nexport type NodeEventHandler = (...args: any[]) => void;\n\n// For APIs that implement `addListener` and `removeListener` methods that may\n// not use the same arguments or return EventEmitter values\n// such as React Native\nexport interface NodeCompatibleEventEmitter {\n  addListener(eventName: string, handler: NodeEventHandler): void | {};\n  removeListener(eventName: string, handler: NodeEventHandler): void | {};\n}\n\n// Use handler types like those in @types/jquery. See:\n// https://github.com/DefinitelyTyped/DefinitelyTyped/blob/847731ba1d7fa6db6b911c0e43aa0afe596e7723/types/jquery/misc.d.ts#L6395\nexport interface JQueryStyleEventEmitter<TContext, T> {\n  on(eventName: string, handler: (this: TContext, t: T, ...args: any[]) => any): void;\n  off(eventName: string, handler: (this: TContext, t: T, ...args: any[]) => any): void;\n}\n\nexport interface EventListenerObject<E> {\n  handleEvent(evt: E): void;\n}\n\nexport interface HasEventTargetAddRemove<E> {\n  addEventListener(\n    type: string,\n    listener: ((evt: E) => void) | EventListenerObject<E> | null,\n    options?: boolean | AddEventListenerOptions\n  ): void;\n  removeEventListener(\n    type: string,\n    listener: ((evt: E) => void) | EventListenerObject<E> | null,\n    options?: EventListenerOptions | boolean\n  ): void;\n}\n\nexport interface EventListenerOptions {\n  capture?: boolean;\n  passive?: boolean;\n  once?: boolean;\n}\n\nexport interface AddEventListenerOptions extends EventListenerOptions {\n  once?: boolean;\n  passive?: boolean;\n}\n\nexport function fromEvent<T>(target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>, eventName: string): Observable<T>;\nexport function fromEvent<T, R>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  resultSelector: (event: T) => R\n): Observable<R>;\nexport function fromEvent<T>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  options: EventListenerOptions\n): Observable<T>;\nexport function fromEvent<T, R>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  options: EventListenerOptions,\n  resultSelector: (event: T) => R\n): Observable<R>;\n\nexport function fromEvent(target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>, eventName: string): Observable<unknown>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function fromEvent<T>(target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>, eventName: string): Observable<T>;\nexport function fromEvent<R>(\n  target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>,\n  eventName: string,\n  resultSelector: (...args: any[]) => R\n): Observable<R>;\n\nexport function fromEvent(\n  target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>,\n  eventName: string\n): Observable<unknown>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function fromEvent<T>(target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>, eventName: string): Observable<T>;\nexport function fromEvent<R>(\n  target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>,\n  eventName: string,\n  resultSelector: (...args: any[]) => R\n): Observable<R>;\n\nexport function fromEvent<T>(\n  target: JQueryStyleEventEmitter<any, T> | ArrayLike<JQueryStyleEventEmitter<any, T>>,\n  eventName: string\n): Observable<T>;\nexport function fromEvent<T, R>(\n  target: JQueryStyleEventEmitter<any, T> | ArrayLike<JQueryStyleEventEmitter<any, T>>,\n  eventName: string,\n  resultSelector: (value: T, ...args: any[]) => R\n): Observable<R>;\n\n/**\n * Creates an Observable that emits events of a specific type coming from the\n * given event target.\n *\n * <span class=\"informal\">Creates an Observable from DOM events, or Node.js\n * EventEmitter events or others.</span>\n *\n * ![](fromEvent.png)\n *\n * `fromEvent` accepts as a first argument event target, which is an object with methods\n * for registering event handler functions. As a second argument it takes string that indicates\n * type of event we want to listen for. `fromEvent` supports selected types of event targets,\n * which are described in detail below. If your event target does not match any of the ones listed,\n * you should use {@link fromEventPattern}, which can be used on arbitrary APIs.\n * When it comes to APIs supported by `fromEvent`, their methods for adding and removing event\n * handler functions have different names, but they all accept a string describing event type\n * and function itself, which will be called whenever said event happens.\n *\n * Every time resulting Observable is subscribed, event handler function will be registered\n * to event target on given event type. When that event fires, value\n * passed as a first argument to registered function will be emitted by output Observable.\n * When Observable is unsubscribed, function will be unregistered from event target.\n *\n * Note that if event target calls registered function with more than one argument, second\n * and following arguments will not appear in resulting stream. In order to get access to them,\n * you can pass to `fromEvent` optional project function, which will be called with all arguments\n * passed to event handler. Output Observable will then emit value returned by project function,\n * instead of the usual value.\n *\n * Remember that event targets listed below are checked via duck typing. It means that\n * no matter what kind of object you have and no matter what environment you work in,\n * you can safely use `fromEvent` on that object if it exposes described methods (provided\n * of course they behave as was described above). So for example if Node.js library exposes\n * event target which has the same method names as DOM EventTarget, `fromEvent` is still\n * a good choice.\n *\n * If the API you use is more callback then event handler oriented (subscribed\n * callback function fires only once and thus there is no need to manually\n * unregister it), you should use {@link bindCallback} or {@link bindNodeCallback}\n * instead.\n *\n * `fromEvent` supports following types of event targets:\n *\n * **DOM EventTarget**\n *\n * This is an object with `addEventListener` and `removeEventListener` methods.\n *\n * In the browser, `addEventListener` accepts - apart from event type string and event\n * handler function arguments - optional third parameter, which is either an object or boolean,\n * both used for additional configuration how and when passed function will be called. When\n * `fromEvent` is used with event target of that type, you can provide this values\n * as third parameter as well.\n *\n * **Node.js EventEmitter**\n *\n * An object with `addListener` and `removeListener` methods.\n *\n * **JQuery-style event target**\n *\n * An object with `on` and `off` methods\n *\n * **DOM NodeList**\n *\n * List of DOM Nodes, returned for example by `document.querySelectorAll` or `Node.childNodes`.\n *\n * Although this collection is not event target in itself, `fromEvent` will iterate over all Nodes\n * it contains and install event handler function in every of them. When returned Observable\n * is unsubscribed, function will be removed from all Nodes.\n *\n * **DOM HtmlCollection**\n *\n * Just as in case of NodeList it is a collection of DOM nodes. Here as well event handler function is\n * installed and removed in each of elements.\n *\n *\n * ## Examples\n *\n * Emit clicks happening on the DOM document\n *\n * ```ts\n * import { fromEvent } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * clicks.subscribe(x => console.log(x));\n *\n * // Results in:\n * // MouseEvent object logged to console every time a click\n * // occurs on the document.\n * ```\n *\n * Use `addEventListener` with capture option\n *\n * ```ts\n * import { fromEvent } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * // note optional configuration parameter which will be passed to addEventListener\n * const clicksInDocument = fromEvent(document, 'click', { capture: true });\n * const clicksInDiv = fromEvent(div, 'click');\n *\n * clicksInDocument.subscribe(() => console.log('document'));\n * clicksInDiv.subscribe(() => console.log('div'));\n *\n * // By default events bubble UP in DOM tree, so normally\n * // when we would click on div in document\n * // \"div\" would be logged first and then \"document\".\n * // Since we specified optional `capture` option, document\n * // will catch event when it goes DOWN DOM tree, so console\n * // will log \"document\" and then \"div\".\n * ```\n *\n * @see {@link bindCallback}\n * @see {@link bindNodeCallback}\n * @see {@link fromEventPattern}\n *\n * @param {FromEventTarget<T>} target The DOM EventTarget, Node.js\n * EventEmitter, JQuery-like event target, NodeList or HTMLCollection to attach the event handler to.\n * @param {string} eventName The event name of interest, being emitted by the\n * `target`.\n * @param {EventListenerOptions} [options] Options to pass through to addEventListener\n * @return {Observable<T>}\n */\nexport function fromEvent<T>(\n  target: any,\n  eventName: string,\n  options?: EventListenerOptions | ((...args: any[]) => T),\n  resultSelector?: (...args: any[]) => T\n): Observable<T> {\n  if (isFunction(options)) {\n    resultSelector = options;\n    options = undefined;\n  }\n  if (resultSelector) {\n    return fromEvent<T>(target, eventName, options as EventListenerOptions).pipe(mapOneOrManyArgs(resultSelector));\n  }\n\n  // Figure out our add and remove methods. In order to do this,\n  // we are going to analyze the target in a preferred order, if\n  // the target matches a given signature, we take the two \"add\" and \"remove\"\n  // method names and apply them to a map to create opposite versions of the\n  // same function. This is because they all operate in duplicate pairs,\n  // `addListener(name, handler)`, `removeListener(name, handler)`, for example.\n  // The call only differs by method name, as to whether or not you're adding or removing.\n  const [add, remove] =\n    // If it is an EventTarget, we need to use a slightly different method than the other two patterns.\n    isEventTarget(target)\n      ? eventTargetMethods.map((methodName) => (handler: any) => target[methodName](eventName, handler, options as EventListenerOptions))\n      : // In all other cases, the call pattern is identical with the exception of the method names.\n      isNodeStyleEventEmitter(target)\n      ? nodeEventEmitterMethods.map(toCommonHandlerRegistry(target, eventName))\n      : isJQueryStyleEventEmitter(target)\n      ? jqueryMethods.map(toCommonHandlerRegistry(target, eventName))\n      : [];\n\n  // If add is falsy, it's because we didn't match a pattern above.\n  // Check to see if it is an ArrayLike, because if it is, we want to\n  // try to apply fromEvent to all of it's items. We do this check last,\n  // because there are may be some types that are both ArrayLike *and* implement\n  // event registry points, and we'd rather delegate to that when possible.\n  if (!add) {\n    if (isArrayLike(target)) {\n      return mergeMap((subTarget: any) => fromEvent(subTarget, eventName, options as EventListenerOptions))(\n        innerFrom(target)\n      ) as Observable<T>;\n    }\n  }\n\n  // If add is falsy and we made it here, it's because we didn't\n  // match any valid target objects above.\n  if (!add) {\n    throw new TypeError('Invalid event target');\n  }\n\n  return new Observable<T>((subscriber) => {\n    // The handler we are going to register. Forwards the event object, by itself, or\n    // an array of arguments to the event handler, if there is more than one argument,\n    // to the consumer.\n    const handler = (...args: any[]) => subscriber.next(1 < args.length ? args : args[0]);\n    // Do the work of adding the handler to the target.\n    add(handler);\n    // When we finalize, we want to remove the handler and free up memory.\n    return () => remove!(handler);\n  });\n}\n\n/**\n * Used to create `add` and `remove` functions to register and unregister event handlers\n * from a target in the most common handler pattern, where there are only two arguments.\n * (e.g.  `on(name, fn)`, `off(name, fn)`, `addListener(name, fn)`, or `removeListener(name, fn)`)\n * @param target The target we're calling methods on\n * @param eventName The event name for the event we're creating register or unregister functions for\n */\nfunction toCommonHandlerRegistry(target: any, eventName: string) {\n  return (methodName: string) => (handler: any) => target[methodName](eventName, handler);\n}\n\n/**\n * Checks to see if the target implements the required node-style EventEmitter methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isNodeStyleEventEmitter(target: any): target is NodeStyleEventEmitter {\n  return isFunction(target.addListener) && isFunction(target.removeListener);\n}\n\n/**\n * Checks to see if the target implements the required jQuery-style EventEmitter methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isJQueryStyleEventEmitter(target: any): target is JQueryStyleEventEmitter<any, any> {\n  return isFunction(target.on) && isFunction(target.off);\n}\n\n/**\n * Checks to see if the target implements the required EventTarget methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isEventTarget(target: any): target is HasEventTargetAddRemove<any> {\n  return isFunction(target.addEventListener) && isFunction(target.removeEventListener);\n}\n", "import { Observable } from '../Observable';\nimport { isFunction } from '../util/isFunction';\nimport { NodeEventHandler } from './fromEvent';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\n\n/* tslint:disable:max-line-length */\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void\n): Observable<T>;\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void,\n  resultSelector?: (...args: any[]) => T\n): Observable<T>;\n/* tslint:enable:max-line-length */\n\n/**\n * Creates an Observable from an arbitrary API for registering event handlers.\n *\n * <span class=\"informal\">When that method for adding event handler was something {@link fromEvent}\n * was not prepared for.</span>\n *\n * ![](fromEventPattern.png)\n *\n * `fromEventPattern` allows you to convert into an Observable any API that supports registering handler functions\n * for events. It is similar to {@link fromEvent}, but far\n * more flexible. In fact, all use cases of {@link fromEvent} could be easily handled by\n * `fromEventPattern` (although in slightly more verbose way).\n *\n * This operator accepts as a first argument an `addHandler` function, which will be injected with\n * handler parameter. That handler is actually an event handler function that you now can pass\n * to API expecting it. `addHandler` will be called whenever Observable\n * returned by the operator is subscribed, so registering handler in API will not\n * necessarily happen when `fromEventPattern` is called.\n *\n * After registration, every time an event that we listen to happens,\n * Observable returned by `fromEventPattern` will emit value that event handler\n * function was called with. Note that if event handler was called with more\n * than one argument, second and following arguments will not appear in the Observable.\n *\n * If API you are using allows to unregister event handlers as well, you can pass to `fromEventPattern`\n * another function - `removeHandler` - as a second parameter. It will be injected\n * with the same handler function as before, which now you can use to unregister\n * it from the API. `removeHandler` will be called when consumer of resulting Observable\n * unsubscribes from it.\n *\n * In some APIs unregistering is actually handled differently. Method registering an event handler\n * returns some kind of token, which is later used to identify which function should\n * be unregistered or it itself has method that unregisters event handler.\n * If that is the case with your API, make sure token returned\n * by registering method is returned by `addHandler`. Then it will be passed\n * as a second argument to `removeHandler`, where you will be able to use it.\n *\n * If you need access to all event handler parameters (not only the first one),\n * or you need to transform them in any way, you can call `fromEventPattern` with optional\n * third parameter - project function which will accept all arguments passed to\n * event handler when it is called. Whatever is returned from project function will appear on\n * resulting stream instead of usual event handlers first argument. This means\n * that default project can be thought of as function that takes its first parameter\n * and ignores the rest.\n *\n * ## Examples\n *\n * Emits clicks happening on the DOM document\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * function addClickHandler(handler) {\n *   document.addEventListener('click', handler);\n * }\n *\n * function removeClickHandler(handler) {\n *   document.removeEventListener('click', handler);\n * }\n *\n * const clicks = fromEventPattern(\n *   addClickHandler,\n *   removeClickHandler\n * );\n * clicks.subscribe(x => console.log(x));\n *\n * // Whenever you click anywhere in the browser, DOM MouseEvent\n * // object will be logged.\n * ```\n *\n * Use with API that returns cancellation token\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * const token = someAPI.registerEventHandler(function() {});\n * someAPI.unregisterEventHandler(token); // this APIs cancellation method accepts\n *                                        // not handler itself, but special token.\n *\n * const someAPIObservable = fromEventPattern(\n *   function(handler) { return someAPI.registerEventHandler(handler); }, // Note that we return the token here...\n *   function(handler, token) { someAPI.unregisterEventHandler(token); }  // ...to then use it here.\n * );\n * ```\n *\n * Use with project function\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * someAPI.registerEventHandler((eventType, eventMessage) => {\n *   console.log(eventType, eventMessage); // Logs 'EVENT_TYPE' 'EVENT_MESSAGE' to console.\n * });\n *\n * const someAPIObservable = fromEventPattern(\n *   handler => someAPI.registerEventHandler(handler),\n *   handler => someAPI.unregisterEventHandler(handler)\n *   (eventType, eventMessage) => eventType + ' --- ' + eventMessage // without that function only 'EVENT_TYPE'\n * );                                                                // would be emitted by the Observable\n *\n * someAPIObservable.subscribe(value => console.log(value));\n *\n * // Logs:\n * // 'EVENT_TYPE --- EVENT_MESSAGE'\n * ```\n *\n * @see {@link fromEvent}\n * @see {@link bindCallback}\n * @see {@link bindNodeCallback}\n *\n * @param {function(handler: Function): any} addHandler A function that takes\n * a `handler` function as argument and attaches it somehow to the actual\n * source of events.\n * @param {function(handler: Function, token?: any): void} [removeHandler] A function that\n * takes a `handler` function as an argument and removes it from the event source. If `addHandler`\n * returns some kind of token, `removeHandler` function will have it as a second parameter.\n * @param {function(...args: any): T} [project] A function to\n * transform results. It takes the arguments from the event handler and\n * should return a single value.\n * @return {Observable<T>} Observable which, when an event happens, emits first parameter\n * passed to registered event handler. Alternatively it emits whatever project function returns\n * at that moment.\n */\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void,\n  resultSelector?: (...args: any[]) => T\n): Observable<T | T[]> {\n  if (resultSelector) {\n    return fromEventPattern<T>(addHandler, removeHandler).pipe(mapOneOrManyArgs(resultSelector));\n  }\n\n  return new Observable<T | T[]>((subscriber) => {\n    const handler = (...e: T[]) => subscriber.next(e.length === 1 ? e[0] : e);\n    const retValue = addHandler(handler);\n    return isFunction(removeHandler) ? () => removeHandler(handler, retValue) : undefined;\n  });\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\nimport { async as asyncScheduler } from '../scheduler/async';\nimport { isScheduler } from '../util/isScheduler';\nimport { isValidDate } from '../util/isDate';\n\n/**\n * Creates an observable that will wait for a specified time period, or exact date, before\n * emitting the number 0.\n *\n * <span class=\"informal\">Used to emit a notification after a delay.</span>\n *\n * This observable is useful for creating delays in code, or racing against other values\n * for ad-hoc timeouts.\n *\n * The `delay` is specified by default in milliseconds, however providing a custom scheduler could\n * create a different behavior.\n *\n * ## Examples\n *\n * Wait 3 seconds and start another observable\n *\n * You might want to use `timer` to delay subscription to an\n * observable by a set amount of time. Here we use a timer with\n * {@link concatMapTo} or {@link concatMap} in order to wait\n * a few seconds and start a subscription to a source.\n *\n * ```ts\n * import { of, timer, concatMap } from 'rxjs';\n *\n * // This could be any observable\n * const source = of(1, 2, 3);\n *\n * timer(3000)\n *   .pipe(concatMap(() => source))\n *   .subscribe(console.log);\n * ```\n *\n * Take all values until the start of the next minute\n *\n * Using a `Date` as the trigger for the first emission, you can\n * do things like wait until midnight to fire an event, or in this case,\n * wait until a new minute starts (chosen so the example wouldn't take\n * too long to run) in order to stop watching a stream. Leveraging\n * {@link takeUntil}.\n *\n * ```ts\n * import { interval, takeUntil, timer } from 'rxjs';\n *\n * // Build a Date object that marks the\n * // next minute.\n * const currentDate = new Date();\n * const startOfNextMinute = new Date(\n *   currentDate.getFullYear(),\n *   currentDate.getMonth(),\n *   currentDate.getDate(),\n *   currentDate.getHours(),\n *   currentDate.getMinutes() + 1\n * );\n *\n * // This could be any observable stream\n * const source = interval(1000);\n *\n * const result = source.pipe(\n *   takeUntil(timer(startOfNextMinute))\n * );\n *\n * result.subscribe(console.log);\n * ```\n *\n * ### Known Limitations\n *\n * - The {@link asyncScheduler} uses `setTimeout` which has limitations for how far in the future it can be scheduled.\n *\n * - If a `scheduler` is provided that returns a timestamp other than an epoch from `now()`, and\n * a `Date` object is passed to the `dueTime` argument, the calculation for when the first emission\n * should occur will be incorrect. In this case, it would be best to do your own calculations\n * ahead of time, and pass a `number` in as the `dueTime`.\n *\n * @param due If a `number`, the amount of time in milliseconds to wait before emitting.\n * If a `Date`, the exact time at which to emit.\n * @param scheduler The scheduler to use to schedule the delay. Defaults to {@link asyncScheduler}.\n */\nexport function timer(due: number | Date, scheduler?: SchedulerLike): Observable<0>;\n\n/**\n * Creates an observable that starts an interval after a specified delay, emitting incrementing numbers -- starting at `0` --\n * on each interval after words.\n *\n * The `delay` and `intervalDuration` are specified by default in milliseconds, however providing a custom scheduler could\n * create a different behavior.\n *\n * ## Example\n *\n * ### Start an interval that starts right away\n *\n * Since {@link interval} waits for the passed delay before starting,\n * sometimes that's not ideal. You may want to start an interval immediately.\n * `timer` works well for this. Here we have both side-by-side so you can\n * see them in comparison.\n *\n * Note that this observable will never complete.\n *\n * ```ts\n * import { timer, interval } from 'rxjs';\n *\n * timer(0, 1000).subscribe(n => console.log('timer', n));\n * interval(1000).subscribe(n => console.log('interval', n));\n * ```\n *\n * ### Known Limitations\n *\n * - The {@link asyncScheduler} uses `setTimeout` which has limitations for how far in the future it can be scheduled.\n *\n * - If a `scheduler` is provided that returns a timestamp other than an epoch from `now()`, and\n * a `Date` object is passed to the `dueTime` argument, the calculation for when the first emission\n * should occur will be incorrect. In this case, it would be best to do your own calculations\n * ahead of time, and pass a `number` in as the `startDue`.\n * @param startDue If a `number`, is the time to wait before starting the interval.\n * If a `Date`, is the exact time at which to start the interval.\n * @param intervalDuration The delay between each value emitted in the interval. Passing a\n * negative number here will result in immediate completion after the first value is emitted, as though\n * no `intervalDuration` was passed at all.\n * @param scheduler The scheduler to use to schedule the delay. Defaults to {@link asyncScheduler}.\n */\nexport function timer(startDue: number | Date, intervalDuration: number, scheduler?: SchedulerLike): Observable<number>;\n\n/**\n * @deprecated The signature allowing `undefined` to be passed for `intervalDuration` will be removed in v8. Use the `timer(dueTime, scheduler?)` signature instead.\n */\nexport function timer(dueTime: number | Date, unused: undefined, scheduler?: SchedulerLike): Observable<0>;\n\nexport function timer(\n  dueTime: number | Date = 0,\n  intervalOrScheduler?: number | SchedulerLike,\n  scheduler: SchedulerLike = asyncScheduler\n): Observable<number> {\n  // Since negative intervalDuration is treated as though no\n  // interval was specified at all, we start with a negative number.\n  let intervalDuration = -1;\n\n  if (intervalOrScheduler != null) {\n    // If we have a second argument, and it's a scheduler,\n    // override the scheduler we had defaulted. Otherwise,\n    // it must be an interval.\n    if (isScheduler(intervalOrScheduler)) {\n      scheduler = intervalOrScheduler;\n    } else {\n      // Note that this *could* be negative, in which case\n      // it's like not passing an intervalDuration at all.\n      intervalDuration = intervalOrScheduler;\n    }\n  }\n\n  return new Observable((subscriber) => {\n    // If a valid date is passed, calculate how long to wait before\n    // executing the first value... otherwise, if it's a number just schedule\n    // that many milliseconds (or scheduler-specified unit size) in the future.\n    let due = isValidDate(dueTime) ? +dueTime - scheduler!.now() : dueTime;\n\n    if (due < 0) {\n      // Ensure we don't schedule in the future.\n      due = 0;\n    }\n\n    // The incrementing value we emit.\n    let n = 0;\n\n    // Start the timer.\n    return scheduler.schedule(function () {\n      if (!subscriber.closed) {\n        // Emit the next value and increment.\n        subscriber.next(n++);\n\n        if (0 <= intervalDuration) {\n          // If we have a interval after the initial timer,\n          // reschedule with the period.\n          this.schedule(undefined, intervalDuration);\n        } else {\n          // We didn't have an interval. So just complete.\n          subscriber.complete();\n        }\n      }\n    }, due);\n  });\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, ObservableInputTuple, SchedulerLike } from '../types';\nimport { mergeAll } from '../operators/mergeAll';\nimport { innerFrom } from './innerFrom';\nimport { EMPTY } from './empty';\nimport { popNumber, popScheduler } from '../util/args';\nimport { from } from './from';\n\nexport function merge<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A[number]>;\nexport function merge<A extends readonly unknown[]>(...sourcesAndConcurrency: [...ObservableInputTuple<A>, number?]): Observable<A[number]>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `mergeAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function merge<A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike?]\n): Observable<A[number]>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `mergeAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function merge<A extends readonly unknown[]>(\n  ...sourcesAndConcurrencyAndScheduler: [...ObservableInputTuple<A>, number?, SchedulerLike?]\n): Observable<A[number]>;\n\n/**\n * Creates an output Observable which concurrently emits all values from every\n * given input Observable.\n *\n * <span class=\"informal\">Flattens multiple Observables together by blending\n * their values into one Observable.</span>\n *\n * ![](merge.png)\n *\n * `merge` subscribes to each given input Observable (as arguments), and simply\n * forwards (without doing any transformation) all the values from all the input\n * Observables to the output Observable. The output Observable only completes\n * once all input Observables have completed. Any error delivered by an input\n * Observable will be immediately emitted on the output Observable.\n *\n * ## Examples\n *\n * Merge together two Observables: 1s interval and clicks\n *\n * ```ts\n * import { merge, fromEvent, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const timer = interval(1000);\n * const clicksOrTimer = merge(clicks, timer);\n * clicksOrTimer.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // timer will emit ascending values, one every second(1000ms) to console\n * // clicks logs MouseEvents to console every time the \"document\" is clicked\n * // Since the two streams are merged you see these happening\n * // as they occur.\n * ```\n *\n * Merge together 3 Observables, but run only 2 concurrently\n *\n * ```ts\n * import { interval, take, merge } from 'rxjs';\n *\n * const timer1 = interval(1000).pipe(take(10));\n * const timer2 = interval(2000).pipe(take(6));\n * const timer3 = interval(500).pipe(take(10));\n *\n * const concurrent = 2; // the argument\n * const merged = merge(timer1, timer2, timer3, concurrent);\n * merged.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // - First timer1 and timer2 will run concurrently\n * // - timer1 will emit a value every 1000ms for 10 iterations\n * // - timer2 will emit a value every 2000ms for 6 iterations\n * // - after timer1 hits its max iteration, timer2 will\n * //   continue, and timer3 will start to run concurrently with timer2\n * // - when timer2 hits its max iteration it terminates, and\n * //   timer3 will continue to emit a value every 500ms until it is complete\n * ```\n *\n * @see {@link mergeAll}\n * @see {@link mergeMap}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n *\n * @param {...ObservableInput} observables Input Observables to merge together.\n * @param {number} [concurrent=Infinity] Maximum number of input\n * Observables being subscribed to concurrently.\n * @param {SchedulerLike} [scheduler=null] The {@link SchedulerLike} to use for managing\n * concurrency of input Observables.\n * @return {Observable} an Observable that emits items that are the result of\n * every input Observable.\n */\nexport function merge(...args: (ObservableInput<unknown> | number | SchedulerLike)[]): Observable<unknown> {\n  const scheduler = popScheduler(args);\n  const concurrent = popNumber(args, Infinity);\n  const sources = args as ObservableInput<unknown>[];\n  return !sources.length\n    ? // No source provided\n      EMPTY\n    : sources.length === 1\n    ? // One source? Just return it.\n      innerFrom(sources[0])\n    : // Merge all sources\n      mergeAll(concurrent)(from(sources, scheduler));\n}\n", "import { Observable } from '../Observable';\nimport { noop } from '../util/noop';\n\n/**\n * An Observable that emits no items to the Observer and never completes.\n *\n * ![](never.png)\n *\n * A simple Observable that emits neither values nor errors nor the completion\n * notification. It can be used for testing purposes or for composing with other\n * Observables. Please note that by never emitting a complete notification, this\n * Observable keeps the subscription from being disposed automatically.\n * Subscriptions need to be manually disposed.\n *\n * ##  Example\n *\n * Emit the number 7, then never emit anything else (not even complete)\n *\n * ```ts\n * import { NEVER, startWith } from 'rxjs';\n *\n * const info = () => console.log('Will not be called');\n *\n * const result = NEVER.pipe(startWith(7));\n * result.subscribe({\n *   next: x => console.log(x),\n *   error: info,\n *   complete: info\n * });\n * ```\n *\n * @see {@link Observable}\n * @see {@link EMPTY}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const NEVER = new Observable<never>(noop);\n\n/**\n * @deprecated Replaced with the {@link NEVER} constant. Will be removed in v8.\n */\nexport function never() {\n  return NEVER;\n}\n", "const { isArray } = Array;\n\n/**\n * Used in operators and functions that accept either a list of arguments, or an array of arguments\n * as a single argument.\n */\nexport function argsOrArgArray<T>(args: (T | T[])[]): T[] {\n  return args.length === 1 && isArray(args[0]) ? args[0] : (args as T[]);\n}\n", "import { OperatorFunction, MonoTypeOperatorFunction, TruthyTypesOf } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function filter<T, S extends T, A>(predicate: (this: A, value: T, index: number) => value is S, thisArg: A): OperatorFunction<T, S>;\nexport function filter<T, S extends T>(predicate: (value: T, index: number) => value is S): OperatorFunction<T, S>;\nexport function filter<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function filter<T, A>(predicate: (this: A, value: T, index: number) => boolean, thisArg: A): MonoTypeOperatorFunction<T>;\nexport function filter<T>(predicate: (value: T, index: number) => boolean): MonoTypeOperatorFunction<T>;\n\n/**\n * Filter items emitted by the source Observable by only emitting those that\n * satisfy a specified predicate.\n *\n * <span class=\"informal\">Like\n * [Array.prototype.filter()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/filter),\n * it only emits a value from the source if it passes a criterion function.</span>\n *\n * ![](filter.png)\n *\n * Similar to the well-known `Array.prototype.filter` method, this operator\n * takes values from the source Observable, passes them through a `predicate`\n * function and only emits those values that yielded `true`.\n *\n * ## Example\n *\n * Emit only click events whose target was a DIV element\n *\n * ```ts\n * import { fromEvent, filter } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * const clicks = fromEvent(document, 'click');\n * const clicksOnDivs = clicks.pipe(filter(ev => (<HTMLElement>ev.target).tagName === 'DIV'));\n * clicksOnDivs.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilChanged}\n * @see {@link distinctUntilKeyChanged}\n * @see {@link ignoreElements}\n * @see {@link partition}\n * @see {@link skip}\n *\n * @param predicate A function that\n * evaluates each value emitted by the source Observable. If it returns `true`,\n * the value is emitted, if `false` the value is not passed to the output\n * Observable. The `index` parameter is the number `i` for the i-th source\n * emission that has happened since the subscription, starting from the number\n * `0`.\n * @param thisArg An optional argument to determine the value of `this`\n * in the `predicate` function.\n * @return A function that returns an Observable that emits items from the\n * source Observable that satisfy the specified `predicate`.\n */\nexport function filter<T>(predicate: (value: T, index: number) => boolean, thisArg?: any): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    // An index passed to our predicate function on each call.\n    let index = 0;\n\n    // Subscribe to the source, all errors and completions are\n    // forwarded to the consumer.\n    source.subscribe(\n      // Call the predicate with the appropriate `this` context,\n      // if the predicate returns `true`, then send the value\n      // to the consumer.\n      createOperatorSubscriber(subscriber, (value) => predicate.call(thisArg, value, index++) && subscriber.next(value))\n    );\n  });\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInputTuple } from '../types';\nimport { innerFrom } from './innerFrom';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { EMPTY } from './empty';\nimport { createOperatorSubscriber } from '../operators/OperatorSubscriber';\nimport { popResultSelector } from '../util/args';\n\nexport function zip<A extends readonly unknown[]>(sources: [...ObservableInputTuple<A>]): Observable<A>;\nexport function zip<A extends readonly unknown[], R>(\n  sources: [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R\n): Observable<R>;\nexport function zip<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A>;\nexport function zip<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelector: [...ObservableInputTuple<A>, (...values: A) => R]\n): Observable<R>;\n\n/**\n * Combines multiple Observables to create an Observable whose values are calculated from the values, in order, of each\n * of its input Observables.\n *\n * If the last parameter is a function, this function is used to compute the created value from the input values.\n * Otherwise, an array of the input values is returned.\n *\n * ## Example\n *\n * Combine age and name from different sources\n *\n * ```ts\n * import { of, zip, map } from 'rxjs';\n *\n * const age$ = of(27, 25, 29);\n * const name$ = of('Foo', 'Bar', 'Beer');\n * const isDev$ = of(true, true, false);\n *\n * zip(age$, name$, isDev$).pipe(\n *   map(([age, name, isDev]) => ({ age, name, isDev }))\n * )\n * .subscribe(x => console.log(x));\n *\n * // Outputs\n * // { age: 27, name: 'Foo', isDev: true }\n * // { age: 25, name: 'Bar', isDev: true }\n * // { age: 29, name: 'Beer', isDev: false }\n * ```\n *\n * @param sources\n * @return {Observable<R>}\n */\nexport function zip(...args: unknown[]): Observable<unknown> {\n  const resultSelector = popResultSelector(args);\n\n  const sources = argsOrArgArray(args) as Observable<unknown>[];\n\n  return sources.length\n    ? new Observable<unknown[]>((subscriber) => {\n        // A collection of buffers of values from each source.\n        // Keyed by the same index with which the sources were passed in.\n        let buffers: unknown[][] = sources.map(() => []);\n\n        // An array of flags of whether or not the sources have completed.\n        // This is used to check to see if we should complete the result.\n        // Keyed by the same index with which the sources were passed in.\n        let completed = sources.map(() => false);\n\n        // When everything is done, release the arrays above.\n        subscriber.add(() => {\n          buffers = completed = null!;\n        });\n\n        // Loop over our sources and subscribe to each one. The index `i` is\n        // especially important here, because we use it in closures below to\n        // access the related buffers and completion properties\n        for (let sourceIndex = 0; !subscriber.closed && sourceIndex < sources.length; sourceIndex++) {\n          innerFrom(sources[sourceIndex]).subscribe(\n            createOperatorSubscriber(\n              subscriber,\n              (value) => {\n                buffers[sourceIndex].push(value);\n                // if every buffer has at least one value in it, then we\n                // can shift out the oldest value from each buffer and emit\n                // them as an array.\n                if (buffers.every((buffer) => buffer.length)) {\n                  const result: any = buffers.map((buffer) => buffer.shift()!);\n                  // Emit the array. If theres' a result selector, use that.\n                  subscriber.next(resultSelector ? resultSelector(...result) : result);\n                  // If any one of the sources is both complete and has an empty buffer\n                  // then we complete the result. This is because we cannot possibly have\n                  // any more values to zip together.\n                  if (buffers.some((buffer, i) => !buffer.length && completed[i])) {\n                    subscriber.complete();\n                  }\n                }\n              },\n              () => {\n                // This source completed. Mark it as complete so we can check it later\n                // if we have to.\n                completed[sourceIndex] = true;\n                // But, if this complete source has nothing in its buffer, then we\n                // can complete the result, because we can't possibly have any more\n                // values from this to zip together with the other values.\n                !buffers[sourceIndex].length && subscriber.complete();\n              }\n            )\n          );\n        }\n\n        // When everything is done, release the arrays above.\n        return () => {\n          buffers = completed = null!;\n        };\n      })\n    : EMPTY;\n}\n", "import { Subscriber } from '../Subscriber';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\n\nimport { operate } from '../util/lift';\nimport { innerFrom } from '../observable/innerFrom';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Ignores source values for a duration determined by another Observable, then\n * emits the most recent value from the source Observable, then repeats this\n * process.\n *\n * <span class=\"informal\">It's like {@link auditTime}, but the silencing\n * duration is determined by a second Observable.</span>\n *\n * ![](audit.svg)\n *\n * `audit` is similar to `throttle`, but emits the last value from the silenced\n * time window, instead of the first value. `audit` emits the most recent value\n * from the source Observable on the output Observable as soon as its internal\n * timer becomes disabled, and ignores source values while the timer is enabled.\n * Initially, the timer is disabled. As soon as the first source value arrives,\n * the timer is enabled by calling the `durationSelector` function with the\n * source value, which returns the \"duration\" Observable. When the duration\n * Observable emits a value, the timer is disabled, then the most\n * recent source value is emitted on the output Observable, and this process\n * repeats for the next source value.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, audit, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(audit(ev => interval(1000)));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link auditTime}\n * @see {@link debounce}\n * @see {@link delayWhen}\n * @see {@link sample}\n * @see {@link throttle}\n *\n * @param durationSelector A function\n * that receives a value from the source Observable, for computing the silencing\n * duration, returned as an Observable or a Promise.\n * @return A function that returns an Observable that performs rate-limiting of\n * emissions from the source Observable.\n */\nexport function audit<T>(durationSelector: (value: T) => ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    let lastValue: T | null = null;\n    let durationSubscriber: Subscriber<any> | null = null;\n    let isComplete = false;\n\n    const endDuration = () => {\n      durationSubscriber?.unsubscribe();\n      durationSubscriber = null;\n      if (hasValue) {\n        hasValue = false;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n      isComplete && subscriber.complete();\n    };\n\n    const cleanupDuration = () => {\n      durationSubscriber = null;\n      isComplete && subscriber.complete();\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          lastValue = value;\n          if (!durationSubscriber) {\n            innerFrom(durationSelector(value)).subscribe(\n              (durationSubscriber = createOperatorSubscriber(subscriber, endDuration, cleanupDuration))\n            );\n          }\n        },\n        () => {\n          isComplete = true;\n          (!hasValue || !durationSubscriber || durationSubscriber.closed) && subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { audit } from './audit';\nimport { timer } from '../observable/timer';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\n\n/**\n * Ignores source values for `duration` milliseconds, then emits the most recent\n * value from the source Observable, then repeats this process.\n *\n * <span class=\"informal\">When it sees a source value, it ignores that plus\n * the next ones for `duration` milliseconds, and then it emits the most recent\n * value from the source.</span>\n *\n * ![](auditTime.png)\n *\n * `auditTime` is similar to `throttleTime`, but emits the last value from the\n * silenced time window, instead of the first value. `auditTime` emits the most\n * recent value from the source Observable on the output Observable as soon as\n * its internal timer becomes disabled, and ignores source values while the\n * timer is enabled. Initially, the timer is disabled. As soon as the first\n * source value arrives, the timer is enabled. After `duration` milliseconds (or\n * the time unit determined internally by the optional `scheduler`) has passed,\n * the timer is disabled, then the most recent source value is emitted on the\n * output Observable, and this process repeats for the next source value.\n * Optionally takes a {@link SchedulerLike} for managing timers.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, auditTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(auditTime(1000));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sampleTime}\n * @see {@link throttleTime}\n *\n * @param {number} duration Time to wait before emitting the most recent source\n * value, measured in milliseconds or the time unit determined internally\n * by the optional `scheduler`.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the rate-limiting behavior.\n * @return A function that returns an Observable that performs rate-limiting of\n * emissions from the source Observable.\n */\nexport function auditTime<T>(duration: number, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  return audit(() => timer(duration, scheduler));\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { arrRemove } from '../util/arrRemove';\n\n/**\n * Buffers the source Observable values until the size hits the maximum\n * `bufferSize` given.\n *\n * <span class=\"informal\">Collects values from the past as an array, and emits\n * that array only when its size reaches `bufferSize`.</span>\n *\n * ![](bufferCount.png)\n *\n * Buffers a number of values from the source Observable by `bufferSize` then\n * emits the buffer and clears it, and starts a new buffer each\n * `startBufferEvery` values. If `startBufferEvery` is not provided or is\n * `null`, then new buffers are started immediately at the start of the source\n * and when each buffer closes and is emitted.\n *\n * ## Examples\n *\n * Emit the last two click events as an array\n *\n * ```ts\n * import { fromEvent, bufferCount } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const buffered = clicks.pipe(bufferCount(2));\n * buffered.subscribe(x => console.log(x));\n * ```\n *\n * On every click, emit the last two click events as an array\n *\n * ```ts\n * import { fromEvent, bufferCount } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const buffered = clicks.pipe(bufferCount(2, 1));\n * buffered.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link buffer}\n * @see {@link bufferTime}\n * @see {@link bufferToggle}\n * @see {@link bufferWhen}\n * @see {@link pairwise}\n * @see {@link windowCount}\n *\n * @param {number} bufferSize The maximum size of the buffer emitted.\n * @param {number} [startBufferEvery] Interval at which to start a new buffer.\n * For example if `startBufferEvery` is `2`, then a new buffer will be started\n * on every other value from the source. A new buffer is started at the\n * beginning of the source by default.\n * @return A function that returns an Observable of arrays of buffered values.\n */\nexport function bufferCount<T>(bufferSize: number, startBufferEvery: number | null = null): OperatorFunction<T, T[]> {\n  // If no `startBufferEvery` value was supplied, then we're\n  // opening and closing on the bufferSize itself.\n  startBufferEvery = startBufferEvery ?? bufferSize;\n\n  return operate((source, subscriber) => {\n    let buffers: T[][] = [];\n    let count = 0;\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          let toEmit: T[][] | null = null;\n\n          // Check to see if we need to start a buffer.\n          // This will start one at the first value, and then\n          // a new one every N after that.\n          if (count++ % startBufferEvery! === 0) {\n            buffers.push([]);\n          }\n\n          // Push our value into our active buffers.\n          for (const buffer of buffers) {\n            buffer.push(value);\n            // Check to see if we're over the bufferSize\n            // if we are, record it so we can emit it later.\n            // If we emitted it now and removed it, it would\n            // mutate the `buffers` array while we're looping\n            // over it.\n            if (bufferSize <= buffer.length) {\n              toEmit = toEmit ?? [];\n              toEmit.push(buffer);\n            }\n          }\n\n          if (toEmit) {\n            // We have found some buffers that are over the\n            // `bufferSize`. Emit them, and remove them from our\n            // buffers list.\n            for (const buffer of toEmit) {\n              arrRemove(buffers, buffer);\n              subscriber.next(buffer);\n            }\n          }\n        },\n        () => {\n          // When the source completes, emit all of our\n          // active buffers.\n          for (const buffer of buffers) {\n            subscriber.next(buffer);\n          }\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer.\n        undefined,\n        () => {\n          // Clean up our memory when we finalize\n          buffers = null!;\n        }\n      )\n    );\n  });\n}\n", "import { Observable } from '../Observable';\n\nimport { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { Subscription } from '../Subscription';\nimport { innerFrom } from '../observable/innerFrom';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { operate } from '../util/lift';\n\n/* tslint:disable:max-line-length */\nexport function catchError<T, O extends ObservableInput<any>>(\n  selector: (err: any, caught: Observable<T>) => O\n): OperatorFunction<T, T | ObservedValueOf<O>>;\n/* tslint:enable:max-line-length */\n\n/**\n * Catches errors on the observable to be handled by returning a new observable or throwing an error.\n *\n * <span class=\"informal\">\n * It only listens to the error channel and ignores notifications.\n * Handles errors from the source observable, and maps them to a new observable.\n * The error may also be rethrown, or a new error can be thrown to emit an error from the result.\n * </span>\n *\n * ![](catch.png)\n *\n * This operator handles errors, but forwards along all other events to the resulting observable.\n * If the source observable terminates with an error, it will map that error to a new observable,\n * subscribe to it, and forward all of its events to the resulting observable.\n *\n * ## Examples\n *\n * Continue with a different Observable when there's an error\n *\n * ```ts\n * import { of, map, catchError } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError(err => of('I', 'II', 'III', 'IV', 'V'))\n *   )\n *   .subscribe(x => console.log(x));\n *   // 1, 2, 3, I, II, III, IV, V\n * ```\n *\n * Retry the caught source Observable again in case of error, similar to `retry()` operator\n *\n * ```ts\n * import { of, map, catchError, take } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError((err, caught) => caught),\n *     take(30)\n *   )\n *   .subscribe(x => console.log(x));\n *   // 1, 2, 3, 1, 2, 3, ...\n * ```\n *\n * Throw a new error when the source Observable throws an error\n *\n * ```ts\n * import { of, map, catchError } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError(err => {\n *       throw 'error in source. Details: ' + err;\n *     })\n *   )\n *   .subscribe({\n *     next: x => console.log(x),\n *     error: err => console.log(err)\n *   });\n *   // 1, 2, 3, error in source. Details: four!\n * ```\n *\n * @see {@link onErrorResumeNext}\n * @see {@link repeat}\n * @see {@link repeatWhen}\n * @see {@link retry }\n * @see {@link retryWhen}\n *\n * @param {function} selector a function that takes as arguments `err`, which is the error, and `caught`, which\n * is the source observable, in case you'd like to \"retry\" that observable by returning it again. Whatever observable\n * is returned by the `selector` will be used to continue the observable chain.\n * @return A function that returns an Observable that originates from either\n * the source or the Observable returned by the `selector` function.\n */\nexport function catchError<T, O extends ObservableInput<any>>(\n  selector: (err: any, caught: Observable<T>) => O\n): OperatorFunction<T, T | ObservedValueOf<O>> {\n  return operate((source, subscriber) => {\n    let innerSub: Subscription | null = null;\n    let syncUnsub = false;\n    let handledResult: Observable<ObservedValueOf<O>>;\n\n    innerSub = source.subscribe(\n      createOperatorSubscriber(subscriber, undefined, undefined, (err) => {\n        handledResult = innerFrom(selector(err, catchError(selector)(source)));\n        if (innerSub) {\n          innerSub.unsubscribe();\n          innerSub = null;\n          handledResult.subscribe(subscriber);\n        } else {\n          // We don't have an innerSub yet, that means the error was synchronous\n          // because the subscribe call hasn't returned yet.\n          syncUnsub = true;\n        }\n      })\n    );\n\n    if (syncUnsub) {\n      // We have a synchronous error, we need to make sure to\n      // finalize right away. This ensures that callbacks in the `finalize` operator are called\n      // at the right time, and that finalization occurs at the expected\n      // time between the source error and the subscription to the\n      // next observable.\n      innerSub.unsubscribe();\n      innerSub = null;\n      handledResult!.subscribe(subscriber);\n    }\n  });\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * A basic scan operation. This is used for `scan` and `reduce`.\n * @param accumulator The accumulator to use\n * @param seed The seed value for the state to accumulate\n * @param hasSeed Whether or not a seed was provided\n * @param emitOnNext Whether or not to emit the state on next\n * @param emitBeforeComplete Whether or not to emit the before completion\n */\n\nexport function scanInternals<V, A, S>(\n  accumulator: (acc: V | A | S, value: V, index: number) => A,\n  seed: S,\n  hasSeed: boolean,\n  emitOnNext: boolean,\n  emitBeforeComplete?: undefined | true\n) {\n  return (source: Observable<V>, subscriber: Subscriber<any>) => {\n    // Whether or not we have state yet. This will only be\n    // false before the first value arrives if we didn't get\n    // a seed value.\n    let hasState = hasSeed;\n    // The state that we're tracking, starting with the seed,\n    // if there is one, and then updated by the return value\n    // from the accumulator on each emission.\n    let state: any = seed;\n    // An index to pass to the accumulator function.\n    let index = 0;\n\n    // Subscribe to our source. All errors and completions are passed through.\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          // Always increment the index.\n          const i = index++;\n          // Set the state\n          state = hasState\n            ? // We already have state, so we can get the new state from the accumulator\n              accumulator(state, value, i)\n            : // We didn't have state yet, a seed value was not provided, so\n\n              // we set the state to the first value, and mark that we have state now\n              ((hasState = true), value);\n\n          // Maybe send it to the consumer.\n          emitOnNext && subscriber.next(state);\n        },\n        // If an onComplete was given, call it, otherwise\n        // just pass through the complete notification to the consumer.\n        emitBeforeComplete &&\n          (() => {\n            hasState && subscriber.next(state);\n            subscriber.complete();\n          })\n      )\n    );\n  };\n}\n", "import { combineLatestInit } from '../observable/combineLatest';\nimport { ObservableInput, ObservableInputTuple, OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\nimport { pipe } from '../util/pipe';\nimport { popResultSelector } from '../util/args';\n\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(\n  sources: [...ObservableInputTuple<A>],\n  project: (...values: [T, ...A]) => R\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(sources: [...ObservableInputTuple<A>]): OperatorFunction<T, [T, ...A]>;\n\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(\n  ...sourcesAndProject: [...ObservableInputTuple<A>, (...values: [T, ...A]) => R]\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(...sources: [...ObservableInputTuple<A>]): OperatorFunction<T, [T, ...A]>;\n\n/**\n * @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8.\n */\nexport function combineLatest<T, R>(...args: (ObservableInput<any> | ((...values: any[]) => R))[]): OperatorFunction<T, unknown> {\n  const resultSelector = popResultSelector(args);\n  return resultSelector\n    ? pipe(combineLatest(...(args as Array<ObservableInput<any>>)), mapOneOrManyArgs(resultSelector))\n    : operate((source, subscriber) => {\n        combineLatestInit([source, ...argsOrArgArray(args)])(subscriber);\n      });\n}\n", "import { ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { combineLatest } from './combineLatest';\n\n/**\n * Create an observable that combines the latest values from all passed observables and the source\n * into arrays and emits them.\n *\n * Returns an observable, that when subscribed to, will subscribe to the source observable and all\n * sources provided as arguments. Once all sources emit at least one value, all of the latest values\n * will be emitted as an array. After that, every time any source emits a value, all of the latest values\n * will be emitted as an array.\n *\n * This is a useful operator for eagerly calculating values based off of changed inputs.\n *\n * ## Example\n *\n * Simple concatenation of values from two inputs\n *\n * ```ts\n * import { fromEvent, combineLatestWith, map } from 'rxjs';\n *\n * // Setup: Add two inputs to the page\n * const input1 = document.createElement('input');\n * document.body.appendChild(input1);\n * const input2 = document.createElement('input');\n * document.body.appendChild(input2);\n *\n * // Get streams of changes\n * const input1Changes$ = fromEvent(input1, 'change');\n * const input2Changes$ = fromEvent(input2, 'change');\n *\n * // Combine the changes by adding them together\n * input1Changes$.pipe(\n *   combineLatestWith(input2Changes$),\n *   map(([e1, e2]) => (<HTMLInputElement>e1.target).value + ' - ' + (<HTMLInputElement>e2.target).value)\n * )\n * .subscribe(x => console.log(x));\n * ```\n *\n * @param otherSources the other sources to subscribe to.\n * @return A function that returns an Observable that emits the latest\n * emissions from both source and provided Observables.\n */\nexport function combineLatestWith<T, A extends readonly unknown[]>(\n  ...otherSources: [...ObservableInputTuple<A>]\n): OperatorFunction<T, Cons<T, A>> {\n  return combineLatest(...otherSources);\n}\n", "import { Subscriber } from '../Subscriber';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { noop } from '../util/noop';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\n\n/**\n * Emits a notification from the source Observable only after a particular time span\n * determined by another Observable has passed without another source emission.\n *\n * <span class=\"informal\">It's like {@link debounceTime}, but the time span of\n * emission silence is determined by a second Observable.</span>\n *\n * ![](debounce.svg)\n *\n * `debounce` delays notifications emitted by the source Observable, but drops previous\n * pending delayed emissions if a new notification arrives on the source Observable.\n * This operator keeps track of the most recent notification from the source\n * Observable, and spawns a duration Observable by calling the\n * `durationSelector` function. The notification is emitted only when the duration\n * Observable emits a next notification, and if no other notification was emitted on\n * the source Observable since the duration Observable was spawned. If a new\n * notification appears before the duration Observable emits, the previous notification will\n * not be emitted and a new duration is scheduled from `durationSelector` is scheduled.\n * If the completing event happens during the scheduled duration the last cached notification\n * is emitted before the completion event is forwarded to the output observable.\n * If the error event happens during the scheduled duration or after it only the error event is\n * forwarded to the output observable. The cache notification is not emitted in this case.\n *\n * Like {@link debounceTime}, this is a rate-limiting operator, and also a\n * delay-like operator since output emissions do not necessarily occur at the\n * same time as they did on the source Observable.\n *\n * ## Example\n *\n * Emit the most recent click after a burst of clicks\n *\n * ```ts\n * import { fromEvent, scan, debounce, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(\n *   scan(i => ++i, 1),\n *   debounce(i => interval(200 * i))\n * );\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link auditTime}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link throttle}\n * @see {@link throttleTime}\n *\n * @param durationSelector A function\n * that receives a value from the source Observable, for computing the timeout\n * duration for each source value, returned as an Observable or a Promise.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified duration Observable returned by\n * `durationSelector`, and may drop some values if they occur too frequently.\n */\nexport function debounce<T>(durationSelector: (value: T) => ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    let lastValue: T | null = null;\n    // The subscriber/subscription for the current debounce, if there is one.\n    let durationSubscriber: Subscriber<any> | null = null;\n\n    const emit = () => {\n      // Unsubscribe any current debounce subscription we have,\n      // we only cared about the first notification from it, and we\n      // want to clean that subscription up as soon as possible.\n      durationSubscriber?.unsubscribe();\n      durationSubscriber = null;\n      if (hasValue) {\n        // We have a value! Free up memory first, then emit the value.\n        hasValue = false;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value: T) => {\n          // Cancel any pending debounce duration. We don't\n          // need to null it out here yet tho, because we're just going\n          // to create another one in a few lines.\n          durationSubscriber?.unsubscribe();\n          hasValue = true;\n          lastValue = value;\n          // Capture our duration subscriber, so we can unsubscribe it when we're notified\n          // and we're going to emit the value.\n          durationSubscriber = createOperatorSubscriber(subscriber, emit, noop);\n          // Subscribe to the duration.\n          innerFrom(durationSelector(value)).subscribe(durationSubscriber);\n        },\n        () => {\n          // Source completed.\n          // Emit any pending debounced values then complete\n          emit();\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer\n        undefined,\n        () => {\n          // Finalization.\n          lastValue = durationSubscriber = null;\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { Subscription } from '../Subscription';\nimport { MonoTypeOperatorFunction, SchedulerAction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits a notification from the source Observable only after a particular time span\n * has passed without another source emission.\n *\n * <span class=\"informal\">It's like {@link delay}, but passes only the most\n * recent notification from each burst of emissions.</span>\n *\n * ![](debounceTime.png)\n *\n * `debounceTime` delays notifications emitted by the source Observable, but drops\n * previous pending delayed emissions if a new notification arrives on the source\n * Observable. This operator keeps track of the most recent notification from the\n * source Observable, and emits that only when `dueTime` has passed\n * without any other notification appearing on the source Observable. If a new value\n * appears before `dueTime` silence occurs, the previous notification will be dropped\n * and will not be emitted and a new `dueTime` is scheduled.\n * If the completing event happens during `dueTime` the last cached notification\n * is emitted before the completion event is forwarded to the output observable.\n * If the error event happens during `dueTime` or after it only the error event is\n * forwarded to the output observable. The cache notification is not emitted in this case.\n *\n * This is a rate-limiting operator, because it is impossible for more than one\n * notification to be emitted in any time window of duration `dueTime`, but it is also\n * a delay-like operator since output emissions do not occur at the same time as\n * they did on the source Observable. Optionally takes a {@link SchedulerLike} for\n * managing timers.\n *\n * ## Example\n *\n * Emit the most recent click after a burst of clicks\n *\n * ```ts\n * import { fromEvent, debounceTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(debounceTime(1000));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link auditTime}\n * @see {@link debounce}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link throttle}\n * @see {@link throttleTime}\n *\n * @param {number} dueTime The timeout duration in milliseconds (or the time\n * unit determined internally by the optional `scheduler`) for the window of\n * time required to wait for emission silence before emitting the most recent\n * source value.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the timeout for each value.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified `dueTime`, and may drop some values\n * if they occur too frequently.\n */\nexport function debounceTime<T>(dueTime: number, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let activeTask: Subscription | null = null;\n    let lastValue: T | null = null;\n    let lastTime: number | null = null;\n\n    const emit = () => {\n      if (activeTask) {\n        // We have a value! Free up memory first, then emit the value.\n        activeTask.unsubscribe();\n        activeTask = null;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n    };\n    function emitWhenIdle(this: SchedulerAction<unknown>) {\n      // This is called `dueTime` after the first value\n      // but we might have received new values during this window!\n\n      const targetTime = lastTime! + dueTime;\n      const now = scheduler.now();\n      if (now < targetTime) {\n        // On that case, re-schedule to the new target\n        activeTask = this.schedule(undefined, targetTime - now);\n        subscriber.add(activeTask);\n        return;\n      }\n\n      emit();\n    }\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value: T) => {\n          lastValue = value;\n          lastTime = scheduler.now();\n\n          // Only set up a task if it's not already up\n          if (!activeTask) {\n            activeTask = scheduler.schedule(emitWhenIdle, dueTime);\n            subscriber.add(activeTask);\n          }\n        },\n        () => {\n          // Source completed.\n          // Emit any pending debounced values then complete\n          emit();\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer.\n        undefined,\n        () => {\n          // Finalization.\n          lastValue = activeTask = null;\n        }\n      )\n    );\n  });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits a given value if the source Observable completes without emitting any\n * `next` value, otherwise mirrors the source Observable.\n *\n * <span class=\"informal\">If the source Observable turns out to be empty, then\n * this operator will emit a default value.</span>\n *\n * ![](defaultIfEmpty.png)\n *\n * `defaultIfEmpty` emits the values emitted by the source Observable or a\n * specified default value if the source Observable is empty (completes without\n * having emitted any `next` value).\n *\n * ## Example\n *\n * If no clicks happen in 5 seconds, then emit 'no clicks'\n *\n * ```ts\n * import { fromEvent, takeUntil, interval, defaultIfEmpty } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const clicksBeforeFive = clicks.pipe(takeUntil(interval(5000)));\n * const result = clicksBeforeFive.pipe(defaultIfEmpty('no clicks'));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link empty}\n * @see {@link last}\n *\n * @param defaultValue The default value used if the source\n * Observable is empty.\n * @return A function that returns an Observable that emits either the\n * specified `defaultValue` if the source Observable emits no items, or the\n * values emitted by the source Observable.\n */\nexport function defaultIfEmpty<T, R>(defaultValue: R): OperatorFunction<T, T | R> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          subscriber.next(value);\n        },\n        () => {\n          if (!hasValue) {\n            subscriber.next(defaultValue!);\n          }\n          subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { EMPTY } from '../observable/empty';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits only the first `count` values emitted by the source Observable.\n *\n * <span class=\"informal\">Takes the first `count` values from the source, then\n * completes.</span>\n *\n * ![](take.png)\n *\n * `take` returns an Observable that emits only the first `count` values emitted\n * by the source Observable. If the source emits fewer than `count` values then\n * all of its values are emitted. After that, it completes, regardless if the\n * source completes.\n *\n * ## Example\n *\n * Take the first 5 seconds of an infinite 1-second interval Observable\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const intervalCount = interval(1000);\n * const takeFive = intervalCount.pipe(take(5));\n * takeFive.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 0\n * // 1\n * // 2\n * // 3\n * // 4\n * ```\n *\n * @see {@link takeLast}\n * @see {@link takeUntil}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param count The maximum number of `next` values to emit.\n * @return A function that returns an Observable that emits only the first\n * `count` values emitted by the source Observable, or all of the values from\n * the source if the source emits fewer than `count` values.\n */\nexport function take<T>(count: number): MonoTypeOperatorFunction<T> {\n  return count <= 0\n    ? // If we are taking no values, that's empty.\n      () => EMPTY\n    : operate((source, subscriber) => {\n        let seen = 0;\n        source.subscribe(\n          createOperatorSubscriber(subscriber, (value) => {\n            // Increment the number of values we have seen,\n            // then check it against the allowed count to see\n            // if we are still letting values through.\n            if (++seen <= count) {\n              subscriber.next(value);\n              // If we have met or passed our allowed count,\n              // we need to complete. We have to do <= here,\n              // because re-entrant code will increment `seen` twice.\n              if (count <= seen) {\n                subscriber.complete();\n              }\n            }\n          })\n        );\n      });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { noop } from '../util/noop';\n\n/**\n * Ignores all items emitted by the source Observable and only passes calls of `complete` or `error`.\n *\n * ![](ignoreElements.png)\n *\n * The `ignoreElements` operator suppresses all items emitted by the source Observable,\n * but allows its termination notification (either `error` or `complete`) to pass through unchanged.\n *\n * If you do not care about the items being emitted by an Observable, but you do want to be notified\n * when it completes or when it terminates with an error, you can apply the `ignoreElements` operator\n * to the Observable, which will ensure that it will never call its observers\u2019 `next` handlers.\n *\n * ## Example\n *\n * Ignore all `next` emissions from the source\n *\n * ```ts\n * import { of, ignoreElements } from 'rxjs';\n *\n * of('you', 'talking', 'to', 'me')\n *   .pipe(ignoreElements())\n *   .subscribe({\n *     next: word => console.log(word),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // result:\n * // 'the end'\n * ```\n *\n * @return A function that returns an empty Observable that only calls\n * `complete` or `error`, based on which one is called by the source\n * Observable.\n */\nexport function ignoreElements(): OperatorFunction<unknown, never> {\n  return operate((source, subscriber) => {\n    source.subscribe(createOperatorSubscriber(subscriber, noop));\n  });\n}\n", "import { OperatorFunction } from '../types';\nimport { map } from './map';\n\n/** @deprecated To be removed in v9. Use {@link map} instead: `map(() => value)`. */\nexport function mapTo<R>(value: R): OperatorFunction<unknown, R>;\n/**\n * @deprecated Do not specify explicit type parameters. Signatures with type parameters\n * that cannot be inferred will be removed in v8. `mapTo` itself will be removed in v9,\n * use {@link map} instead: `map(() => value)`.\n * */\nexport function mapTo<T, R>(value: R): OperatorFunction<T, R>;\n\n/**\n * Emits the given constant value on the output Observable every time the source\n * Observable emits a value.\n *\n * <span class=\"informal\">Like {@link map}, but it maps every source value to\n * the same output value every time.</span>\n *\n * ![](mapTo.png)\n *\n * Takes a constant `value` as argument, and emits that whenever the source\n * Observable emits a value. In other words, ignores the actual source value,\n * and simply uses the emission moment to know when to emit the given `value`.\n *\n * ## Example\n *\n * Map every click to the string `'Hi'`\n *\n * ```ts\n * import { fromEvent, mapTo } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const greetings = clicks.pipe(mapTo('Hi'));\n *\n * greetings.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link map}\n *\n * @param value The value to map each source value to.\n * @return A function that returns an Observable that emits the given `value`\n * every time the source Observable emits.\n * @deprecated To be removed in v9. Use {@link map} instead: `map(() => value)`.\n */\nexport function mapTo<R>(value: R): OperatorFunction<unknown, R> {\n  return map(() => value);\n}\n", "import { Observable } from '../Observable';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { concat } from '../observable/concat';\nimport { take } from './take';\nimport { ignoreElements } from './ignoreElements';\nimport { mapTo } from './mapTo';\nimport { mergeMap } from './mergeMap';\nimport { innerFrom } from '../observable/innerFrom';\n\n/** @deprecated The `subscriptionDelay` parameter will be removed in v8. */\nexport function delayWhen<T>(\n  delayDurationSelector: (value: T, index: number) => ObservableInput<any>,\n  subscriptionDelay: Observable<any>\n): MonoTypeOperatorFunction<T>;\nexport function delayWhen<T>(delayDurationSelector: (value: T, index: number) => ObservableInput<any>): MonoTypeOperatorFunction<T>;\n\n/**\n * Delays the emission of items from the source Observable by a given time span\n * determined by the emissions of another Observable.\n *\n * <span class=\"informal\">It's like {@link delay}, but the time span of the\n * delay duration is determined by a second Observable.</span>\n *\n * ![](delayWhen.png)\n *\n * `delayWhen` operator shifts each emitted value from the source Observable by\n * a time span determined by another Observable. When the source emits a value,\n * the `delayDurationSelector` function is called with the value emitted from\n * the source Observable as the first argument to the `delayDurationSelector`.\n * The `delayDurationSelector` function should return an {@link ObservableInput},\n * that is internally converted to an Observable that is called the \"duration\"\n * Observable.\n *\n * The source value is emitted on the output Observable only when the \"duration\"\n * Observable emits ({@link guide/glossary-and-semantics#next next}s) any value.\n * Upon that, the \"duration\" Observable gets unsubscribed.\n *\n * Before RxJS V7, the {@link guide/glossary-and-semantics#complete completion}\n * of the \"duration\" Observable would have been triggering the emission of the\n * source value to the output Observable, but with RxJS V7, this is not the case\n * anymore.\n *\n * Only next notifications (from the \"duration\" Observable) trigger values from\n * the source Observable to be passed to the output Observable. If the \"duration\"\n * Observable only emits the complete notification (without next), the value\n * emitted by the source Observable will never get to the output Observable - it\n * will be swallowed. If the \"duration\" Observable errors, the error will be\n * propagated to the output Observable.\n *\n * Optionally, `delayWhen` takes a second argument, `subscriptionDelay`, which\n * is an Observable. When `subscriptionDelay` emits its first value or\n * completes, the source Observable is subscribed to and starts behaving like\n * described in the previous paragraph. If `subscriptionDelay` is not provided,\n * `delayWhen` will subscribe to the source Observable as soon as the output\n * Observable is subscribed.\n *\n * ## Example\n *\n * Delay each click by a random amount of time, between 0 and 5 seconds\n *\n * ```ts\n * import { fromEvent, delayWhen, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const delayedClicks = clicks.pipe(\n *   delayWhen(() => interval(Math.random() * 5000))\n * );\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link delay}\n * @see {@link throttle}\n * @see {@link throttleTime}\n * @see {@link debounce}\n * @see {@link debounceTime}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link audit}\n * @see {@link auditTime}\n *\n * @param delayDurationSelector A function that returns an `ObservableInput` for\n * each `value` emitted by the source Observable, which is then used to delay the\n * emission of that `value` on the output Observable until the `ObservableInput`\n * returned from this function emits a next value. When called, beside `value`,\n * this function receives a zero-based `index` of the emission order.\n * @param subscriptionDelay An Observable that triggers the subscription to the\n * source Observable once it emits any value.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by an amount of time specified by the Observable\n * returned by `delayDurationSelector`.\n */\nexport function delayWhen<T>(\n  delayDurationSelector: (value: T, index: number) => ObservableInput<any>,\n  subscriptionDelay?: Observable<any>\n): MonoTypeOperatorFunction<T> {\n  if (subscriptionDelay) {\n    // DEPRECATED PATH\n    return (source: Observable<T>) =>\n      concat(subscriptionDelay.pipe(take(1), ignoreElements()), source.pipe(delayWhen(delayDurationSelector)));\n  }\n\n  return mergeMap((value, index) => innerFrom(delayDurationSelector(value, index)).pipe(take(1), mapTo(value)));\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { delayWhen } from './delayWhen';\nimport { timer } from '../observable/timer';\n\n/**\n * Delays the emission of items from the source Observable by a given timeout or\n * until a given Date.\n *\n * <span class=\"informal\">Time shifts each item by some specified amount of\n * milliseconds.</span>\n *\n * ![](delay.svg)\n *\n * If the delay argument is a Number, this operator time shifts the source\n * Observable by that amount of time expressed in milliseconds. The relative\n * time intervals between the values are preserved.\n *\n * If the delay argument is a Date, this operator time shifts the start of the\n * Observable execution until the given date occurs.\n *\n * ## Examples\n *\n * Delay each click by one second\n *\n * ```ts\n * import { fromEvent, delay } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const delayedClicks = clicks.pipe(delay(1000)); // each click emitted after 1 second\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * Delay all clicks until a future date happens\n *\n * ```ts\n * import { fromEvent, delay } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const date = new Date('March 15, 2050 12:00:00'); // in the future\n * const delayedClicks = clicks.pipe(delay(date)); // click emitted only after that date\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link delayWhen}\n * @see {@link throttle}\n * @see {@link throttleTime}\n * @see {@link debounce}\n * @see {@link debounceTime}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link audit}\n * @see {@link auditTime}\n *\n * @param {number|Date} due The delay duration in milliseconds (a `number`) or\n * a `Date` until which the emission of the source items is delayed.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the time-shift for each item.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified timeout or Date.\n */\nexport function delay<T>(due: number | Date, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  const duration = timer(due, scheduler);\n  return delayWhen(() => duration);\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { identity } from '../util/identity';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function distinctUntilChanged<T>(comparator?: (previous: T, current: T) => boolean): MonoTypeOperatorFunction<T>;\nexport function distinctUntilChanged<T, K>(\n  comparator: (previous: K, current: K) => boolean,\n  keySelector: (value: T) => K\n): MonoTypeOperatorFunction<T>;\n\n/**\n * Returns a result {@link Observable} that emits all values pushed by the source observable if they\n * are distinct in comparison to the last value the result observable emitted.\n *\n * When provided without parameters or with the first parameter (`{@link distinctUntilChanged#comparator comparator}`),\n * it behaves like this:\n *\n * 1. It will always emit the first value from the source.\n * 2. For all subsequent values pushed by the source, they will be compared to the previously emitted values\n *    using the provided `comparator` or an `===` equality check.\n * 3. If the value pushed by the source is determined to be unequal by this check, that value is emitted and\n *    becomes the new \"previously emitted value\" internally.\n *\n * When the second parameter (`{@link distinctUntilChanged#keySelector keySelector}`) is provided, the behavior\n * changes:\n *\n * 1. It will always emit the first value from the source.\n * 2. The `keySelector` will be run against all values, including the first value.\n * 3. For all values after the first, the selected key will be compared against the key selected from\n *    the previously emitted value using the `comparator`.\n * 4. If the keys are determined to be unequal by this check, the value (not the key), is emitted\n *    and the selected key from that value is saved for future comparisons against other keys.\n *\n * ## Examples\n *\n * A very basic example with no `{@link distinctUntilChanged#comparator comparator}`. Note that `1` is emitted more than once,\n * because it's distinct in comparison to the _previously emitted_ value,\n * not in comparison to _all other emitted values_.\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * of(1, 1, 1, 2, 2, 2, 1, 1, 3, 3)\n *   .pipe(distinctUntilChanged())\n *   .subscribe(console.log);\n * // Logs: 1, 2, 1, 3\n * ```\n *\n * With a `{@link distinctUntilChanged#comparator comparator}`, you can do custom comparisons. Let's say\n * you only want to emit a value when all of its components have\n * changed:\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * const totallyDifferentBuilds$ = of(\n *   { engineVersion: '1.1.0', transmissionVersion: '1.2.0' },\n *   { engineVersion: '1.1.0', transmissionVersion: '1.4.0' },\n *   { engineVersion: '1.3.0', transmissionVersion: '1.4.0' },\n *   { engineVersion: '1.3.0', transmissionVersion: '1.5.0' },\n *   { engineVersion: '2.0.0', transmissionVersion: '1.5.0' }\n * ).pipe(\n *   distinctUntilChanged((prev, curr) => {\n *     return (\n *       prev.engineVersion === curr.engineVersion ||\n *       prev.transmissionVersion === curr.transmissionVersion\n *     );\n *   })\n * );\n *\n * totallyDifferentBuilds$.subscribe(console.log);\n *\n * // Logs:\n * // { engineVersion: '1.1.0', transmissionVersion: '1.2.0' }\n * // { engineVersion: '1.3.0', transmissionVersion: '1.4.0' }\n * // { engineVersion: '2.0.0', transmissionVersion: '1.5.0' }\n * ```\n *\n * You can also provide a custom `{@link distinctUntilChanged#comparator comparator}` to check that emitted\n * changes are only in one direction. Let's say you only want to get\n * the next record temperature:\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * const temps$ = of(30, 31, 20, 34, 33, 29, 35, 20);\n *\n * const recordHighs$ = temps$.pipe(\n *   distinctUntilChanged((prevHigh, temp) => {\n *     // If the current temp is less than\n *     // or the same as the previous record,\n *     // the record hasn't changed.\n *     return temp <= prevHigh;\n *   })\n * );\n *\n * recordHighs$.subscribe(console.log);\n * // Logs: 30, 31, 34, 35\n * ```\n *\n * Selecting update events only when the `updatedBy` field shows\n * the account changed hands.\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * // A stream of updates to a given account\n * const accountUpdates$ = of(\n *   { updatedBy: 'blesh', data: [] },\n *   { updatedBy: 'blesh', data: [] },\n *   { updatedBy: 'ncjamieson', data: [] },\n *   { updatedBy: 'ncjamieson', data: [] },\n *   { updatedBy: 'blesh', data: [] }\n * );\n *\n * // We only want the events where it changed hands\n * const changedHands$ = accountUpdates$.pipe(\n *   distinctUntilChanged(undefined, update => update.updatedBy)\n * );\n *\n * changedHands$.subscribe(console.log);\n * // Logs:\n * // { updatedBy: 'blesh', data: Array[0] }\n * // { updatedBy: 'ncjamieson', data: Array[0] }\n * // { updatedBy: 'blesh', data: Array[0] }\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilKeyChanged}\n *\n * @param comparator A function used to compare the previous and current keys for\n * equality. Defaults to a `===` check.\n * @param keySelector Used to select a key value to be passed to the `comparator`.\n *\n * @return A function that returns an Observable that emits items from the\n * source Observable with distinct values.\n */\nexport function distinctUntilChanged<T, K>(\n  comparator?: (previous: K, current: K) => boolean,\n  keySelector: (value: T) => K = identity as (value: T) => K\n): MonoTypeOperatorFunction<T> {\n  // We've been allowing `null` do be passed as the `compare`, so we can't do\n  // a default value for the parameter, because that will only work\n  // for `undefined`.\n  comparator = comparator ?? defaultCompare;\n\n  return operate((source, subscriber) => {\n    // The previous key, used to compare against keys selected\n    // from new arrivals to determine \"distinctiveness\".\n    let previousKey: K;\n    // Whether or not this is the first value we've gotten.\n    let first = true;\n\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        // We always call the key selector.\n        const currentKey = keySelector(value);\n\n        // If it's the first value, we always emit it.\n        // Otherwise, we compare this key to the previous key, and\n        // if the comparer returns false, we emit.\n        if (first || !comparator!(previousKey, currentKey)) {\n          // Update our state *before* we emit the value\n          // as emission can be the source of re-entrant code\n          // in functional libraries like this. We only really\n          // need to do this if it's the first value, or if the\n          // key we're tracking in previous needs to change.\n          first = false;\n          previousKey = currentKey;\n\n          // Emit the value!\n          subscriber.next(value);\n        }\n      })\n    );\n  });\n}\n\nfunction defaultCompare(a: any, b: any) {\n  return a === b;\n}\n", "import { distinctUntilChanged } from './distinctUntilChanged';\nimport { MonoTypeOperatorFunction } from '../types';\n\n/* tslint:disable:max-line-length */\nexport function distinctUntilKeyChanged<T>(key: keyof T): MonoTypeOperatorFunction<T>;\nexport function distinctUntilKeyChanged<T, K extends keyof T>(key: K, compare: (x: T[K], y: T[K]) => boolean): MonoTypeOperatorFunction<T>;\n/* tslint:enable:max-line-length */\n\n/**\n * Returns an Observable that emits all items emitted by the source Observable that are distinct by comparison from the previous item,\n * using a property accessed by using the key provided to check if the two items are distinct.\n *\n * If a comparator function is provided, then it will be called for each item to test for whether or not that value should be emitted.\n *\n * If a comparator function is not provided, an equality check is used by default.\n *\n * ## Examples\n *\n * An example comparing the name of persons\n *\n * ```ts\n * import { of, distinctUntilKeyChanged } from 'rxjs';\n *\n * of(\n *   { age: 4, name: 'Foo' },\n *   { age: 7, name: 'Bar' },\n *   { age: 5, name: 'Foo' },\n *   { age: 6, name: 'Foo' }\n * ).pipe(\n *   distinctUntilKeyChanged('name')\n * )\n * .subscribe(x => console.log(x));\n *\n * // displays:\n * // { age: 4, name: 'Foo' }\n * // { age: 7, name: 'Bar' }\n * // { age: 5, name: 'Foo' }\n * ```\n *\n * An example comparing the first letters of the name\n *\n * ```ts\n * import { of, distinctUntilKeyChanged } from 'rxjs';\n *\n * of(\n *   { age: 4, name: 'Foo1' },\n *   { age: 7, name: 'Bar' },\n *   { age: 5, name: 'Foo2' },\n *   { age: 6, name: 'Foo3' }\n * ).pipe(\n *   distinctUntilKeyChanged('name', (x, y) => x.substring(0, 3) === y.substring(0, 3))\n * )\n * .subscribe(x => console.log(x));\n *\n * // displays:\n * // { age: 4, name: 'Foo1' }\n * // { age: 7, name: 'Bar' }\n * // { age: 5, name: 'Foo2' }\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilChanged}\n *\n * @param {string} key String key for object property lookup on each item.\n * @param {function} [compare] Optional comparison function called to test if an item is distinct from the previous item in the source.\n * @return A function that returns an Observable that emits items from the\n * source Observable with distinct values based on the key specified.\n */\nexport function distinctUntilKeyChanged<T, K extends keyof T>(key: K, compare?: (x: T[K], y: T[K]) => boolean): MonoTypeOperatorFunction<T> {\n  return distinctUntilChanged((x: T, y: T) => compare ? compare(x[key], y[key]) : x[key] === y[key]);\n}\n", "import { EmptyError } from '../util/EmptyError';\nimport { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * If the source observable completes without emitting a value, it will emit\n * an error. The error will be created at that time by the optional\n * `errorFactory` argument, otherwise, the error will be {@link EmptyError}.\n *\n * ![](throwIfEmpty.png)\n *\n * ## Example\n *\n * Throw an error if the document wasn't clicked within 1 second\n *\n * ```ts\n * import { fromEvent, takeUntil, timer, throwIfEmpty } from 'rxjs';\n *\n * const click$ = fromEvent(document, 'click');\n *\n * click$.pipe(\n *   takeUntil(timer(1000)),\n *   throwIfEmpty(() => new Error('The document was not clicked within 1 second'))\n * )\n * .subscribe({\n *   next() {\n *    console.log('The document was clicked');\n *   },\n *   error(err) {\n *     console.error(err.message);\n *   }\n * });\n * ```\n *\n * @param errorFactory A factory function called to produce the\n * error to be thrown when the source observable completes without emitting a\n * value.\n * @return A function that returns an Observable that throws an error if the\n * source Observable completed without emitting.\n */\nexport function throwIfEmpty<T>(errorFactory: () => any = defaultErrorFactory): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          subscriber.next(value);\n        },\n        () => (hasValue ? subscriber.complete() : subscriber.error(errorFactory()))\n      )\n    );\n  });\n}\n\nfunction defaultErrorFactory() {\n  return new EmptyError();\n}\n", "/** prettier */\nimport { Observable } from '../Observable';\nimport { concat } from '../observable/concat';\nimport { of } from '../observable/of';\nimport { MonoTypeOperatorFunction, SchedulerLike, OperatorFunction, ValueFromArray } from '../types';\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function endWith<T>(scheduler: SchedulerLike): MonoTypeOperatorFunction<T>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function endWith<T, A extends unknown[] = T[]>(\n  ...valuesAndScheduler: [...A, SchedulerLike]\n): OperatorFunction<T, T | ValueFromArray<A>>;\n\nexport function endWith<T, A extends unknown[] = T[]>(...values: A): OperatorFunction<T, T | ValueFromArray<A>>;\n\n/**\n * Returns an observable that will emit all values from the source, then synchronously emit\n * the provided value(s) immediately after the source completes.\n *\n * NOTE: Passing a last argument of a Scheduler is _deprecated_, and may result in incorrect\n * types in TypeScript.\n *\n * This is useful for knowing when an observable ends. Particularly when paired with an\n * operator like {@link takeUntil}\n *\n * ![](endWith.png)\n *\n * ## Example\n *\n * Emit values to know when an interval starts and stops. The interval will\n * stop when a user clicks anywhere on the document.\n *\n * ```ts\n * import { interval, map, fromEvent, startWith, takeUntil, endWith } from 'rxjs';\n *\n * const ticker$ = interval(5000).pipe(\n *   map(() => 'tick')\n * );\n *\n * const documentClicks$ = fromEvent(document, 'click');\n *\n * ticker$.pipe(\n *   startWith('interval started'),\n *   takeUntil(documentClicks$),\n *   endWith('interval ended by click')\n * )\n * .subscribe(x => console.log(x));\n *\n * // Result (assuming a user clicks after 15 seconds)\n * // 'interval started'\n * // 'tick'\n * // 'tick'\n * // 'tick'\n * // 'interval ended by click'\n * ```\n *\n * @see {@link startWith}\n * @see {@link concat}\n * @see {@link takeUntil}\n *\n * @param values Items you want the modified Observable to emit last.\n * @return A function that returns an Observable that emits all values from the\n * source, then synchronously emits the provided value(s) immediately after the\n * source completes.\n */\nexport function endWith<T>(...values: Array<T | SchedulerLike>): MonoTypeOperatorFunction<T> {\n  return (source: Observable<T>) => concat(source, of(...values)) as Observable<T>;\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\n\n/**\n * Returns an Observable that mirrors the source Observable, but will call a specified function when\n * the source terminates on complete or error.\n * The specified function will also be called when the subscriber explicitly unsubscribes.\n *\n * ## Examples\n *\n * Execute callback function when the observable completes\n *\n * ```ts\n * import { interval, take, finalize } from 'rxjs';\n *\n * // emit value in sequence every 1 second\n * const source = interval(1000);\n * const example = source.pipe(\n *   take(5), //take only the first 5 values\n *   finalize(() => console.log('Sequence complete')) // Execute when the observable completes\n * );\n * const subscribe = example.subscribe(val => console.log(val));\n *\n * // results:\n * // 0\n * // 1\n * // 2\n * // 3\n * // 4\n * // 'Sequence complete'\n * ```\n *\n * Execute callback function when the subscriber explicitly unsubscribes\n *\n * ```ts\n * import { interval, finalize, tap, noop, timer } from 'rxjs';\n *\n * const source = interval(100).pipe(\n *   finalize(() => console.log('[finalize] Called')),\n *   tap({\n *     next: () => console.log('[next] Called'),\n *     error: () => console.log('[error] Not called'),\n *     complete: () => console.log('[tap complete] Not called')\n *   })\n * );\n *\n * const sub = source.subscribe({\n *   next: x => console.log(x),\n *   error: noop,\n *   complete: () => console.log('[complete] Not called')\n * });\n *\n * timer(150).subscribe(() => sub.unsubscribe());\n *\n * // results:\n * // '[next] Called'\n * // 0\n * // '[finalize] Called'\n * ```\n *\n * @param {function} callback Function to be called when source terminates.\n * @return A function that returns an Observable that mirrors the source, but\n * will call the specified function on termination.\n */\nexport function finalize<T>(callback: () => void): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    // TODO: This try/finally was only added for `useDeprecatedSynchronousErrorHandling`.\n    // REMOVE THIS WHEN THAT HOT GARBAGE IS REMOVED IN V8.\n    try {\n      source.subscribe(subscriber);\n    } finally {\n      subscriber.add(callback);\n    }\n  });\n}\n", "import { Observable } from '../Observable';\nimport { EmptyError } from '../util/EmptyError';\nimport { OperatorFunction, TruthyTypesOf } from '../types';\nimport { filter } from './filter';\nimport { take } from './take';\nimport { defaultIfEmpty } from './defaultIfEmpty';\nimport { throwIfEmpty } from './throwIfEmpty';\nimport { identity } from '../util/identity';\n\nexport function first<T, D = T>(predicate?: null, defaultValue?: D): OperatorFunction<T, T | D>;\nexport function first<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function first<T, D>(predicate: BooleanConstructor, defaultValue: D): OperatorFunction<T, TruthyTypesOf<T> | D>;\nexport function first<T, S extends T>(\n  predicate: (value: T, index: number, source: Observable<T>) => value is S,\n  defaultValue?: S\n): OperatorFunction<T, S>;\nexport function first<T, S extends T, D>(\n  predicate: (value: T, index: number, source: Observable<T>) => value is S,\n  defaultValue: D\n): OperatorFunction<T, S | D>;\nexport function first<T, D = T>(\n  predicate: (value: T, index: number, source: Observable<T>) => boolean,\n  defaultValue?: D\n): OperatorFunction<T, T | D>;\n\n/**\n * Emits only the first value (or the first value that meets some condition)\n * emitted by the source Observable.\n *\n * <span class=\"informal\">Emits only the first value. Or emits only the first\n * value that passes some test.</span>\n *\n * ![](first.png)\n *\n * If called with no arguments, `first` emits the first value of the source\n * Observable, then completes. If called with a `predicate` function, `first`\n * emits the first value of the source that matches the specified condition. Throws an error if\n * `defaultValue` was not provided and a matching element is not found.\n *\n * ## Examples\n *\n * Emit only the first click that happens on the DOM\n *\n * ```ts\n * import { fromEvent, first } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(first());\n * result.subscribe(x => console.log(x));\n * ```\n *\n * Emits the first click that happens on a DIV\n *\n * ```ts\n * import { fromEvent, first } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(first(ev => (<HTMLElement>ev.target).tagName === 'DIV'));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link filter}\n * @see {@link find}\n * @see {@link take}\n *\n * @throws {EmptyError} Delivers an EmptyError to the Observer's `error`\n * callback if the Observable completes before any `next` notification was sent.\n * This is how `first()` is different from {@link take}(1) which completes instead.\n *\n * @param {function(value: T, index: number, source: Observable<T>): boolean} [predicate]\n * An optional function called with each item to test for condition matching.\n * @param {D} [defaultValue] The default value emitted in case no valid value\n * was found on the source.\n * @return A function that returns an Observable that emits the first item that\n * matches the condition.\n */\nexport function first<T, D>(\n  predicate?: ((value: T, index: number, source: Observable<T>) => boolean) | null,\n  defaultValue?: D\n): OperatorFunction<T, T | D> {\n  const hasDefaultValue = arguments.length >= 2;\n  return (source: Observable<T>) =>\n    source.pipe(\n      predicate ? filter((v, i) => predicate(v, i, source)) : identity,\n      take(1),\n      hasDefaultValue ? defaultIfEmpty(defaultValue!) : throwIfEmpty(() => new EmptyError())\n    );\n}\n", "import { EMPTY } from '../observable/empty';\nimport { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Waits for the source to complete, then emits the last N values from the source,\n * as specified by the `count` argument.\n *\n * ![](takeLast.png)\n *\n * `takeLast` results in an observable that will hold values up to `count` values in memory,\n * until the source completes. It then pushes all values in memory to the consumer, in the\n * order they were received from the source, then notifies the consumer that it is\n * complete.\n *\n * If for some reason the source completes before the `count` supplied to `takeLast` is reached,\n * all values received until that point are emitted, and then completion is notified.\n *\n * **Warning**: Using `takeLast` with an observable that never completes will result\n * in an observable that never emits a value.\n *\n * ## Example\n *\n * Take the last 3 values of an Observable with many values\n *\n * ```ts\n * import { range, takeLast } from 'rxjs';\n *\n * const many = range(1, 100);\n * const lastThree = many.pipe(takeLast(3));\n * lastThree.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeUntil}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param count The maximum number of values to emit from the end of\n * the sequence of values emitted by the source Observable.\n * @return A function that returns an Observable that emits at most the last\n * `count` values emitted by the source Observable.\n */\nexport function takeLast<T>(count: number): MonoTypeOperatorFunction<T> {\n  return count <= 0\n    ? () => EMPTY\n    : operate((source, subscriber) => {\n        // This buffer will hold the values we are going to emit\n        // when the source completes. Since we only want to take the\n        // last N values, we can't emit until we're sure we're not getting\n        // any more values.\n        let buffer: T[] = [];\n        source.subscribe(\n          createOperatorSubscriber(\n            subscriber,\n            (value) => {\n              // Add the most recent value onto the end of our buffer.\n              buffer.push(value);\n              // If our buffer is now larger than the number of values we\n              // want to take, we remove the oldest value from the buffer.\n              count < buffer.length && buffer.shift();\n            },\n            () => {\n              // The source completed, we now know what are last values\n              // are, emit them in the order they were received.\n              for (const value of buffer) {\n                subscriber.next(value);\n              }\n              subscriber.complete();\n            },\n            // Errors are passed through to the consumer\n            undefined,\n            () => {\n              // During finalization release the values in our buffer.\n              buffer = null!;\n            }\n          )\n        );\n      });\n}\n", "import { ObservableInput, ObservableInputTuple, OperatorFunction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { mergeAll } from './mergeAll';\nimport { popNumber, popScheduler } from '../util/args';\nimport { from } from '../observable/from';\n\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndConcurrency: [...ObservableInputTuple<A>, number]\n): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike]\n): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndConcurrencyAndScheduler: [...ObservableInputTuple<A>, number, SchedulerLike]\n): OperatorFunction<T, T | A[number]>;\n\nexport function merge<T>(...args: unknown[]): OperatorFunction<T, unknown> {\n  const scheduler = popScheduler(args);\n  const concurrent = popNumber(args, Infinity);\n  args = argsOrArgArray(args);\n\n  return operate((source, subscriber) => {\n    mergeAll(concurrent)(from([source, ...(args as ObservableInput<T>[])], scheduler)).subscribe(subscriber);\n  });\n}\n", "import { ObservableInputTuple, OperatorFunction } from '../types';\nimport { merge } from './merge';\n\n/**\n * Merge the values from all observables to a single observable result.\n *\n * Creates an observable, that when subscribed to, subscribes to the source\n * observable, and all other sources provided as arguments. All values from\n * every source are emitted from the resulting subscription.\n *\n * When all sources complete, the resulting observable will complete.\n *\n * When any source errors, the resulting observable will error.\n *\n * ## Example\n *\n * Joining all outputs from multiple user input event streams\n *\n * ```ts\n * import { fromEvent, map, mergeWith } from 'rxjs';\n *\n * const clicks$ = fromEvent(document, 'click').pipe(map(() => 'click'));\n * const mousemoves$ = fromEvent(document, 'mousemove').pipe(map(() => 'mousemove'));\n * const dblclicks$ = fromEvent(document, 'dblclick').pipe(map(() => 'dblclick'));\n *\n * mousemoves$\n *   .pipe(mergeWith(clicks$, dblclicks$))\n *   .subscribe(x => console.log(x));\n *\n * // result (assuming user interactions)\n * // 'mousemove'\n * // 'mousemove'\n * // 'mousemove'\n * // 'click'\n * // 'click'\n * // 'dblclick'\n * ```\n *\n * @see {@link merge}\n *\n * @param otherSources the sources to combine the current source with.\n * @return A function that returns an Observable that merges the values from\n * all given Observables.\n */\nexport function mergeWith<T, A extends readonly unknown[]>(\n  ...otherSources: [...ObservableInputTuple<A>]\n): OperatorFunction<T, T | A[number]> {\n  return merge(...otherSources);\n}\n", "import { Subscription } from '../Subscription';\nimport { EMPTY } from '../observable/empty';\nimport { operate } from '../util/lift';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { timer } from '../observable/timer';\n\nexport interface RepeatConfig {\n  /**\n   * The number of times to repeat the source. Defaults to `Infinity`.\n   */\n  count?: number;\n\n  /**\n   * If a `number`, will delay the repeat of the source by that number of milliseconds.\n   * If a function, it will provide the number of times the source has been subscribed to,\n   * and the return value should be a valid observable input that will notify when the source\n   * should be repeated. If the notifier observable is empty, the result will complete.\n   */\n  delay?: number | ((count: number) => ObservableInput<any>);\n}\n\n/**\n * Returns an Observable that will resubscribe to the source stream when the source stream completes.\n *\n * <span class=\"informal\">Repeats all values emitted on the source. It's like {@link retry}, but for non error cases.</span>\n *\n * ![](repeat.png)\n *\n * Repeat will output values from a source until the source completes, then it will resubscribe to the\n * source a specified number of times, with a specified delay. Repeat can be particularly useful in\n * combination with closing operators like {@link take}, {@link takeUntil}, {@link first}, or {@link takeWhile},\n * as it can be used to restart a source again from scratch.\n *\n * Repeat is very similar to {@link retry}, where {@link retry} will resubscribe to the source in the error case, but\n * `repeat` will resubscribe if the source completes.\n *\n * Note that `repeat` will _not_ catch errors. Use {@link retry} for that.\n *\n * - `repeat(0)` returns an empty observable\n * - `repeat()` will repeat forever\n * - `repeat({ delay: 200 })` will repeat forever, with a delay of 200ms between repetitions.\n * - `repeat({ count: 2, delay: 400 })` will repeat twice, with a delay of 400ms between repetitions.\n * - `repeat({ delay: (count) => timer(count * 1000) })` will repeat forever, but will have a delay that grows by one second for each repetition.\n *\n * ## Example\n *\n * Repeat a message stream\n *\n * ```ts\n * import { of, repeat } from 'rxjs';\n *\n * const source = of('Repeat message');\n * const result = source.pipe(repeat(3));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results\n * // 'Repeat message'\n * // 'Repeat message'\n * // 'Repeat message'\n * ```\n *\n * Repeat 3 values, 2 times\n *\n * ```ts\n * import { interval, take, repeat } from 'rxjs';\n *\n * const source = interval(1000);\n * const result = source.pipe(take(3), repeat(2));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results every second\n * // 0\n * // 1\n * // 2\n * // 0\n * // 1\n * // 2\n * ```\n *\n * Defining two complex repeats with delays on the same source.\n * Note that the second repeat cannot be called until the first\n * repeat as exhausted it's count.\n *\n * ```ts\n * import { defer, of, repeat } from 'rxjs';\n *\n * const source = defer(() => {\n *    return of(`Hello, it is ${new Date()}`)\n * });\n *\n * source.pipe(\n *    // Repeat 3 times with a delay of 1 second between repetitions\n *    repeat({\n *      count: 3,\n *      delay: 1000,\n *    }),\n *\n *    // *Then* repeat forever, but with an exponential step-back\n *    // maxing out at 1 minute.\n *    repeat({\n *      delay: (count) => timer(Math.min(60000, 2 ^ count * 1000))\n *    })\n * )\n * ```\n *\n * @see {@link repeatWhen}\n * @see {@link retry}\n *\n * @param count The number of times the source Observable items are repeated, a count of 0 will yield\n * an empty Observable.\n */\nexport function repeat<T>(countOrConfig?: number | RepeatConfig): MonoTypeOperatorFunction<T> {\n  let count = Infinity;\n  let delay: RepeatConfig['delay'];\n\n  if (countOrConfig != null) {\n    if (typeof countOrConfig === 'object') {\n      ({ count = Infinity, delay } = countOrConfig);\n    } else {\n      count = countOrConfig;\n    }\n  }\n\n  return count <= 0\n    ? () => EMPTY\n    : operate((source, subscriber) => {\n        let soFar = 0;\n        let sourceSub: Subscription | null;\n\n        const resubscribe = () => {\n          sourceSub?.unsubscribe();\n          sourceSub = null;\n          if (delay != null) {\n            const notifier = typeof delay === 'number' ? timer(delay) : innerFrom(delay(soFar));\n            const notifierSubscriber = createOperatorSubscriber(subscriber, () => {\n              notifierSubscriber.unsubscribe();\n              subscribeToSource();\n            });\n            notifier.subscribe(notifierSubscriber);\n          } else {\n            subscribeToSource();\n          }\n        };\n\n        const subscribeToSource = () => {\n          let syncUnsub = false;\n          sourceSub = source.subscribe(\n            createOperatorSubscriber(subscriber, undefined, () => {\n              if (++soFar < count) {\n                if (sourceSub) {\n                  resubscribe();\n                } else {\n                  syncUnsub = true;\n                }\n              } else {\n                subscriber.complete();\n              }\n            })\n          );\n\n          if (syncUnsub) {\n            resubscribe();\n          }\n        };\n\n        subscribeToSource();\n      });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { scanInternals } from './scanInternals';\n\nexport function scan<V, A = V>(accumulator: (acc: A | V, value: V, index: number) => A): OperatorFunction<V, V | A>;\nexport function scan<V, A>(accumulator: (acc: A, value: V, index: number) => A, seed: A): OperatorFunction<V, A>;\nexport function scan<V, A, S>(accumulator: (acc: A | S, value: V, index: number) => A, seed: S): OperatorFunction<V, A>;\n\n// TODO: link to a \"redux pattern\" section in the guide (location TBD)\n\n/**\n * Useful for encapsulating and managing state. Applies an accumulator (or \"reducer function\")\n * to each value from the source after an initial state is established -- either via\n * a `seed` value (second argument), or from the first value from the source.\n *\n * <span class=\"informal\">It's like {@link reduce}, but emits the current\n * accumulation state after each update</span>\n *\n * ![](scan.png)\n *\n * This operator maintains an internal state and emits it after processing each value as follows:\n *\n * 1. First value arrives\n *   - If a `seed` value was supplied (as the second argument to `scan`), let `state = seed` and `value = firstValue`.\n *   - If NO `seed` value was supplied (no second argument), let `state = firstValue` and go to 3.\n * 2. Let `state = accumulator(state, value)`.\n *   - If an error is thrown by `accumulator`, notify the consumer of an error. The process ends.\n * 3. Emit `state`.\n * 4. Next value arrives, let `value = nextValue`, go to 2.\n *\n * ## Examples\n *\n * An average of previous numbers. This example shows how\n * not providing a `seed` can prime the stream with the\n * first value from the source.\n *\n * ```ts\n * import { of, scan, map } from 'rxjs';\n *\n * const numbers$ = of(1, 2, 3);\n *\n * numbers$\n *   .pipe(\n *     // Get the sum of the numbers coming in.\n *     scan((total, n) => total + n),\n *     // Get the average by dividing the sum by the total number\n *     // received so far (which is 1 more than the zero-based index).\n *     map((sum, index) => sum / (index + 1))\n *   )\n *   .subscribe(console.log);\n * ```\n *\n * The Fibonacci sequence. This example shows how you can use\n * a seed to prime accumulation process. Also... you know... Fibonacci.\n * So important to like, computers and stuff that its whiteboarded\n * in job interviews. Now you can show them the Rx version! (Please don't, haha)\n *\n * ```ts\n * import { interval, scan, map, startWith } from 'rxjs';\n *\n * const firstTwoFibs = [0, 1];\n * // An endless stream of Fibonacci numbers.\n * const fibonacci$ = interval(1000).pipe(\n *   // Scan to get the fibonacci numbers (after 0, 1)\n *   scan(([a, b]) => [b, a + b], firstTwoFibs),\n *   // Get the second number in the tuple, it's the one you calculated\n *   map(([, n]) => n),\n *   // Start with our first two digits :)\n *   startWith(...firstTwoFibs)\n * );\n *\n * fibonacci$.subscribe(console.log);\n * ```\n *\n * @see {@link expand}\n * @see {@link mergeScan}\n * @see {@link reduce}\n * @see {@link switchScan}\n *\n * @param accumulator A \"reducer function\". This will be called for each value after an initial state is\n * acquired.\n * @param seed The initial state. If this is not provided, the first value from the source will\n * be used as the initial state, and emitted without going through the accumulator. All subsequent values\n * will be processed by the accumulator function. If this is provided, all values will go through\n * the accumulator function.\n * @return A function that returns an Observable of the accumulated values.\n */\nexport function scan<V, A, S>(accumulator: (acc: V | A | S, value: V, index: number) => A, seed?: S): OperatorFunction<V, V | A> {\n  // providing a seed of `undefined` *should* be valid and trigger\n  // hasSeed! so don't use `seed !== undefined` checks!\n  // For this reason, we have to check it here at the original call site\n  // otherwise inside Operator/Subscriber we won't know if `undefined`\n  // means they didn't provide anything or if they literally provided `undefined`\n  return operate(scanInternals(accumulator, seed as S, arguments.length >= 2, true));\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { Subject } from '../Subject';\nimport { SafeSubscriber } from '../Subscriber';\nimport { Subscription } from '../Subscription';\nimport { MonoTypeOperatorFunction, SubjectLike, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\n\nexport interface ShareConfig<T> {\n  /**\n   * The factory used to create the subject that will connect the source observable to\n   * multicast consumers.\n   */\n  connector?: () => SubjectLike<T>;\n  /**\n   * If `true`, the resulting observable will reset internal state on error from source and return to a \"cold\" state. This\n   * allows the resulting observable to be \"retried\" in the event of an error.\n   * If `false`, when an error comes from the source it will push the error into the connecting subject, and the subject\n   * will remain the connecting subject, meaning the resulting observable will not go \"cold\" again, and subsequent retries\n   * or resubscriptions will resubscribe to that same subject. In all cases, RxJS subjects will emit the same error again, however\n   * {@link ReplaySubject} will also push its buffered values before pushing the error.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnError?: boolean | ((error: any) => ObservableInput<any>);\n  /**\n   * If `true`, the resulting observable will reset internal state on completion from source and return to a \"cold\" state. This\n   * allows the resulting observable to be \"repeated\" after it is done.\n   * If `false`, when the source completes, it will push the completion through the connecting subject, and the subject\n   * will remain the connecting subject, meaning the resulting observable will not go \"cold\" again, and subsequent repeats\n   * or resubscriptions will resubscribe to that same subject.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnComplete?: boolean | (() => ObservableInput<any>);\n  /**\n   * If `true`, when the number of subscribers to the resulting observable reaches zero due to those subscribers unsubscribing, the\n   * internal state will be reset and the resulting observable will return to a \"cold\" state. This means that the next\n   * time the resulting observable is subscribed to, a new subject will be created and the source will be subscribed to\n   * again.\n   * If `false`, when the number of subscribers to the resulting observable reaches zero due to unsubscription, the subject\n   * will remain connected to the source, and new subscriptions to the result will be connected through that same subject.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnRefCountZero?: boolean | (() => ObservableInput<any>);\n}\n\nexport function share<T>(): MonoTypeOperatorFunction<T>;\n\nexport function share<T>(options: ShareConfig<T>): MonoTypeOperatorFunction<T>;\n\n/**\n * Returns a new Observable that multicasts (shares) the original Observable. As long as there is at least one\n * Subscriber this Observable will be subscribed and emitting data. When all subscribers have unsubscribed it will\n * unsubscribe from the source Observable. Because the Observable is multicasting it makes the stream `hot`.\n * This is an alias for `multicast(() => new Subject()), refCount()`.\n *\n * The subscription to the underlying source Observable can be reset (unsubscribe and resubscribe for new subscribers),\n * if the subscriber count to the shared observable drops to 0, or if the source Observable errors or completes. It is\n * possible to use notifier factories for the resets to allow for behaviors like conditional or delayed resets. Please\n * note that resetting on error or complete of the source Observable does not behave like a transparent retry or restart\n * of the source because the error or complete will be forwarded to all subscribers and their subscription will be\n * closed. Only new subscribers after a reset on error or complete happened will cause a fresh subscription to the\n * source. To achieve transparent retries or restarts pipe the source through appropriate operators before sharing.\n *\n * ![](share.png)\n *\n * ## Example\n *\n * Generate new multicast Observable from the `source` Observable value\n *\n * ```ts\n * import { interval, tap, map, take, share } from 'rxjs';\n *\n * const source = interval(1000).pipe(\n *   tap(x => console.log('Processing: ', x)),\n *   map(x => x * x),\n *   take(6),\n *   share()\n * );\n *\n * source.subscribe(x => console.log('subscription 1: ', x));\n * source.subscribe(x => console.log('subscription 2: ', x));\n *\n * // Logs:\n * // Processing: 0\n * // subscription 1: 0\n * // subscription 2: 0\n * // Processing: 1\n * // subscription 1: 1\n * // subscription 2: 1\n * // Processing: 2\n * // subscription 1: 4\n * // subscription 2: 4\n * // Processing: 3\n * // subscription 1: 9\n * // subscription 2: 9\n * // Processing: 4\n * // subscription 1: 16\n * // subscription 2: 16\n * // Processing: 5\n * // subscription 1: 25\n * // subscription 2: 25\n * ```\n *\n * ## Example with notifier factory: Delayed reset\n *\n * ```ts\n * import { interval, take, share, timer } from 'rxjs';\n *\n * const source = interval(1000).pipe(\n *   take(3),\n *   share({\n *     resetOnRefCountZero: () => timer(1000)\n *   })\n * );\n *\n * const subscriptionOne = source.subscribe(x => console.log('subscription 1: ', x));\n * setTimeout(() => subscriptionOne.unsubscribe(), 1300);\n *\n * setTimeout(() => source.subscribe(x => console.log('subscription 2: ', x)), 1700);\n *\n * setTimeout(() => source.subscribe(x => console.log('subscription 3: ', x)), 5000);\n *\n * // Logs:\n * // subscription 1:  0\n * // (subscription 1 unsubscribes here)\n * // (subscription 2 subscribes here ~400ms later, source was not reset)\n * // subscription 2:  1\n * // subscription 2:  2\n * // (subscription 2 unsubscribes here)\n * // (subscription 3 subscribes here ~2000ms later, source did reset before)\n * // subscription 3:  0\n * // subscription 3:  1\n * // subscription 3:  2\n * ```\n *\n * @see {@link shareReplay}\n *\n * @return A function that returns an Observable that mirrors the source.\n */\nexport function share<T>(options: ShareConfig<T> = {}): MonoTypeOperatorFunction<T> {\n  const { connector = () => new Subject<T>(), resetOnError = true, resetOnComplete = true, resetOnRefCountZero = true } = options;\n  // It's necessary to use a wrapper here, as the _operator_ must be\n  // referentially transparent. Otherwise, it cannot be used in calls to the\n  // static `pipe` function - to create a partial pipeline.\n  //\n  // The _operator function_ - the function returned by the _operator_ - will\n  // not be referentially transparent - as it shares its source - but the\n  // _operator function_ is called when the complete pipeline is composed via a\n  // call to a source observable's `pipe` method - not when the static `pipe`\n  // function is called.\n  return (wrapperSource) => {\n    let connection: SafeSubscriber<T> | undefined;\n    let resetConnection: Subscription | undefined;\n    let subject: SubjectLike<T> | undefined;\n    let refCount = 0;\n    let hasCompleted = false;\n    let hasErrored = false;\n\n    const cancelReset = () => {\n      resetConnection?.unsubscribe();\n      resetConnection = undefined;\n    };\n    // Used to reset the internal state to a \"cold\"\n    // state, as though it had never been subscribed to.\n    const reset = () => {\n      cancelReset();\n      connection = subject = undefined;\n      hasCompleted = hasErrored = false;\n    };\n    const resetAndUnsubscribe = () => {\n      // We need to capture the connection before\n      // we reset (if we need to reset).\n      const conn = connection;\n      reset();\n      conn?.unsubscribe();\n    };\n\n    return operate<T, T>((source, subscriber) => {\n      refCount++;\n      if (!hasErrored && !hasCompleted) {\n        cancelReset();\n      }\n\n      // Create the subject if we don't have one yet. Grab a local reference to\n      // it as well, which avoids non-null assertions when using it and, if we\n      // connect to it now, then error/complete need a reference after it was\n      // reset.\n      const dest = (subject = subject ?? connector());\n\n      // Add the finalization directly to the subscriber - instead of returning it -\n      // so that the handling of the subscriber's unsubscription will be wired\n      // up _before_ the subscription to the source occurs. This is done so that\n      // the assignment to the source connection's `closed` property will be seen\n      // by synchronous firehose sources.\n      subscriber.add(() => {\n        refCount--;\n\n        // If we're resetting on refCount === 0, and it's 0, we only want to do\n        // that on \"unsubscribe\", really. Resetting on error or completion is a different\n        // configuration.\n        if (refCount === 0 && !hasErrored && !hasCompleted) {\n          resetConnection = handleReset(resetAndUnsubscribe, resetOnRefCountZero);\n        }\n      });\n\n      // The following line adds the subscription to the subscriber passed.\n      // Basically, `subscriber === dest.subscribe(subscriber)` is `true`.\n      dest.subscribe(subscriber);\n\n      if (\n        !connection &&\n        // Check this shareReplay is still activate - it can be reset to 0\n        // and be \"unsubscribed\" _before_ it actually subscribes.\n        // If we were to subscribe then, it'd leak and get stuck.\n        refCount > 0\n      ) {\n        // We need to create a subscriber here - rather than pass an observer and\n        // assign the returned subscription to connection - because it's possible\n        // for reentrant subscriptions to the shared observable to occur and in\n        // those situations we want connection to be already-assigned so that we\n        // don't create another connection to the source.\n        connection = new SafeSubscriber({\n          next: (value) => dest.next(value),\n          error: (err) => {\n            hasErrored = true;\n            cancelReset();\n            resetConnection = handleReset(reset, resetOnError, err);\n            dest.error(err);\n          },\n          complete: () => {\n            hasCompleted = true;\n            cancelReset();\n            resetConnection = handleReset(reset, resetOnComplete);\n            dest.complete();\n          },\n        });\n        innerFrom(source).subscribe(connection);\n      }\n    })(wrapperSource);\n  };\n}\n\nfunction handleReset<T extends unknown[] = never[]>(\n  reset: () => void,\n  on: boolean | ((...args: T) => ObservableInput<any>),\n  ...args: T\n): Subscription | undefined {\n  if (on === true) {\n    reset();\n    return;\n  }\n\n  if (on === false) {\n    return;\n  }\n\n  const onSubscriber = new SafeSubscriber({\n    next: () => {\n      onSubscriber.unsubscribe();\n      reset();\n    },\n  });\n\n  return innerFrom(on(...args)).subscribe(onSubscriber);\n}\n", "import { ReplaySubject } from '../ReplaySubject';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { share } from './share';\n\nexport interface ShareReplayConfig {\n  bufferSize?: number;\n  windowTime?: number;\n  refCount: boolean;\n  scheduler?: SchedulerLike;\n}\n\nexport function shareReplay<T>(config: ShareReplayConfig): MonoTypeOperatorFunction<T>;\nexport function shareReplay<T>(bufferSize?: number, windowTime?: number, scheduler?: SchedulerLike): MonoTypeOperatorFunction<T>;\n\n/**\n * Share source and replay specified number of emissions on subscription.\n *\n * This operator is a specialization of `replay` that connects to a source observable\n * and multicasts through a `ReplaySubject` constructed with the specified arguments.\n * A successfully completed source will stay cached in the `shareReplay`ed observable forever,\n * but an errored source can be retried.\n *\n * ## Why use `shareReplay`?\n *\n * You generally want to use `shareReplay` when you have side-effects or taxing computations\n * that you do not wish to be executed amongst multiple subscribers.\n * It may also be valuable in situations where you know you will have late subscribers to\n * a stream that need access to previously emitted values.\n * This ability to replay values on subscription is what differentiates {@link share} and `shareReplay`.\n *\n * ## Reference counting\n *\n * By default `shareReplay` will use `refCount` of false, meaning that it will _not_ unsubscribe the\n * source when the reference counter drops to zero, i.e. the inner `ReplaySubject` will _not_ be unsubscribed\n * (and potentially run for ever).\n * This is the default as it is expected that `shareReplay` is often used to keep around expensive to setup\n * observables which we want to keep running instead of having to do the expensive setup again.\n *\n * As of RXJS version 6.4.0 a new overload signature was added to allow for manual control over what\n * happens when the operators internal reference counter drops to zero.\n * If `refCount` is true, the source will be unsubscribed from once the reference count drops to zero, i.e.\n * the inner `ReplaySubject` will be unsubscribed. All new subscribers will receive value emissions from a\n * new `ReplaySubject` which in turn will cause a new subscription to the source observable.\n *\n * ## Examples\n *\n * Example with a third subscriber coming late to the party\n *\n * ```ts\n * import { interval, take, shareReplay } from 'rxjs';\n *\n * const shared$ = interval(2000).pipe(\n *   take(6),\n *   shareReplay(3)\n * );\n *\n * shared$.subscribe(x => console.log('sub A: ', x));\n * shared$.subscribe(y => console.log('sub B: ', y));\n *\n * setTimeout(() => {\n *   shared$.subscribe(y => console.log('sub C: ', y));\n * }, 11000);\n *\n * // Logs:\n * // (after ~2000 ms)\n * // sub A: 0\n * // sub B: 0\n * // (after ~4000 ms)\n * // sub A: 1\n * // sub B: 1\n * // (after ~6000 ms)\n * // sub A: 2\n * // sub B: 2\n * // (after ~8000 ms)\n * // sub A: 3\n * // sub B: 3\n * // (after ~10000 ms)\n * // sub A: 4\n * // sub B: 4\n * // (after ~11000 ms, sub C gets the last 3 values)\n * // sub C: 2\n * // sub C: 3\n * // sub C: 4\n * // (after ~12000 ms)\n * // sub A: 5\n * // sub B: 5\n * // sub C: 5\n * ```\n *\n * Example for `refCount` usage\n *\n * ```ts\n * import { Observable, tap, interval, shareReplay, take } from 'rxjs';\n *\n * const log = <T>(name: string, source: Observable<T>) => source.pipe(\n *   tap({\n *     subscribe: () => console.log(`${ name }: subscribed`),\n *     next: value => console.log(`${ name }: ${ value }`),\n *     complete: () => console.log(`${ name }: completed`),\n *     finalize: () => console.log(`${ name }: unsubscribed`)\n *   })\n * );\n *\n * const obs$ = log('source', interval(1000));\n *\n * const shared$ = log('shared', obs$.pipe(\n *   shareReplay({ bufferSize: 1, refCount: true }),\n *   take(2)\n * ));\n *\n * shared$.subscribe(x => console.log('sub A: ', x));\n * shared$.subscribe(y => console.log('sub B: ', y));\n *\n * // PRINTS:\n * // shared: subscribed <-- reference count = 1\n * // source: subscribed\n * // shared: subscribed <-- reference count = 2\n * // source: 0\n * // shared: 0\n * // sub A: 0\n * // shared: 0\n * // sub B: 0\n * // source: 1\n * // shared: 1\n * // sub A: 1\n * // shared: completed <-- take(2) completes the subscription for sub A\n * // shared: unsubscribed <-- reference count = 1\n * // shared: 1\n * // sub B: 1\n * // shared: completed <-- take(2) completes the subscription for sub B\n * // shared: unsubscribed <-- reference count = 0\n * // source: unsubscribed <-- replaySubject unsubscribes from source observable because the reference count dropped to 0 and refCount is true\n *\n * // In case of refCount being false, the unsubscribe is never called on the source and the source would keep on emitting, even if no subscribers\n * // are listening.\n * // source: 2\n * // source: 3\n * // source: 4\n * // ...\n * ```\n *\n * @see {@link publish}\n * @see {@link share}\n * @see {@link publishReplay}\n *\n * @param configOrBufferSize Maximum element count of the replay buffer or {@link ShareReplayConfig configuration}\n * object.\n * @param windowTime Maximum time length of the replay buffer in milliseconds.\n * @param scheduler Scheduler where connected observers within the selector function\n * will be invoked on.\n * @return A function that returns an Observable sequence that contains the\n * elements of a sequence produced by multicasting the source sequence within a\n * selector function.\n */\nexport function shareReplay<T>(\n  configOrBufferSize?: ShareReplayConfig | number,\n  windowTime?: number,\n  scheduler?: SchedulerLike\n): MonoTypeOperatorFunction<T> {\n  let bufferSize: number;\n  let refCount = false;\n  if (configOrBufferSize && typeof configOrBufferSize === 'object') {\n    ({ bufferSize = Infinity, windowTime = Infinity, refCount = false, scheduler } = configOrBufferSize);\n  } else {\n    bufferSize = (configOrBufferSize ?? Infinity) as number;\n  }\n  return share<T>({\n    connector: () => new ReplaySubject(bufferSize, windowTime, scheduler),\n    resetOnError: true,\n    resetOnComplete: false,\n    resetOnRefCountZero: refCount,\n  });\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { filter } from './filter';\n\n/**\n * Returns an Observable that skips the first `count` items emitted by the source Observable.\n *\n * ![](skip.png)\n *\n * Skips the values until the sent notifications are equal or less than provided skip count. It raises\n * an error if skip count is equal or more than the actual number of emits and source raises an error.\n *\n * ## Example\n *\n * Skip the values before the emission\n *\n * ```ts\n * import { interval, skip } from 'rxjs';\n *\n * // emit every half second\n * const source = interval(500);\n * // skip the first 10 emitted values\n * const result = source.pipe(skip(10));\n *\n * result.subscribe(value => console.log(value));\n * // output: 10...11...12...13...\n * ```\n *\n * @see {@link last}\n * @see {@link skipWhile}\n * @see {@link skipUntil}\n * @see {@link skipLast}\n *\n * @param {Number} count - The number of times, items emitted by source Observable should be skipped.\n * @return A function that returns an Observable that skips the first `count`\n * values emitted by the source Observable.\n */\nexport function skip<T>(count: number): MonoTypeOperatorFunction<T> {\n  return filter((_, index) => count <= index);\n}\n", "import { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { noop } from '../util/noop';\n\n/**\n * Returns an Observable that skips items emitted by the source Observable until a second Observable emits an item.\n *\n * The `skipUntil` operator causes the observable stream to skip the emission of values until the passed in observable\n * emits the first value. This can be particularly useful in combination with user interactions, responses of HTTP\n * requests or waiting for specific times to pass by.\n *\n * ![](skipUntil.png)\n *\n * Internally, the `skipUntil` operator subscribes to the passed in `notifier` `ObservableInput` (which gets converted\n * to an Observable) in order to recognize the emission of its first value. When `notifier` emits next, the operator\n * unsubscribes from it and starts emitting the values of the *source* observable until it completes or errors. It\n * will never let the *source* observable emit any values if the `notifier` completes or throws an error without\n * emitting a value before.\n *\n * ## Example\n *\n * In the following example, all emitted values of the interval observable are skipped until the user clicks anywhere\n * within the page\n *\n * ```ts\n * import { interval, fromEvent, skipUntil } from 'rxjs';\n *\n * const intervalObservable = interval(1000);\n * const click = fromEvent(document, 'click');\n *\n * const emitAfterClick = intervalObservable.pipe(\n *   skipUntil(click)\n * );\n * // clicked at 4.6s. output: 5...6...7...8........ or\n * // clicked at 7.3s. output: 8...9...10..11.......\n * emitAfterClick.subscribe(value => console.log(value));\n * ```\n *\n * @see {@link last}\n * @see {@link skip}\n * @see {@link skipWhile}\n * @see {@link skipLast}\n *\n * @param notifier An `ObservableInput` that has to emit an item before the source Observable elements begin to\n * be mirrored by the resulting Observable.\n * @return A function that returns an Observable that skips items from the\n * source Observable until the `notifier` Observable emits an item, then emits the\n * remaining items.\n */\nexport function skipUntil<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let taking = false;\n\n    const skipSubscriber = createOperatorSubscriber(\n      subscriber,\n      () => {\n        skipSubscriber?.unsubscribe();\n        taking = true;\n      },\n      noop\n    );\n\n    innerFrom(notifier).subscribe(skipSubscriber);\n\n    source.subscribe(createOperatorSubscriber(subscriber, (value) => taking && subscriber.next(value)));\n  });\n}\n", "import { concat } from '../observable/concat';\nimport { OperatorFunction, SchedulerLike, ValueFromArray } from '../types';\nimport { popScheduler } from '../util/args';\nimport { operate } from '../util/lift';\n\n// Devs are more likely to pass null or undefined than they are a scheduler\n// without accompanying values. To make things easier for (naughty) devs who\n// use the `strictNullChecks: false` TypeScript compiler option, these\n// overloads with explicit null and undefined values are included.\n\nexport function startWith<T>(value: null): OperatorFunction<T, T | null>;\nexport function startWith<T>(value: undefined): OperatorFunction<T, T | undefined>;\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function startWith<T, A extends readonly unknown[] = T[]>(\n  ...valuesAndScheduler: [...A, SchedulerLike]\n): OperatorFunction<T, T | ValueFromArray<A>>;\nexport function startWith<T, A extends readonly unknown[] = T[]>(...values: A): OperatorFunction<T, T | ValueFromArray<A>>;\n\n/**\n * Returns an observable that, at the moment of subscription, will synchronously emit all\n * values provided to this operator, then subscribe to the source and mirror all of its emissions\n * to subscribers.\n *\n * This is a useful way to know when subscription has occurred on an existing observable.\n *\n * <span class=\"informal\">First emits its arguments in order, and then any\n * emissions from the source.</span>\n *\n * ![](startWith.png)\n *\n * ## Examples\n *\n * Emit a value when a timer starts.\n *\n * ```ts\n * import { timer, map, startWith } from 'rxjs';\n *\n * timer(1000)\n *   .pipe(\n *     map(() => 'timer emit'),\n *     startWith('timer start')\n *   )\n *   .subscribe(x => console.log(x));\n *\n * // results:\n * // 'timer start'\n * // 'timer emit'\n * ```\n *\n * @param values Items you want the modified Observable to emit first.\n * @return A function that returns an Observable that synchronously emits\n * provided values before subscribing to the source Observable.\n *\n * @see {@link endWith}\n * @see {@link finalize}\n * @see {@link concat}\n */\nexport function startWith<T, D>(...values: D[]): OperatorFunction<T, T | D> {\n  const scheduler = popScheduler(values);\n  return operate((source, subscriber) => {\n    // Here we can't pass `undefined` as a scheduler, because if we did, the\n    // code inside of `concat` would be confused by the `undefined`, and treat it\n    // like an invalid observable. So we have to split it two different ways.\n    (scheduler ? concat(values, source, scheduler) : concat(values, source)).subscribe(subscriber);\n  });\n}\n", "import { Subscriber } from '../Subscriber';\nimport { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { innerFrom } from '../observable/innerFrom';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/* tslint:disable:max-line-length */\nexport function switchMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function switchMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: undefined\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function switchMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R\n): OperatorFunction<T, R>;\n/* tslint:enable:max-line-length */\n\n/**\n * Projects each source value to an Observable which is merged in the output\n * Observable, emitting values only from the most recently projected Observable.\n *\n * <span class=\"informal\">Maps each value to an Observable, then flattens all of\n * these inner Observables using {@link switchAll}.</span>\n *\n * ![](switchMap.png)\n *\n * Returns an Observable that emits items based on applying a function that you\n * supply to each item emitted by the source Observable, where that function\n * returns an (so-called \"inner\") Observable. Each time it observes one of these\n * inner Observables, the output Observable begins emitting the items emitted by\n * that inner Observable. When a new inner Observable is emitted, `switchMap`\n * stops emitting items from the earlier-emitted inner Observable and begins\n * emitting items from the new one. It continues to behave like this for\n * subsequent inner Observables.\n *\n * ## Example\n *\n * Generate new Observable according to source Observable values\n *\n * ```ts\n * import { of, switchMap } from 'rxjs';\n *\n * const switched = of(1, 2, 3).pipe(switchMap(x => of(x, x ** 2, x ** 3)));\n * switched.subscribe(x => console.log(x));\n * // outputs\n * // 1\n * // 1\n * // 1\n * // 2\n * // 4\n * // 8\n * // 3\n * // 9\n * // 27\n * ```\n *\n * Restart an interval Observable on every click event\n *\n * ```ts\n * import { fromEvent, switchMap, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(switchMap(() => interval(1000)));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link concatMap}\n * @see {@link exhaustMap}\n * @see {@link mergeMap}\n * @see {@link switchAll}\n * @see {@link switchMapTo}\n *\n * @param {function(value: T, index: number): ObservableInput} project A function\n * that, when applied to an item emitted by the source Observable, returns an\n * Observable.\n * @return A function that returns an Observable that emits the result of\n * applying the projection function (and the optional deprecated\n * `resultSelector`) to each item emitted by the source Observable and taking\n * only the values from the most recently projected inner Observable.\n */\nexport function switchMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector?: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R\n): OperatorFunction<T, ObservedValueOf<O> | R> {\n  return operate((source, subscriber) => {\n    let innerSubscriber: Subscriber<ObservedValueOf<O>> | null = null;\n    let index = 0;\n    // Whether or not the source subscription has completed\n    let isComplete = false;\n\n    // We only complete the result if the source is complete AND we don't have an active inner subscription.\n    // This is called both when the source completes and when the inners complete.\n    const checkComplete = () => isComplete && !innerSubscriber && subscriber.complete();\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          // Cancel the previous inner subscription if there was one\n          innerSubscriber?.unsubscribe();\n          let innerIndex = 0;\n          const outerIndex = index++;\n          // Start the next inner subscription\n          innerFrom(project(value, outerIndex)).subscribe(\n            (innerSubscriber = createOperatorSubscriber(\n              subscriber,\n              // When we get a new inner value, next it through. Note that this is\n              // handling the deprecate result selector here. This is because with this architecture\n              // it ends up being smaller than using the map operator.\n              (innerValue) => subscriber.next(resultSelector ? resultSelector(value, innerValue, outerIndex, innerIndex++) : innerValue),\n              () => {\n                // The inner has completed. Null out the inner subscriber to\n                // free up memory and to signal that we have no inner subscription\n                // currently.\n                innerSubscriber = null!;\n                checkComplete();\n              }\n            ))\n          );\n        },\n        () => {\n          isComplete = true;\n          checkComplete();\n        }\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { noop } from '../util/noop';\n\n/**\n * Emits the values emitted by the source Observable until a `notifier`\n * Observable emits a value.\n *\n * <span class=\"informal\">Lets values pass until a second Observable,\n * `notifier`, emits a value. Then, it completes.</span>\n *\n * ![](takeUntil.png)\n *\n * `takeUntil` subscribes and begins mirroring the source Observable. It also\n * monitors a second Observable, `notifier` that you provide. If the `notifier`\n * emits a value, the output Observable stops mirroring the source Observable\n * and completes. If the `notifier` doesn't emit any value and completes\n * then `takeUntil` will pass all values.\n *\n * ## Example\n *\n * Tick every second until the first click happens\n *\n * ```ts\n * import { interval, fromEvent, takeUntil } from 'rxjs';\n *\n * const source = interval(1000);\n * const clicks = fromEvent(document, 'click');\n * const result = source.pipe(takeUntil(clicks));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeLast}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param {Observable} notifier The Observable whose first emitted value will\n * cause the output Observable of `takeUntil` to stop emitting values from the\n * source Observable.\n * @return A function that returns an Observable that emits the values from the\n * source Observable until `notifier` emits its first value.\n */\nexport function takeUntil<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    innerFrom(notifier).subscribe(createOperatorSubscriber(subscriber, () => subscriber.complete(), noop));\n    !subscriber.closed && source.subscribe(subscriber);\n  });\n}\n", "import { OperatorFunction, MonoTypeOperatorFunction, TruthyTypesOf } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function takeWhile<T>(predicate: BooleanConstructor, inclusive: true): MonoTypeOperatorFunction<T>;\nexport function takeWhile<T>(predicate: BooleanConstructor, inclusive: false): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function takeWhile<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function takeWhile<T, S extends T>(predicate: (value: T, index: number) => value is S): OperatorFunction<T, S>;\nexport function takeWhile<T, S extends T>(predicate: (value: T, index: number) => value is S, inclusive: false): OperatorFunction<T, S>;\nexport function takeWhile<T>(predicate: (value: T, index: number) => boolean, inclusive?: boolean): MonoTypeOperatorFunction<T>;\n\n/**\n * Emits values emitted by the source Observable so long as each value satisfies\n * the given `predicate`, and then completes as soon as this `predicate` is not\n * satisfied.\n *\n * <span class=\"informal\">Takes values from the source only while they pass the\n * condition given. When the first value does not satisfy, it completes.</span>\n *\n * ![](takeWhile.png)\n *\n * `takeWhile` subscribes and begins mirroring the source Observable. Each value\n * emitted on the source is given to the `predicate` function which returns a\n * boolean, representing a condition to be satisfied by the source values. The\n * output Observable emits the source values until such time as the `predicate`\n * returns false, at which point `takeWhile` stops mirroring the source\n * Observable and completes the output Observable.\n *\n * ## Example\n *\n * Emit click events only while the clientX property is greater than 200\n *\n * ```ts\n * import { fromEvent, takeWhile } from 'rxjs';\n *\n * const clicks = fromEvent<PointerEvent>(document, 'click');\n * const result = clicks.pipe(takeWhile(ev => ev.clientX > 200));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeLast}\n * @see {@link takeUntil}\n * @see {@link skip}\n *\n * @param {function(value: T, index: number): boolean} predicate A function that\n * evaluates a value emitted by the source Observable and returns a boolean.\n * Also takes the (zero-based) index as the second argument.\n * @param {boolean} inclusive When set to `true` the value that caused\n * `predicate` to return `false` will also be emitted.\n * @return A function that returns an Observable that emits values from the\n * source Observable so long as each value satisfies the condition defined by\n * the `predicate`, then completes.\n */\nexport function takeWhile<T>(predicate: (value: T, index: number) => boolean, inclusive = false): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let index = 0;\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        const result = predicate(value, index++);\n        (result || inclusive) && subscriber.next(value);\n        !result && subscriber.complete();\n      })\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, Observer } from '../types';\nimport { isFunction } from '../util/isFunction';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { identity } from '../util/identity';\n\n/**\n * An extension to the {@link Observer} interface used only by the {@link tap} operator.\n *\n * It provides a useful set of callbacks a user can register to do side-effects in\n * cases other than what the usual {@link Observer} callbacks are\n * ({@link guide/glossary-and-semantics#next next},\n * {@link guide/glossary-and-semantics#error error} and/or\n * {@link guide/glossary-and-semantics#complete complete}).\n *\n * ## Example\n *\n * ```ts\n * import { fromEvent, switchMap, tap, interval, take } from 'rxjs';\n *\n * const source$ = fromEvent(document, 'click');\n * const result$ = source$.pipe(\n *   switchMap((_, i) => i % 2 === 0\n *     ? fromEvent(document, 'mousemove').pipe(\n *         tap({\n *           subscribe: () => console.log('Subscribed to the mouse move events after click #' + i),\n *           unsubscribe: () => console.log('Mouse move events #' + i + ' unsubscribed'),\n *           finalize: () => console.log('Mouse move events #' + i + ' finalized')\n *         })\n *       )\n *     : interval(1_000).pipe(\n *         take(5),\n *         tap({\n *           subscribe: () => console.log('Subscribed to the 1-second interval events after click #' + i),\n *           unsubscribe: () => console.log('1-second interval events #' + i + ' unsubscribed'),\n *           finalize: () => console.log('1-second interval events #' + i + ' finalized')\n *         })\n *       )\n *   )\n * );\n *\n * const subscription = result$.subscribe({\n *   next: console.log\n * });\n *\n * setTimeout(() => {\n *   console.log('Unsubscribe after 60 seconds');\n *   subscription.unsubscribe();\n * }, 60_000);\n * ```\n */\nexport interface TapObserver<T> extends Observer<T> {\n  /**\n   * The callback that `tap` operator invokes at the moment when the source Observable\n   * gets subscribed to.\n   */\n  subscribe: () => void;\n  /**\n   * The callback that `tap` operator invokes when an explicit\n   * {@link guide/glossary-and-semantics#unsubscription unsubscribe} happens. It won't get invoked on\n   * `error` or `complete` events.\n   */\n  unsubscribe: () => void;\n  /**\n   * The callback that `tap` operator invokes when any kind of\n   * {@link guide/glossary-and-semantics#finalization finalization} happens - either when\n   * the source Observable `error`s or `complete`s or when it gets explicitly unsubscribed\n   * by the user. There is no difference in using this callback or the {@link finalize}\n   * operator, but if you're already using `tap` operator, you can use this callback\n   * instead. You'd get the same result in either case.\n   */\n  finalize: () => void;\n}\nexport function tap<T>(observerOrNext?: Partial<TapObserver<T>> | ((value: T) => void)): MonoTypeOperatorFunction<T>;\n/** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\nexport function tap<T>(\n  next?: ((value: T) => void) | null,\n  error?: ((error: any) => void) | null,\n  complete?: (() => void) | null\n): MonoTypeOperatorFunction<T>;\n\n/**\n * Used to perform side-effects for notifications from the source observable\n *\n * <span class=\"informal\">Used when you want to affect outside state with a notification without altering the notification</span>\n *\n * ![](tap.png)\n *\n * Tap is designed to allow the developer a designated place to perform side effects. While you _could_ perform side-effects\n * inside of a `map` or a `mergeMap`, that would make their mapping functions impure, which isn't always a big deal, but will\n * make it so you can't do things like memoize those functions. The `tap` operator is designed solely for such side-effects to\n * help you remove side-effects from other operations.\n *\n * For any notification, next, error, or complete, `tap` will call the appropriate callback you have provided to it, via a function\n * reference, or a partial observer, then pass that notification down the stream.\n *\n * The observable returned by `tap` is an exact mirror of the source, with one exception: Any error that occurs -- synchronously -- in a handler\n * provided to `tap` will be emitted as an error from the returned observable.\n *\n * > Be careful! You can mutate objects as they pass through the `tap` operator's handlers.\n *\n * The most common use of `tap` is actually for debugging. You can place a `tap(console.log)` anywhere\n * in your observable `pipe`, log out the notifications as they are emitted by the source returned by the previous\n * operation.\n *\n * ## Examples\n *\n * Check a random number before it is handled. Below is an observable that will use a random number between 0 and 1,\n * and emit `'big'` or `'small'` depending on the size of that number. But we wanted to log what the original number\n * was, so we have added a `tap(console.log)`.\n *\n * ```ts\n * import { of, tap, map } from 'rxjs';\n *\n * of(Math.random()).pipe(\n *   tap(console.log),\n *   map(n => n > 0.5 ? 'big' : 'small')\n * ).subscribe(console.log);\n * ```\n *\n * Using `tap` to analyze a value and force an error. Below is an observable where in our system we only\n * want to emit numbers 3 or less we get from another source. We can force our observable to error\n * using `tap`.\n *\n * ```ts\n * import { of, tap } from 'rxjs';\n *\n * const source = of(1, 2, 3, 4, 5);\n *\n * source.pipe(\n *   tap(n => {\n *     if (n > 3) {\n *       throw new TypeError(`Value ${ n } is greater than 3`);\n *     }\n *   })\n * )\n * .subscribe({ next: console.log, error: err => console.log(err.message) });\n * ```\n *\n * We want to know when an observable completes before moving on to the next observable. The system\n * below will emit a random series of `'X'` characters from 3 different observables in sequence. The\n * only way we know when one observable completes and moves to the next one, in this case, is because\n * we have added a `tap` with the side effect of logging to console.\n *\n * ```ts\n * import { of, concatMap, interval, take, map, tap } from 'rxjs';\n *\n * of(1, 2, 3).pipe(\n *   concatMap(n => interval(1000).pipe(\n *     take(Math.round(Math.random() * 10)),\n *     map(() => 'X'),\n *     tap({ complete: () => console.log(`Done with ${ n }`) })\n *   ))\n * )\n * .subscribe(console.log);\n * ```\n *\n * @see {@link finalize}\n * @see {@link TapObserver}\n *\n * @param observerOrNext A next handler or partial observer\n * @param error An error handler\n * @param complete A completion handler\n * @return A function that returns an Observable identical to the source, but\n * runs the specified Observer or callback(s) for each item.\n */\nexport function tap<T>(\n  observerOrNext?: Partial<TapObserver<T>> | ((value: T) => void) | null,\n  error?: ((e: any) => void) | null,\n  complete?: (() => void) | null\n): MonoTypeOperatorFunction<T> {\n  // We have to check to see not only if next is a function,\n  // but if error or complete were passed. This is because someone\n  // could technically call tap like `tap(null, fn)` or `tap(null, null, fn)`.\n  const tapObserver =\n    isFunction(observerOrNext) || error || complete\n      ? // tslint:disable-next-line: no-object-literal-type-assertion\n        ({ next: observerOrNext as Exclude<typeof observerOrNext, Partial<TapObserver<T>>>, error, complete } as Partial<TapObserver<T>>)\n      : observerOrNext;\n\n  return tapObserver\n    ? operate((source, subscriber) => {\n        tapObserver.subscribe?.();\n        let isUnsub = true;\n        source.subscribe(\n          createOperatorSubscriber(\n            subscriber,\n            (value) => {\n              tapObserver.next?.(value);\n              subscriber.next(value);\n            },\n            () => {\n              isUnsub = false;\n              tapObserver.complete?.();\n              subscriber.complete();\n            },\n            (err) => {\n              isUnsub = false;\n              tapObserver.error?.(err);\n              subscriber.error(err);\n            },\n            () => {\n              if (isUnsub) {\n                tapObserver.unsubscribe?.();\n              }\n              tapObserver.finalize?.();\n            }\n          )\n        );\n      })\n    : // Tap was called with no valid tap observer or handler\n      // (e.g. `tap(null, null, null)` or `tap(null)` or `tap()`)\n      // so we're going to just mirror the source.\n      identity;\n}\n", "import { Subscription } from '../Subscription';\n\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\n\n/**\n * An object interface used by {@link throttle} or {@link throttleTime} that ensure\n * configuration options of these operators.\n *\n * @see {@link throttle}\n * @see {@link throttleTime}\n */\nexport interface ThrottleConfig {\n  /**\n   * If `true`, the resulting Observable will emit the first value from the source\n   * Observable at the **start** of the \"throttling\" process (when starting an\n   * internal timer that prevents other emissions from the source to pass through).\n   * If `false`, it will not emit the first value from the source Observable at the\n   * start of the \"throttling\" process.\n   *\n   * If not provided, defaults to: `true`.\n   */\n  leading?: boolean;\n  /**\n   * If `true`, the resulting Observable will emit the last value from the source\n   * Observable at the **end** of the \"throttling\" process (when ending an internal\n   * timer that prevents other emissions from the source to pass through).\n   * If `false`, it will not emit the last value from the source Observable at the\n   * end of the \"throttling\" process.\n   *\n   * If not provided, defaults to: `false`.\n   */\n  trailing?: boolean;\n}\n\n/**\n * Emits a value from the source Observable, then ignores subsequent source\n * values for a duration determined by another Observable, then repeats this\n * process.\n *\n * <span class=\"informal\">It's like {@link throttleTime}, but the silencing\n * duration is determined by a second Observable.</span>\n *\n * ![](throttle.svg)\n *\n * `throttle` emits the source Observable values on the output Observable\n * when its internal timer is disabled, and ignores source values when the timer\n * is enabled. Initially, the timer is disabled. As soon as the first source\n * value arrives, it is forwarded to the output Observable, and then the timer\n * is enabled by calling the `durationSelector` function with the source value,\n * which returns the \"duration\" Observable. When the duration Observable emits a\n * value, the timer is disabled, and this process repeats for the\n * next source value.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, throttle, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(throttle(() => interval(1000)));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounce}\n * @see {@link delayWhen}\n * @see {@link sample}\n * @see {@link throttleTime}\n *\n * @param durationSelector A function that receives a value from the source\n * Observable, for computing the silencing duration for each source value,\n * returned as an `ObservableInput`.\n * @param config A configuration object to define `leading` and `trailing`\n * behavior. Defaults to `{ leading: true, trailing: false }`.\n * @return A function that returns an Observable that performs the throttle\n * operation to limit the rate of emissions from the source.\n */\nexport function throttle<T>(durationSelector: (value: T) => ObservableInput<any>, config?: ThrottleConfig): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    const { leading = true, trailing = false } = config ?? {};\n    let hasValue = false;\n    let sendValue: T | null = null;\n    let throttled: Subscription | null = null;\n    let isComplete = false;\n\n    const endThrottling = () => {\n      throttled?.unsubscribe();\n      throttled = null;\n      if (trailing) {\n        send();\n        isComplete && subscriber.complete();\n      }\n    };\n\n    const cleanupThrottling = () => {\n      throttled = null;\n      isComplete && subscriber.complete();\n    };\n\n    const startThrottle = (value: T) =>\n      (throttled = innerFrom(durationSelector(value)).subscribe(createOperatorSubscriber(subscriber, endThrottling, cleanupThrottling)));\n\n    const send = () => {\n      if (hasValue) {\n        // Ensure we clear out our value and hasValue flag\n        // before we emit, otherwise reentrant code can cause\n        // issues here.\n        hasValue = false;\n        const value = sendValue!;\n        sendValue = null;\n        // Emit the value.\n        subscriber.next(value);\n        !isComplete && startThrottle(value);\n      }\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        // Regarding the presence of throttled.closed in the following\n        // conditions, if a synchronous duration selector is specified - weird,\n        // but legal - an already-closed subscription will be assigned to\n        // throttled, so the subscription's closed property needs to be checked,\n        // too.\n        (value) => {\n          hasValue = true;\n          sendValue = value;\n          !(throttled && !throttled.closed) && (leading ? send() : startThrottle(value));\n        },\n        () => {\n          isComplete = true;\n          !(trailing && hasValue && throttled && !throttled.closed) && subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { throttle, ThrottleConfig } from './throttle';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { timer } from '../observable/timer';\n\n/**\n * Emits a value from the source Observable, then ignores subsequent source\n * values for `duration` milliseconds, then repeats this process.\n *\n * <span class=\"informal\">Lets a value pass, then ignores source values for the\n * next `duration` milliseconds.</span>\n *\n * ![](throttleTime.png)\n *\n * `throttleTime` emits the source Observable values on the output Observable\n * when its internal timer is disabled, and ignores source values when the timer\n * is enabled. Initially, the timer is disabled. As soon as the first source\n * value arrives, it is forwarded to the output Observable, and then the timer\n * is enabled. After `duration` milliseconds (or the time unit determined\n * internally by the optional `scheduler`) has passed, the timer is disabled,\n * and this process repeats for the next source value. Optionally takes a\n * {@link SchedulerLike} for managing timers.\n *\n * ## Examples\n *\n * ### Limit click rate\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, throttleTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(throttleTime(1000));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link auditTime}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sampleTime}\n * @see {@link throttle}\n *\n * @param duration Time to wait before emitting another value after\n * emitting the last value, measured in milliseconds or the time unit determined\n * internally by the optional `scheduler`.\n * @param scheduler The {@link SchedulerLike} to use for\n * managing the timers that handle the throttling. Defaults to {@link asyncScheduler}.\n * @param config A configuration object to define `leading` and\n * `trailing` behavior. Defaults to `{ leading: true, trailing: false }`.\n * @return A function that returns an Observable that performs the throttle\n * operation to limit the rate of emissions from the source.\n */\nexport function throttleTime<T>(\n  duration: number,\n  scheduler: SchedulerLike = asyncScheduler,\n  config?: ThrottleConfig\n): MonoTypeOperatorFunction<T> {\n  const duration$ = timer(duration, scheduler);\n  return throttle(() => duration$, config);\n}\n", "import { OperatorFunction, ObservableInputTuple } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { identity } from '../util/identity';\nimport { noop } from '../util/noop';\nimport { popResultSelector } from '../util/args';\n\nexport function withLatestFrom<T, O extends unknown[]>(...inputs: [...ObservableInputTuple<O>]): OperatorFunction<T, [T, ...O]>;\n\nexport function withLatestFrom<T, O extends unknown[], R>(\n  ...inputs: [...ObservableInputTuple<O>, (...value: [T, ...O]) => R]\n): OperatorFunction<T, R>;\n\n/**\n * Combines the source Observable with other Observables to create an Observable\n * whose values are calculated from the latest values of each, only when the\n * source emits.\n *\n * <span class=\"informal\">Whenever the source Observable emits a value, it\n * computes a formula using that value plus the latest values from other input\n * Observables, then emits the output of that formula.</span>\n *\n * ![](withLatestFrom.png)\n *\n * `withLatestFrom` combines each value from the source Observable (the\n * instance) with the latest values from the other input Observables only when\n * the source emits a value, optionally using a `project` function to determine\n * the value to be emitted on the output Observable. All input Observables must\n * emit at least one value before the output Observable will emit a value.\n *\n * ## Example\n *\n * On every click event, emit an array with the latest timer event plus the click event\n *\n * ```ts\n * import { fromEvent, interval, withLatestFrom } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const timer = interval(1000);\n * const result = clicks.pipe(withLatestFrom(timer));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link combineLatest}\n *\n * @param {ObservableInput} other An input Observable to combine with the source\n * Observable. More than one input Observables may be given as argument.\n * @param {Function} [project] Projection function for combining values\n * together. Receives all values in order of the Observables passed, where the\n * first parameter is a value from the source Observable. (e.g.\n * `a.pipe(withLatestFrom(b, c), map(([a1, b1, c1]) => a1 + b1 + c1))`). If this is not\n * passed, arrays will be emitted on the output Observable.\n * @return A function that returns an Observable of projected values from the\n * most recent values from each input Observable, or an array of the most\n * recent values from each input Observable.\n */\nexport function withLatestFrom<T, R>(...inputs: any[]): OperatorFunction<T, R | any[]> {\n  const project = popResultSelector(inputs) as ((...args: any[]) => R) | undefined;\n\n  return operate((source, subscriber) => {\n    const len = inputs.length;\n    const otherValues = new Array(len);\n    // An array of whether or not the other sources have emitted. Matched with them by index.\n    // TODO: At somepoint, we should investigate the performance implications here, and look\n    // into using a `Set()` and checking the `size` to see if we're ready.\n    let hasValue = inputs.map(() => false);\n    // Flipped true when we have at least one value from all other sources and\n    // we are ready to start emitting values.\n    let ready = false;\n\n    // Other sources. Note that here we are not checking `subscriber.closed`,\n    // this causes all inputs to be subscribed to, even if nothing can be emitted\n    // from them. This is an important distinction because subscription constitutes\n    // a side-effect.\n    for (let i = 0; i < len; i++) {\n      innerFrom(inputs[i]).subscribe(\n        createOperatorSubscriber(\n          subscriber,\n          (value) => {\n            otherValues[i] = value;\n            if (!ready && !hasValue[i]) {\n              // If we're not ready yet, flag to show this observable has emitted.\n              hasValue[i] = true;\n              // Intentionally terse code.\n              // If all of our other observables have emitted, set `ready` to `true`,\n              // so we know we can start emitting values, then clean up the `hasValue` array,\n              // because we don't need it anymore.\n              (ready = hasValue.every(identity)) && (hasValue = null!);\n            }\n          },\n          // Completing one of the other sources has\n          // no bearing on the completion of our result.\n          noop\n        )\n      );\n    }\n\n    // Source subscription\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        if (ready) {\n          // We have at least one value from the other sources. Go ahead and emit.\n          const values = [value, ...otherValues];\n          subscriber.next(project ? project(...values) : values);\n        }\n      })\n    );\n  });\n}\n", "import { zip as zipStatic } from '../observable/zip';\nimport { ObservableInput, ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { operate } from '../util/lift';\n\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[]>(otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[], R>(\n  otherInputsAndProject: [...ObservableInputTuple<A>],\n  project: (...values: Cons<T, A>) => R\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[]>(...otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[], R>(\n  ...otherInputsAndProject: [...ObservableInputTuple<A>, (...values: Cons<T, A>) => R]\n): OperatorFunction<T, R>;\n\n/**\n * @deprecated Replaced with {@link zipWith}. Will be removed in v8.\n */\nexport function zip<T, R>(...sources: Array<ObservableInput<any> | ((...values: Array<any>) => R)>): OperatorFunction<T, any> {\n  return operate((source, subscriber) => {\n    zipStatic(source as ObservableInput<any>, ...(sources as Array<ObservableInput<any>>)).subscribe(subscriber);\n  });\n}\n", "import { ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { zip } from './zip';\n\n/**\n * Subscribes to the source, and the observable inputs provided as arguments, and combines their values, by index, into arrays.\n *\n * What is meant by \"combine by index\": The first value from each will be made into a single array, then emitted,\n * then the second value from each will be combined into a single array and emitted, then the third value\n * from each will be combined into a single array and emitted, and so on.\n *\n * This will continue until it is no longer able to combine values of the same index into an array.\n *\n * After the last value from any one completed source is emitted in an array, the resulting observable will complete,\n * as there is no way to continue \"zipping\" values together by index.\n *\n * Use-cases for this operator are limited. There are memory concerns if one of the streams is emitting\n * values at a much faster rate than the others. Usage should likely be limited to streams that emit\n * at a similar pace, or finite streams of known length.\n *\n * In many cases, authors want `combineLatestWith` and not `zipWith`.\n *\n * @param otherInputs other observable inputs to collate values from.\n * @return A function that returns an Observable that emits items by index\n * combined from the source Observable and provided Observables, in form of an\n * array.\n */\nexport function zipWith<T, A extends readonly unknown[]>(...otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>> {\n  return zip(...otherInputs);\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ReplaySubject,\n  Subject,\n  fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject<Document> {\n  const document$ = new ReplaySubject<Document>(1)\n  fromEvent(document, \"DOMContentLoaded\", { once: true })\n    .subscribe(() => document$.next(document))\n\n  /* Return document */\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return (\n    document.activeElement?.shadowRoot?.activeElement as HTMLElement ??\n    document.activeElement as HTMLElement ??\n    undefined\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounceTime,\n  distinctUntilChanged,\n  fromEvent,\n  map,\n  merge,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Focus observable\n *\n * Previously, this observer used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n */\nconst observer$ = merge(\n  fromEvent(document.body, \"focusin\"),\n  fromEvent(document.body, \"focusout\")\n)\n  .pipe(\n    debounceTime(1),\n    startWith(undefined),\n    map(() => getActiveElement() || document.body),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      map(active => el.contains(active)),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounce,\n  defer,\n  fromEvent,\n  identity,\n  map,\n  merge,\n  startWith,\n  timer\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element hover\n *\n * The second parameter allows to specify a timeout in milliseconds after which\n * the hover state will be reset to `false`. This is useful for tooltips which\n * should disappear after a certain amount of time, in order to allow the user\n * to move the cursor from the host to the tooltip.\n *\n * @param el - Element\n * @param timeout - Timeout\n *\n * @returns Element hover observable\n */\nexport function watchElementHover(\n  el: HTMLElement, timeout?: number\n): Observable<boolean> {\n  return defer(() => merge(\n    fromEvent(el, \"mouseenter\").pipe(map(() => true)),\n    fromEvent(el, \"mouseleave\").pipe(map(() => false))\n  )\n    .pipe(\n      timeout ? debounce(active => timer(+!active * timeout)) : identity,\n      startWith(el.matches(\":hover\"))\n    )\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n  & JSXInternal.HTMLAttributes\n  & JSXInternal.SVGAttributes\n  & Record<string, any>\n\n/**\n * Child element\n */\ntype Child =\n  | ChildNode\n  | HTMLElement\n  | Text\n  | string\n  | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n  /* Handle primitive types (including raw HTML) */\n  if (typeof child === \"string\" || typeof child === \"number\") {\n    el.innerHTML += child.toString()\n\n  /* Handle nodes */\n  } else if (child instanceof Node) {\n    el.appendChild(child)\n\n  /* Handle nested children */\n  } else if (Array.isArray(child)) {\n    for (const node of child)\n      appendChild(el, node)\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h<T extends keyof HTMLElementTagNameMap>(\n  tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n  const el = document.createElement(tag)\n\n  /* Set attributes, if any */\n  if (attributes)\n    for (const attr of Object.keys(attributes)) {\n      if (typeof attributes[attr] === \"undefined\")\n        continue\n\n      /* Set default attribute or boolean */\n      if (typeof attributes[attr] !== \"boolean\")\n        el.setAttribute(attr, attributes[attr])\n      else\n        el.setAttribute(attr, \"\")\n    }\n\n  /* Append child nodes */\n  for (const child of children)\n    appendChild(el, child)\n\n  /* Return element */\n  return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n  namespace JSX {\n    type Element = HTMLElement\n    type IntrinsicElements = JSXInternal.IntrinsicElements\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n  if (value > 999) {\n    const digits = +((value - 950) % 1000 > 99)\n    return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n  } else {\n    return value.toString()\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  switchMap,\n  take,\n  throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it wasn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable<void> {\n  const script = h(\"script\", { src })\n  return defer(() => {\n    document.head.appendChild(script)\n    return merge(\n      fromEvent(script, \"load\"),\n      fromEvent(script, \"error\")\n        .pipe(\n          switchMap(() => (\n            throwError(() => new ReferenceError(`Invalid script: ${src}`))\n          ))\n        )\n    )\n      .pipe(\n        map(() => undefined),\n        finalize(() => document.head.removeChild(script)),\n        take(1)\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"../../../script\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n  width: number                        /* Element width */\n  height: number                       /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject<ResizeObserverEntry>()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * If the browser doesn't have a `ResizeObserver` implementation available, a\n * polyfill is automatically downloaded from unpkg.com. This is also compatible\n * with the built-in privacy plugin, which will download the polyfill and put\n * it alongside the built site for self-hosting.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => (\n  typeof ResizeObserver === \"undefined\"\n    ? watchScript(\"https://unpkg.com/resize-observer-polyfill\")\n    : of(undefined)\n))\n  .pipe(\n    map(() => new ResizeObserver(entries => (\n      entries.forEach(entry => entry$.next(entry))\n    ))),\n    switchMap(observer => merge(NEVER, of(observer)).pipe(\n      finalize(() => observer.disconnect())\n    )),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n\n  // Compute target element - since inline elements cannot be observed by the\n  // current `ResizeObserver` implementation as provided by browsers, we need\n  // to determine the first containing parent element and use that one as a\n  // target, while we always compute the actual size from the element.\n  let target = el\n  while (target.clientWidth === 0)\n    if (target.parentElement)\n      target = target.parentElement\n    else\n      break\n\n  // Observe target element and recompute element size on resize - as described\n  // above, the target element is not necessarily the element of interest\n  return observer$.pipe(\n    tap(observer => observer.observe(target)),\n    switchMap(observer => entry$.pipe(\n      filter(entry => entry.target === target),\n      finalize(() => observer.unobserve(target))\n    )),\n    map(() => getElementSize(el)),\n    startWith(getElementSize(el))\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.scrollWidth,\n    height: el.scrollHeight\n  }\n}\n\n/**\n * Retrieve the overflowing container of an element, if any\n *\n * @param el - Element\n *\n * @returns Overflowing container or nothing\n */\nexport function getElementContainer(\n  el: HTMLElement\n): HTMLElement | undefined {\n  let parent = el.parentElement\n  while (parent)\n    if (\n      el.scrollWidth  <= parent.scrollWidth &&\n      el.scrollHeight <= parent.scrollHeight\n    )\n      parent = (el = parent).parentElement\n    else\n      break\n\n  /* Return overflowing container */\n  return parent ? el : undefined\n}\n\n/**\n * Retrieve all overflowing containers of an element, if any\n *\n * Note that this function has a slightly different behavior, so we should at\n * some point consider refactoring how overflowing containers are handled.\n *\n * @param el - Element\n *\n * @returns Overflowing containers\n */\nexport function getElementContainers(\n  el: HTMLElement\n): HTMLElement[] {\n  const containers: HTMLElement[] = []\n\n  // Walk up the DOM tree until we find an overflowing container\n  let parent = el.parentElement\n  while (parent) {\n    if (\n      el.clientWidth  > parent.clientWidth ||\n      el.clientHeight > parent.clientHeight\n    )\n      containers.push(parent)\n\n    // Continue with parent element\n    parent = (el = parent).parentElement\n  }\n\n  // If the page is short, the body might not be overflowing and there might be\n  // no other containers, which is why we need to make sure the body is present\n  if (containers.length === 0)\n    containers.push(document.documentElement)\n\n  // Return overflowing containers\n  return containers\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { watchElementSize } from \"../../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.offsetLeft,\n    y: el.offsetTop\n  }\n}\n\n/**\n * Retrieve absolute element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffsetAbsolute(\n  el: HTMLElement\n): ElementOffset {\n  const rect = el.getBoundingClientRect()\n  return {\n    x: rect.x + window.scrollX,\n    y: rect.y + window.scrollY\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(window, \"load\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementOffset(el)),\n      startWith(getElementOffset(el))\n    )\n}\n\n/**\n * Watch absolute element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffsetAbsolute(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    watchElementOffset(el),\n    watchElementSize(document.body) // @todo find a better way for this\n  )\n    .pipe(\n      map(() => getElementOffsetAbsolute(el)),\n      startWith(getElementOffsetAbsolute(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.scrollLeft,\n    y: el.scrollTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(el, \"scroll\"),\n    fromEvent(window, \"scroll\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElementContentSize,\n  getElementSize,\n  watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject<IntersectionObserverEntry>()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new IntersectionObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  }, {\n    threshold: 0\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(({ isIntersecting }) => isIntersecting)\n        )\n      )\n    )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n  el: HTMLElement, threshold = 16\n): Observable<boolean> {\n  return watchElementContentOffset(el)\n    .pipe(\n      map(({ y }) => {\n        const visible = getElementSize(el)\n        const content = getElementContentSize(el)\n        return y >= (\n          content.height - visible.height - threshold\n        )\n      }),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n  | \"drawer\"                           /* Toggle for drawer */\n  | \"search\"                           /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record<Toggle, HTMLInputElement> = {\n  drawer: getElement(\"[data-md-toggle=drawer]\"),\n  search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n  return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n  if (toggles[name].checked !== value)\n    toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable<boolean> {\n  const el = toggles[name]\n  return fromEvent(el, \"change\")\n    .pipe(\n      map(() => el.checked),\n      startWith(el.checked)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  share,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n  | \"global\"                           /* Global */\n  | \"search\"                           /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n  mode: KeyboardMode                   /* Keyboard mode */\n  type: string                         /* Key type */\n  claim(): void                        /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n  el: HTMLElement, type: string\n): boolean {\n  switch (el.constructor) {\n\n    /* Input elements */\n    case HTMLInputElement:\n      /* @ts-expect-error - omit unnecessary type cast */\n      if (el.type === \"radio\")\n        return /^Arrow/.test(type)\n      else\n        return true\n\n    /* Select element and textarea */\n    case HTMLSelectElement:\n    case HTMLTextAreaElement:\n      return true\n\n    /* Everything else */\n    default:\n      return el.isContentEditable\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch composition events\n *\n * @returns Composition observable\n */\nexport function watchComposition(): Observable<boolean> {\n  return merge(\n    fromEvent(window, \"compositionstart\").pipe(map(() => true)),\n    fromEvent(window, \"compositionend\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(false)\n    )\n}\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable<Keyboard> {\n  const keyboard$ = fromEvent<KeyboardEvent>(window, \"keydown\")\n    .pipe(\n      filter(ev => !(ev.metaKey || ev.ctrlKey)),\n      map(ev => ({\n        mode: getToggle(\"search\") ? \"search\" : \"global\",\n        type: ev.key,\n        claim() {\n          ev.preventDefault()\n          ev.stopPropagation()\n        }\n      } as Keyboard)),\n      filter(({ mode, type }) => {\n        if (mode === \"global\") {\n          const active = getActiveElement()\n          if (typeof active !== \"undefined\")\n            return !isSusceptibleToKeyboard(active, type)\n        }\n        return true\n      }),\n      share()\n    )\n\n  /* Don't emit during composition events - see https://bit.ly/3te3Wl8 */\n  return watchComposition()\n    .pipe(\n      switchMap(active => !active ? keyboard$ : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n  return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * If instant navigation is enabled, this function creates a temporary anchor\n * element, sets the `href` attribute, appends it to the body, clicks it, and\n * then removes it again. The event will bubble up the DOM and trigger be\n * intercepted by the instant loading business logic.\n *\n * Note that we must append and remove the anchor element, or the event will\n * not bubble up the DOM, making it impossible to intercept it.\n *\n * @param url - URL to navigate to\n * @param navigate - Force navigation\n */\nexport function setLocation(\n  url: URL | HTMLLinkElement, navigate = false\n): void {\n  if (feature(\"navigation.instant\") && !navigate) {\n    const el = h(\"a\", { href: url.href })\n    document.body.appendChild(el)\n    el.click()\n    el.remove()\n\n  // If we're not using instant navigation, and the page should not be reloaded\n  // just instruct the browser to navigate to the given URL\n  } else {\n    location.href = url.href\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject<URL> {\n  return new Subject<URL>()\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n  return location.hash.slice(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n  const el = h(\"a\", { href: hash })\n  el.addEventListener(\"click\", ev => ev.stopPropagation())\n  el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @param location$ - Location observable\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(\n  location$: Observable<URL>\n): Observable<string> {\n  return merge(\n    fromEvent<HashChangeEvent>(window, \"hashchange\"),\n    location$\n  )\n    .pipe(\n      map(getLocationHash),\n      startWith(getLocationHash()),\n      filter(hash => hash.length > 0),\n      shareReplay(1)\n    )\n}\n\n/**\n * Watch location target\n *\n * @param location$ - Location observable\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(\n  location$: Observable<URL>\n): Observable<HTMLElement> {\n  return watchLocationHash(location$)\n    .pipe(\n      map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n      filter(el => typeof el !== \"undefined\")\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  fromEvent,\n  fromEventPattern,\n  map,\n  merge,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable<boolean> {\n  const media = matchMedia(query)\n  return fromEventPattern<boolean>(next => (\n    media.addListener(() => next(media.matches))\n  ))\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable<boolean> {\n  const media = matchMedia(\"print\")\n  return merge(\n    fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n    fromEvent(window, \"afterprint\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at<T>(\n  query$: Observable<boolean>, factory: () => Observable<T>\n): Observable<T> {\n  return query$\n    .pipe(\n      switchMap(active => active ? factory() : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  map,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Options\n */\ninterface Options {\n  progress$?: Subject<number>          // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * This function returns an observable that emits the response as a blob and\n * completes, or emits an error if the request failed. The caller can cancel\n * the request by unsubscribing at any time, which will automatically abort\n * the inflight request and complete the observable.\n *\n * Note that we use `XMLHTTPRequest` not because we're nostalgic, but because\n * it's the only way to get progress events for downloads and also allow for\n * cancellation of requests, as the official Fetch API does not support this\n * yet, even though we're already in 2024.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function request(\n  url: URL | string, options?: Options\n): Observable<Blob> {\n  return new Observable<Blob>(observer => {\n    const req = new XMLHttpRequest()\n    req.open(\"GET\", `${url}`)\n    req.responseType = \"blob\"\n\n    // Handle response\n    req.addEventListener(\"load\", () => {\n      if (req.status >= 200 && req.status < 300) {\n        observer.next(req.response)\n        observer.complete()\n\n      // Every response that is not in the 2xx range is considered an error\n      } else {\n        observer.error(new Error(req.statusText))\n      }\n    })\n\n    // Handle network errors\n    req.addEventListener(\"error\", () => {\n      observer.error(new Error(\"Network error\"))\n    })\n\n    // Handle aborted requests\n    req.addEventListener(\"abort\", () => {\n      observer.complete()\n    })\n\n    // Handle download progress\n    if (typeof options?.progress$ !== \"undefined\") {\n      req.addEventListener(\"progress\", event => {\n        if (event.lengthComputable) {\n          options.progress$!.next((event.loaded / event.total) * 100)\n\n        // Hack: Chromium doesn't report the total number of bytes if content\n        // is compressed, so we need this fallback - see https://t.ly/ZXofI\n        } else {\n          const length = req.getResponseHeader(\"Content-Length\") ?? 0\n          options.progress$!.next((event.loaded / +length) * 100)\n        }\n      })\n\n      // Immediately set progress to 5% to indicate that we're loading\n      options.progress$.next(5)\n    }\n\n    // Send request and automatically abort request upon unsubscription\n    req.send()\n    return () => req.abort()\n  })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON<T>(\n  url: URL | string, options?: Options\n): Observable<T> {\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(body => JSON.parse(body) as T),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch HTML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestHTML(\n  url: URL | string, options?: Options\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/html\")),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n  url: URL | string, options?: Options\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/xml\")),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n  return {\n    x: Math.max(0, scrollX),\n    y: Math.max(0, scrollY)\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable<ViewportOffset> {\n  return merge(\n    fromEvent(window, \"scroll\", { passive: true }),\n    fromEvent(window, \"resize\", { passive: true })\n  )\n    .pipe(\n      map(getViewportOffset),\n      startWith(getViewportOffset())\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n  width: number                        /* Viewport width */\n  height: number                       /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n  return {\n    width:  innerWidth,\n    height: innerHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable<ViewportSize> {\n  return fromEvent(window, \"resize\", { passive: true })\n    .pipe(\n      map(getViewportSize),\n      startWith(getViewportSize())\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  map,\n  shareReplay\n} from \"rxjs\"\n\nimport {\n  ViewportOffset,\n  watchViewportOffset\n} from \"../offset\"\nimport {\n  ViewportSize,\n  watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n  offset: ViewportOffset               /* Viewport offset */\n  size: ViewportSize                   /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable<Viewport> {\n  return combineLatest([\n    watchViewportOffset(),\n    watchViewportSize()\n  ])\n    .pipe(\n      map(([offset, size]) => ({ offset, size })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilKeyChanged,\n  map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Viewport> {\n  const size$ = viewport$\n    .pipe(\n      distinctUntilKeyChanged(\"size\")\n    )\n\n  /* Compute element offset */\n  const offset$ = combineLatest([size$, header$])\n    .pipe(\n      map(() => getElementOffset(el))\n    )\n\n  /* Compute relative viewport, return hot observable */\n  return combineLatest([header$, viewport$, offset$])\n    .pipe(\n      map(([{ height }, { offset, size }, { x, y }]) => ({\n        offset: {\n          x: offset.x - x,\n          y: offset.y - y + height\n        },\n        size\n      }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  mergeWith,\n  share,\n  takeUntil\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n  type: unknown                        /* Message type */\n  data?: unknown                       /* Message data */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create an observable for receiving from a web worker\n *\n * @template T - Data type\n *\n * @param worker - Web worker\n *\n * @returns Message observable\n */\nfunction recv<T>(worker: Worker): Observable<T> {\n  return fromEvent<MessageEvent<T>, T>(worker, \"message\", ev => ev.data)\n}\n\n/**\n * Create a subject for sending to a web worker\n *\n * @template T - Data type\n *\n * @param worker - Web worker\n *\n * @returns Message subject\n */\nfunction send<T>(worker: Worker): Subject<T> {\n  const send$ = new Subject<T>()\n  send$.subscribe(data => worker.postMessage(data))\n\n  /* Return message subject */\n  return send$\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a bidirectional communication channel to a web worker\n *\n * @template T - Data type\n *\n * @param url - Worker URL\n * @param worker - Worker\n *\n * @returns Worker subject\n */\nexport function watchWorker<T extends WorkerMessage>(\n  url: string, worker = new Worker(url)\n): Subject<T> {\n  const recv$ = recv<T>(worker)\n  const send$ = send<T>(worker)\n\n  /* Create worker subject and forward messages */\n  const worker$ = new Subject<T>()\n  worker$.subscribe(send$)\n\n  /* Return worker subject */\n  const done$ = send$.pipe(ignoreElements(), endWith(true))\n  return worker$\n    .pipe(\n      ignoreElements(),\n      mergeWith(recv$.pipe(takeUntil(done$))),\n      share()\n    ) as Subject<T>\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n  | \"announce.dismiss\"                 /* Dismissable announcement bar */\n  | \"content.code.annotate\"            /* Code annotations */\n  | \"content.code.copy\"                /* Code copy button */\n  | \"content.lazy\"                     /* Lazy content elements */\n  | \"content.tabs.link\"                /* Link content tabs */\n  | \"content.tooltips\"                 /* Tooltips */\n  | \"header.autohide\"                  /* Hide header */\n  | \"navigation.expand\"                /* Automatic expansion */\n  | \"navigation.indexes\"               /* Section pages */\n  | \"navigation.instant\"               /* Instant navigation */\n  | \"navigation.instant.progress\"      /* Instant navigation progress */\n  | \"navigation.sections\"              /* Section navigation */\n  | \"navigation.tabs\"                  /* Tabs navigation */\n  | \"navigation.tabs.sticky\"           /* Tabs navigation (sticky) */\n  | \"navigation.top\"                   /* Back-to-top button */\n  | \"navigation.tracking\"              /* Anchor tracking */\n  | \"search.highlight\"                 /* Search highlighting */\n  | \"search.share\"                     /* Search sharing */\n  | \"search.suggest\"                   /* Search suggestions */\n  | \"toc.follow\"                       /* Following table of contents */\n  | \"toc.integrate\"                    /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n  | \"clipboard.copy\"                   /* Copy to clipboard */\n  | \"clipboard.copied\"                 /* Copied to clipboard */\n  | \"search.result.placeholder\"        /* Type to start searching */\n  | \"search.result.none\"               /* No matching documents */\n  | \"search.result.one\"                /* 1 matching document */\n  | \"search.result.other\"              /* # matching documents */\n  | \"search.result.more.one\"           /* 1 more on this page */\n  | \"search.result.more.other\"         /* # more on this page */\n  | \"search.result.term.missing\"       /* Missing */\n  | \"select.version\"                   /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations =\n  Record<Translation, string>\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n  provider: \"mike\"                     /* Version provider */\n  default?: string | string[]          /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n  base: string                         /* Base URL */\n  features: Flag[]                     /* Feature flags */\n  translations: Translations           /* Translations */\n  search: string                       /* Search worker URL */\n  tags?: Record<string, string>        /* Tags mapping */\n  version?: Versioning                 /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n  return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n  return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n  key: Translation, value?: string | number\n): string {\n  return typeof value !== \"undefined\"\n    ? config.translations[key].replace(\"#\", value.toString())\n    : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n  | \"announce\"                         /* Announcement bar */\n  | \"container\"                        /* Container */\n  | \"consent\"                          /* Consent */\n  | \"content\"                          /* Content */\n  | \"dialog\"                           /* Dialog */\n  | \"header\"                           /* Header */\n  | \"header-title\"                     /* Header title */\n  | \"header-topic\"                     /* Header topic */\n  | \"main\"                             /* Main area */\n  | \"outdated\"                         /* Version warning */\n  | \"palette\"                          /* Color palette */\n  | \"progress\"                         /* Progress indicator */\n  | \"search\"                           /* Search */\n  | \"search-query\"                     /* Search input */\n  | \"search-result\"                    /* Search results */\n  | \"search-share\"                     /* Search sharing */\n  | \"search-suggest\"                   /* Search suggestions */\n  | \"sidebar\"                          /* Sidebar */\n  | \"skip\"                             /* Skip link */\n  | \"source\"                           /* Repository information */\n  | \"tabs\"                             /* Navigation tabs */\n  | \"toc\"                              /* Table of contents */\n  | \"top\"                              /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n  T extends {} = {},\n  U extends HTMLElement = HTMLElement\n> =\n  T & {\n    ref: U                             /* Component reference */\n  }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n  \"announce\": HTMLElement              /* Announcement bar */\n  \"container\": HTMLElement             /* Container */\n  \"consent\": HTMLElement               /* Consent */\n  \"content\": HTMLElement               /* Content */\n  \"dialog\": HTMLElement                /* Dialog */\n  \"header\": HTMLElement                /* Header */\n  \"header-title\": HTMLElement          /* Header title */\n  \"header-topic\": HTMLElement          /* Header topic */\n  \"main\": HTMLElement                  /* Main area */\n  \"outdated\": HTMLElement              /* Version warning */\n  \"palette\": HTMLElement               /* Color palette */\n  \"progress\": HTMLElement              /* Progress indicator */\n  \"search\": HTMLElement                /* Search */\n  \"search-query\": HTMLInputElement     /* Search input */\n  \"search-result\": HTMLElement         /* Search results */\n  \"search-share\": HTMLAnchorElement    /* Search sharing */\n  \"search-suggest\": HTMLElement        /* Search suggestions */\n  \"sidebar\": HTMLElement               /* Sidebar */\n  \"skip\": HTMLAnchorElement            /* Skip link */\n  \"source\": HTMLAnchorElement          /* Repository information */\n  \"tabs\": HTMLElement                  /* Navigation tabs */\n  \"toc\": HTMLElement                   /* Table of contents */\n  \"top\": HTMLAnchorElement             /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n  return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n  return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n  hash: number                        /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n  el: HTMLElement\n): Observable<Announce> {\n  const button = getElement(\".md-typeset > :first-child\", el)\n  return fromEvent(button, \"click\", { once: true })\n    .pipe(\n      map(() => getElement(\".md-typeset\", el)),\n      map(content => ({ hash: __md_hash(content.innerHTML) }))\n    )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n  el: HTMLElement\n): Observable<Component<Announce>> {\n  if (!feature(\"announce.dismiss\") || !el.childElementCount)\n    return EMPTY\n\n  /* Support instant navigation - see https://t.ly/3FTme */\n  if (!el.hidden) {\n    const content = getElement(\".md-typeset\", el)\n    if (__md_hash(content.innerHTML) === __md_get(\"__announce\"))\n      el.hidden = true\n  }\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Announce>()\n    push$.subscribe(({ hash }) => {\n      el.hidden = true\n\n      /* Persist preference in local storage */\n      __md_set<number>(\"__announce\", hash)\n    })\n\n    /* Create and return component */\n    return watchAnnounce(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n  hidden: boolean                      /* Consent is hidden */\n}\n\n/**\n * Consent defaults\n */\nexport interface ConsentDefaults {\n  analytics?: boolean                  /* Consent for Analytics */\n  github?: boolean                     /* Consent for GitHub */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n  el: HTMLElement, { target$ }: WatchOptions\n): Observable<Consent> {\n  return target$\n    .pipe(\n      map(target => ({ hidden: target !== el }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Consent>> {\n  const internal$ = new Subject<Consent>()\n  internal$.subscribe(({ hidden }) => {\n    el.hidden = hidden\n  })\n\n  /* Create and return component */\n  return watchConsent(el, options)\n    .pipe(\n      tap(state => internal$.next(state)),\n      finalize(() => internal$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip style\n */\nexport type TooltipStyle =\n  | \"inline\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a tooltip\n *\n * @param id - Tooltip identifier\n * @param style - Tooltip style\n *\n * @returns Element\n */\nexport function renderTooltip(\n  id?: string, style?: TooltipStyle\n): HTMLElement {\n  if (style === \"inline\") { // @todo refactor control flow\n    return (\n      <div class=\"md-tooltip md-tooltip--inline\" id={id} role=\"tooltip\">\n        <div class=\"md-tooltip__inner md-typeset\"></div>\n      </div>\n    )\n  } else {\n    return (\n      <div class=\"md-tooltip\" id={id} role=\"tooltip\">\n        <div class=\"md-tooltip__inner md-typeset\"></div>\n      </div>\n    )\n  }\n}\n\n// @todo: rename\nexport function renderInlineTooltip2(\n  ...children: ComponentChild[]\n): HTMLElement {\n  return (\n    <div class=\"md-tooltip2\" role=\"tooltip\">\n      <div class=\"md-tooltip2__inner md-typeset\">\n        {children}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\nimport { renderTooltip } from \"../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an annotation\n *\n * @param id - Annotation identifier\n * @param prefix - Tooltip identifier prefix\n *\n * @returns Element\n */\nexport function renderAnnotation(\n  id: string | number, prefix?: string\n): HTMLElement {\n  prefix = prefix ? `${prefix}_annotation_${id}` : undefined\n\n  /* Render tooltip with anchor, if given */\n  if (prefix) {\n    const anchor = prefix ? `#${prefix}` : undefined\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <a href={anchor} class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </a>\n      </aside>\n    )\n  } else {\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <span class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </span>\n      </aside>\n    )\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n  return (\n    <button\n      class=\"md-clipboard md-icon\"\n      title={translation(\"clipboard.copy\")}\n      data-clipboard-target={`#${id} > code`}\n    ></button>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { SearchItem } from \"~/integrations/search\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n  TEASER = 1,                          /* Render teaser */\n  PARENT = 2                           /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n  document: SearchItem, flag: Flag\n): HTMLElement {\n  const parent = flag & Flag.PARENT\n  const teaser = flag & Flag.TEASER\n\n  /* Render missing query terms */\n  const missing = Object.keys(document.terms)\n    .filter(key => !document.terms[key])\n    .reduce<ComponentChild[]>((list, key) => [\n      ...list, <del>{key}</del>, \" \"\n    ], [])\n    .slice(0, -1)\n\n  /* Assemble query string for highlighting */\n  const config = configuration()\n  const url = new URL(document.location, config.base)\n  if (feature(\"search.highlight\"))\n    url.searchParams.set(\"h\", Object.entries(document.terms)\n      .filter(([, match]) => match)\n      .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n    )\n\n  /* Render article or section, depending on flags */\n  const { tags } = configuration()\n  return (\n    <a href={`${url}`} class=\"md-search-result__link\" tabIndex={-1}>\n      <article\n        class=\"md-search-result__article md-typeset\"\n        data-md-score={document.score.toFixed(2)}\n      >\n        {parent > 0 && <div class=\"md-search-result__icon md-icon\"></div>}\n        {parent > 0 && <h1>{document.title}</h1>}\n        {parent <= 0 && <h2>{document.title}</h2>}\n        {teaser > 0 && document.text.length > 0 &&\n          document.text\n        }\n        {document.tags && document.tags.map(tag => {\n          const type = tags\n            ? tag in tags\n              ? `md-tag-icon md-tag--${tags[tag]}`\n              : \"md-tag-icon\"\n            : \"\"\n          return (\n            <span class={`md-tag ${type}`}>{tag}</span>\n          )\n        })}\n        {teaser > 0 && missing.length > 0 &&\n          <p class=\"md-search-result__terms\">\n            {translation(\"search.result.term.missing\")}: {...missing}\n          </p>\n        }\n      </article>\n    </a>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n  result: SearchItem[]\n): HTMLElement {\n  const threshold = result[0].score\n  const docs = [...result]\n\n  const config = configuration()\n\n  /* Find and extract parent article */\n  const parent = docs.findIndex(doc => {\n    const l = `${new URL(doc.location, config.base)}` // @todo hacky\n    return !l.includes(\"#\")\n  })\n  const [article] = docs.splice(parent, 1)\n\n  /* Determine last index above threshold */\n  let index = docs.findIndex(doc => doc.score < threshold)\n  if (index === -1)\n    index = docs.length\n\n  /* Partition sections */\n  const best = docs.slice(0, index)\n  const more = docs.slice(index)\n\n  /* Render children */\n  const children = [\n    renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n    ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n    ...more.length ? [\n      <details class=\"md-search-result__more\">\n        <summary tabIndex={-1}>\n          <div>\n            {more.length > 0 && more.length === 1\n              ? translation(\"search.result.more.one\")\n              : translation(\"search.result.more.other\", more.length)\n            }\n          </div>\n        </summary>\n        {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n      </details>\n    ] : []\n  ]\n\n  /* Render search result */\n  return (\n    <li class=\"md-search-result__item\">\n      {children}\n    </li>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n  return (\n    <ul class=\"md-source__facts\">\n      {Object.entries(facts).map(([key, value]) => (\n        <li class={`md-source__fact md-source__fact--${key}`}>\n          {typeof value === \"number\" ? round(value) : value}\n        </li>\n      ))}\n    </ul>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n  | \"prev\"\n  | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n  type: TabbedControlType\n): HTMLElement {\n  const classes = `tabbed-control tabbed-control--${type}`\n  return (\n    <div class={classes} hidden>\n      <button class=\"tabbed-button\" tabIndex={-1} aria-hidden=\"true\"></button>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n  return (\n    <div class=\"md-typeset__scrollwrap\">\n      <div class=\"md-typeset__table\">\n        {table}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version properties\n */\nexport interface VersionProperties {\n  hidden?: boolean                     /* Version is hidden */\n}\n\n/**\n * Version\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\n  properties?: VersionProperties       /* Version properties */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n  const config = configuration()\n\n  /* Ensure trailing slash - see https://bit.ly/3rL5u3f */\n  const url = new URL(`../${version.version}/`, config.base)\n  return (\n    <li class=\"md-version__item\">\n      <a href={`${url}`} class=\"md-version__link\">\n        {version.title}\n      </a>\n    </li>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n  versions: Version[], active: Version\n): HTMLElement {\n  versions = versions.filter(version => !version.properties?.hidden)\n  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version\")}\n      >\n        {active.title}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  BehaviorSubject,\n  EMPTY,\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  combineLatest,\n  debounce,\n  defer,\n  distinctUntilChanged,\n  endWith,\n  filter,\n  finalize,\n  first,\n  ignoreElements,\n  map,\n  mergeMap,\n  observeOn,\n  queueScheduler,\n  share,\n  startWith,\n  switchMap,\n  tap,\n  throttleTime,\n  timer,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  Viewport,\n  getElement,\n  getElementContainers,\n  getElementOffsetAbsolute,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementHover\n} from \"~/browser\"\nimport { renderInlineTooltip2 } from \"~/templates\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip\n */\nexport interface Tooltip {\n  active: boolean                      // Tooltip is active\n  offset: ElementOffset                // Tooltip offset\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dependencies\n */\ninterface Dependencies {\n  content$: Observable<HTMLElement>    // Tooltip content observable\n  viewport$: Observable<Viewport>      // Viewport observable\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for tooltips\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch tooltip\n *\n * This function tracks the tooltip host element, and deduces the active state\n * and offset of the tooltip from it. The active state is determined by whether\n * the host element is focused or hovered, and the offset is determined by the\n * host element's absolute position in the document.\n *\n * @param el - Tooltip host element\n *\n * @returns Tooltip observable\n */\nexport function watchTooltip2(\n  el: HTMLElement\n): Observable<Tooltip> {\n\n  // Compute whether tooltip should be shown - we need to watch both focus and\n  // hover events on the host element and emit if one of them is active. In case\n  // of a hover event, we keep the element visible for a short amount of time\n  // after the pointer left the host element for a better user experience.\n  const active$ =\n    combineLatest([\n      watchElementFocus(el),\n      watchElementHover(el)\n    ])\n      .pipe(\n        map(([focus, hover]) => focus || hover),\n        distinctUntilChanged()\n      )\n\n  // We need to determine all parent elements of the host element that are\n  // currently scrollable, as they might affect the position of the tooltip\n  // depending on their horizontal of vertical offset. We must track all of\n  // them and recompute the position of the tooltip if they change.\n  const offset$ =\n    defer(() => getElementContainers(el)).pipe(\n      mergeMap(watchElementContentOffset),\n      throttleTime(1),\n      map(() => getElementOffsetAbsolute(el))\n    )\n\n  // Only track parent elements and compute offset of the tooltip host if the\n  // tooltip should be shown - we defer the computation of the offset until the\n  // tooltip becomes active for the first time. This is necessary, because we\n  // must also keep the tooltip active as long as it is focused or hovered.\n  return active$.pipe(\n    first(active => active),\n    switchMap(() => combineLatest([active$, offset$])),\n    map(([active, offset]) => ({ active, offset })),\n    share()\n  )\n}\n\n/**\n * Mount tooltip\n *\n * This function renders a tooltip with the content from the provided `content$`\n * observable as passed via the dependencies. If the returned element has a role\n * of type `dialog`, the tooltip is considered to be interactive, and rendered\n * either above or below the host element, depending on the available space.\n *\n * If the returned element has a role of type `tooltip`, the tooltip is always\n * rendered below the host element and considered to be non-interactive. This\n * allows us to reuse the same positioning logic for both interactive and\n * non-interactive tooltips, as it is largely the same.\n *\n * @param el - Tooltip host element\n * @param dependencies - Dependencies\n *\n * @returns Tooltip component observable\n */\nexport function mountTooltip2(\n  el: HTMLElement, dependencies: Dependencies\n): Observable<Component<Tooltip>> {\n  const { content$, viewport$ } = dependencies\n\n  // Compute unique tooltip id - this is necessary to associate the tooltip host\n  // element with the tooltip element for ARIA purposes\n  const id = `__tooltip2_${sequence++}`\n\n  // Create component on subscription\n  return defer(() => {\n    const push$ = new Subject<Tooltip>()\n\n    // Create subject to track tooltip presence and visibility - we use another\n    // purely internal subject to track the tooltip's presence and visibility,\n    // as the tooltip should be visible if the host element or tooltip itself\n    // is focused or hovered to allow for smooth pointer migration\n    const show$ = new BehaviorSubject(false)\n    push$.pipe(ignoreElements(), endWith(false))\n      .subscribe(show$)\n\n    // Create observable controlling tooltip element - we create and attach the\n    // tooltip only if it is actually present, in order to keep the number of\n    // elements low. We need to keep the tooltip visible for a short time after\n    // the pointer left the host element or tooltip itself. For this, we use an\n    // inner subscription to the tooltip observable, which we terminate when the\n    // tooltip should not be shown, automatically removing the element. Moreover\n    // we use the queue scheduler, which will schedule synchronously in case the\n    // tooltip should be shown, and asynchronously if it should be hidden.\n    const node$ = show$.pipe(\n      debounce(active => timer(+!active * 250, queueScheduler)),\n      distinctUntilChanged(),\n      switchMap(active => active ? content$ : EMPTY),\n      tap(node => node.id = id),\n      share()\n    )\n\n    // Compute tooltip presence and visibility - the tooltip should be shown if\n    // the host element or the tooltip itself is focused or hovered\n    combineLatest([\n      push$.pipe(map(({ active }) => active)),\n      node$.pipe(\n        switchMap(node => watchElementHover(node, 250)),\n        startWith(false)\n      )\n    ])\n      .pipe(map(states => states.some(active => active)))\n      .subscribe(show$)\n\n    // Compute tooltip origin - we need to compute the tooltip origin depending\n    // on the position of the host element, the viewport size, as well as the\n    // actual size of the tooltip, if positioned above. The tooltip must about\n    // to be rendered for this to be correct, which is why we do it here.\n    const origin$ = show$.pipe(\n      filter(active => active),\n      withLatestFrom(node$, viewport$),\n      map(([_, node, { size }]) => {\n        const host = el.getBoundingClientRect()\n        const x = host.width / 2\n\n        // If the tooltip is non-interactive, we always render it below the\n        // actual element because all operating systems do it that way\n        if (node.role === \"tooltip\") {\n          return { x, y: 8 + host.height }\n\n        // Otherwise, we determine where there is more space, and render the\n        // tooltip either above or below the host element\n        } else if (host.y >= size.height / 2) {\n          const { height } = getElementSize(node)\n          return { x, y: -16 - height }\n        } else {\n          return { x, y: +16 + host.height }\n        }\n      })\n    )\n\n    // Update tooltip position - we always need to update the position of the\n    // tooltip, as it might change depending on the viewport offset of the host\n    combineLatest([node$, push$, origin$])\n      .subscribe(([node, { offset }, origin]) => {\n        node.style.setProperty(\"--md-tooltip-host-x\", `${offset.x}px`)\n        node.style.setProperty(\"--md-tooltip-host-y\", `${offset.y}px`)\n\n        // Update tooltip origin - this is mainly set to determine the position\n        // of the tooltip tail, to show the direction it is originating from\n        node.style.setProperty(\"--md-tooltip-x\", `${origin.x}px`)\n        node.style.setProperty(\"--md-tooltip-y\", `${origin.y}px`)\n\n        // Update tooltip render location, i.e., whether the tooltip is shown\n        // above or below the host element, depending on the available space\n        node.classList.toggle(\"md-tooltip2--top\",    origin.y <  0)\n        node.classList.toggle(\"md-tooltip2--bottom\", origin.y >= 0)\n      })\n\n    // Update tooltip width - we only explicitly set the width of the tooltip\n    // if it is non-interactive, in case it should always be rendered centered\n    show$.pipe(\n      filter(active => active),\n      withLatestFrom(node$, (_, node) => node),\n      filter(node => node.role === \"tooltip\")\n    )\n      .subscribe(node => {\n        const size = getElementSize(getElement(\":scope > *\", node))\n\n        // Set tooltip width and remove tail by setting it to a width of zero -\n        // if authors want to keep the tail, we can move this to CSS later\n        node.style.setProperty(\"--md-tooltip-width\", `${size.width}px`)\n        node.style.setProperty(\"--md-tooltip-tail\",  `${0}px`)\n      })\n\n    // Update tooltip visibility - we defer to the next animation frame, because\n    // the tooltip must first be added to the document before we make it appear,\n    // or it will appear instantly without delay. Additionally, we need to keep\n    // the tooltip visible for a short time after the pointer left the host.\n    show$.pipe(\n      distinctUntilChanged(),\n      observeOn(animationFrameScheduler),\n      withLatestFrom(node$)\n    )\n      .subscribe(([active, node]) => {\n        node.classList.toggle(\"md-tooltip2--active\", active)\n      })\n\n    // Set up ARIA attributes when tooltip is visible\n    combineLatest([\n      show$.pipe(filter(active => active)),\n      node$\n    ])\n      .subscribe(([_, node]) => {\n        if (node.role === \"dialog\") {\n          el.setAttribute(\"aria-controls\", id)\n          el.setAttribute(\"aria-haspopup\", \"dialog\")\n        } else {\n          el.setAttribute(\"aria-describedby\", id)\n        }\n      })\n\n    // Remove ARIA attributes when tooltip is hidden\n    show$.pipe(filter(active => !active))\n      .subscribe(() => {\n        el.removeAttribute(\"aria-controls\")\n        el.removeAttribute(\"aria-describedby\")\n        el.removeAttribute(\"aria-haspopup\")\n      })\n\n    // Create and return component\n    return watchTooltip2(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n\n// ----------------------------------------------------------------------------\n\n/**\n * Mount inline tooltip\n *\n * @todo refactor this function\n *\n * @param el - Tooltip host element\n * @param dependencies - Dependencies\n * @param container - Container\n *\n * @returns Tooltip component observable\n */\nexport function mountInlineTooltip2(\n  el: HTMLElement, { viewport$ }: { viewport$: Observable<Viewport> },\n  container = document.body\n): Observable<Component<Tooltip>> {\n  return mountTooltip2(el, {\n    content$: new Observable<HTMLElement>(observer => {\n      const title = el.title\n      const node = renderInlineTooltip2(title)\n      observer.next(node)\n      el.removeAttribute(\"title\")\n      // Append tooltip and remove on unsubscription\n      container.append(node)\n      return () => {\n        node.remove()\n        el.setAttribute(\"title\", title)\n      }\n    }),\n    viewport$\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  debounceTime,\n  defer,\n  delay,\n  endWith,\n  filter,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  switchMap,\n  take,\n  takeUntil,\n  tap,\n  throttleTime,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getActiveElement,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementOffset,\n  watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n  active: boolean                      /* Annotation is active */\n  offset: ElementOffset                /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Annotation> {\n  const offset$ = defer(() => combineLatest([\n    watchElementOffset(el),\n    watchElementContentOffset(container)\n  ]))\n    .pipe(\n      map(([{ x, y }, scroll]): ElementOffset => {\n        const { width, height } = getElementSize(el)\n        return ({\n          x: x - scroll.x + width  / 2,\n          y: y - scroll.y + height / 2\n        })\n      })\n    )\n\n  /* Actively watch annotation on focus */\n  return watchElementFocus(el)\n    .pipe(\n      switchMap(active => offset$\n        .pipe(\n          map(offset => ({ active, offset })),\n          take(+!active || Infinity)\n        )\n      )\n    )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n  el: HTMLElement, container: HTMLElement, { target$ }: MountOptions\n): Observable<Component<Annotation>> {\n  const [tooltip, index] = Array.from(el.children)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Annotation>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.style.removeProperty(\"--md-tooltip-x\")\n        el.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Start animation only when annotation is visible */\n    watchElementVisibility(el)\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(visible => {\n          el.toggleAttribute(\"data-md-visible\", visible)\n        })\n\n    /* Toggle tooltip presence to mitigate empty lines when copying */\n    merge(\n      push$.pipe(filter(({ active }) => active)),\n      push$.pipe(debounceTime(250), filter(({ active }) => !active))\n    )\n      .subscribe({\n\n        /* Handle emission */\n        next({ active }) {\n          if (active)\n            el.prepend(tooltip)\n          else\n            tooltip.remove()\n        },\n\n        /* Handle complete */\n        complete() {\n          el.prepend(tooltip)\n        }\n      })\n\n    /* Toggle tooltip visibility */\n    push$\n      .pipe(\n        auditTime(16, animationFrameScheduler)\n      )\n        .subscribe(({ active }) => {\n          tooltip.classList.toggle(\"md-tooltip--active\", active)\n        })\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(125, animationFrameScheduler),\n        filter(() => !!el.offsetParent),\n        map(() => el.offsetParent!.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next(origin) {\n            if (origin)\n              el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n            else\n              el.style.removeProperty(\"--md-tooltip-0\")\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-tooltip-0\")\n          }\n        })\n\n    /* Allow to copy link without scrolling to anchor */\n    fromEvent<MouseEvent>(index, \"click\")\n      .pipe(\n        takeUntil(done$),\n        filter(ev => !(ev.metaKey || ev.ctrlKey))\n      )\n        .subscribe(ev => {\n          ev.stopPropagation()\n          ev.preventDefault()\n        })\n\n    /* Allow to open link in new tab or blur on close */\n    fromEvent<MouseEvent>(index, \"mousedown\")\n      .pipe(\n        takeUntil(done$),\n        withLatestFrom(push$)\n      )\n        .subscribe(([ev, { active }]) => {\n\n          /* Open in new tab */\n          if (ev.button !== 0 || ev.metaKey || ev.ctrlKey) {\n            ev.preventDefault()\n\n          /* Close annotation */\n          } else if (active) {\n            ev.preventDefault()\n\n            /* Focus parent annotation, if any */\n            const parent = el.parentElement!.closest(\".md-annotation\")\n            if (parent instanceof HTMLElement)\n              parent.focus()\n            else\n              getActiveElement()?.blur()\n          }\n        })\n\n    /* Open and focus annotation on location target */\n    target$\n      .pipe(\n        takeUntil(done$),\n        filter(target => target === tooltip),\n        delay(125)\n      )\n        .subscribe(() => el.focus())\n\n    /* Create and return component */\n    return watchAnnotation(el, container)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  endWith,\n  finalize,\n  ignoreElements,\n  merge,\n  share,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation hosts in the containing element\n *\n * @param container - Containing element\n *\n * @returns Annotation hosts\n */\nfunction findHosts(container: HTMLElement): HTMLElement[] {\n  return container.tagName === \"CODE\"\n    ? getElements(\".c, .c1, .cm\", container)\n    : [container]\n}\n\n/**\n * Find all annotation markers in the containing element\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findMarkers(container: HTMLElement): Text[] {\n  const markers: Text[] = []\n  for (const el of findHosts(container)) {\n    const nodes: Text[] = []\n\n    /* Find all text nodes in current element */\n    const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n    for (let node = it.nextNode(); node; node = it.nextNode())\n      nodes.push(node as Text)\n\n    /* Find all markers in each text node */\n    for (let text of nodes) {\n      let match: RegExpExecArray | null\n\n      /* Split text at marker and add to list */\n      while ((match = /(\\(\\d+\\))(!)?/.exec(text.textContent!))) {\n        const [, id, force] = match\n        if (typeof force === \"undefined\") {\n          const marker = text.splitText(match.index)\n          text = marker.splitText(id.length)\n          markers.push(marker)\n\n        /* Replace entire text with marker */\n        } else {\n          text.textContent = id\n          markers.push(text)\n          break\n        }\n      }\n    }\n  }\n  return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n  target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n  el: HTMLElement, container: HTMLElement, { target$, print$ }: MountOptions\n): Observable<Component<Annotation>> {\n\n  /* Compute prefix for tooltip anchors */\n  const parent = container.closest(\"[id]\")\n  const prefix = parent?.id\n\n  /* Find and replace all markers with empty annotations */\n  const annotations = new Map<string, HTMLElement>()\n  for (const marker of findMarkers(container)) {\n    const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n    if (getOptionalElement(`:scope > li:nth-child(${id})`, el)) {\n      annotations.set(id, renderAnnotation(id, prefix))\n      marker.replaceWith(annotations.get(id)!)\n    }\n  }\n\n  /* Keep list if there are no annotations to render */\n  if (annotations.size === 0)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n\n    /* Retrieve container pairs for swapping */\n    const pairs: [HTMLElement, HTMLElement][] = []\n    for (const [id, annotation] of annotations)\n      pairs.push([\n        getElement(\".md-typeset\", annotation),\n        getElement(`:scope > li:nth-child(${id})`, el)\n      ])\n\n    /* Handle print mode - see https://bit.ly/3rgPdpt */\n    print$.pipe(takeUntil(done$))\n      .subscribe(active => {\n        el.hidden = !active\n\n        /* Add class to discern list element */\n        el.classList.toggle(\"md-annotation-list\", active)\n\n        /* Show annotations in code block or list (print) */\n        for (const [inner, child] of pairs)\n          if (!active)\n            swap(child, inner)\n          else\n            swap(inner, child)\n      })\n\n    /* Create and return component */\n    return merge(...[...annotations]\n      .map(([, annotation]) => (\n        mountAnnotation(annotation, container, { target$ })\n      ))\n    )\n      .pipe(\n        finalize(() => push$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable, defer } from \"rxjs\"\n\nimport { Component } from \"../../../_\"\nimport { Annotation } from \"../_\"\nimport { mountAnnotationList } from \"../list\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find list element directly following a block\n *\n * @param el - Annotation block element\n *\n * @returns List element or nothing\n */\nfunction findList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation block\n *\n * @param el - Annotation block element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Annotation>> {\n  return defer(() => {\n    const list = findList(el)\n    return typeof list !== \"undefined\"\n      ? mountAnnotationList(list, el, options)\n      : EMPTY\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  map,\n  mergeWith,\n  switchMap,\n  take,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  getElements,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport {\n  Tooltip,\n  mountInlineTooltip2\n} from \"~/components/tooltip2\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block overflow\n */\nexport interface Overflow {\n  scrollable: boolean                  /* Code block overflows */\n}\n\n/**\n * Code block\n */\nexport type CodeBlock =\n  | Overflow\n  | Annotation\n  | Tooltip\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for code blocks\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findCandidateList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n  el: HTMLElement\n): Observable<Overflow> {\n  return watchElementSize(el)\n    .pipe(\n      map(({ width }) => {\n        const content = getElementContentSize(el)\n        return {\n          scrollable: content.width > width\n        }\n      }),\n      distinctUntilKeyChanged(\"scrollable\")\n    )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * Note that code blocks may be mounted eagerly or lazily. If they're mounted\n * lazily (on first visibility), code annotation anchor links will not work,\n * as they are evaluated on initial page load, and code annotations in general\n * might feel a little bumpier.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<CodeBlock>> {\n  const { matches: hover } = matchMedia(\"(hover)\")\n\n  /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n  const factory$ = defer(() => {\n    const push$ = new Subject<Overflow>()\n    const done$ = push$.pipe(takeLast(1))\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    const content$: Array<Observable<Component<CodeBlock>>> = []\n    if (ClipboardJS.isSupported()) {\n      if (el.closest(\".copy\") || (\n        feature(\"content.code.copy\") && !el.closest(\".no-copy\")\n      )) {\n        const parent = el.closest(\"pre\")!\n        parent.id = `__code_${sequence++}`\n\n        /* Mount tooltip, if enabled */\n        const button = renderClipboardButton(parent.id)\n        parent.insertBefore(button, el)\n        if (feature(\"content.tooltips\"))\n          content$.push(mountInlineTooltip2(button, { viewport$ }))\n      }\n    }\n\n    /* Handle code annotations */\n    const container = el.closest(\".highlight\")\n    if (container instanceof HTMLElement) {\n      const list = findCandidateList(container)\n\n      /* Mount code annotations, if enabled */\n      if (typeof list !== \"undefined\" && (\n        container.classList.contains(\"annotate\") ||\n        feature(\"content.code.annotate\")\n      )) {\n        const annotations$ = mountAnnotationList(list, el, options)\n        content$.push(\n          watchElementSize(container)\n            .pipe(\n              takeUntil(done$),\n              map(({ width, height }) => width && height),\n              distinctUntilChanged(),\n              switchMap(active => active ? annotations$ : EMPTY)\n            )\n        )\n      }\n    }\n\n    // If the code block has line spans, we can add this additional class to\n    // the code block element, which fixes the problem for highlighted code\n    // lines not stretching to the entirety of the screen when the code block\n    // overflows, e.g., on mobile - see\n    const spans = getElements(\":scope > span[id]\", el)\n    if (spans.length)\n      el.classList.add(\"md-code__content\")\n\n    /* Create and return component */\n    return watchCodeBlock(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state })),\n        mergeWith(...content$)\n      )\n  })\n\n  /* Mount code block lazily */\n  if (feature(\"content.lazy\"))\n    return watchElementVisibility(el)\n      .pipe(\n        filter(visible => visible),\n        take(1),\n        switchMap(() => factory$)\n      )\n\n  /* Mount code block */\n  return factory$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n  action: \"open\" | \"close\"             /* Details state */\n  reveal?: boolean                     /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n  el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable<Details> {\n  let open = true\n  return merge(\n\n    /* Open and focus details on location target */\n    target$\n      .pipe(\n        map(target => target.closest(\"details:not([open])\")!),\n        filter(details => el === details),\n        map(() => ({\n          action: \"open\", reveal: true\n        }) as Details)\n      ),\n\n    /* Open details on print and close afterwards */\n    print$\n      .pipe(\n        filter(active => active || !open),\n        tap(() => open = el.open),\n        map(active => ({\n          action: active ? \"open\" : \"close\"\n        }) as Details)\n      )\n  )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n  el: HTMLDetailsElement, options: MountOptions\n): Observable<Component<Details>> {\n  return defer(() => {\n    const push$ = new Subject<Details>()\n    push$.subscribe(({ action, reveal }) => {\n      el.toggleAttribute(\"open\", action === \"open\")\n      if (reveal)\n        el.scrollIntoView()\n    })\n\n    /* Create and return component */\n    return watchDetails(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", ".node circle,.node ellipse,.node path,.node polygon,.node rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}marker{fill:var(--md-mermaid-edge-color)!important}.edgeLabel .label rect{fill:#0000}.label{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.label foreignObject{line-height:normal;overflow:visible}.label div .edgeLabel{color:var(--md-mermaid-label-fg-color)}.edgeLabel,.edgeLabel rect,.label div .edgeLabel{background-color:var(--md-mermaid-label-bg-color)}.edgeLabel,.edgeLabel rect{fill:var(--md-mermaid-label-bg-color);color:var(--md-mermaid-edge-color)}.edgePath .path,.flowchart-link{stroke:var(--md-mermaid-edge-color);stroke-width:.05rem}.edgePath .arrowheadPath{fill:var(--md-mermaid-edge-color);stroke:none}.cluster rect{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}.cluster span{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}g #flowchart-circleEnd,g #flowchart-circleStart,g #flowchart-crossEnd,g #flowchart-crossStart,g #flowchart-pointEnd,g #flowchart-pointStart{stroke:none}g.classGroup line,g.classGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.classGroup text{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.classLabel .box{fill:var(--md-mermaid-label-bg-color);background-color:var(--md-mermaid-label-bg-color);opacity:1}.classLabel .label{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.node .divider{stroke:var(--md-mermaid-node-fg-color)}.relation{stroke:var(--md-mermaid-edge-color)}.cardinality{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.cardinality text{fill:inherit!important}defs #classDiagram-compositionEnd,defs #classDiagram-compositionStart,defs #classDiagram-dependencyEnd,defs #classDiagram-dependencyStart,defs #classDiagram-extensionEnd,defs #classDiagram-extensionStart{fill:var(--md-mermaid-edge-color)!important;stroke:var(--md-mermaid-edge-color)!important}defs #classDiagram-aggregationEnd,defs #classDiagram-aggregationStart{fill:var(--md-mermaid-label-bg-color)!important;stroke:var(--md-mermaid-edge-color)!important}g.stateGroup rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}g.stateGroup .state-title{fill:var(--md-mermaid-label-fg-color)!important;font-family:var(--md-mermaid-font-family)}g.stateGroup .composit{fill:var(--md-mermaid-label-bg-color)}.nodeLabel,.nodeLabel p{color:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.node circle.state-end,.node circle.state-start,.start-state{fill:var(--md-mermaid-edge-color);stroke:none}.end-state-inner,.end-state-outer{fill:var(--md-mermaid-edge-color)}.end-state-inner,.node circle.state-end{stroke:var(--md-mermaid-label-bg-color)}.transition{stroke:var(--md-mermaid-edge-color)}[id^=state-fork] rect,[id^=state-join] rect{fill:var(--md-mermaid-edge-color)!important;stroke:none!important}.statediagram-cluster.statediagram-cluster .inner{fill:var(--md-default-bg-color)}.statediagram-cluster rect{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.statediagram-state rect.divider{fill:var(--md-default-fg-color--lightest);stroke:var(--md-default-fg-color--lighter)}defs #statediagram-barbEnd{stroke:var(--md-mermaid-edge-color)}.attributeBoxEven,.attributeBoxOdd{fill:var(--md-mermaid-node-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityBox{fill:var(--md-mermaid-label-bg-color);stroke:var(--md-mermaid-node-fg-color)}.entityLabel{fill:var(--md-mermaid-label-fg-color);font-family:var(--md-mermaid-font-family)}.relationshipLabelBox{fill:var(--md-mermaid-label-bg-color);fill-opacity:1;background-color:var(--md-mermaid-label-bg-color);opacity:1}.relationshipLabel{fill:var(--md-mermaid-label-fg-color)}.relationshipLine{stroke:var(--md-mermaid-edge-color)}defs #ONE_OR_MORE_END *,defs #ONE_OR_MORE_START *,defs #ONLY_ONE_END *,defs #ONLY_ONE_START *,defs #ZERO_OR_MORE_END *,defs #ZERO_OR_MORE_START *,defs #ZERO_OR_ONE_END *,defs #ZERO_OR_ONE_START *{stroke:var(--md-mermaid-edge-color)!important}defs #ZERO_OR_MORE_END circle,defs #ZERO_OR_MORE_START circle{fill:var(--md-mermaid-label-bg-color)}.actor{fill:var(--md-mermaid-sequence-actor-bg-color);stroke:var(--md-mermaid-sequence-actor-border-color)}text.actor>tspan{fill:var(--md-mermaid-sequence-actor-fg-color);font-family:var(--md-mermaid-font-family)}line{stroke:var(--md-mermaid-sequence-actor-line-color)}.actor-man circle,.actor-man line{fill:var(--md-mermaid-sequence-actorman-bg-color);stroke:var(--md-mermaid-sequence-actorman-line-color)}.messageLine0,.messageLine1{stroke:var(--md-mermaid-sequence-message-line-color)}.note{fill:var(--md-mermaid-sequence-note-bg-color);stroke:var(--md-mermaid-sequence-note-border-color)}.loopText,.loopText>tspan,.messageText,.noteText>tspan{stroke:none;font-family:var(--md-mermaid-font-family)!important}.messageText{fill:var(--md-mermaid-sequence-message-fg-color)}.loopText,.loopText>tspan{fill:var(--md-mermaid-sequence-loop-fg-color)}.noteText>tspan{fill:var(--md-mermaid-sequence-note-fg-color)}#arrowhead path{fill:var(--md-mermaid-sequence-message-line-color);stroke:none}.loopLine{fill:var(--md-mermaid-sequence-loop-bg-color);stroke:var(--md-mermaid-sequence-loop-border-color)}.labelBox{fill:var(--md-mermaid-sequence-label-bg-color);stroke:none}.labelText,.labelText>span{fill:var(--md-mermaid-sequence-label-fg-color);font-family:var(--md-mermaid-font-family)}.sequenceNumber{fill:var(--md-mermaid-sequence-number-fg-color)}rect.rect{fill:var(--md-mermaid-sequence-box-bg-color);stroke:none}rect.rect+text.text{fill:var(--md-mermaid-sequence-box-fg-color)}defs #sequencenumber{fill:var(--md-mermaid-sequence-number-bg-color)!important}", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable<void>\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable<void> {\n  return typeof mermaid === \"undefined\" || mermaid instanceof Element\n    ? watchScript(\"https://unpkg.com/mermaid@10.7.0/dist/mermaid.min.js\")\n    : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n  el: HTMLElement\n): Observable<Component<Mermaid>> {\n  el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n  mermaid$ ||= fetchScripts()\n    .pipe(\n      tap(() => mermaid.initialize({\n        startOnLoad: false,\n        themeCSS,\n        sequence: {\n          actorFontSize: \"16px\", // Hack: mitigate https://bit.ly/3y0NEi3\n          messageFontSize: \"16px\",\n          noteFontSize: \"16px\"\n        }\n      })),\n      map(() => undefined),\n      shareReplay(1)\n    )\n\n  /* Render diagram */\n  mermaid$.subscribe(async () => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${sequence++}`\n\n    /* Create host element to replace code block */\n    const host = h(\"div\", { class: \"mermaid\" })\n    const text = el.textContent\n\n    /* Render and inject diagram */\n    const { svg, fn } = await mermaid.render(id, text)\n\n    /* Create a shadow root and inject diagram */\n    const shadow = host.attachShadow({ mode: \"closed\" })\n    shadow.innerHTML = svg\n\n    /* Replace code block with diagram and bind functions */\n    el.replaceWith(host)\n    fn?.(shadow)\n  })\n\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n  el: HTMLElement\n): Observable<Component<DataTable>> {\n  el.replaceWith(sentinel)\n  sentinel.replaceWith(renderTable(el))\n\n  /* Create and return component */\n  return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  endWith,\n  filter,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  skip,\n  startWith,\n  subscribeOn,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContentOffset,\n  getElementContentSize,\n  getElementOffset,\n  getElementSize,\n  getElements,\n  watchElementContentOffset,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n  active: HTMLLabelElement             /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param inputs - Content tabs input elements\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  inputs: HTMLInputElement[]\n): Observable<ContentTabs> {\n  const initial = inputs.find(input => input.checked) || inputs[0]\n  return merge(...inputs.map(input => fromEvent(input, \"change\")\n    .pipe(\n      map(() => getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`))\n    )\n  ))\n    .pipe(\n      startWith(getElement<HTMLLabelElement>(`label[for=\"${initial.id}\"]`)),\n      map(active => ({ active }))\n    )\n}\n\n/**\n * Mount content tabs\n *\n * @param el - Content tabs element\n * @param options - Options\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n  el: HTMLElement, { viewport$, target$ }: MountOptions\n): Observable<Component<ContentTabs>> {\n  const container = getElement(\".tabbed-labels\", el)\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\n\n  /* Render content tab previous button for pagination */\n  const prev = renderTabbedControl(\"prev\")\n  el.append(prev)\n\n  /* Render content tab next button for pagination */\n  const next = renderTabbedControl(\"next\")\n  el.append(next)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    combineLatest([push$, watchElementSize(el)])\n      .pipe(\n        takeUntil(done$),\n        auditTime(1, animationFrameScheduler)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n            el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n            /* Scroll container to active content tab */\n            const content = getElementContentOffset(container)\n            if (\n              offset.x         < content.x              ||\n              offset.x + width > content.x + size.width\n            )\n              container.scrollTo({\n                left: Math.max(0, offset.x - 16),\n                behavior: \"smooth\"\n              })\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-indicator-x\")\n            el.style.removeProperty(\"--md-indicator-width\")\n          }\n        })\n\n    /* Hide content tab buttons on borders */\n    combineLatest([\n      watchElementContentOffset(container),\n      watchElementSize(container)\n    ])\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(([offset, size]) => {\n          const content = getElementContentSize(container)\n          prev.hidden = offset.x < 16\n          next.hidden = offset.x > content.width - size.width - 16\n        })\n\n    /* Paginate content tab container on click */\n    merge(\n      fromEvent(prev, \"click\").pipe(map(() => -1)),\n      fromEvent(next, \"click\").pipe(map(() => +1))\n    )\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Switch to content tab target */\n    target$\n      .pipe(\n        takeUntil(done$),\n        filter(input => inputs.includes(input as HTMLInputElement))\n      )\n        .subscribe(input => input.click())\n\n    /* Add link to each content tab label */\n    container.classList.add(\"tabbed-labels--linked\")\n    for (const input of inputs) {\n      const label = getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`)\n      label.replaceChildren(h(\"a\", {\n        href: `#${label.htmlFor}`,\n        tabIndex: -1\n      }, ...Array.from(label.childNodes)))\n\n      /* Allow to copy link without scrolling to anchor */\n      fromEvent<MouseEvent>(label.firstElementChild!, \"click\")\n        .pipe(\n          takeUntil(done$),\n          filter(ev => !(ev.metaKey || ev.ctrlKey)),\n          tap(ev => {\n            ev.preventDefault()\n            ev.stopPropagation()\n          })\n        )\n          // @todo we might need to remove the anchor link on complete\n          .subscribe(() => {\n            history.replaceState({}, \"\", `#${label.htmlFor}`)\n            label.click()\n          })\n    }\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.pipe(\n        skip(1),\n        withLatestFrom(viewport$)\n      )\n        .subscribe(([{ active }, { offset }]) => {\n          const tab = active.innerText.trim()\n          if (active.hasAttribute(\"data-md-switching\")) {\n            active.removeAttribute(\"data-md-switching\")\n\n          /* Determine viewport offset of active tab */\n          } else {\n            const y = el.offsetTop - offset.y\n\n            /* Passively activate other tabs */\n            for (const set of getElements(\"[data-tabs]\"))\n              for (const input of getElements<HTMLInputElement>(\n                \":scope > input\", set\n              )) {\n                const label = getElement(`label[for=\"${input.id}\"]`)\n                if (\n                  label !== active &&\n                  label.innerText.trim() === tab\n                ) {\n                  label.setAttribute(\"data-md-switching\", \"\")\n                  input.click()\n                  break\n                }\n              }\n\n            /* Bring active tab into view */\n            window.scrollTo({\n              top: el.offsetTop - y\n            })\n\n            /* Persist active tabs in local storage */\n            const tabs = __md_get<string[]>(\"__tabs\") || []\n            __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n          }\n        })\n\n    /* Pause media (audio, video) on switch - see https://bit.ly/3Bk6cel */\n    push$.pipe(takeUntil(done$))\n      .subscribe(() => {\n        for (const media of getElements<HTMLAudioElement>(\"audio, video\", el))\n          media.pause()\n      })\n\n    /* Create and return component */\n    return watchElementVisibility(el)\n      .pipe(\n        switchMap(() => watchContentTabs(inputs)),\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { Viewport, getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport {\n  Tooltip,\n  mountInlineTooltip2\n} from \"../../tooltip2\"\nimport {\n  Annotation,\n  mountAnnotationBlock\n} from \"../annotation\"\nimport {\n  CodeBlock,\n  mountCodeBlock\n} from \"../code\"\nimport {\n  Details,\n  mountDetails\n} from \"../details\"\nimport {\n  Mermaid,\n  mountMermaid\n} from \"../mermaid\"\nimport {\n  DataTable,\n  mountDataTable\n} from \"../table\"\nimport {\n  ContentTabs,\n  mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n  | Annotation\n  | CodeBlock\n  | ContentTabs\n  | DataTable\n  | Details\n  | Mermaid\n  | Tooltip\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n  el: HTMLElement, { viewport$, target$, print$ }: MountOptions\n): Observable<Component<Content>> {\n  return merge(\n\n    /* Annotations */\n    ...getElements(\".annotate:not(.highlight)\", el)\n      .map(child => mountAnnotationBlock(child, { target$, print$ })),\n\n    /* Code blocks */\n    ...getElements(\"pre:not(.mermaid) > code\", el)\n      .map(child => mountCodeBlock(child, { target$, print$ })),\n\n    /* Mermaid diagrams */\n    ...getElements(\"pre.mermaid\", el)\n      .map(child => mountMermaid(child)),\n\n    /* Data tables */\n    ...getElements(\"table:not([class])\", el)\n      .map(child => mountDataTable(child)),\n\n    /* Details */\n    ...getElements(\"details\", el)\n      .map(child => mountDetails(child, { target$, print$ })),\n\n    /* Content tabs */\n    ...getElements(\"[data-tabs]\", el)\n      .map(child => mountContentTabs(child, { viewport$, target$ })),\n\n    /* Tooltips */\n    ...getElements(\"[title]\", el)\n      .filter(() => feature(\"content.tooltips\"))\n      .map(child => mountInlineTooltip2(child, { viewport$ }))\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  delay,\n  finalize,\n  map,\n  merge,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n  message: string                      /* Dialog message */\n  active: boolean                      /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n  _el: HTMLElement, { alert$ }: WatchOptions\n): Observable<Dialog> {\n  return alert$\n    .pipe(\n      switchMap(message => merge(\n        of(true),\n        of(false).pipe(delay(2000))\n      )\n        .pipe(\n          map(active => ({ message, active }))\n        )\n      )\n    )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Dialog>> {\n  const inner = getElement(\".md-typeset\", el)\n  return defer(() => {\n    const push$ = new Subject<Dialog>()\n    push$.subscribe(({ message, active }) => {\n      el.classList.toggle(\"md-dialog--active\", active)\n      inner.textContent = message\n    })\n\n    /* Create and return component */\n    return watchDialog(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  subscribeOn,\n  tap,\n  throttleTime\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getElement,\n  getElementContainer,\n  getElementOffset,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementHover\n} from \"~/browser\"\nimport { renderTooltip } from \"~/templates\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tooltip\n */\nexport interface Tooltip {\n  active: boolean                      /* Tooltip is active */\n  offset: ElementOffset                /* Tooltip offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for tooltips\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch tooltip\n *\n * This function will append the tooltip temporarily to compute its width,\n * which is necessary for correct centering, and then removing it again.\n *\n * @param el - Tooltip element\n * @param host - Host element\n *\n * @returns Tooltip observable\n */\nexport function watchTooltip(\n  el: HTMLElement, host: HTMLElement\n): Observable<Tooltip> {\n  document.body.append(el)\n\n  /* Compute width and remove tooltip immediately */\n  const { width } = getElementSize(el)\n  el.style.setProperty(\"--md-tooltip-width\", `${width}px`)\n  el.remove()\n\n  /* Retrieve and watch containing element */\n  const container = getElementContainer(host)\n  const scroll$ =\n    typeof container !== \"undefined\"\n      ? watchElementContentOffset(container)\n      : of({ x: 0, y: 0 })\n\n  /* Compute tooltip visibility */\n  const active$ = merge(\n    watchElementFocus(host),\n    watchElementHover(host)\n  )\n    .pipe(\n      distinctUntilChanged()\n    )\n\n  /* Compute tooltip offset */\n  return combineLatest([active$, scroll$])\n    .pipe(\n      map(([active, scroll]) => {\n        let { x, y } = getElementOffset(host)\n        const size = getElementSize(host)\n\n        /**\n         * Experimental: fix handling of tables - see https://bit.ly/3TQEj5O\n         *\n         * If this proves to be a viable fix, we should refactor tooltip\n         * positioning and somehow streamline the current process. This might\n         * also fix positioning for annotations inside tables, which is another\n         * limitation.\n         */\n        const table = host.closest(\"table\")\n        if (table && host.parentElement) {\n          x += table.offsetLeft + host.parentElement.offsetLeft\n          y += table.offsetTop  + host.parentElement.offsetTop\n        }\n        return {\n          active,\n          offset: {\n            x: x - scroll.x + size.width  / 2 - width / 2,\n            y: y - scroll.y + size.height + 8\n          }\n        }\n      })\n    )\n}\n\n/**\n * Mount tooltip\n *\n * @param el - Host element\n *\n * @returns Tooltip component observable\n */\nexport function mountTooltip(\n  el: HTMLElement\n): Observable<Component<Tooltip>> {\n  const title = el.title\n  if (!title.length)\n    return EMPTY\n\n  /* Render tooltip and set title from host element */\n  const id = `__tooltip_${sequence++}`\n  const tooltip = renderTooltip(id, \"inline\")\n  const typeset = getElement(\".md-typeset\", tooltip)\n  typeset.innerHTML = title\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Tooltip>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        tooltip.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        tooltip.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        tooltip.style.removeProperty(\"--md-tooltip-x\")\n        tooltip.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Toggle tooltip presence to mitigate empty lines when copying */\n    merge(\n      push$.pipe(filter(({ active }) => active)),\n      push$.pipe(debounceTime(250), filter(({ active }) => !active))\n    )\n      .subscribe({\n\n        /* Handle emission */\n        next({ active }) {\n          if (active) {\n            el.insertAdjacentElement(\"afterend\", tooltip)\n            el.setAttribute(\"aria-describedby\", id)\n            el.removeAttribute(\"title\")\n          } else {\n            tooltip.remove()\n            el.removeAttribute(\"aria-describedby\")\n            el.setAttribute(\"title\", title)\n          }\n        },\n\n        /* Handle complete */\n        complete() {\n          tooltip.remove()\n          el.removeAttribute(\"aria-describedby\")\n          el.setAttribute(\"title\", title)\n        }\n      })\n\n    /* Toggle tooltip visibility */\n    push$\n      .pipe(\n        auditTime(16, animationFrameScheduler)\n      )\n        .subscribe(({ active }) => {\n          tooltip.classList.toggle(\"md-tooltip--active\", active)\n        })\n\n    // @todo - refactor positioning together with annotations \u2013 there are\n    // several things that overlap and are identical in handling\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(125, animationFrameScheduler),\n        filter(() => !!el.offsetParent),\n        map(() => el.offsetParent!.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n      .subscribe({\n\n        /* Handle emission */\n        next(origin) {\n          if (origin)\n            tooltip.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n          else\n            tooltip.style.removeProperty(\"--md-tooltip-0\")\n        },\n\n        /* Handle complete */\n        complete() {\n          tooltip.style.removeProperty(\"--md-tooltip-0\")\n        }\n      })\n\n    /* Create and return component */\n    return watchTooltip(tooltip, el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  combineLatestWith,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  from,\n  ignoreElements,\n  map,\n  mergeMap,\n  mergeWith,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\nimport {\n  Tooltip,\n  mountTooltip\n} from \"../../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n  height: number                       /* Header visible height */\n  hidden: boolean                      /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable<boolean> {\n  if (!feature(\"header.autohide\"))\n    return of(false)\n\n  /* Compute direction and turning point */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => [a < b, b] as const),\n      distinctUntilKeyChanged(0)\n    )\n\n  /* Compute whether header should be hidden */\n  const hidden$ = combineLatest([viewport$, direction$])\n    .pipe(\n      filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n      map(([, [direction]]) => direction),\n      distinctUntilChanged()\n    )\n\n  /* Compute threshold for hiding */\n  const search$ = watchToggle(\"search\")\n  return combineLatest([viewport$, search$])\n    .pipe(\n      map(([{ offset }, search]) => offset.y > 400 && !search),\n      distinctUntilChanged(),\n      switchMap(active => active ? hidden$ : of(false)),\n      startWith(false)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n  el: HTMLElement, options: WatchOptions\n): Observable<Header> {\n  return defer(() => combineLatest([\n    watchElementSize(el),\n    isHidden(options)\n  ]))\n    .pipe(\n      map(([{ height }, hidden]) => ({\n        height,\n        hidden\n      })),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.hidden === b.hidden\n      )),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n  el: HTMLElement, { header$, main$ }: MountOptions\n): Observable<Component<Header | Tooltip>> {\n  return defer(() => {\n    const push$ = new Subject<Main>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$\n      .pipe(\n        distinctUntilKeyChanged(\"active\"),\n        combineLatestWith(header$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Mount tooltips, if enabled */\n    const tooltips = from(getElements(\"[title]\", el))\n      .pipe(\n        filter(() => feature(\"content.tooltips\")),\n        mergeMap(child => mountTooltip(child))\n      )\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state })),\n        mergeWith(tooltips.pipe(takeUntil(done$)))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElementSize,\n  getOptionalElement,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n  active: boolean                      /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<HeaderTitle> {\n  return watchViewportAt(el, { viewport$, header$ })\n    .pipe(\n      map(({ offset: { y } }) => {\n        const { height } = getElementSize(el)\n        return {\n          active: y >= height\n        }\n      }),\n      distinctUntilKeyChanged(\"active\")\n    )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<HeaderTitle>> {\n  return defer(() => {\n    const push$ = new Subject<HeaderTitle>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ active }) {\n        el.classList.toggle(\"md-header__title--active\", active)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.classList.remove(\"md-header__title--active\")\n      }\n    })\n\n    /* Obtain headline, if any */\n    const heading = getOptionalElement(\".md-content h1\")\n    if (typeof heading === \"undefined\")\n      return EMPTY\n\n    /* Create and return component */\n    return watchHeaderTitle(heading, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  map,\n  switchMap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n  offset: number                       /* Main area top offset */\n  height: number                       /* Main area visible height */\n  active: boolean                      /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Main> {\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      map(({ height }) => height),\n      distinctUntilChanged()\n    )\n\n  /* Compute the main area's top and bottom borders */\n  const border$ = adjust$\n    .pipe(\n      switchMap(() => watchElementSize(el)\n        .pipe(\n          map(({ height }) => ({\n            top:    el.offsetTop,\n            bottom: el.offsetTop + height\n          })),\n          distinctUntilKeyChanged(\"bottom\")\n        )\n      )\n    )\n\n  /* Compute the main area's offset, visible height and if we scrolled past */\n  return combineLatest([adjust$, border$, viewport$])\n    .pipe(\n      map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n        height = Math.max(0, height\n          - Math.max(0, top    - y,  header)\n          - Math.max(0, height + y - bottom)\n        )\n        return {\n          offset: top - header,\n          height,\n          active: top - header <= y\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.offset === b.offset &&\n        a.height === b.height &&\n        a.active === b.active\n      ))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  defer,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  repeat,\n  shareReplay,\n  skip,\n  startWith,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements, watchMedia } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n  media?: string                       /* Media query */\n  scheme?: string                      /* Color scheme */\n  primary?: string                     /* Primary color */\n  accent?: string                      /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n  index: number                        /* Palette index */\n  color: PaletteColor                  /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n  inputs: HTMLInputElement[]\n): Observable<Palette> {\n  const current = __md_get<Palette>(\"__palette\") || {\n    index: inputs.findIndex(input => matchMedia(\n      input.getAttribute(\"data-md-color-media\")!\n    ).matches)\n  }\n\n  /* Emit changes in color palette */\n  const index = Math.max(0, Math.min(current.index, inputs.length - 1))\n  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\").pipe(map(() => input))),\n      startWith(inputs[index]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\n          media:   input.getAttribute(\"data-md-color-media\"),\n          scheme:  input.getAttribute(\"data-md-color-scheme\"),\n          primary: input.getAttribute(\"data-md-color-primary\"),\n          accent:  input.getAttribute(\"data-md-color-accent\")\n        }\n      } as Palette)),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n  el: HTMLElement\n): Observable<Component<Palette>> {\n  const inputs = getElements<HTMLInputElement>(\"input\", el)\n  const meta = h(\"meta\", { name: \"theme-color\" })\n  document.head.appendChild(meta)\n\n  // Add color scheme meta tag\n  const scheme = h(\"meta\", { name: \"color-scheme\" })\n  document.head.appendChild(scheme)\n\n  /* Mount component on subscription */\n  const media$ = watchMedia(\"(prefers-color-scheme: light)\")\n  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Retrieve color palette for system preference */\n      if (palette.color.media === \"(prefers-color-scheme)\") {\n        const media = matchMedia(\"(prefers-color-scheme: light)\")\n        const input = document.querySelector(media.matches\n          ? \"[data-md-color-media='(prefers-color-scheme: light)']\"\n          : \"[data-md-color-media='(prefers-color-scheme: dark)']\"\n        )!\n\n        /* Retrieve colors for system preference */\n        palette.color.scheme  = input.getAttribute(\"data-md-color-scheme\")!\n        palette.color.primary = input.getAttribute(\"data-md-color-primary\")!\n        palette.color.accent  = input.getAttribute(\"data-md-color-accent\")!\n      }\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* Set toggle visibility */\n      for (let index = 0; index < inputs.length; index++) {\n        const label = inputs[index].nextElementSibling\n        if (label instanceof HTMLElement)\n          label.hidden = palette.index !== index\n      }\n\n      /* Persist preference in local storage */\n      __md_set(\"__palette\", palette)\n    })\n\n    // Handle color switch on Enter or Space - see https://t.ly/YIhVj\n    fromEvent<KeyboardEvent>(el, \"keydown\").pipe(\n      filter(ev => ev.key === \"Enter\"),\n      withLatestFrom(push$, (_, palette) => palette)\n    )\n      .subscribe(({ index }) => {\n        index = (index + 1) % inputs.length\n        inputs[index].click()\n        inputs[index].focus()\n      })\n\n    /* Update theme-color meta tag */\n    push$\n      .pipe(\n        map(() => {\n          const header = getComponentElement(\"header\")\n          const style  = window.getComputedStyle(header)\n\n          // Set color scheme\n          scheme.content = style.colorScheme\n\n          /* Return color in hexadecimal format */\n          return style.backgroundColor.match(/\\d+/g)!\n            .map(value => (+value).toString(16).padStart(2, \"0\"))\n            .join(\"\")\n        })\n      )\n        .subscribe(color => meta.content = `#${color}`)\n\n    /* Revert transition durations after color switch */\n    push$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    return watchPalette(inputs)\n      .pipe(\n        takeUntil(media$.pipe(skip(1))),\n        repeat(),\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Progress indicator\n */\nexport interface Progress {\n  value: number                        // Progress value\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  progress$: Subject<number>           // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount progress indicator\n *\n * @param el - Progress indicator element\n * @param options - Options\n *\n * @returns Progress indicator component observable\n */\nexport function mountProgress(\n  el: HTMLElement, { progress$ }: MountOptions\n): Observable<Component<Progress>> {\n\n  // Mount component on subscription\n  return defer(() => {\n    const push$ = new Subject<Progress>()\n    push$.subscribe(({ value }) => {\n      el.style.setProperty(\"--md-progress-value\", `${value}`)\n    })\n\n    // Create and return component\n    return progress$\n      .pipe(\n        tap(value => push$.next({ value })),\n        finalize(() => push$.complete()),\n        map(value => ({ ref: el, value }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n  el.setAttribute(\"data-md-copying\", \"\")\n  const copy = el.closest(\"[data-copy]\")\n  const text = copy\n    ? copy.getAttribute(\"data-copy\")!\n    : el.innerText\n  el.removeAttribute(\"data-md-copying\")\n  return text.trimEnd()\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n  { alert$ }: SetupOptions\n): void {\n  if (ClipboardJS.isSupported()) {\n    new Observable<ClipboardJS.Event>(subscriber => {\n      new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n        text: el => (\n          el.getAttribute(\"data-clipboard-text\")! ||\n          extract(getElement(\n            el.getAttribute(\"data-clipboard-target\")!\n          ))\n        )\n      })\n        .on(\"success\", ev => subscriber.next(ev))\n    })\n      .pipe(\n        tap(ev => {\n          const trigger = ev.trigger as HTMLElement\n          trigger.focus()\n        }),\n        map(() => translation(\"clipboard.copied\"))\n      )\n        .subscribe(alert$)\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  catchError,\n  map,\n  of\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  requestXML\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = Map<string, URL[]>\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Resolve URL to the given base URL\n *\n * When serving the site with instant navigation, MkDocs will set the hostname\n * to the value as specified in `dev_addr`, but the browser allows for several\n * hostnames to be used: `localhost`, `127.0.0.1` or even `0.0.0.0`, depending\n * on configuration. This function resolves the URL to the given hostname.\n *\n * @param url - URL\n * @param base - Base URL\n *\n * @returns Resolved URL\n */\nfunction resolve(url: URL, base: URL) {\n  url.protocol = base.protocol\n  url.hostname = base.hostname\n  return url\n}\n\n/**\n * Extract sitemap from document\n *\n * This function extracts the URLs and alternate links from the document, and\n * associates alternate links to the original URL as found in `loc`, allowing\n * the browser to navigate to the correct page when switching languages. The\n * format of the sitemap is expected to adhere to:\n *\n * ``` xml\n * <urlset>\n *   <url>\n *     <loc>...</loc>\n *     <xhtml:link rel=\"alternate\" hreflang=\"en\" href=\"...\"/>\n *     <xhtml:link rel=\"alternate\" hreflang=\"de\" href=\"...\"/>\n *     ...\n *   </url>\n *   ...\n * </urlset>\n * ```\n *\n * @param document - Document\n * @param base - Base URL\n *\n * @returns Sitemap\n */\nfunction extract(document: Document, base: URL): Sitemap {\n  const sitemap: Sitemap = new Map()\n  for (const el of getElements(\"url\", document)) {\n    const url = getElement(\"loc\", el)\n\n    // Create entry for location and add it to the list of links\n    const links = [resolve(new URL(url.textContent!), base)]\n    sitemap.set(`${links[0]}`, links)\n\n    // Attach alternate links to current entry\n    for (const link of getElements(\"[rel=alternate]\", el)) {\n      const href = link.getAttribute(\"href\")\n      if (href != null)\n        links.push(resolve(new URL(href), base))\n    }\n  }\n\n  // Return sitemap\n  return sitemap\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * If a network or parsing error occurs, we just default to an empty sitemap,\n * which means the caller should fall back to regular navigation.\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base: URL | string): Observable<Sitemap> {\n  return requestXML(new URL(\"sitemap.xml\", base))\n    .pipe(\n      map(document => extract(document, new URL(base))),\n      catchError(() => of(new Map())),\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  combineLatestWith,\n  concat,\n  debounceTime,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  of,\n  share,\n  switchMap,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  requestHTML,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\n\nimport { Sitemap, fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  location$: Subject<URL>              // Location subject\n  viewport$: Observable<Viewport>      // Viewport observable\n  progress$: Subject<number>           // Progress subject\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Handle clicks on internal URLs while skipping external URLs\n *\n * @param ev - Mouse event\n * @param sitemap - Sitemap\n *\n * @returns URL observable\n */\nfunction handle(\n  ev: MouseEvent, sitemap: Sitemap\n): Observable<URL> {\n  if (!(ev.target instanceof Element))\n    return EMPTY\n\n  // Skip, as target is not within a link - clicks on non-link elements are\n  // also captured, which we need to exclude from processing\n  const el = ev.target.closest(\"a\")\n  if (el === null)\n    return EMPTY\n\n  // Skip, as link opens in new window - we now know we have captured a click\n  // on a link, but the link either has a `target` property defined, or the\n  // user pressed the `meta` or `ctrl` key to open it in a new window. Thus,\n  // we need to filter this event as well.\n  if (el.target || ev.metaKey || ev.ctrlKey)\n    return EMPTY\n\n  // Next, we must check if the URL is relevant for us, i.e., if it's an\n  // internal link to a page that is managed by MkDocs. Only then we can be\n  // sure that the structure of the page to be loaded adheres to the current\n  // document structure and can subsequently be injected into it without doing\n  // a full reload. For this reason, we must canonicalize the URL by removing\n  // all search parameters and hash fragments.\n  const url = new URL(el.href)\n  url.search = url.hash = \"\"\n\n  // Skip, if URL is not included in the sitemap - this could be the case when\n  // linking between versions or languages, or to another page that the author\n  // included as part of the build, but that is not managed by MkDocs. In that\n  // case we must not continue with instant navigation.\n  if (!sitemap.has(`${url}`))\n    return EMPTY\n\n  // We now know that we have a link to an internal page, so we prevent the\n  // browser from navigation and emit the URL for instant navigation. Note that\n  // this also includes anchor links, which means we need to implement anchor\n  // positioning ourselves. The reason for this is that if we wouldn't manage\n  // anchor links as well, scroll restoration will not work correctly (e.g.\n  // following an anchor link and scrolling).\n  ev.preventDefault()\n  return of(new URL(el.href))\n}\n\n/**\n * Create a map of head elements for lookup and replacement\n *\n * @param document - Document\n *\n * @returns Tag map\n */\nfunction head(document: Document): Map<string, HTMLElement> {\n  const tags = new Map<string, HTMLElement>()\n  for (const el of getElements(\":scope > *\", document.head))\n    tags.set(el.outerHTML, el)\n\n  // Return tag map\n  return tags\n}\n\n/**\n * Resolve relative URLs in the given document\n *\n * This function resolves relative `href` and `src` attributes, which can belong\n * to all sorts of tags, like meta tags, links, images, scripts and more.\n *\n * @param document - Document\n *\n * @returns Document observable\n */\nfunction resolve(document: Document): Observable<Document> {\n  for (const el of getElements(\"[href], [src]\", document))\n    for (const key of [\"href\", \"src\"]) {\n      const value = el.getAttribute(key)\n      if (value && !/^(?:[a-z]+:)?\\/\\//i.test(value)) {\n        // @ts-expect-error - trick: self-assign to resolve URL\n        el[key] = el[key]\n        break\n      }\n    }\n\n  // Return document observable\n  return of(document)\n}\n\n/**\n * Inject the contents of a document into the current one\n *\n * @param next - Next document\n *\n * @returns Document observable\n */\nfunction inject(next: Document): Observable<Document> {\n  for (const selector of [\n    \"[data-md-component=announce]\",\n    \"[data-md-component=container]\",\n    \"[data-md-component=header-topic]\",\n    \"[data-md-component=outdated]\",\n    \"[data-md-component=logo]\",\n    \"[data-md-component=skip]\",\n    ...feature(\"navigation.tabs.sticky\")\n      ? [\"[data-md-component=tabs]\"]\n      : []\n  ]) {\n    const source = getOptionalElement(selector)\n    const target = getOptionalElement(selector, next)\n    if (\n      typeof source !== \"undefined\" &&\n      typeof target !== \"undefined\"\n    ) {\n      source.replaceWith(target)\n    }\n  }\n\n  // Update meta tags\n  const tags = head(document)\n  for (const [html, el] of head(next))\n    if (tags.has(html))\n      tags.delete(html)\n    else\n      document.head.appendChild(el)\n\n  // Remove meta tags that are not present in the new document\n  for (const el of tags.values()) {\n    const name = el.getAttribute(\"name\")\n    // @todo - find a better way to handle attributes we add dynamically in\n    // other components without mounting components on every navigation, as\n    // this might impact overall performance - see https://t.ly/ehp_O\n    if (name !== \"theme-color\" && name !== \"color-scheme\")\n      el.remove()\n  }\n\n  // After components and meta tags were replaced, re-evaluate scripts\n  // that were provided by the author as part of Markdown files\n  const container = getComponentElement(\"container\")\n  return concat(getElements(\"script\", container))\n    .pipe(\n      switchMap(el => {\n        const script = next.createElement(\"script\")\n        if (el.src) {\n          for (const name of el.getAttributeNames())\n            script.setAttribute(name, el.getAttribute(name)!)\n          el.replaceWith(script)\n\n          // Complete when script is loaded\n          return new Observable(observer => {\n            script.onload = () => observer.complete()\n          })\n\n        // Complete immediately\n        } else {\n          script.textContent = el.textContent\n          el.replaceWith(script)\n          return EMPTY\n        }\n      }),\n      ignoreElements(),\n      endWith(document)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant navigation\n *\n * This is a heavily orchestrated operation - see inline comments to learn how\n * this works with Material for MkDocs, and how you can hook into it.\n *\n * @param options - Options\n *\n * @returns Document observable\n */\nexport function setupInstantNavigation(\n  { location$, viewport$, progress$ }: SetupOptions\n): Observable<Document> {\n  const config = configuration()\n  if (location.protocol === \"file:\")\n    return EMPTY\n\n  // Load sitemap immediately, so we have it available when the user initiates\n  // the first navigation request without any perceivable delay\n  const sitemap$ = fetchSitemap(config.base)\n\n  // Since we might be on a slow connection, the user might trigger multiple\n  // instant navigation events that overlap. MkDocs produces relative URLs for\n  // all internal links, which becomes a problem in this case, because we need\n  // to change the base URL the moment the user clicks a link that should be\n  // intercepted in order to be consistent with popstate, which means that the\n  // base URL would now be incorrect when resolving another relative link from\n  // the same site. For this reason we always resolve all relative links to\n  // absolute links, so we can be sure this never happens.\n  of(document)\n    .subscribe(resolve)\n\n  // --------------------------------------------------------------------------\n  // Navigation interception\n  // --------------------------------------------------------------------------\n\n  // Intercept navigation - to keep the number of event listeners down we use\n  // the fact that uncaptured events bubble up to the body. This has the nice\n  // property that we don't need to detach and then re-attach event listeners\n  // when the document is replaced after a navigation event.\n  const instant$ =\n    fromEvent<MouseEvent>(document.body, \"click\")\n      .pipe(\n        combineLatestWith(sitemap$),\n        switchMap(([ev, sitemap]) => handle(ev, sitemap)),\n        share()\n      )\n\n  // Intercept history change events, e.g. when the user uses the browser's\n  // back or forward buttons, and emit new location for fetching and parsing\n  const history$ =\n    fromEvent<PopStateEvent>(window, \"popstate\")\n      .pipe(\n        map(getLocation),\n        share()\n      )\n\n  // While it would be better UX to defer navigation events until the document\n  // is fully fetched and parsed, we must schedule it here to synchronize with\n  // popstate events, as they are emitted immediately. Moreover we need to\n  // store the current viewport offset for scroll restoration later on.\n  instant$.pipe(withLatestFrom(viewport$))\n    .subscribe(([url, { offset }]) => {\n      history.replaceState(offset, \"\")\n      history.pushState(null, \"\", url)\n    })\n\n  // Emit URLs that should be fetched via instant navigation on location subject\n  // which was passed into this function. The state of instant navigation can be\n  // intercepted by other parts of the application, which can synchronously back\n  // up or restore state before or after instant navigation happens.\n  merge(instant$, history$)\n    .subscribe(location$)\n\n  // --------------------------------------------------------------------------\n  // Fetching and parsing\n  // --------------------------------------------------------------------------\n\n  // Fetch document - we deduplicate requests to the same location, so we don't\n  // end up with multiple requests for the same page. We use `switchMap`, since\n  // we want to cancel the previous request when a new one is triggered, which\n  // is automatically handled by the observable returned by `request`. This is\n  // essential to ensure a good user experience, as we don't want to load pages\n  // that are not needed anymore, e.g., when the user clicks multiple links in\n  // quick succession or on slow connections. If the request fails for some\n  // reason, we fall back and use regular navigation, forcing a reload.\n  const document$ =\n    location$.pipe(\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(url => requestHTML(url, { progress$ })\n        .pipe(\n          catchError(() => {\n            setLocation(url, true)\n            return EMPTY\n          })\n        )\n      ),\n\n      // The document was successfully fetched and parsed, so we can inject its\n      // contents into the currently active document\n      switchMap(resolve),\n      switchMap(inject),\n      share()\n    )\n\n  // --------------------------------------------------------------------------\n  // Scroll restoration\n  // --------------------------------------------------------------------------\n\n  // Handle scroll restoration - we must restore the viewport offset after the\n  // document has been fetched and injected, and every time the user clicks an\n  // anchor that leads to an element on the same page, which might also happen\n  // when the user uses the back or forward button.\n  merge(\n    document$.pipe(withLatestFrom(location$, (_, url) => url)),\n\n    // Handle instant navigation events that are triggered by the user clicking\n    // on an anchor link with a hash fragment different from the current one, as\n    // well as from popstate events, which are emitted when the user navigates\n    // back and forth between pages. We use a two-layered subscription to scope\n    // the scroll restoration to the current page, as we don't need to restore\n    // the viewport offset when the user navigates to a different page, as this\n    // is already handled by the previous observable.\n    location$.pipe(\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(() => location$),\n      distinctUntilKeyChanged(\"hash\"),\n    ),\n\n    // Handle instant navigation events that are triggered by the user clicking\n    // on an anchor link with the same hash fragment as the current one in the\n    // URL. It is essential that we only intercept those from instant navigation\n    // events and not from history change events, or we'll end up in and endless\n    // loop. The top-level history entry must be removed, as it will be replaced\n    // with a new one, which would otherwise lead to a duplicate entry.\n    location$.pipe(\n      distinctUntilChanged((a, b) => (\n        a.pathname === b.pathname &&\n        a.hash     === b.hash\n      )),\n      switchMap(() => instant$),\n      tap(() => history.back())\n    )\n  )\n    .subscribe(url => {\n\n      // Check if the current history entry has a state, which happens when the\n      // user presses the back or forward button to visit a page we've already\n      // seen. If there's no state, it means a new page was visited and we must\n      // scroll to the top, unless an anchor is given.\n      if (history.state !== null || !url.hash) {\n        window.scrollTo(0, history.state?.y ?? 0)\n      } else {\n        history.scrollRestoration = \"auto\"\n        setLocationHash(url.hash)\n        history.scrollRestoration = \"manual\"\n      }\n    })\n\n  // Disable scroll restoration when an instant navigation event occurs, so the\n  // browser does not immediately set the viewport offset to the prior history\n  // entry, scrolling to the position on the same page, which would look odd.\n  // Instead, we manually restore the position once the page has loaded.\n  location$.subscribe(() => {\n    history.scrollRestoration = \"manual\"\n  })\n\n  // Enable scroll restoration before window unloads - this is essential to\n  // ensure that full reloads (F5) restore the viewport offset correctly. If\n  // only popstate events wouldn't reset the viewport offset prior to their\n  // emission, we could just reset this in popstate. Meh.\n  fromEvent(window, \"beforeunload\")\n    .subscribe(() => {\n      history.scrollRestoration = \"auto\"\n    })\n\n  // Track viewport offset, so we can restore it when the user navigates back\n  // and forth between pages. Note that this must be debounced and cannot be\n  // done in popstate, as popstate has already removed the entry from the\n  // history, which means it is too late.\n  viewport$.pipe(\n    distinctUntilKeyChanged(\"offset\"),\n    debounceTime(100)\n  )\n    .subscribe(({ offset }) => {\n      history.replaceState(offset, \"\")\n    })\n\n  // Return document observable\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchConfig } from \"../config\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search configuration\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchConfig\n): SearchHighlightFactoryFn {\n  // Hack: temporarily remove pure lookaheads and lookbehinds\n  const regex = config.separator.split(\"|\").map(term => {\n    const temp = term.replace(/(\\(\\?[!=<][^)]+\\))/g, \"\")\n    return temp.length === 0 ? \"\uFFFD\" : term\n  })\n    .join(\"|\")\n\n  const separator = new RegExp(regex, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator}|)(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => escapeHTML(value)\n      .replace(match, highlight)\n      .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchResult } from \"../../_\"\nimport { SearchIndex } from \"../../config\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n  SETUP,                               /* Search index setup */\n  READY,                               /* Search index ready */\n  QUERY,                               /* Search query */\n  RESULT                               /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n  type: SearchMessageType.SETUP        /* Message type */\n  data: SearchIndex                    /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n  type: SearchMessageType.READY        /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n  type: SearchMessageType.QUERY        /* Message type */\n  data: string                         /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n  type: SearchMessageType.RESULT       /* Message type */\n  data: SearchResult                   /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n  | SearchSetupMessage\n  | SearchReadyMessage\n  | SearchQueryMessage\n  | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n  message: SearchMessage\n): message is SearchReadyMessage {\n  return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n  message: SearchMessage\n): message is SearchResultMessage {\n  return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ObservableInput,\n  Subject,\n  first,\n  merge,\n  of,\n  switchMap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { watchToggle, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../config\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates and initializes a web worker that is used for search,\n * so that the user interface doesn't freeze. In general, the application does\n * not care how search is implemented, as long as the web worker conforms to\n * the format expected by the application as defined in `SearchMessage`. This\n * allows the author to implement custom search functionality, by providing a\n * custom web worker via configuration.\n *\n * Material for MkDocs' built-in search implementation makes use of Lunr.js, an\n * efficient and fast implementation for client-side search. Leveraging a tiny\n * iframe-based web worker shim, search is even supported for the `file://`\n * protocol, enabling search for local non-hosted builds.\n *\n * If the protocol is `file://`, search initialization is deferred to mitigate\n * freezing, as it's now synchronous by design - see https://bit.ly/3C521EO\n *\n * @see https://bit.ly/3igvtQv - How to implement custom search\n *\n * @param url - Worker URL\n * @param index$ - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n  url: string, index$: ObservableInput<SearchIndex>\n): Subject<SearchMessage> {\n  const worker$ = watchWorker<SearchMessage>(url)\n  merge(\n    of(location.protocol !== \"file:\"),\n    watchToggle(\"search\")\n  )\n    .pipe(\n      first(active => active),\n      switchMap(() => index$)\n    )\n      .subscribe(({ config, docs }) => worker$.next({\n        type: SearchMessageType.SETUP,\n        data: {\n          config,\n          docs,\n          options: {\n            suggest: feature(\"search.suggest\")\n          }\n        }\n      }))\n\n  /* Return search worker */\n  return worker$\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n  { document$ }: SetupOptions\n): void {\n  const config = configuration()\n  const versions$ = requestJSON<Version[]>(\n    new URL(\"../versions.json\", config.base)\n  )\n    .pipe(\n      catchError(() => EMPTY) // @todo refactor instant loading\n    )\n\n  /* Determine current version */\n  const current$ = versions$\n    .pipe(\n      map(versions => {\n        const [, current] = config.base.match(/([^/]+)\\/?$/)!\n        return versions.find(({ version, aliases }) => (\n          version === current || aliases.includes(current)\n        )) || versions[0]\n      })\n    )\n\n  /* Intercept inter-version navigation */\n  versions$\n    .pipe(\n      map(versions => new Map(versions.map(version => [\n        `${new URL(`../${version.version}/`, config.base)}`,\n        version\n      ]))),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          withLatestFrom(current$),\n          switchMap(([ev, current]) => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target && urls.has(el.href)) {\n                const url = el.href\n                // This is a temporary hack to detect if a version inside the\n                // version selector or on another part of the site was clicked.\n                // If we're inside the version selector, we definitely want to\n                // find the same page, as we might have different deployments\n                // due to aliases. However, if we're outside the version\n                // selector, we must abort here, because we might otherwise\n                // interfere with instant navigation. We need to refactor this\n                // at some point together with instant navigation.\n                //\n                // See https://github.com/squidfunk/mkdocs-material/issues/4012\n                if (!ev.target.closest(\".md-version\")) {\n                  const version = urls.get(url)!\n                  if (version === current)\n                    return EMPTY\n                }\n                ev.preventDefault()\n                return of(url)\n              }\n            }\n            return EMPTY\n          }),\n          switchMap(url => {\n            const { version } = urls.get(url)!\n            return fetchSitemap(new URL(url))\n              .pipe(\n                map(sitemap => {\n                  const location = getLocation()\n                  const path = location.href.replace(config.base, \"\")\n                  return sitemap.has(path.split(\"#\")[0])\n                    ? new URL(`../${version}/${path}`, config.base)\n                    : new URL(url)\n                })\n              )\n          })\n        )\n      )\n    )\n      .subscribe(url => setLocation(url, true))\n\n  /* Render version selector and warning */\n  combineLatest([versions$, current$])\n    .subscribe(([versions, current]) => {\n      const topic = getElement(\".md-header__topic\")\n      topic.appendChild(renderVersionSelector(versions, current))\n    })\n\n  /* Integrate outdated version banner with instant navigation */\n  document$.pipe(switchMap(() => current$))\n    .subscribe(current => {\n\n      /* Check if version state was already determined */\n      let outdated = __md_get(\"__outdated\", sessionStorage)\n      if (outdated === null) {\n        outdated = true\n\n        /* Obtain and normalize default versions */\n        let ignored = config.version?.default || \"latest\"\n        if (!Array.isArray(ignored))\n          ignored = [ignored]\n\n        /* Check if version is considered a default */\n        main: for (const ignore of ignored)\n          for (const version of current.aliases.concat(current.version))\n            if (new RegExp(ignore, \"i\").test(version)) {\n              outdated = false\n              break main\n            }\n\n        /* Persist version state in session storage */\n        __md_set(\"__outdated\", outdated, sessionStorage)\n      }\n\n      /* Unhide outdated version banner */\n      if (outdated)\n        for (const warning of getComponentElements(\"outdated\"))\n          warning.hidden = false\n    })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  first,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  shareReplay,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getLocation,\n  setToggle,\n  watchElementFocus,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchMessageType,\n  isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n  value: string                        /* Query value */\n  focus: boolean                       /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param options - Options\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n  el: HTMLInputElement, { worker$ }: WatchOptions\n): Observable<SearchQuery> {\n\n  /* Support search deep linking */\n  const { searchParams } = getLocation()\n  if (searchParams.has(\"q\")) {\n    setToggle(\"search\", true)\n\n    /* Set query from parameter */\n    el.value = searchParams.get(\"q\")!\n    el.focus()\n\n    /* Remove query parameter on close */\n    watchToggle(\"search\")\n      .pipe(\n        first(active => !active)\n      )\n        .subscribe(() => {\n          const url = getLocation()\n          url.searchParams.delete(\"q\")\n          history.replaceState({}, \"\", `${url}`)\n        })\n  }\n\n  /* Intercept focus and input events */\n  const focus$ = watchElementFocus(el)\n  const value$ = merge(\n    worker$.pipe(first(isSearchReadyMessage)),\n    fromEvent(el, \"keyup\"),\n    focus$\n  )\n    .pipe(\n      map(() => el.value),\n      distinctUntilChanged()\n    )\n\n  /* Combine into single observable */\n  return combineLatest([value$, focus$])\n    .pipe(\n      map(([value, focus]) => ({ value, focus })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param options - Options\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n  el: HTMLInputElement, { worker$ }: MountOptions\n): Observable<Component<SearchQuery, HTMLInputElement>> {\n  const push$ = new Subject<SearchQuery>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n\n  /* Handle value change */\n  combineLatest([\n    worker$.pipe(first(isSearchReadyMessage)),\n    push$\n  ], (_, query) => query)\n    .pipe(\n      distinctUntilKeyChanged(\"value\")\n    )\n      .subscribe(({ value }) => worker$.next({\n        type: SearchMessageType.QUERY,\n        data: value\n      }))\n\n  /* Handle focus change */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"focus\")\n    )\n      .subscribe(({ focus }) => {\n        if (focus)\n          setToggle(\"search\", focus)\n      })\n\n  /* Handle reset */\n  fromEvent(el.form!, \"reset\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(() => el.focus())\n\n  // Focus search query on label click - note that this is necessary to bring\n  // up the keyboard on iOS and other mobile platforms, as the search dialog is\n  // not visible at first, and programatically focusing an input element must\n  // be triggered by a user interaction - see https://t.ly/Cb30n\n  const label = getElement(\"header [for=__search]\")\n  fromEvent(label, \"click\")\n    .subscribe(() => el.focus())\n\n  /* Create and return component */\n  return watchSearchQuery(el, { worker$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  bufferCount,\n  filter,\n  finalize,\n  first,\n  fromEvent,\n  map,\n  merge,\n  mergeMap,\n  of,\n  share,\n  skipUntil,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom,\n  zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getElement,\n  getOptionalElement,\n  watchElementBoundary,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchResult,\n  isSearchReadyMessage,\n  isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n  el: HTMLElement, { worker$, query$ }: MountOptions\n): Observable<Component<SearchResult>> {\n  const push$ = new Subject<SearchResult>()\n  const boundary$ = watchElementBoundary(el.parentElement!)\n    .pipe(\n      filter(Boolean)\n    )\n\n  /* Retrieve container */\n  const container = el.parentElement!\n\n  /* Retrieve nested components */\n  const meta = getElement(\":scope > :first-child\", el)\n  const list = getElement(\":scope > :last-child\", el)\n\n  /* Reveal to accessibility tree \u2013 see https://bit.ly/3iAA7t8 */\n  watchToggle(\"search\")\n    .subscribe(active => list.setAttribute(\n      \"role\", active ? \"list\" : \"presentation\"\n    ))\n\n  /* Update search result metadata */\n  push$\n    .pipe(\n      withLatestFrom(query$),\n      skipUntil(worker$.pipe(first(isSearchReadyMessage)))\n    )\n      .subscribe(([{ items }, { value }]) => {\n        switch (items.length) {\n\n          /* No results */\n          case 0:\n            meta.textContent = value.length\n              ? translation(\"search.result.none\")\n              : translation(\"search.result.placeholder\")\n            break\n\n          /* One result */\n          case 1:\n            meta.textContent = translation(\"search.result.one\")\n            break\n\n          /* Multiple result */\n          default:\n            const count = round(items.length)\n            meta.textContent = translation(\"search.result.other\", count)\n        }\n      })\n\n  /* Render search result item */\n  const render$ = push$\n    .pipe(\n      tap(() => list.innerHTML = \"\"),\n      switchMap(({ items }) => merge(\n        of(...items.slice(0, 10)),\n        of(...items.slice(10))\n          .pipe(\n            bufferCount(4),\n            zipWith(boundary$),\n            switchMap(([chunk]) => chunk)\n          )\n      )),\n      map(renderSearchResultItem),\n      share()\n    )\n\n  /* Update search result list */\n  render$.subscribe(item => list.appendChild(item))\n  render$\n    .pipe(\n      mergeMap(item => {\n        const details = getOptionalElement(\"details\", item)\n        if (typeof details === \"undefined\")\n          return EMPTY\n\n        /* Keep position of details element stable */\n        return fromEvent(details, \"toggle\")\n          .pipe(\n            takeUntil(push$),\n            map(() => details)\n          )\n      })\n    )\n      .subscribe(details => {\n        if (\n          details.open === false &&\n          details.offsetTop <= container.scrollTop\n        )\n          container.scrollTo({ top: details.offsetTop })\n      })\n\n  /* Filter search result message */\n  const result$ = worker$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n  url: URL                             /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n  _el: HTMLElement, { query$ }: WatchOptions\n): Observable<SearchShare> {\n  return query$\n    .pipe(\n      map(({ value }) => {\n        const url = getLocation()\n        url.hash = \"\"\n\n        /* Compute readable query strings */\n        value = value\n          .replace(/\\s+/g, \"+\")        /* Collapse whitespace */\n          .replace(/&/g, \"%26\")        /* Escape '&' character */\n          .replace(/=/g, \"%3D\")        /* Escape '=' character */\n\n        /* Replace query string */\n        url.search = `q=${value}`\n        return { url }\n      })\n    )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n  el: HTMLAnchorElement, options: MountOptions\n): Observable<Component<SearchShare>> {\n  const push$ = new Subject<SearchShare>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n  push$.subscribe(({ url }) => {\n    el.setAttribute(\"data-clipboard-text\", el.href)\n    el.href = `${url}`\n  })\n\n  /* Prevent following of link */\n  fromEvent(el, \"click\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(ev => ev.preventDefault())\n\n  /* Create and return component */\n  return watchSearchShare(el, options)\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  combineLatestWith,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  observeOn,\n  tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchResult,\n  isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n  el: HTMLElement, { worker$, keyboard$ }: MountOptions\n): Observable<Component<SearchSuggest>> {\n  const push$ = new Subject<SearchResult>()\n\n  /* Retrieve query component and track all changes */\n  const query  = getComponentElement(\"search-query\")\n  const query$ = merge(\n    fromEvent(query, \"keydown\"),\n    fromEvent(query, \"focus\")\n  )\n    .pipe(\n      observeOn(asyncScheduler),\n      map(() => query.value),\n      distinctUntilChanged(),\n    )\n\n  /* Update search suggestions */\n  push$\n    .pipe(\n      combineLatestWith(query$),\n      map(([{ suggest }, value]) => {\n        const words = value.split(/([\\s-]+)/)\n        if (suggest?.length && words[words.length - 1]) {\n          const last = suggest[suggest.length - 1]\n          if (last.startsWith(words[words.length - 1]))\n            words[words.length - 1] = last\n        } else {\n          words.length = 0\n        }\n        return words\n      })\n    )\n      .subscribe(words => el.innerHTML = words\n        .join(\"\")\n        .replace(/\\s/g, \"&nbsp;\")\n      )\n\n  /* Set up search keyboard handlers */\n  keyboard$\n    .pipe(\n      filter(({ mode }) => mode === \"search\")\n    )\n      .subscribe(key => {\n        switch (key.type) {\n\n          /* Right arrow: accept current suggestion */\n          case \"ArrowRight\":\n            if (\n              el.innerText.length &&\n              query.selectionStart === query.value.length\n            )\n              query.value = el.innerText\n            break\n        }\n      })\n\n  /* Filter search result message */\n  const result$ = worker$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  ObservableInput,\n  filter,\n  fromEvent,\n  merge,\n  mergeWith\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  Keyboard,\n  getActiveElement,\n  getElements,\n  setToggle\n} from \"~/browser\"\nimport {\n  SearchIndex,\n  SearchResult,\n  setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n  Component,\n  getComponentElement,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SearchQuery,\n  mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n  SearchShare,\n  mountSearchShare\n} from \"../share\"\nimport {\n  SearchSuggest,\n  mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n  | SearchQuery\n  | SearchResult\n  | SearchShare\n  | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n  el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable<Component<Search>> {\n  const config = configuration()\n  try {\n    const worker$ = setupSearchWorker(config.search, index$)\n\n    /* Retrieve query and result components */\n    const query  = getComponentElement(\"search-query\", el)\n    const result = getComponentElement(\"search-result\", el)\n\n    /* Always close search on result selection */\n    fromEvent<PointerEvent>(el, \"click\")\n      .pipe(\n        filter(({ target }) => (\n          target instanceof Element && !!target.closest(\"a\")\n        ))\n      )\n        .subscribe(() => setToggle(\"search\", false))\n\n    /* Set up search keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"search\")\n      )\n        .subscribe(key => {\n          const active = getActiveElement()\n          switch (key.type) {\n\n            /* Enter: go to first (best) result */\n            case \"Enter\":\n              if (active === query) {\n                const anchors = new Map<HTMLAnchorElement, number>()\n                for (const anchor of getElements<HTMLAnchorElement>(\n                  \":first-child [href]\", result\n                )) {\n                  const article = anchor.firstElementChild!\n                  anchors.set(anchor, parseFloat(\n                    article.getAttribute(\"data-md-score\")!\n                  ))\n                }\n\n                /* Go to result with highest score, if any */\n                if (anchors.size) {\n                  const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n                  best.click()\n                }\n\n                /* Otherwise omit form submission */\n                key.claim()\n              }\n              break\n\n            /* Escape or Tab: close search */\n            case \"Escape\":\n            case \"Tab\":\n              setToggle(\"search\", false)\n              query.blur()\n              break\n\n            /* Vertical arrows: select previous or next search result */\n            case \"ArrowUp\":\n            case \"ArrowDown\":\n              if (typeof active === \"undefined\") {\n                query.focus()\n              } else {\n                const els = [query, ...getElements(\n                  \":not(details) > [href], summary, details[open] [href]\",\n                  result\n                )]\n                const i = Math.max(0, (\n                  Math.max(0, els.indexOf(active)) + els.length + (\n                    key.type === \"ArrowUp\" ? -1 : +1\n                  )\n                ) % els.length)\n                els[i].focus()\n              }\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n\n            /* All other keys: hand to search query */\n            default:\n              if (query !== getActiveElement())\n                query.focus()\n          }\n        })\n\n    /* Set up global keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"global\")\n      )\n        .subscribe(key => {\n          switch (key.type) {\n\n            /* Open search and select query */\n            case \"f\":\n            case \"s\":\n            case \"/\":\n              query.focus()\n              query.select()\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n          }\n        })\n\n    /* Create and return component */\n    const query$ = mountSearchQuery(query, { worker$ })\n    return merge(\n      query$,\n      mountSearchResult(result, { worker$, query$ })\n    )\n      .pipe(\n        mergeWith(\n\n          /* Search sharing */\n          ...getComponentElements(\"search-share\", el)\n            .map(child => mountSearchShare(child, { query$ })),\n\n          /* Search suggestions */\n          ...getComponentElements(\"search-suggest\", el)\n            .map(child => mountSearchSuggest(child, { worker$, keyboard$ }))\n        )\n      )\n\n  /* Gracefully handle broken search */\n  } catch (err) {\n    el.hidden = true\n    return NEVER\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  ObservableInput,\n  combineLatest,\n  filter,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n  SearchIndex,\n  setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n  nodes: Map<ChildNode, string>        /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  location$: Observable<URL>           /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n  el: HTMLElement, { index$, location$ }: MountOptions\n): Observable<Component<SearchHighlight>> {\n  return combineLatest([\n    index$,\n    location$\n      .pipe(\n        startWith(getLocation()),\n        filter(url => !!url.searchParams.get(\"h\"))\n      )\n  ])\n    .pipe(\n      map(([index, url]) => setupSearchHighlighter(index.config)(\n        url.searchParams.get(\"h\")!\n      )),\n      map(fn => {\n        const nodes = new Map<ChildNode, string>()\n\n        /* Traverse text nodes and collect matches */\n        const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n        for (let node = it.nextNode(); node; node = it.nextNode()) {\n          if (node.parentElement?.offsetHeight) {\n            const original = node.textContent!\n            const replaced = fn(original)\n            if (replaced.length > original.length)\n              nodes.set(node as ChildNode, replaced)\n          }\n        }\n\n        /* Replace original nodes with matches */\n        for (const [node, text] of nodes) {\n          const { childNodes } = h(\"span\", null, text)\n          node.replaceWith(...Array.from(childNodes))\n        }\n\n        /* Return component */\n        return { ref: el, nodes }\n      })\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  distinctUntilChanged,\n  endWith,\n  finalize,\n  first,\n  from,\n  fromEvent,\n  ignoreElements,\n  map,\n  mergeMap,\n  observeOn,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElement,\n  getElementOffset,\n  getElementSize,\n  getElements\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n  height: number                       /* Sidebar height */\n  locked: boolean                      /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n  el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable<Sidebar> {\n  const parent = el.closest<HTMLElement>(\".md-grid\")!\n  const adjust =\n    parent.offsetTop -\n    parent.parentElement!.offsetTop\n\n  /* Compute the sidebar's available height and if it should be locked */\n  return combineLatest([main$, viewport$])\n    .pipe(\n      map(([{ offset, height }, { offset: { y } }]) => {\n        height = height\n          + Math.min(adjust, Math.max(0, y - offset))\n          - adjust\n        return {\n          height,\n          locked: y >= offset + adjust\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.locked === b.locked\n      ))\n    )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n  el: HTMLElement, { header$, ...options }: MountOptions\n): Observable<Component<Sidebar>> {\n  const inner = getElement(\".md-sidebar__scrollwrap\", el)\n  const { y } = getElementOffset(inner)\n  return defer(() => {\n    const push$ = new Subject<Sidebar>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    const next$ = push$\n      .pipe(\n        auditTime(0, animationFrameScheduler)\n      )\n\n    /* Update sidebar height and offset */\n    next$.pipe(withLatestFrom(header$))\n      .subscribe({\n\n        /* Handle emission */\n        next([{ height }, { height: offset }]) {\n          inner.style.height = `${height - 2 * y}px`\n          el.style.top       = `${offset}px`\n        },\n\n        /* Handle complete */\n        complete() {\n          inner.style.height = \"\"\n          el.style.top       = \"\"\n        }\n      })\n\n    /* Bring active item into view on initial load */\n    next$.pipe(first())\n      .subscribe(() => {\n        for (const item of getElements(\".md-nav__link--active[href]\", el)) {\n          if (!item.clientHeight) // skip invisible toc in left sidebar\n            continue\n          const container = item.closest<HTMLElement>(\".md-sidebar__scrollwrap\")!\n          if (typeof container !== \"undefined\") {\n            const offset = item.offsetTop - container.offsetTop\n            const { height } = getElementSize(container)\n            container.scrollTo({\n              top: offset - height / 2\n            })\n          }\n        }\n      })\n\n    /* Handle accessibility for expandable items, see https://bit.ly/3jaod9p */\n    from(getElements<HTMLLabelElement>(\"label[tabindex]\", el))\n      .pipe(\n        mergeMap(label => fromEvent(label, \"click\")\n          .pipe(\n            observeOn(asyncScheduler),\n            map(() => label),\n            takeUntil(done$)\n          )\n        )\n      )\n        .subscribe(label => {\n          const input = getElement<HTMLInputElement>(`[id=\"${label.htmlFor}\"]`)\n          const nav = getElement(`[aria-labelledby=\"${label.id}\"]`)\n          nav.setAttribute(\"aria-expanded\", `${input.checked}`)\n        })\n\n    /* Create and return component */\n    return watchSidebar(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n  tag_name: string                     /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n  user: string, repo?: string\n): Observable<SourceFacts> {\n  if (typeof repo !== \"undefined\") {\n    const url = `https://api.github.com/repos/${user}/${repo}`\n    return zip(\n\n      /* Fetch version */\n      requestJSON<Release>(`${url}/releases/latest`)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(release => ({\n            version: release.tag_name\n          })),\n          defaultIfEmpty({})\n        ),\n\n      /* Fetch stars and forks */\n      requestJSON<Repo>(url)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(info => ({\n            stars: info.stargazers_count,\n            forks: info.forks_count\n          })),\n          defaultIfEmpty({})\n        )\n    )\n      .pipe(\n        map(([release, info]) => ({ ...release, ...info }))\n      )\n\n  /* User or organization */\n  } else {\n    const url = `https://api.github.com/users/${user}`\n    return requestJSON<User>(url)\n      .pipe(\n        map(info => ({\n          repositories: info.public_repos\n        })),\n        defaultIfEmpty({})\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n  base: string, project: string\n): Observable<SourceFacts> {\n  const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n  return requestJSON<ProjectSchema>(url)\n    .pipe(\n      catchError(() => EMPTY), // @todo refactor instant loading\n      map(({ star_count, forks_count }) => ({\n        stars: star_count,\n        forks: forks_count\n      })),\n      defaultIfEmpty({})\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n  stars?: number                       /* Number of stars */\n  forks?: number                       /* Number of forks */\n  version?: string                     /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n  repositories?: number                /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n  | RepositoryFacts\n  | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n  url: string\n): Observable<SourceFacts> {\n\n  /* Try to match GitHub repository */\n  let match = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)\n  if (match) {\n    const [, user, repo] = match\n    return fetchSourceFactsFromGitHub(user, repo)\n  }\n\n  /* Try to match GitLab repository */\n  match = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)\n  if (match) {\n    const [, base, slug] = match\n    return fetchSourceFactsFromGitLab(base, slug)\n  }\n\n  /* Fallback */\n  return EMPTY\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  defer,\n  filter,\n  finalize,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { ConsentDefaults } from \"~/components/consent\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport {\n  Component,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SourceFacts,\n  fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n  facts: SourceFacts                   /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable<Source>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n  el: HTMLAnchorElement\n): Observable<Source> {\n  return fetch$ ||= defer(() => {\n    const cached = __md_get<SourceFacts>(\"__source\", sessionStorage)\n    if (cached) {\n      return of(cached)\n    } else {\n\n      /* Check if consent is configured and was given */\n      const els = getComponentElements(\"consent\")\n      if (els.length) {\n        const consent = __md_get<ConsentDefaults>(\"__consent\")\n        if (!(consent && consent.github))\n          return EMPTY\n      }\n\n      /* Fetch repository facts */\n      return fetchSourceFacts(el.href)\n        .pipe(\n          tap(facts => __md_set(\"__source\", facts, sessionStorage))\n        )\n    }\n  })\n    .pipe(\n      catchError(() => EMPTY),\n      filter(facts => Object.keys(facts).length > 0),\n      map(facts => ({ facts })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n  el: HTMLAnchorElement\n): Observable<Component<Source>> {\n  const inner = getElement(\":scope > :last-child\", el)\n  return defer(() => {\n    const push$ = new Subject<Source>()\n    push$.subscribe(({ facts }) => {\n      inner.appendChild(renderSourceFacts(facts))\n      inner.classList.add(\"md-source__repository--active\")\n    })\n\n    /* Create and return component */\n    return watchSource(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n  hidden: boolean                      /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Tabs> {\n  return watchElementSize(document.body)\n    .pipe(\n      switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n      map(({ offset: { y } }) => {\n        return {\n          hidden: y >= 10\n        }\n      }),\n      distinctUntilKeyChanged(\"hidden\")\n    )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Tabs>> {\n  return defer(() => {\n    const push$ = new Subject<Tabs>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ hidden }) {\n        el.hidden = hidden\n      },\n\n      /* Handle complete */\n      complete() {\n        el.hidden = false\n      }\n    })\n\n    /* Create and return component */\n    return (\n      feature(\"navigation.tabs.sticky\")\n        ? of({ hidden: false })\n        : watchTabs(el, options)\n    )\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  bufferCount,\n  combineLatestWith,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  finalize,\n  ignoreElements,\n  map,\n  merge,\n  observeOn,\n  of,\n  repeat,\n  scan,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContainer,\n  getElementSize,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  watchElementSize\n} from \"~/browser\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n  prev: HTMLAnchorElement[][]          /* Anchors (previous) */\n  next: HTMLAnchorElement[][]          /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<TableOfContents> {\n  const table = new Map<HTMLAnchorElement, HTMLElement>()\n\n  /* Compute anchor-to-target mapping */\n  const anchors = getElements<HTMLAnchorElement>(\".md-nav__link\", el)\n  for (const anchor of anchors) {\n    const id = decodeURIComponent(anchor.hash.substring(1))\n    const target = getOptionalElement(`[id=\"${id}\"]`)\n    if (typeof target !== \"undefined\")\n      table.set(anchor, target)\n  }\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n      map(({ height }) => {\n        const main = getComponentElement(\"main\")\n        const grid = getElement(\":scope > :first-child\", main)\n        return height + 0.8 * (\n          grid.offsetTop -\n          main.offsetTop\n        )\n      }),\n      share()\n    )\n\n  /* Compute partition of previous and next anchors */\n  const partition$ = watchElementSize(document.body)\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n\n      /* Build index to map anchor paths to vertical offsets */\n      switchMap(body => defer(() => {\n        let path: HTMLAnchorElement[] = []\n        return of([...table].reduce((index, [anchor, target]) => {\n          while (path.length) {\n            const last = table.get(path[path.length - 1])!\n            if (last.tagName >= target.tagName) {\n              path.pop()\n            } else {\n              break\n            }\n          }\n\n          /* If the current anchor is hidden, continue with its parent */\n          let offset = target.offsetTop\n          while (!offset && target.parentElement) {\n            target = target.parentElement\n            offset = target.offsetTop\n          }\n\n          /* Fix anchor offsets in tables - see https://bit.ly/3CUFOcn */\n          let parent = target.offsetParent as HTMLElement\n          for (; parent; parent = parent.offsetParent as HTMLElement)\n            offset += parent.offsetTop\n\n          /* Map reversed anchor path to vertical offset */\n          return index.set(\n            [...path = [...path, anchor]].reverse(),\n            offset\n          )\n        }, new Map<HTMLAnchorElement[], number>()))\n      })\n        .pipe(\n\n          /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n          map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n          combineLatestWith(adjust$),\n\n          /* Re-compute partition when viewport offset changes */\n          switchMap(([index, adjust]) => viewport$\n            .pipe(\n              scan(([prev, next], { offset: { y }, size }) => {\n                const last = y + size.height >= Math.floor(body.height)\n\n                /* Look forward */\n                while (next.length) {\n                  const [, offset] = next[0]\n                  if (offset - adjust < y || last) {\n                    prev = [...prev, next.shift()!]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Look backward */\n                while (prev.length) {\n                  const [, offset] = prev[prev.length - 1]\n                  if (offset - adjust >= y && !last) {\n                    next = [prev.pop()!, ...next]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Return partition */\n                return [prev, next]\n              }, [[], [...index]]),\n              distinctUntilChanged((a, b) => (\n                a[0] === b[0] &&\n                a[1] === b[1]\n              ))\n            )\n          )\n        )\n      )\n    )\n\n  /* Compute and return anchor list migrations */\n  return partition$\n    .pipe(\n      map(([prev, next]) => ({\n        prev: prev.map(([path]) => path),\n        next: next.map(([path]) => path)\n      })),\n\n      /* Extract anchor list migrations */\n      startWith({ prev: [], next: [] }),\n      bufferCount(2, 1),\n      map(([a, b]) => {\n\n        /* Moving down */\n        if (a.prev.length < b.prev.length) {\n          return {\n            prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n            next: []\n          }\n\n        /* Moving up */\n        } else {\n          return {\n            prev: b.prev.slice(-1),\n            next: b.next.slice(0, b.next.length - a.next.length)\n          }\n        }\n      })\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<TableOfContents>> {\n  return defer(() => {\n    const push$ = new Subject<TableOfContents>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$.subscribe(({ prev, next }) => {\n\n      /* Look forward */\n      for (const [anchor] of next) {\n        anchor.classList.remove(\"md-nav__link--passed\")\n        anchor.classList.remove(\"md-nav__link--active\")\n      }\n\n      /* Look backward */\n      for (const [index, [anchor]] of prev.entries()) {\n        anchor.classList.add(\"md-nav__link--passed\")\n        anchor.classList.toggle(\n          \"md-nav__link--active\",\n          index === prev.length - 1\n        )\n      }\n    })\n\n    /* Set up following, if enabled */\n    if (feature(\"toc.follow\")) {\n\n      /* Toggle smooth scrolling only for anchor clicks */\n      const smooth$ = merge(\n        viewport$.pipe(debounceTime(1), map(() => undefined)),\n        viewport$.pipe(debounceTime(250), map(() => \"smooth\" as const))\n      )\n\n      /* Bring active anchor into view */ // @todo: refactor\n      push$\n        .pipe(\n          filter(({ prev }) => prev.length > 0),\n          combineLatestWith(main$.pipe(observeOn(asyncScheduler))),\n          withLatestFrom(smooth$)\n        )\n          .subscribe(([[{ prev }], behavior]) => {\n            const [anchor] = prev[prev.length - 1]\n            if (anchor.offsetHeight) {\n\n              /* Retrieve overflowing container and scroll */\n              const container = getElementContainer(anchor)\n              if (typeof container !== \"undefined\") {\n                const offset = anchor.offsetTop - container.offsetTop\n                const { height } = getElementSize(container)\n                container.scrollTo({\n                  top: offset - height / 2,\n                  behavior\n                })\n              }\n            }\n          })\n    }\n\n    /* Set up anchor tracking, if enabled */\n    if (feature(\"navigation.tracking\"))\n      viewport$\n        .pipe(\n          takeUntil(done$),\n          distinctUntilKeyChanged(\"offset\"),\n          debounceTime(250),\n          skip(1),\n          takeUntil(target$.pipe(skip(1))),\n          repeat({ delay: 250 }),\n          withLatestFrom(push$)\n        )\n          .subscribe(([, { prev }]) => {\n            const url = getLocation()\n\n            /* Set hash fragment to active anchor */\n            const anchor = prev[prev.length - 1]\n            if (anchor && anchor.length) {\n              const [active] = anchor\n              const { hash } = new URL(active.href)\n              if (url.hash !== hash) {\n                url.hash = hash\n                history.replaceState({}, \"\", `${url}`)\n              }\n\n            /* Reset anchor when at the top */\n            } else {\n              url.hash = \"\"\n              history.replaceState({}, \"\", `${url}`)\n            }\n          })\n\n    /* Create and return component */\n    return watchTableOfContents(el, { viewport$, header$ })\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  repeat,\n  skip,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n  hidden: boolean                      /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n  _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable<BackToTop> {\n\n  /* Compute direction */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => a > b && b > 0),\n      distinctUntilChanged()\n    )\n\n  /* Compute whether main area is active */\n  const active$ = main$\n    .pipe(\n      map(({ active }) => active)\n    )\n\n  /* Compute threshold for hiding */\n  return combineLatest([active$, direction$])\n    .pipe(\n      map(([active, direction]) => !(active && direction)),\n      distinctUntilChanged(),\n      takeUntil(target$.pipe(skip(1))),\n      endWith(true),\n      repeat({ delay: 250 }),\n      map(hidden => ({ hidden }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<BackToTop>> {\n  const push$ = new Subject<BackToTop>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n  push$.subscribe({\n\n    /* Handle emission */\n    next({ hidden }) {\n      el.hidden = hidden\n      if (hidden) {\n        el.setAttribute(\"tabindex\", \"-1\")\n        el.blur()\n      } else {\n        el.removeAttribute(\"tabindex\")\n      }\n    },\n\n    /* Handle complete */\n    complete() {\n      el.style.top = \"\"\n      el.hidden = true\n      el.removeAttribute(\"tabindex\")\n    }\n  })\n\n  /* Watch header height */\n  header$\n    .pipe(\n      takeUntil(done$),\n      distinctUntilKeyChanged(\"height\")\n    )\n      .subscribe(({ height }) => {\n        el.style.top = `${height + 16}px`\n      })\n\n  /* Go back to top */\n  fromEvent(el, \"click\")\n    .subscribe(ev => {\n      ev.preventDefault()\n      window.scrollTo({ top: 0 })\n    })\n\n  /* Create and return component */\n  return watchBackToTop(el, { viewport$, main$, target$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  finalize,\n  map,\n  mergeMap,\n  skip,\n  switchMap,\n  take,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElements,\n  watchElementVisibility\n} from \"~/browser\"\nimport { mountInlineTooltip2 } from \"~/components/tooltip2\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch ellipsis\n *\n * This function will fetch all elements that are shortened with ellipsis, and\n * filter those which are visible. Once they become visible, they stay in that\n * state, even though they may be hidden again. This optimization is necessary\n * to reduce pressure on the browser, with elements fading in and out of view.\n *\n * @param options - Options\n */\nexport function patchEllipsis(\n  { document$, viewport$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\".md-ellipsis\")),\n      mergeMap(el => watchElementVisibility(el)\n        .pipe(\n          takeUntil(document$.pipe(skip(1))),\n          filter(visible => visible),\n          map(() => el),\n          take(1)\n        )\n      ),\n      filter(el => el.offsetWidth < el.scrollWidth),\n      mergeMap(el => {\n        const text = el.innerText\n        const host = el.closest(\"a\") || el\n        host.title = text\n\n        /* Mount tooltip */\n        return mountInlineTooltip2(host, { viewport$ })\n          .pipe(\n            takeUntil(document$.pipe(skip(1))),\n            finalize(() => host.removeAttribute(\"title\"))\n          )\n      })\n    )\n      .subscribe()\n\n  // @todo move this outside of here and fix memleaks\n  document$\n    .pipe(\n      switchMap(() => getElements(\".md-status\")),\n      mergeMap(el => mountInlineTooltip2(el, { viewport$ }))\n    )\n      .subscribe()\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  takeWhile,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n  { document$, tablet$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements<HTMLInputElement>(\n        \".md-toggle--indeterminate\"\n      )),\n      tap(el => {\n        el.indeterminate = true\n        el.checked = false\n      }),\n      mergeMap(el => fromEvent(el, \"change\")\n        .pipe(\n          takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n          map(() => el)\n        )\n      ),\n      withLatestFrom(tablet$)\n    )\n      .subscribe(([el, tablet]) => {\n        el.classList.remove(\"md-toggle--indeterminate\")\n        if (tablet)\n          el.checked = false\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n  return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n  { document$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\"[data-md-scrollfix]\")),\n      tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n      filter(isAppleDevice),\n      mergeMap(el => fromEvent(el, \"touchstart\")\n        .pipe(\n          map(() => el)\n        )\n      )\n    )\n      .subscribe(el => {\n        const top = el.scrollTop\n\n        /* We're at the top of the container */\n        if (top === 0) {\n          el.scrollTop = 1\n\n        /* We're at the bottom of the container */\n        } else if (top + el.offsetHeight === el.scrollHeight) {\n          el.scrollTop = top - 1\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  delay,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n  { viewport$, tablet$ }: PatchOptions\n): void {\n  combineLatest([watchToggle(\"search\"), tablet$])\n    .pipe(\n      map(([active, tablet]) => active && !tablet),\n      switchMap(active => of(active)\n        .pipe(\n          delay(active ? 400 : 100)\n        )\n      ),\n      withLatestFrom(viewport$)\n    )\n      .subscribe(([active, { offset: { y }}]) => {\n        if (active) {\n          document.body.setAttribute(\"data-md-scrolllock\", \"\")\n          document.body.style.top = `-${y}px`\n        } else {\n          const value = -1 * parseInt(document.body.style.top, 10)\n          document.body.removeAttribute(\"data-md-scrolllock\")\n          document.body.style.top = \"\"\n          if (value)\n            window.scrollTo(0, value)\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n"],
-  "mappings": 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diff --git a/docs/site/assets/javascripts/lunr/min/lunr.ar.min.js b/docs/site/assets/javascripts/lunr/min/lunr.ar.min.js
index 9b06c26..248ddc5 100644
--- a/docs/site/assets/javascripts/lunr/min/lunr.ar.min.js
+++ b/docs/site/assets/javascripts/lunr/min/lunr.ar.min.js
@@ -1 +1 @@
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الاولى التى التي الثاني الثانية الذاتي الذى الذي الذين السابق الف اللائي اللاتي اللتان اللتيا اللتين اللذان اللذين اللواتي الماضي المقبل الوقت الى اليوم اما امام امس ان انبرى انقلب انه انها او اول اي ايار ايام ايضا ب بات باسم بان بخ برس بسبب بس بشكل بضع بطان بعد بعض بك بكم بكما بكن بل بلى بما بماذا بمن بن بنا به بها بي بيد بين بس بله بئس تان تانك تبدل تجاه تحول تلقاء تلك تلكم تلكما تم تينك تين ته تي ثلاثة ثم ثم ثمة ثم جعل جلل جميع جير حار حاشا حاليا حاي حتى حرى حسب حم حوالى حول حيث حيثما حين حي حبذا حتى حذار خلا خلال دون دونك ذا ذات ذاك ذانك ذان ذلك ذلكم ذلكما ذلكن ذو ذوا ذواتا ذواتي ذيت ذينك ذين ذه ذي راح رجع رويدك ريث رب زيارة سبحان سرعان سنة سنوات سوف سوى ساء ساءما شبه شخصا شرع شتان صار صباح صفر صه صه ضد ضمن طاق طالما طفق طق ظل عاد عام عاما عامة عدا عدة عدد عدم عسى عشر عشرة علق على عليك عليه عليها عل عن عند عندما عوض عين عدس عما غدا غير  ف فان فلان فو فى في فيم فيما فيه فيها قال قام قبل قد قط قلما قوة كانما كاين كاي كاين كاد كان كانت كذا كذلك كرب كل كلا كلاهما كلتا كلم كليكما كليهما كلما كلا كم كما كي كيت كيف كيفما كان كخ لئن لا لات لاسيما لدن لدى لعمر لقاء لك لكم لكما لكن لكنما لكي لكيلا للامم لم لما لما لن لنا له لها لو لوكالة لولا لوما لي لست لست لستم لستما لستن لست لسن لعل لكن ليت ليس ليسا ليستا ليست ليسوا لسنا ما ماانفك مابرح مادام ماذا مازال مافتئ مايو متى مثل مذ مساء مع معاذ مقابل مكانكم مكانكما مكانكن مكانك مليار مليون مما ممن من منذ منها مه مهما من من نحن نحو نعم نفس نفسه نهاية نخ نعما نعم ها هاؤم هاك هاهنا هب هذا هذه هكذا هل هلم هلا هم هما هن هنا هناك هنالك هو هي هيا هيت هيا هؤلاء هاتان هاتين هاته هاتي هج هذا هذان هذين هذه هذي هيهات و وا واحد واضاف واضافت واكد وان واها واوضح وراءك وفي وقال وقالت وقد وقف وكان وكانت ولا ولم ومن وهو وهي ويكان وي وشكان يكون يمكن يوم ايان".split(" ").indexOf(e.word)>=0)return!0},e.normalizeHamzaAndAlef=function(){return 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عدد عدم عسى عشر عشرة علق على عليك عليه عليها عل عن عند عندما عوض عين عدس عما غدا غير  ف فان فلان فو فى في فيم فيما فيه فيها قال قام قبل قد قط قلما قوة كانما كاين كاي كاين كاد كان كانت كذا كذلك كرب كل كلا كلاهما كلتا كلم كليكما كليهما كلما كلا كم كما كي كيت كيف كيفما كان كخ لئن لا لات لاسيما لدن لدى لعمر لقاء لك لكم لكما لكن لكنما لكي لكيلا للامم لم لما لما لن لنا له لها لو لوكالة لولا لوما لي لست لست لستم لستما لستن لست لسن لعل لكن ليت ليس ليسا ليستا ليست ليسوا لسنا ما ماانفك مابرح مادام ماذا مازال مافتئ مايو متى مثل مذ مساء مع معاذ مقابل مكانكم مكانكما مكانكن مكانك مليار مليون مما ممن من منذ منها مه مهما من من نحن نحو نعم نفس نفسه نهاية نخ نعما نعم ها هاؤم هاك هاهنا هب هذا هذه هكذا هل هلم هلا هم هما هن هنا هناك هنالك هو هي هيا هيت هيا هؤلاء هاتان هاتين هاته هاتي هج هذا هذان هذين هذه هذي هيهات وا واحد واضاف واضافت واكد وان واها واوضح وراءك وفي وقال وقالت وقد وقف وكان وكانت ولا ولم ومن وهو وهي ويكان وي وشكان يكون يمكن يوم ايان".split(" ")),e.Pipeline.registerFunction(e.ar.stopWordFilter,"stopWordFilter-ar")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.el.min.js b/docs/site/assets/javascripts/lunr/min/lunr.el.min.js
deleted file mode 100644
index ace017b..0000000
--- a/docs/site/assets/javascripts/lunr/min/lunr.el.min.js
+++ /dev/null
@@ -1 +0,0 @@
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null!==o&&(s=o[1]+l[o[2]]),null!==(o=/^(.+?)(ΑΔΕΣ|ΑΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΚ|ΜΑΜ|ΜΑΝ|ΜΠΑΜΠ|ΠΑΤΕΡ|ΓΙΑΓΙ|ΝΤΑΝΤ|ΚΥΡ|ΘΕΙ|ΠΕΘΕΡ|ΜΟΥΣΑΜ|ΚΑΠΛΑΜ|ΠΑΡ|ΨΑΡ|ΤΖΟΥΡ|ΤΑΜΠΟΥΡ|ΓΑΛΑΤ|ΦΑΦΛΑΤ)$/.test(o[1])||(s+="ΑΔ")),null!==(o=/^(.+?)(ΕΔΕΣ|ΕΔΩΝ)$/.exec(s))&&(s=o[1],/(ΟΠ|ΙΠ|ΕΜΠ|ΥΠ|ΓΗΠ|ΔΑΠ|ΚΡΑΣΠ|ΜΙΛ)$/.test(o[1])&&(s+="ΕΔ")),null!==(o=/^(.+?)(ΟΥΔΕΣ|ΟΥΔΩΝ)$/.exec(s))&&(s=o[1],/(ΑΡΚ|ΚΑΛΙΑΚ|ΠΕΤΑΛ|ΛΙΧ|ΠΛΕΞ|ΣΚ|Σ|ΦΛ|ΦΡ|ΒΕΛ|ΛΟΥΛ|ΧΝ|ΣΠ|ΤΡΑΓ|ΦΕ)$/.test(o[1])&&(s+="ΟΥΔ")),null!==(o=/^(.+?)(ΕΩΣ|ΕΩΝ|ΕΑΣ|ΕΑ)$/.exec(s))&&(s=o[1],/^(Θ|Δ|ΕΛ|ΓΑΛ|Ν|Π|ΙΔ|ΠΑΡ|ΣΤΕΡ|ΟΡΦ|ΑΝΔΡ|ΑΝΤΡ)$/.test(o[1])&&(s+="Ε")),null!==(o=/^(.+?)(ΕΙΟ|ΕΙΟΣ|ΕΙΟΙ|ΕΙΑ|ΕΙΑΣ|ΕΙΕΣ|ΕΙΟΥ|ΕΙΟΥΣ|ΕΙΩΝ)$/.exec(s))&&o[1].length>4&&(s=o[1]),null!==(o=/^(.+?)(ΙΟΥΣ|ΙΑΣ|ΙΕΣ|ΙΟΣ|ΙΟΥ|ΙΟΙ|ΙΩΝ|ΙΟΝ|ΙΑ|ΙΟ)$/.exec(s))&&(s=o[1],(t(s)||s.length<2||/^(ΑΓ|ΑΓΓΕΛ|ΑΓΡ|ΑΕΡ|ΑΘΛ|ΑΚΟΥΣ|ΑΞ|ΑΣ|Β|ΒΙΒΛ|ΒΥΤ|Γ|ΓΙΑΓ|ΓΩΝ|Δ|ΔΑΝ|ΔΗΛ|ΔΗΜ|ΔΟΚΙΜ|ΕΛ|ΖΑΧΑΡ|ΗΛ|ΗΠ|ΙΔ|ΙΣΚ|ΙΣΤ|ΙΟΝ|ΙΩΝ|ΚΙΜΩΛ|ΚΟΛΟΝ|ΚΟΡ|ΚΤΗΡ|ΚΥΡ|ΛΑΓ|ΛΟΓ|ΜΑΓ|ΜΠΑΝ|ΜΠΡ|ΝΑΥΤ|ΝΟΤ|ΟΠΑΛ|ΟΞ|ΟΡ|ΟΣ|ΠΑΝΑΓ|ΠΑΤΡ|ΠΗΛ|ΠΗΝ|ΠΛΑΙΣ|ΠΟΝΤ|ΡΑΔ|ΡΟΔ|ΣΚ|ΣΚΟΡΠ|ΣΟΥΝ|ΣΠΑΝ|ΣΤΑΔ|ΣΥΡ|ΤΗΛ|ΤΙΜ|ΤΟΚ|ΤΟΠ|ΤΡΟΧ|ΦΙΛ|ΦΩΤ|Χ|ΧΙΛ|ΧΡΩΜ|ΧΩΡ)$/.test(o[1]))&&(s+="Ι"),/^(ΠΑΛ)$/.test(o[1])&&(s+="ΑΙ")),null!==(o=/^(.+?)(ΙΚΟΣ|ΙΚΟΝ|ΙΚΕΙΣ|ΙΚΟΙ|ΙΚΕΣ|ΙΚΟΥΣ|ΙΚΗ|ΙΚΗΣ|ΙΚΟ|ΙΚΑ|ΙΚΟΥ|ΙΚΩΝ|ΙΚΩΣ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΑΔ|ΑΛ|ΑΜΑΝ|ΑΜΕΡ|ΑΜΜΟΧΑΛ|ΑΝΗΘ|ΑΝΤΙΔ|ΑΠΛ|ΑΤΤ|ΑΦΡ|ΒΑΣ|ΒΡΩΜ|ΓΕΝ|ΓΕΡ|Δ|ΔΙΚΑΝ|ΔΥΤ|ΕΙΔ|ΕΝΔ|ΕΞΩΔ|ΗΘ|ΘΕΤ|ΚΑΛΛΙΝ|ΚΑΛΠ|ΚΑΤΑΔ|ΚΟΥΖΙΝ|ΚΡ|ΚΩΔ|ΛΟΓ|Μ|ΜΕΡ|ΜΟΝΑΔ|ΜΟΥΛ|ΜΟΥΣ|ΜΠΑΓΙΑΤ|ΜΠΑΝ|ΜΠΟΛ|ΜΠΟΣ|ΜΥΣΤ|Ν|ΝΙΤ|ΞΙΚ|ΟΠΤ|ΠΑΝ|ΠΕΤΣ|ΠΙΚΑΝΤ|ΠΙΤΣ|ΠΛΑΣΤ|ΠΛΙΑΤΣ|ΠΟΝΤ|ΠΟΣΤΕΛΝ|ΠΡΩΤΟΔ|ΣΕΡΤ|ΣΗΜΑΝΤ|ΣΤΑΤ|ΣΥΝΑΔ|ΣΥΝΟΜΗΛ|ΤΕΛ|ΤΕΧΝ|ΤΡΟΠ|ΤΣΑΜ|ΥΠΟΔ|Φ|ΦΙΛΟΝ|ΦΥΛΟΔ|ΦΥΣ|ΧΑΣ)$/.test(o[1])||/(ΦΟΙΝ)$/.test(o[1]))&&(s+="ΙΚ")),"ΑΓΑΜΕ"===s&&(s="ΑΓΑΜ"),null!==(o=/^(.+?)(ΑΓΑΜΕ|ΗΣΑΜΕ|ΟΥΣΑΜΕ|ΗΚΑΜΕ|ΗΘΗΚΑΜΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΑΜΕ)$/.exec(s))&&(s=o[1],/^(ΑΝΑΠ|ΑΠΟΘ|ΑΠΟΚ|ΑΠΟΣΤ|ΒΟΥΒ|ΞΕΘ|ΟΥΛ|ΠΕΘ|ΠΙΚΡ|ΠΟΤ|ΣΙΧ|Χ)$/.test(o[1])&&(s+="ΑΜ")),null!==(o=/^(.+?)(ΑΓΑΝΕ|ΗΣΑΝΕ|ΟΥΣΑΝΕ|ΙΟΝΤΑΝΕ|ΙΟΤΑΝΕ|ΙΟΥΝΤΑΝΕ|ΟΝΤΑΝΕ|ΟΤΑΝΕ|ΟΥΝΤΑΝΕ|ΗΚΑΝΕ|ΗΘΗΚΑΝΕ)$/.exec(s))&&(s=o[1],/^(ΤΡ|ΤΣ)$/.test(o[1])&&(s+="ΑΓΑΝ")),null!==(o=/^(.+?)(ΑΝΕ)$/.exec(s))&&(s=o[1],(r(s)||/^(ΒΕΤΕΡ|ΒΟΥΛΚ|ΒΡΑΧΜ|Γ|ΔΡΑΔΟΥΜ|Θ|ΚΑΛΠΟΥΖ|ΚΑΣΤΕΛ|ΚΟΡΜΟΡ|ΛΑΟΠΛ|ΜΩΑΜΕΘ|Μ|ΜΟΥΣΟΥΛΜΑΝ|ΟΥΛ|Π|ΠΕΛΕΚ|ΠΛ|ΠΟΛΙΣ|ΠΟΡΤΟΛ|ΣΑΡΑΚΑΤΣ|ΣΟΥΛΤ|ΤΣΑΡΛΑΤ|ΟΡΦ|ΤΣΙΓΓ|ΤΣΟΠ|ΦΩΤΟΣΤΕΦ|Χ|ΨΥΧΟΠΛ|ΑΓ|ΟΡΦ|ΓΑΛ|ΓΕΡ|ΔΕΚ|ΔΙΠΛ|ΑΜΕΡΙΚΑΝ|ΟΥΡ|ΠΙΘ|ΠΟΥΡΙΤ|Σ|ΖΩΝΤ|ΙΚ|ΚΑΣΤ|ΚΟΠ|ΛΙΧ|ΛΟΥΘΗΡ|ΜΑΙΝΤ|ΜΕΛ|ΣΙΓ|ΣΠ|ΣΤΕΓ|ΤΡΑΓ|ΤΣΑΓ|Φ|ΕΡ|ΑΔΑΠ|ΑΘΙΓΓ|ΑΜΗΧ|ΑΝΙΚ|ΑΝΟΡΓ|ΑΠΗΓ|ΑΠΙΘ|ΑΤΣΙΓΓ|ΒΑΣ|ΒΑΣΚ|ΒΑΘΥΓΑΛ|ΒΙΟΜΗΧ|ΒΡΑΧΥΚ|ΔΙΑΤ|ΔΙΑΦ|ΕΝΟΡΓ|ΘΥΣ|ΚΑΠΝΟΒΙΟΜΗΧ|ΚΑΤΑΓΑΛ|ΚΛΙΒ|ΚΟΙΛΑΡΦ|ΛΙΒ|ΜΕΓΛΟΒΙΟΜΗΧ|ΜΙΚΡΟΒΙΟΜΗΧ|ΝΤΑΒ|ΞΗΡΟΚΛΙΒ|ΟΛΙΓΟΔΑΜ|ΟΛΟΓΑΛ|ΠΕΝΤΑΡΦ|ΠΕΡΗΦ|ΠΕΡΙΤΡ|ΠΛΑΤ|ΠΟΛΥΔΑΠ|ΠΟΛΥΜΗΧ|ΣΤΕΦ|ΤΑΒ|ΤΕΤ|ΥΠΕΡΗΦ|ΥΠΟΚΟΠ|ΧΑΜΗΛΟΔΑΠ|ΨΗΛΟΤΑΒ)$/.test(o[1]))&&(s+="ΑΝ")),null!==(o=/^(.+?)(ΗΣΕΤΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΕΤΕ)$/.exec(s))&&(s=o[1],(r(s)||/(ΟΔ|ΑΙΡ|ΦΟΡ|ΤΑΘ|ΔΙΑΘ|ΣΧ|ΕΝΔ|ΕΥΡ|ΤΙΘ|ΥΠΕΡΘ|ΡΑΘ|ΕΝΘ|ΡΟΘ|ΣΘ|ΠΥΡ|ΑΙΝ|ΣΥΝΔ|ΣΥΝ|ΣΥΝΘ|ΧΩΡ|ΠΟΝ|ΒΡ|ΚΑΘ|ΕΥΘ|ΕΚΘ|ΝΕΤ|ΡΟΝ|ΑΡΚ|ΒΑΡ|ΒΟΛ|ΩΦΕΛ)$/.test(o[1])||/^(ΑΒΑΡ|ΒΕΝ|ΕΝΑΡ|ΑΒΡ|ΑΔ|ΑΘ|ΑΝ|ΑΠΛ|ΒΑΡΟΝ|ΝΤΡ|ΣΚ|ΚΟΠ|ΜΠΟΡ|ΝΙΦ|ΠΑΓ|ΠΑΡΑΚΑΛ|ΣΕΡΠ|ΣΚΕΛ|ΣΥΡΦ|ΤΟΚ|Υ|Δ|ΕΜ|ΘΑΡΡ|Θ)$/.test(o[1]))&&(s+="ΕΤ")),null!==(o=/^(.+?)(ΟΝΤΑΣ|ΩΝΤΑΣ)$/.exec(s))&&(s=o[1],/^ΑΡΧ$/.test(o[1])&&(s+="ΟΝΤ"),/ΚΡΕ$/.test(o[1])&&(s+="ΩΝΤ")),null!==(o=/^(.+?)(ΟΜΑΣΤΕ|ΙΟΜΑΣΤΕ)$/.exec(s))&&(s=o[1],/^ΟΝ$/.test(o[1])&&(s+="ΟΜΑΣΤ")),null!==(o=/^(.+?)(ΙΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(Π|ΑΠ|ΣΥΜΠ|ΑΣΥΜΠ|ΑΚΑΤΑΠ|ΑΜΕΤΑΜΦ)$/.test(o[1])&&(s+="ΙΕΣΤ")),null!==(o=/^(.+?)(ΕΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΛ|ΑΡ|ΕΚΤΕΛ|Ζ|Μ|Ξ|ΠΑΡΑΚΑΛ|ΠΡΟ|ΝΙΣ)$/.test(o[1])&&(s+="ΕΣΤ")),null!==(o=/^(.+?)(ΗΘΗΚΑ|ΗΘΗΚΕΣ|ΗΘΗΚΕ)$/.exec(s))&&(s=o[1]),null!==(o=/^(.+?)(ΗΚΑ|ΗΚΕΣ|ΗΚΕ)$/.exec(s))&&(s=o[1],(/(ΣΚΩΛ|ΣΚΟΥΛ|ΝΑΡΘ|ΣΦ|ΟΘ|ΠΙΘ)$/.test(o[1])||/^(ΔΙΑΘ|Θ|ΠΑΡΑΚΑΤΑΘ|ΠΡΟΣΘ|ΣΥΝΘ)$/.test(o[1]))&&(s+="ΗΚ")),null!==(o=/^(.+?)(ΟΥΣΑ|ΟΥΣΕΣ|ΟΥΣΕ)$/.exec(s))&&(s=o[1],(t(s)||/^(ΦΑΡΜΑΚ|ΧΑΔ|ΑΓΚ|ΑΝΑΡΡ|ΒΡΟΜ|ΕΚΛΙΠ|ΛΑΜΠΙΔ|ΛΕΧ|Μ|ΠΑΤ|Ρ|Λ|ΜΕΔ|ΜΕΣΑΖ|ΥΠΟΤΕΙΝ|ΑΜ|ΑΙΘ|ΑΝΗΚ|ΔΕΣΠΟΖ|ΕΝΔΙΑΦΕΡ)$/.test(o[1])||/(ΠΟΔΑΡ|ΒΛΕΠ|ΠΑΝΤΑΧ|ΦΡΥΔ|ΜΑΝΤΙΛ|ΜΑΛΛ|ΚΥΜΑΤ|ΛΑΧ|ΛΗΓ|ΦΑΓ|ΟΜ|ΠΡΩΤ)$/.test(o[1]))&&(s+="ΟΥΣ")),null!==(o=/^(.+?)(ΑΓΑ|ΑΓΕΣ|ΑΓΕ)$/.exec(s))&&(s=o[1],(/^(ΑΒΑΣΤ|ΠΟΛΥΦ|ΑΔΗΦ|ΠΑΜΦ|Ρ|ΑΣΠ|ΑΦ|ΑΜΑΛ|ΑΜΑΛΛΙ|ΑΝΥΣΤ|ΑΠΕΡ|ΑΣΠΑΡ|ΑΧΑΡ|ΔΕΡΒΕΝ|ΔΡΟΣΟΠ|ΞΕΦ|ΝΕΟΠ|ΝΟΜΟΤ|ΟΛΟΠ|ΟΜΟΤ|ΠΡΟΣΤ|ΠΡΟΣΩΠΟΠ|ΣΥΜΠ|ΣΥΝΤ|Τ|ΥΠΟΤ|ΧΑΡ|ΑΕΙΠ|ΑΙΜΟΣΤ|ΑΝΥΠ|ΑΠΟΤ|ΑΡΤΙΠ|ΔΙΑΤ|ΕΝ|ΕΠΙΤ|ΚΡΟΚΑΛΟΠ|ΣΙΔΗΡΟΠ|Λ|ΝΑΥ|ΟΥΛΑΜ|ΟΥΡ|Π|ΤΡ|Μ)$/.test(o[1])||/(ΟΦ|ΠΕΛ|ΧΟΡΤ|ΛΛ|ΣΦ|ΡΠ|ΦΡ|ΠΡ|ΛΟΧ|ΣΜΗΝ)$/.test(o[1])&&!/^(ΨΟΦ|ΝΑΥΛΟΧ)$/.test(o[1])||/(ΚΟΛΛ)$/.test(o[1]))&&(s+="ΑΓ")),null!==(o=/^(.+?)(ΗΣΕ|ΗΣΟΥ|ΗΣΑ)$/.exec(s))&&(s=o[1],/^(Ν|ΧΕΡΣΟΝ|ΔΩΔΕΚΑΝ|ΕΡΗΜΟΝ|ΜΕΓΑΛΟΝ|ΕΠΤΑΝ|Ι)$/.test(o[1])&&(s+="ΗΣ")),null!==(o=/^(.+?)(ΗΣΤΕ)$/.exec(s))&&(s=o[1],/^(ΑΣΒ|ΣΒ|ΑΧΡ|ΧΡ|ΑΠΛ|ΑΕΙΜΝ|ΔΥΣΧΡ|ΕΥΧΡ|ΚΟΙΝΟΧΡ|ΠΑΛΙΜΨ)$/.test(o[1])&&(s+="ΗΣΤ")),null!==(o=/^(.+?)(ΟΥΝΕ|ΗΣΟΥΝΕ|ΗΘΟΥΝΕ)$/.exec(s))&&(s=o[1],/^(Ν|Ρ|ΣΠΙ|ΣΤΡΑΒΟΜΟΥΤΣ|ΚΑΚΟΜΟΥΤΣ|ΕΞΩΝ)$/.test(o[1])&&(s+="ΟΥΝ")),null!==(o=/^(.+?)(ΟΥΜΕ|ΗΣΟΥΜΕ|ΗΘΟΥΜΕ)$/.exec(s))&&(s=o[1],/^(ΠΑΡΑΣΟΥΣ|Φ|Χ|ΩΡΙΟΠΛ|ΑΖ|ΑΛΛΟΣΟΥΣ|ΑΣΟΥΣ)$/.test(o[1])&&(s+="ΟΥΜ")),null!=(o=/^(.+?)(ΜΑΤΟΙ|ΜΑΤΟΥΣ|ΜΑΤΟ|ΜΑΤΑ|ΜΑΤΩΣ|ΜΑΤΩΝ|ΜΑΤΟΣ|ΜΑΤΕΣ|ΜΑΤΗ|ΜΑΤΗΣ|ΜΑΤΟΥ)$/.exec(s))&&(s=o[1]+"Μ",/^(ΓΡΑΜ)$/.test(o[1])?s+="Α":/^(ΓΕ|ΣΤΑ)$/.test(o[1])&&(s+="ΑΤ")),null!==(o=/^(.+?)(ΟΥΑ)$/.exec(s))&&(s=o[1]+"ΟΥ"),n.length===s.length&&null!==(o=/^(.+?)(Α|ΑΓΑΤΕ|ΑΓΑΝ|ΑΕΙ|ΑΜΑΙ|ΑΝ|ΑΣ|ΑΣΑΙ|ΑΤΑΙ|ΑΩ|Ε|ΕΙ|ΕΙΣ|ΕΙΤΕ|ΕΣΑΙ|ΕΣ|ΕΤΑΙ|Ι|ΙΕΜΑΙ|ΙΕΜΑΣΤΕ|ΙΕΤΑΙ|ΙΕΣΑΙ|ΙΕΣΑΣΤΕ|ΙΟΜΑΣΤΑΝ|ΙΟΜΟΥΝ|ΙΟΜΟΥΝΑ|ΙΟΝΤΑΝ|ΙΟΝΤΟΥΣΑΝ|ΙΟΣΑΣΤΑΝ|ΙΟΣΑΣΤΕ|ΙΟΣΟΥΝ|ΙΟΣΟΥΝΑ|ΙΟΤΑΝ|ΙΟΥΜΑ|ΙΟΥΜΑΣΤΕ|ΙΟΥΝΤΑΙ|ΙΟΥΝΤΑΝ|Η|ΗΔΕΣ|ΗΔΩΝ|ΗΘΕΙ|ΗΘΕΙΣ|ΗΘΕΙΤΕ|ΗΘΗΚΑΤΕ|ΗΘΗΚΑΝ|ΗΘΟΥΝ|ΗΘΩ|ΗΚΑΤΕ|ΗΚΑΝ|ΗΣ|ΗΣΑΝ|ΗΣΑΤΕ|ΗΣΕΙ|ΗΣΕΣ|ΗΣΟΥΝ|ΗΣΩ|Ο|ΟΙ|ΟΜΑΙ|ΟΜΑΣΤΑΝ|ΟΜΟΥΝ|ΟΜΟΥΝΑ|ΟΝΤΑΙ|ΟΝΤΑΝ|ΟΝΤΟΥΣΑΝ|ΟΣ|ΟΣΑΣΤΑΝ|ΟΣΑΣΤΕ|ΟΣΟΥΝ|ΟΣΟΥΝΑ|ΟΤΑΝ|ΟΥ|ΟΥΜΑΙ|ΟΥΜΑΣΤΕ|ΟΥΝ|ΟΥΝΤΑΙ|ΟΥΝΤΑΝ|ΟΥΣ|ΟΥΣΑΝ|ΟΥΣΑΤΕ|Υ||ΥΑ|ΥΣ|Ω|ΩΝ|ΟΙΣ)$/.exec(s))&&(s=o[1]),null!=(o=/^(.+?)(ΕΣΤΕΡ|ΕΣΤΑΤ|ΟΤΕΡ|ΟΤΑΤ|ΥΤΕΡ|ΥΤΑΤ|ΩΤΕΡ|ΩΤΑΤ)$/.exec(s))&&(/^(ΕΞ|ΕΣ|ΑΝ|ΚΑΤ|Κ|ΠΡ)$/.test(o[1])||(s=o[1]),/^(ΚΑ|Μ|ΕΛΕ|ΛΕ|ΔΕ)$/.test(o[1])&&(s+="ΥΤ")),s}var l={"ΦΑΓΙΑ":"ΦΑ","ΦΑΓΙΟΥ":"ΦΑ","ΦΑΓΙΩΝ":"ΦΑ","ΣΚΑΓΙΑ":"ΣΚΑ","ΣΚΑΓΙΟΥ":"ΣΚΑ","ΣΚΑΓΙΩΝ":"ΣΚΑ","ΣΟΓΙΟΥ":"ΣΟ","ΣΟΓΙΑ":"ΣΟ","ΣΟΓΙΩΝ":"ΣΟ","ΤΑΤΟΓΙΑ":"ΤΑΤΟ","ΤΑΤΟΓΙΟΥ":"ΤΑΤΟ","ΤΑΤΟΓΙΩΝ":"ΤΑΤΟ","ΚΡΕΑΣ":"ΚΡΕ","ΚΡΕΑΤΟΣ":"ΚΡΕ","ΚΡΕΑΤΑ":"ΚΡΕ","ΚΡΕΑΤΩΝ":"ΚΡΕ","ΠΕΡΑΣ":"ΠΕΡ","ΠΕΡΑΤΟΣ":"ΠΕΡ","ΠΕΡΑΤΑ":"ΠΕΡ","ΠΕΡΑΤΩΝ":"ΠΕΡ","ΤΕΡΑΣ":"ΤΕΡ","ΤΕΡΑΤΟΣ":"ΤΕΡ","ΤΕΡΑΤΑ":"ΤΕΡ","ΤΕΡΑΤΩΝ":"ΤΕΡ","ΦΩΣ":"ΦΩ","ΦΩΤΟΣ":"ΦΩ","ΦΩΤΑ":"ΦΩ","ΦΩΤΩΝ":"ΦΩ","ΚΑΘΕΣΤΩΣ":"ΚΑΘΕΣΤ","ΚΑΘΕΣΤΩΤΟΣ":"ΚΑΘΕΣΤ","ΚΑΘΕΣΤΩΤΑ":"ΚΑΘΕΣΤ","ΚΑΘΕΣΤΩΤΩΝ":"ΚΑΘΕΣΤ","ΓΕΓΟΝΟΣ":"ΓΕΓΟΝ","ΓΕΓΟΝΟΤΟΣ":"ΓΕΓΟΝ","ΓΕΓΟΝΟΤΑ":"ΓΕΓΟΝ","ΓΕΓΟΝΟΤΩΝ":"ΓΕΓΟΝ","ΕΥΑ":"ΕΥ"},i=["ΑΚΡΙΒΩΣ","ΑΛΑ","ΑΛΛΑ","ΑΛΛΙΩΣ","ΑΛΛΟΤΕ","ΑΜΑ","ΑΝΩ","ΑΝΑ","ΑΝΑΜΕΣΑ","ΑΝΑΜΕΤΑΞΥ","ΑΝΕΥ","ΑΝΤΙ","ΑΝΤΙΠΕΡΑ","ΑΝΤΙΟ","ΑΞΑΦΝΑ","ΑΠΟ","ΑΠΟΨΕ","ΑΡΑ","ΑΡΑΓΕ","ΑΥΡΙΟ","ΑΦΟΙ","ΑΦΟΥ","ΑΦΟΤΟΥ","ΒΡΕ","ΓΕΙΑ","ΓΙΑ","ΓΙΑΤΙ","ΓΡΑΜΜΑ","ΔΕΗ","ΔΕΝ","ΔΗΛΑΔΗ","ΔΙΧΩΣ","ΔΥΟ","ΕΑΝ","ΕΓΩ","ΕΔΩ","ΕΔΑ","ΕΙΘΕ","ΕΙΜΑΙ","ΕΙΜΑΣΤΕ","ΕΙΣΑΙ","ΕΙΣΑΣΤΕ","ΕΙΝΑΙ","ΕΙΣΤΕ","ΕΙΤΕ","ΕΚΕΙ","ΕΚΟ","ΕΛΑ","ΕΜΑΣ","ΕΜΕΙΣ","ΕΝΤΕΛΩΣ","ΕΝΤΟΣ","ΕΝΤΩΜΕΤΑΞΥ","ΕΝΩ","ΕΞΙ","ΕΞΙΣΟΥ","ΕΞΗΣ","ΕΞΩ","ΕΟΚ","ΕΠΑΝΩ","ΕΠΕΙΔΗ","ΕΠΕΙΤΑ","ΕΠΙ","ΕΠΙΣΗΣ","ΕΠΟΜΕΝΩΣ","ΕΠΤΑ","ΕΣΑΣ","ΕΣΕΙΣ","ΕΣΤΩ","ΕΣΥ","ΕΣΩ","ΕΤΣΙ","ΕΥΓΕ","ΕΦΕ","ΕΦΕΞΗΣ","ΕΧΤΕΣ","ΕΩΣ","ΗΔΗ","ΗΜΙ","ΗΠΑ","ΗΤΟΙ","ΘΕΣ","ΙΔΙΩΣ","ΙΔΗ","ΙΚΑ","ΙΣΩΣ","ΚΑΘΕ","ΚΑΘΕΤΙ","ΚΑΘΟΛΟΥ","ΚΑΘΩΣ","ΚΑΙ","ΚΑΝ","ΚΑΠΟΤΕ","ΚΑΠΟΥ","ΚΑΤΑ","ΚΑΤΙ","ΚΑΤΟΠΙΝ","ΚΑΤΩ","ΚΕΙ","ΚΙΧ","ΚΚΕ","ΚΟΛΑΝ","ΚΥΡΙΩΣ","ΚΩΣ","ΜΑΚΑΡΙ","ΜΑΛΙΣΤΑ","ΜΑΛΛΟΝ","ΜΑΙ","ΜΑΟ","ΜΑΟΥΣ","ΜΑΣ","ΜΕΘΑΥΡΙΟ","ΜΕΣ","ΜΕΣΑ","ΜΕΤΑ","ΜΕΤΑΞΥ","ΜΕΧΡΙ","ΜΗΔΕ","ΜΗΝ","ΜΗΠΩΣ","ΜΗΤΕ","ΜΙΑ","ΜΙΑΣ","ΜΙΣ","ΜΜΕ","ΜΟΛΟΝΟΤΙ","ΜΟΥ","ΜΠΑ","ΜΠΑΣ","ΜΠΟΥΦΑΝ","ΜΠΡΟΣ","ΝΑΙ","ΝΕΣ","ΝΤΑ","ΝΤΕ","ΞΑΝΑ","ΟΗΕ","ΟΚΤΩ","ΟΜΩΣ","ΟΝΕ","ΟΠΑ","ΟΠΟΥ","ΟΠΩΣ","ΟΣΟ","ΟΤΑΝ","ΟΤΕ","ΟΤΙ","ΟΥΤΕ","ΟΧΙ","ΠΑΛΙ","ΠΑΝ","ΠΑΝΟ","ΠΑΝΤΟΤΕ","ΠΑΝΤΟΥ","ΠΑΝΤΩΣ","ΠΑΝΩ","ΠΑΡΑ","ΠΕΡΑ","ΠΕΡΙ","ΠΕΡΙΠΟΥ","ΠΙΑ","ΠΙΟ","ΠΙΣΩ","ΠΛΑΙ","ΠΛΕΟΝ","ΠΛΗΝ","ΠΟΤΕ","ΠΟΥ","ΠΡΟ","ΠΡΟΣ","ΠΡΟΧΤΕΣ","ΠΡΟΧΘΕΣ","ΡΟΔΙ","ΠΩΣ","ΣΑΙ","ΣΑΣ","ΣΑΝ","ΣΕΙΣ","ΣΙΑ","ΣΚΙ","ΣΟΙ","ΣΟΥ","ΣΡΙ","ΣΥΝ","ΣΥΝΑΜΑ","ΣΧΕΔΟΝ","ΤΑΔΕ","ΤΑΞΙ","ΤΑΧΑ","ΤΕΙ","ΤΗΝ","ΤΗΣ","ΤΙΠΟΤΑ","ΤΙΠΟΤΕ","ΤΙΣ","ΤΟΝ","ΤΟΤΕ","ΤΟΥ","ΤΟΥΣ","ΤΣΑ","ΤΣΕ","ΤΣΙ","ΤΣΟΥ","ΤΩΝ","ΥΠΟ","ΥΠΟΨΗ","ΥΠΟΨΙΝ","ΥΣΤΕΡΑ","ΦΕΤΟΣ","ΦΙΣ","ΦΠΑ","ΧΑΦ","ΧΘΕΣ","ΧΤΕΣ","ΧΩΡΙΣ","ΩΣ","ΩΣΑΝ","ΩΣΟΤΟΥ","ΩΣΠΟΥ","ΩΣΤΕ","ΩΣΤΟΣΟ"],s=new RegExp("^[ΑΒΓΔΕΖΗΘΙΚΛΜΝΞΟΠΡΣΤΥΦΧΨΩ]+$");return function(e){return"function"==typeof e.update?e.update(function(e){return n(e.toUpperCase()).toLowerCase()}):n(e.toUpperCase()).toLowerCase()}}(),e.Pipeline.registerFunction(e.el.stemmer,"stemmer-el"),e.el.stopWordFilter=e.generateStopWordFilter("αλλα αν αντι απο αυτα αυτεσ αυτη αυτο αυτοι αυτοσ αυτουσ αυτων για δε δεν εαν ειμαι ειμαστε ειναι εισαι ειστε εκεινα εκεινεσ εκεινη εκεινο εκεινοι εκεινοσ εκεινουσ εκεινων ενω επι η θα ισωσ κ και κατα κι μα με μετα μη μην να ο οι ομωσ οπωσ οσο οτι παρα ποια ποιεσ ποιο ποιοι ποιοσ ποιουσ ποιων που προσ πωσ σε στη στην στο στον τα την τησ το τον τοτε του των ωσ".split(" ")),e.Pipeline.registerFunction(e.el.stopWordFilter,"stopWordFilter-el"),e.el.normilizer=function(){var e={"Ά":"Α","ά":"α","Έ":"Ε","έ":"ε","Ή":"Η","ή":"η","Ί":"Ι","ί":"ι","Ό":"Ο","ο":"ο","Ύ":"Υ","ύ":"υ","Ώ":"Ω","ώ":"ω","Ϊ":"Ι","ϊ":"ι","Ϋ":"Υ","ϋ":"υ","ΐ":"ι","ΰ":"υ"};return function(t){if("function"==typeof t.update)return t.update(function(t){for(var r="",n=0;n<t.length;n++)r+=e[t.charAt(n)]||t.charAt(n);return r});for(var r="",n=0;n<t.length;n++)r+=e[t.charAt(n)]||t.charAt(n);return r}}(),e.Pipeline.registerFunction(e.el.normilizer,"normilizer-el")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.he.min.js b/docs/site/assets/javascripts/lunr/min/lunr.he.min.js
deleted file mode 100644
index b863d3e..0000000
--- a/docs/site/assets/javascripts/lunr/min/lunr.he.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.he=function(){this.pipeline.reset(),this.pipeline.add(e.he.trimmer,e.he.stopWordFilter,e.he.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.he.stemmer))},e.he.wordCharacters="֑-״א-תa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.he.trimmer=e.trimmerSupport.generateTrimmer(e.he.wordCharacters),e.Pipeline.registerFunction(e.he.trimmer,"trimmer-he"),e.he.stemmer=function(){var e=this;return e.result=!1,e.preRemoved=!1,e.sufRemoved=!1,e.pre={pre1:"ה ו י ת",pre2:"ב כ ל מ ש כש",pre3:"הב הכ הל המ הש בש לכ",pre4:"וב וכ ול ומ וש",pre5:"מה שה כל",pre6:"מב מכ מל ממ מש",pre7:"בה בו בי בת כה כו כי כת לה לו לי לת",pre8:"ובה ובו ובי ובת וכה וכו וכי וכת ולה ולו ולי ולת"},e.suf={suf1:"ך כ ם ן נ",suf2:"ים ות וך וכ ום ון ונ הם הן יכ יך ינ ים",suf3:"תי תך תכ תם תן תנ",suf4:"ותי ותך ותכ ותם ותן ותנ",suf5:"נו כם כן הם הן",suf6:"ונו וכם וכן והם והן",suf7:"תכם תכן תנו תהם תהן",suf8:"הוא היא הם הן אני אתה את אנו אתם אתן",suf9:"ני נו כי כו כם כן תי תך תכ תם תן",suf10:"י ך כ ם ן נ ת"},e.patterns=JSON.parse('{"hebrewPatterns": [{"pt1": [{"c": "ה", "l": 0}]}, {"pt2": [{"c": "ו", "l": 0}]}, {"pt3": [{"c": "י", "l": 0}]}, {"pt4": [{"c": "ת", "l": 0}]}, {"pt5": [{"c": "מ", "l": 0}]}, {"pt6": [{"c": "ל", "l": 0}]}, {"pt7": [{"c": "ב", "l": 0}]}, {"pt8": [{"c": "כ", "l": 0}]}, {"pt9": [{"c": "ש", "l": 0}]}, {"pt10": [{"c": "כש", "l": 0}]}, {"pt11": [{"c": "בה", "l": 0}]}, {"pt12": [{"c": "וב", "l": 0}]}, {"pt13": [{"c": "וכ", "l": 0}]}, {"pt14": [{"c": "ול", "l": 0}]}, {"pt15": [{"c": "ומ", "l": 0}]}, {"pt16": [{"c": "וש", "l": 0}]}, {"pt17": [{"c": "הב", "l": 0}]}, {"pt18": [{"c": "הכ", "l": 0}]}, {"pt19": [{"c": "הל", "l": 0}]}, {"pt20": [{"c": "המ", "l": 0}]}, {"pt21": [{"c": "הש", "l": 0}]}, {"pt22": [{"c": "מה", "l": 0}]}, {"pt23": [{"c": "שה", "l": 0}]}, {"pt24": [{"c": "כל", "l": 0}]}]}'),e.execArray=["cleanWord","removeDiacritics","removeStopWords","normalizeHebrewCharacters"],e.stem=function(){var r=0;for(e.result=!1,e.preRemoved=!1,e.sufRemoved=!1;r<e.execArray.length&&1!=e.result;)e.result=e[e.execArray[r]](),r++},e.setCurrent=function(r){e.word=r},e.getCurrent=function(){return e.word},e.cleanWord=function(){return!!new RegExp("[^֑-״א-ת]").test("")},e.removeDiacritics=function(){var r=new RegExp("[ְ-ֿ]","g");return e.word=e.word.replace(r,""),!1},e.removeStopWords=function(){if("אבל או אולי אותו אותי אותך אותם אותן אותנו אז אחר אחרות אחרי אחריכן אחרים אחרת אי איזה איך אין איפה אל אלה אלו אם אנחנו אני אף אפשר את אתה אתכם אתכן אתם אתן באיזה באיזו בגלל בין בלבד בעבור בעזרת בכל בכן בלי במידה במקום שבו ברוב בשביל בשעה ש בתוך גם דרך הוא היא היה היי היכן היתה היתי הם הן הנה הסיבה שבגללה הרי ואילו ואת זאת זה זות יהיה יוכל יוכלו יותר מדי יכול יכולה יכולות יכולים יכל יכלה יכלו יש כאן כאשר כולם כולן כזה כי כיצד כך כל כלל כמו כן כפי כש לא לאו לאיזותך לאן לבין לה להיות להם להן לו לזה לזות לי לך לכם לכן למה למעלה למעלה מ למטה למטה מ למעט למקום שבו למרות לנו לעבר לעיכן לפיכך לפני מאד מאחורי מאיזו סיבה מאין מאיפה מבלי מבעד מדוע מה מהיכן מול מחוץ מי מידע מכאן מכל מכן מלבד מן מנין מסוגל מעט מעטים מעל מצד מקום בו מתחת מתי נגד נגר נו עד עז על עלי עליו עליה עליהם עליך עלינו עם עצמה עצמהם עצמהן עצמו עצמי עצמם עצמן עצמנו פה רק שוב של שלה שלהם שלהן שלו שלי שלך שלכה שלכם שלכן שלנו שם תהיה תחת".split(" ").indexOf(e.word)>=0)return!0},e.normalizeHebrewCharacters=function(){return e.word=e.word.replace("ך","כ"),e.word=e.word.replace("ם","מ"),e.word=e.word.replace("ן","נ"),e.word=e.word.replace("ף","פ"),e.word=e.word.replace("ץ","צ"),!1},function(r){return"function"==typeof r.update?r.update(function(r){return e.setCurrent(r),e.stem(),e.getCurrent()}):(e.setCurrent(r),e.stem(),e.getCurrent())}}(),e.Pipeline.registerFunction(e.he.stemmer,"stemmer-he"),e.he.stopWordFilter=e.generateStopWordFilter("אבל או אולי אותו אותי אותך אותם אותן אותנו אז אחר אחרות אחרי אחריכן אחרים אחרת אי איזה איך אין איפה אל אלה אלו אם אנחנו אני אף אפשר את אתה אתכם אתכן אתם אתן באיזה באיזו בגלל בין בלבד בעבור בעזרת בכל בכן בלי במידה במקום שבו ברוב בשביל בשעה ש בתוך גם דרך הוא היא היה היי היכן היתה היתי הם הן הנה הסיבה שבגללה הרי ואילו ואת זאת זה זות יהיה יוכל יוכלו יותר מדי יכול יכולה יכולות יכולים יכל יכלה יכלו יש כאן כאשר כולם כולן כזה כי כיצד כך כל כלל כמו כן כפי כש לא לאו לאיזותך לאן לבין לה להיות להם להן לו לזה לזות לי לך לכם לכן למה למעלה למעלה מ למטה למטה מ למעט למקום שבו למרות לנו לעבר לעיכן לפיכך לפני מאד מאחורי מאיזו סיבה מאין מאיפה מבלי מבעד מדוע מה מהיכן מול מחוץ מי מידע מכאן מכל מכן מלבד מן מנין מסוגל מעט מעטים מעל מצד מקום בו מתחת מתי נגד נגר נו עד עז על עלי עליו עליה עליהם עליך עלינו עם עצמה עצמהם עצמהן עצמו עצמי עצמם עצמן עצמנו פה רק שוב של שלה שלהם שלהן שלו שלי שלך שלכה שלכם שלכן שלנו שם תהיה תחת".split(" ")),e.Pipeline.registerFunction(e.he.stopWordFilter,"stopWordFilter-he")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.hy.min.js b/docs/site/assets/javascripts/lunr/min/lunr.hy.min.js
deleted file mode 100644
index b37f792..0000000
--- a/docs/site/assets/javascripts/lunr/min/lunr.hy.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hy=function(){this.pipeline.reset(),this.pipeline.add(e.hy.trimmer,e.hy.stopWordFilter)},e.hy.wordCharacters="[A-Za-z԰-֏ff-ﭏ]",e.hy.trimmer=e.trimmerSupport.generateTrimmer(e.hy.wordCharacters),e.Pipeline.registerFunction(e.hy.trimmer,"trimmer-hy"),e.hy.stopWordFilter=e.generateStopWordFilter("դու և եք էիր էիք հետո նաև նրանք որը վրա է որ պիտի են այս մեջ ն իր ու ի այդ որոնք այն կամ էր մի ես համար այլ իսկ էին ենք հետ ին թ էինք մենք նրա նա դուք եմ էի ըստ որպես ում".split(" ")),e.Pipeline.registerFunction(e.hy.stopWordFilter,"stopWordFilter-hy"),e.hy.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.hy.stemmer,"stemmer-hy")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.kn.min.js b/docs/site/assets/javascripts/lunr/min/lunr.kn.min.js
deleted file mode 100644
index 1cef9be..0000000
--- a/docs/site/assets/javascripts/lunr/min/lunr.kn.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.kn=function(){this.pipeline.reset(),this.pipeline.add(e.kn.trimmer,e.kn.stopWordFilter,e.kn.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.kn.stemmer))},e.kn.wordCharacters="ಀ-಄ಅ-ಔಕ-ಹಾ-ೌ಼-ಽೕ-ೖೝ-ೞೠ-ೡೢ-ೣ೤೥೦-೯ೱ-ೳ",e.kn.trimmer=e.trimmerSupport.generateTrimmer(e.kn.wordCharacters),e.Pipeline.registerFunction(e.kn.trimmer,"trimmer-kn"),e.kn.stopWordFilter=e.generateStopWordFilter("ಮತ್ತು ಈ ಒಂದು ರಲ್ಲಿ ಹಾಗೂ ಎಂದು ಅಥವಾ ಇದು ರ ಅವರು ಎಂಬ ಮೇಲೆ ಅವರ ತನ್ನ ಆದರೆ ತಮ್ಮ ನಂತರ ಮೂಲಕ ಹೆಚ್ಚು ನ ಆ ಕೆಲವು ಅನೇಕ ಎರಡು ಹಾಗು ಪ್ರಮುಖ ಇದನ್ನು ಇದರ ಸುಮಾರು ಅದರ ಅದು ಮೊದಲ ಬಗ್ಗೆ ನಲ್ಲಿ ರಂದು ಇತರ ಅತ್ಯಂತ ಹೆಚ್ಚಿನ ಸಹ ಸಾಮಾನ್ಯವಾಗಿ ನೇ ಹಲವಾರು ಹೊಸ ದಿ ಕಡಿಮೆ ಯಾವುದೇ ಹೊಂದಿದೆ ದೊಡ್ಡ ಅನ್ನು ಇವರು ಪ್ರಕಾರ ಇದೆ ಮಾತ್ರ ಕೂಡ ಇಲ್ಲಿ ಎಲ್ಲಾ ವಿವಿಧ ಅದನ್ನು ಹಲವು ರಿಂದ ಕೇವಲ ದ ದಕ್ಷಿಣ ಗೆ ಅವನ ಅತಿ ನೆಯ ಬಹಳ ಕೆಲಸ ಎಲ್ಲ ಪ್ರತಿ ಇತ್ಯಾದಿ ಇವು ಬೇರೆ ಹೀಗೆ ನಡುವೆ ಇದಕ್ಕೆ ಎಸ್ ಇವರ ಮೊದಲು ಶ್ರೀ ಮಾಡುವ ಇದರಲ್ಲಿ ರೀತಿಯ ಮಾಡಿದ ಕಾಲ ಅಲ್ಲಿ ಮಾಡಲು ಅದೇ ಈಗ ಅವು ಗಳು ಎ ಎಂಬುದು ಅವನು ಅಂದರೆ ಅವರಿಗೆ ಇರುವ ವಿಶೇಷ ಮುಂದೆ ಅವುಗಳ ಮುಂತಾದ ಮೂಲ ಬಿ ಮೀ ಒಂದೇ ಇನ್ನೂ ಹೆಚ್ಚಾಗಿ ಮಾಡಿ ಅವರನ್ನು ಇದೇ ಯ ರೀತಿಯಲ್ಲಿ ಜೊತೆ ಅದರಲ್ಲಿ ಮಾಡಿದರು ನಡೆದ ಆಗ ಮತ್ತೆ ಪೂರ್ವ ಆತ ಬಂದ ಯಾವ ಒಟ್ಟು ಇತರೆ ಹಿಂದೆ ಪ್ರಮಾಣದ ಗಳನ್ನು ಕುರಿತು ಯು ಆದ್ದರಿಂದ ಅಲ್ಲದೆ ನಗರದ ಮೇಲಿನ ಏಕೆಂದರೆ ರಷ್ಟು ಎಂಬುದನ್ನು ಬಾರಿ ಎಂದರೆ ಹಿಂದಿನ ಆದರೂ ಆದ ಸಂಬಂಧಿಸಿದ ಮತ್ತೊಂದು ಸಿ ಆತನ ".split(" ")),e.kn.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.kn.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var n=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(n).split("|")},e.Pipeline.registerFunction(e.kn.stemmer,"stemmer-kn"),e.Pipeline.registerFunction(e.kn.stopWordFilter,"stopWordFilter-kn")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.ko.min.js b/docs/site/assets/javascripts/lunr/min/lunr.ko.min.js
deleted file mode 100644
index eaf9dab..0000000
--- a/docs/site/assets/javascripts/lunr/min/lunr.ko.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ko=function(){this.pipeline.reset(),this.pipeline.add(e.ko.trimmer,e.ko.stopWordFilter)},e.ko.wordCharacters="[A-Za-z가-힣]",e.ko.trimmer=e.trimmerSupport.generateTrimmer(e.ko.wordCharacters),e.Pipeline.registerFunction(e.ko.trimmer,"trimmer-ko"),e.ko.stopWordFilter=e.generateStopWordFilter("아 휴 아이구 아이쿠 아이고 어 나 우리 저희 따라 의해 을 를 에 의 가 으로 로 에게 뿐이다 의거하여 근거하여 입각하여 기준으로 예하면 예를 들면 예를 들자면 저 소인 소생 저희 지말고 하지마 하지마라 다른 물론 또한 그리고 비길수 없다 해서는 안된다 뿐만 아니라 만이 아니다 만은 아니다 막론하고 관계없이 그치지 않다 그러나 그런데 하지만 든간에 논하지 않다 따지지 않다 설사 비록 더라도 아니면 만 못하다 하는 편이 낫다 불문하고 향하여 향해서 향하다 쪽으로 틈타 이용하여 타다 오르다 제외하고 이 외에 이 밖에 하여야 비로소 한다면 몰라도 외에도 이곳 여기 부터 기점으로 따라서 할 생각이다 하려고하다 이리하여 그리하여 그렇게 함으로써 하지만 일때 할때 앞에서 중에서 보는데서 으로써 로써 까지 해야한다 일것이다 반드시 할줄알다 할수있다 할수있어 임에 틀림없다 한다면 등 등등 제 겨우 단지 다만 할뿐 딩동 댕그 대해서 대하여 대하면 훨씬 얼마나 얼마만큼 얼마큼 남짓 여 얼마간 약간 다소 좀 조금 다수 몇 얼마 지만 하물며 또한 그러나 그렇지만 하지만 이외에도 대해 말하자면 뿐이다 다음에 반대로 반대로 말하자면 이와 반대로 바꾸어서 말하면 바꾸어서 한다면 만약 그렇지않으면 까악 툭 딱 삐걱거리다 보드득 비걱거리다 꽈당 응당 해야한다 에 가서 각 각각 여러분 각종 각자 제각기 하도록하다 와 과 그러므로 그래서 고로 한 까닭에 하기 때문에 거니와 이지만 대하여 관하여 관한 과연 실로 아니나다를가 생각한대로 진짜로 한적이있다 하곤하였다 하 하하 허허 아하 거바 와 오 왜 어째서 무엇때문에 어찌 하겠는가 무슨 어디 어느곳 더군다나 하물며 더욱이는 어느때 언제 야 이봐 어이 여보시오 흐흐 흥 휴 헉헉 헐떡헐떡 영차 여차 어기여차 끙끙 아야 앗 아야 콸콸 졸졸 좍좍 뚝뚝 주룩주룩 솨 우르르 그래도 또 그리고 바꾸어말하면 바꾸어말하자면 혹은 혹시 답다 및 그에 따르는 때가 되어 즉 지든지 설령 가령 하더라도 할지라도 일지라도 지든지 몇 거의 하마터면 인젠 이젠 된바에야 된이상 만큼\t어찌됏든 그위에 게다가 점에서 보아 비추어 보아 고려하면 하게될것이다 일것이다 비교적 좀 보다더 비하면 시키다 하게하다 할만하다 의해서 연이서 이어서 잇따라 뒤따라 뒤이어 결국 의지하여 기대여 통하여 자마자 더욱더 불구하고 얼마든지 마음대로 주저하지 않고 곧 즉시 바로 당장 하자마자 밖에 안된다 하면된다 그래 그렇지 요컨대 다시 말하자면 바꿔 말하면 즉 구체적으로 말하자면 시작하여 시초에 이상 허 헉 허걱 바와같이 해도좋다 해도된다 게다가 더구나 하물며 와르르 팍 퍽 펄렁 동안 이래 하고있었다 이었다 에서 로부터 까지 예하면 했어요 해요 함께 같이 더불어 마저 마저도 양자 모두 습니다 가까스로 하려고하다 즈음하여 다른 다른 방면으로 해봐요 습니까 했어요 말할것도 없고 무릎쓰고 개의치않고 하는것만 못하다 하는것이 낫다 매 매번 들 모 어느것 어느 로써 갖고말하자면 어디 어느쪽 어느것 어느해 어느 년도 라 해도 언젠가 어떤것 어느것 저기 저쪽 저것 그때 그럼 그러면 요만한걸 그래 그때 저것만큼 그저 이르기까지 할 줄 안다 할 힘이 있다 너 너희 당신 어찌 설마 차라리 할지언정 할지라도 할망정 할지언정 구토하다 게우다 토하다 메쓰겁다 옆사람 퉤 쳇 의거하여 근거하여 의해 따라 힘입어 그 다음 버금 두번째로 기타 첫번째로 나머지는 그중에서 견지에서 형식으로 쓰여 입장에서 위해서 단지 의해되다 하도록시키다 뿐만아니라 반대로 전후 전자 앞의것 잠시 잠깐 하면서 그렇지만 다음에 그러한즉 그런즉 남들 아무거나 어찌하든지 같다 비슷하다 예컨대 이럴정도로 어떻게 만약 만일 위에서 서술한바와같이 인 듯하다 하지 않는다면 만약에 무엇 무슨 어느 어떤 아래윗 조차 한데 그럼에도 불구하고 여전히 심지어 까지도 조차도 하지 않도록 않기 위하여 때 시각 무렵 시간 동안 어때 어떠한 하여금 네 예 우선 누구 누가 알겠는가 아무도 줄은모른다 줄은 몰랏다 하는 김에 겸사겸사 하는바 그런 까닭에 한 이유는 그러니 그러니까 때문에 그 너희 그들 너희들 타인 것 것들 너 위하여 공동으로 동시에 하기 위하여 어찌하여 무엇때문에 붕붕 윙윙 나 우리 엉엉 휘익 윙윙 오호 아하 어쨋든 만 못하다\t하기보다는 차라리 하는 편이 낫다 흐흐 놀라다 상대적으로 말하자면 마치 아니라면 쉿 그렇지 않으면 그렇지 않다면 안 그러면 아니었다면 하든지 아니면 이라면 좋아 알았어 하는것도 그만이다 어쩔수 없다 하나 일 일반적으로 일단 한켠으로는 오자마자 이렇게되면 이와같다면 전부 한마디 한항목 근거로 하기에 아울러 하지 않도록 않기 위해서 이르기까지 이 되다 로 인하여 까닭으로 이유만으로 이로 인하여 그래서 이 때문에 그러므로 그런 까닭에 알 수 있다 결론을 낼 수 있다 으로 인하여 있다 어떤것 관계가 있다 관련이 있다 연관되다 어떤것들 에 대해 이리하여 그리하여 여부 하기보다는 하느니 하면 할수록 운운 이러이러하다 하구나 하도다 다시말하면 다음으로 에 있다 에 달려 있다 우리 우리들 오히려 하기는한데 어떻게 어떻해 어찌됏어 어때 어째서 본대로 자 이 이쪽 여기 이것 이번 이렇게말하자면 이런 이러한 이와 같은 요만큼 요만한 것 얼마 안 되는 것 이만큼 이 정도의 이렇게 많은 것 이와 같다 이때 이렇구나 것과 같이 끼익 삐걱 따위 와 같은 사람들 부류의 사람들 왜냐하면 중의하나 오직 오로지 에 한하다 하기만 하면 도착하다 까지 미치다 도달하다 정도에 이르다 할 지경이다 결과에 이르다 관해서는 여러분 하고 있다 한 후 혼자 자기 자기집 자신 우에 종합한것과같이 총적으로 보면 총적으로 말하면 총적으로 대로 하다 으로서 참 그만이다 할 따름이다 쿵 탕탕 쾅쾅 둥둥 봐 봐라 아이야 아니 와아 응 아이 참나 년 월 일 령 영 일 이 삼 사 오 육 륙 칠 팔 구 이천육 이천칠 이천팔 이천구 하나 둘 셋 넷 다섯 여섯 일곱 여덟 아홉 령 영".split(" ")),e.Pipeline.registerFunction(e.ko.stopWordFilter,"stopWordFilter-ko"),e.ko.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.ko.stemmer,"stemmer-ko")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.sa.min.js b/docs/site/assets/javascripts/lunr/min/lunr.sa.min.js
deleted file mode 100644
index 50ee564..0000000
--- a/docs/site/assets/javascripts/lunr/min/lunr.sa.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sa=function(){this.pipeline.reset(),this.pipeline.add(e.sa.trimmer,e.sa.stopWordFilter,e.sa.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sa.stemmer))},e.sa.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿ꣠-꣱ꣲ-ꣷ꣸-ꣻ꣼-ꣽꣾ-ꣿᆰ0-ᆰ9",e.sa.trimmer=e.trimmerSupport.generateTrimmer(e.sa.wordCharacters),e.Pipeline.registerFunction(e.sa.trimmer,"trimmer-sa"),e.sa.stopWordFilter=e.generateStopWordFilter('तथा अयम्‌ एकम्‌ इत्यस्मिन्‌ तथा तत्‌ वा अयम्‌ इत्यस्य ते आहूत उपरि तेषाम्‌  किन्तु तेषाम्‌ तदा इत्यनेन अधिकः इत्यस्य तत्‌ केचन बहवः द्वि तथा महत्वपूर्णः अयम्‌ अस्य  विषये अयं अस्ति तत्‌ प्रथमः विषये इत्युपरि इत्युपरि इतर अधिकतमः अधिकः अपि सामान्यतया ठ इतरेतर नूतनम्‌ द  न्यूनम्‌ कश्चित्‌ वा विशालः द  सः अस्ति तदनुसारम् तत्र अस्ति केवलम्‌ अपि अत्र सर्वे विविधाः तत्‌ बहवः यतः इदानीम्‌ द  दक्षिण इत्यस्मै तस्य उपरि नथ अतीव कार्यम्‌ सर्वे एकैकम्‌ इत्यादि। एते सन्ति  उत इत्थम्‌ मध्ये एतदर्थं . स कस्य प्रथमः श्री. करोति अस्मिन् प्रकारः निर्मिता कालः तत्र कर्तुं  समान अधुना ते सन्ति स एकः अस्ति सः अर्थात् तेषां कृते . स्थितम्  विशेषः अग्रिम तेषाम्‌ समान स्रोतः ख म समान इदानीमपि अधिकतया करोतु ते समान इत्यस्य वीथी सह यस्मिन्  कृतवान्‌ धृतः तदा पुनः पूर्वं सः आगतः किम्‌ कुल इतर पुरा  मात्रा स विषये उ अतएव अपि नगरस्य  उपरि यतः प्रतिशतं  कतरः कालः साधनानि भूत तथापि जात सम्बन्धि अन्यत्‌ ग अतः अस्माकं स्वकीयाः अस्माकं इदानीं अन्तः इत्यादयः भवन्तः इत्यादयः एते एताः तस्य अस्य इदम् एते तेषां तेषां तेषां तान् तेषां तेषां तेषां समानः सः एकः च तादृशाः बहवः अन्ये च वदन्ति यत् कियत् कस्मै  कस्मै  यस्मै  यस्मै  यस्मै  यस्मै न अतिनीचः किन्तु प्रथमं सम्पूर्णतया  ततः चिरकालानन्तरं पुस्तकं सम्पूर्णतया अन्तः  किन्तु अत्र वा इह इव श्रद्धाय अवशिष्यते  परन्तु अन्ये वर्गाः सन्ति ते सन्ति शक्नुवन्ति सर्वे मिलित्वा सर्वे एकत्र"'.split(" ")),e.sa.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.sa.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var i=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(i).split("|")},e.Pipeline.registerFunction(e.sa.stemmer,"stemmer-sa"),e.Pipeline.registerFunction(e.sa.stopWordFilter,"stopWordFilter-sa")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.ta.min.js b/docs/site/assets/javascripts/lunr/min/lunr.ta.min.js
deleted file mode 100644
index a644bed..0000000
--- a/docs/site/assets/javascripts/lunr/min/lunr.ta.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ta=function(){this.pipeline.reset(),this.pipeline.add(e.ta.trimmer,e.ta.stopWordFilter,e.ta.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ta.stemmer))},e.ta.wordCharacters="஀-உஊ-ஏஐ-ஙச-ட஠-னப-யர-ஹ஺-ிீ-௉ொ-௏ௐ-௙௚-௟௠-௩௪-௯௰-௹௺-௿a-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ta.trimmer=e.trimmerSupport.generateTrimmer(e.ta.wordCharacters),e.Pipeline.registerFunction(e.ta.trimmer,"trimmer-ta"),e.ta.stopWordFilter=e.generateStopWordFilter("அங்கு அங்கே அது அதை அந்த அவர் அவர்கள் அவள் அவன் அவை ஆக ஆகவே ஆகையால் ஆதலால் ஆதலினால் ஆனாலும் ஆனால் இங்கு இங்கே இது இதை இந்த இப்படி இவர் இவர்கள் இவள் இவன் இவை இவ்வளவு உனக்கு உனது உன் உன்னால் எங்கு எங்கே எது எதை எந்த எப்படி எவர் எவர்கள் எவள் எவன் எவை எவ்வளவு எனக்கு எனது எனவே என் என்ன என்னால் ஏது ஏன் தனது தன்னால் தானே தான் நாங்கள் நாம் நான் நீ நீங்கள்".split(" ")),e.ta.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.ta.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.ta.stemmer,"stemmer-ta"),e.Pipeline.registerFunction(e.ta.stopWordFilter,"stopWordFilter-ta")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.te.min.js b/docs/site/assets/javascripts/lunr/min/lunr.te.min.js
deleted file mode 100644
index 9fa7a93..0000000
--- a/docs/site/assets/javascripts/lunr/min/lunr.te.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.te=function(){this.pipeline.reset(),this.pipeline.add(e.te.trimmer,e.te.stopWordFilter,e.te.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.te.stemmer))},e.te.wordCharacters="ఀ-ఄఅ-ఔక-హా-ౌౕ-ౖౘ-ౚౠ-ౡౢ-ౣ౦-౯౸-౿఼ఽ్ౝ౷౤౥",e.te.trimmer=e.trimmerSupport.generateTrimmer(e.te.wordCharacters),e.Pipeline.registerFunction(e.te.trimmer,"trimmer-te"),e.te.stopWordFilter=e.generateStopWordFilter("అందరూ అందుబాటులో అడగండి అడగడం అడ్డంగా అనుగుణంగా అనుమతించు అనుమతిస్తుంది అయితే ఇప్పటికే ఉన్నారు ఎక్కడైనా ఎప్పుడు ఎవరైనా ఎవరో ఏ ఏదైనా ఏమైనప్పటికి ఒక ఒకరు కనిపిస్తాయి కాదు కూడా గా గురించి చుట్టూ చేయగలిగింది తగిన తర్వాత దాదాపు దూరంగా నిజంగా పై ప్రకారం ప్రక్కన మధ్య మరియు మరొక మళ్ళీ మాత్రమే మెచ్చుకో వద్ద వెంట వేరుగా వ్యతిరేకంగా సంబంధం".split(" ")),e.te.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.te.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.te.stemmer,"stemmer-te"),e.Pipeline.registerFunction(e.te.stopWordFilter,"stopWordFilter-te")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/min/lunr.zh.min.js b/docs/site/assets/javascripts/lunr/min/lunr.zh.min.js
index fda66e9..7727bbe 100644
--- a/docs/site/assets/javascripts/lunr/min/lunr.zh.min.js
+++ b/docs/site/assets/javascripts/lunr/min/lunr.zh.min.js
@@ -1 +1 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 為 以 于 於 上 他 而 后 後 之 来 來 及 了 因 下 可 到 由 这 這 与 與 也 此 但 并 並 个 個 其 已 无 無 小 我 们 們 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 當 从 從 得 打 凡 儿 兒 尔 爾 该 該 各 给 給 跟 和 何 还 還 即 几 幾 既 看 据 據 距 靠 啦 另 么 麽 每 嘛 拿 哪 您 凭 憑 且 却 卻 让 讓 仍 啥 如 若 使 谁 誰 虽 雖 随 隨 同 所 她 哇 嗡 往 些 向 沿 哟 喲 用 咱 则 則 怎 曾 至 致 着 著 诸 諸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
\ No newline at end of file
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("nodejieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 以 于 上 他 而 后 之 来 及 了 因 下 可 到 由 这 与 也 此 但 并 个 其 已 无 小 我 们 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 从 到 得 打 凡 儿 尔 该 各 给 跟 和 何 还 即 几 既 看 据 距 靠 啦 了 另 么 每 们 嘛 拿 哪 那 您 凭 且 却 让 仍 啥 如 若 使 谁 虽 随 同 所 她 哇 嗡 往 哪 些 向 沿 哟 用 于 咱 则 怎 曾 至 致 着 诸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
\ No newline at end of file
diff --git a/docs/site/assets/javascripts/lunr/wordcut.js b/docs/site/assets/javascripts/lunr/wordcut.js
index 0d898c9..146f4b4 100644
--- a/docs/site/assets/javascripts/lunr/wordcut.js
+++ b/docs/site/assets/javascripts/lunr/wordcut.js
@@ -68,7 +68,7 @@ var WordcutDict = {
 
   init: function (dictPathFile, withDefault, words) {
     withDefault = withDefault || false
-    var defaultDict = path.normalize(__dirname + "/..") + "/data/tdict-*.txt";
+    defaultDict = path.normalize(__dirname + "/..") + "/data/tdict-*.txt";
     this.dict=[]
     var dictPathIsDefined = dictPathFile !== undefined
     var dictPath = (withDefault || !dictPathIsDefined) ? [defaultDict]: [];
@@ -106,7 +106,7 @@ var WordcutDict = {
     finalize = finalize===undefined || finalize;
     
     for (var i = 0; i < 1; i++) {
-      var words = "ก.ก.\nก.ก.น.\nก.ข.ค.\nก.ค.\nก.จ.\nก.ช.น.\nก.ฌ.\nก.ต.\nก.ต.ง.\nก.ต.ช.\nก.ตร.\nก.ท.\nก.น.ช.\nก.บช.\nก.บถ.\nก.ป.ส.\nก.พ.\nก.ม.\nก.ย.\nก.ร.\nก.ล.ต.\nก.ว.\nก.ศ.ว.\nก.ส.ท.\nก.ส.ธ.\nก.ส.อ.\nก.อ.\nกก.ตชด.\nกก.ตร.น.\nกก.ภ.จว.\nกก.รสช.\nกกบ.ขส.ทบ.\nกกล.รพน.\nกง.กห.\nกง.ทบ.\nกง.ทร.\nกซข.ป.\nกซม.ป.\nกทม.กรุงเทพมหานคร\nกบ.ทบ.\nกป.สป.\nกพ.ทบ.\nกพ.ทร.\nกพ.ทหาร\nกร.ทบ.\nกรป.กลาง\nกรอ.พอ.\nกศ.ด.\nกศ.บ.\nกศ.บป.\nกศ.ม.\nกษ.ด.\nกษ.บ.\nกษ.ม.\nกส.ด.\nกส.ทบ.\nกส.บ.\nกส.ม.\nกอ.ปค.\nกอ.รพน.\nกอ.รมน.\nกอ.รสต.\nข.ต.ว.\nขว.ทบ.\nขว.ทร.\nขว.ทหาร\nขส.ทบ.\nขส.ทร.\nขส.ทอ.\nค.ด.\nค.บ.\nค.พ.ศ.\nค.ม.\nค.ร.น.\nค.ร.ฟ.\nค.ร.ม.\nค.ศ.\nค.อ.ด.\nค.อ.บ.\nค.อ.ม.\nคศ.ด.\nคศ.บ.\nคศ.ม.\nง.ด.\nจ.จ.\nจ.จ.จ.\nจ.ช.\nจ.ต.\nจ.ท.\nจ.ป.ร.\nจ.ม.\nจ.ศ.\nจ.ส.ต.\nจ.ส.ท.\nจ.ส.อ.\nจ.อ.\nจ.อ.ร.\nจ.๑๘\nจก.ธน.\nจก.สน.\nช.ค.\nช.ค.บ.\nช.พ.ค.\nช.ส.\nช.ส.ค.\nฌ.ป.ค.\nฌ.ศ.ร.\nฌ.ส.อ.\nฐท.สห.\nด.ช.\nด.ญ.\nด.ต.\nด.ศ.ค.\nด.ศ.ร.\nดย.ทร.\nต.ก.\nต.ค.\nต.จ.\nต.จ.ว.\nต.ช.\nต.ต.\nต.บ.\nต.ม.\nต.ร.\nต.ศ.ร.\nต.ห.\nต.อ.\nต.อ.จ.\nตร.กม.\nตร.ซม.\nตร.ต.\nตร.ทล.\nตร.น.\nตร.ปม.\nตร.ภ.\nตร.ม.\nตร.รฟ.\nตร.ว.\nตร.ส.\nตร.สข.\nท.จ.\nท.จ.ว.\nท.ช.\nท.ญ.\nท.ด.\nท.ท.ท.\nท.ทบ.\nท.บ.\nท.พ.\nท.ม.\nท.ศ.\nทก.ด.\nทก.บ.\nทก.ม.\nทส.ปช.\nทส.รมว.กห.\nทุ.ส.นิ.ม.\nธ.ก.ส.\nธ.ค.\nธ.ญ\nธ.บ.\nน.ช.\nน.ญ.\nน.ด.\nน.ต.\nน.ท.\nน.น.\nน.บ.\nน.บ.ท.\nน.ป.ท.\nน.พ.\nน.ม.\nน.ร.\nน.ว.\nน.ศ.\nน.ส.\nน.ส.พ.\nน.ส.๓\nน.สพ.\nน.อ.\nนปพ.ภ.\nนศ.ด.\nนศ.บ.\nนศ.ม.\nบ.ก.\nบ.ข.ส.\nบ.ช.\nบ.ด.ท.\nบ.ตร.\nบ.ภ.\nบ.ม.\nบก.จร.\nบก.ตชด.\nบก.ตม.\nบก.ทล.\nบก.น.\nบก.ป.\nบก.ปค.\nบก.ปม.\nบก.ภ.เขต\nบก.รน.\nบก.รฟ.\nบก.ร้อย.ตชด.\nบก.ส.\nบกข.ป.\nบจพ.ป.\nบช.ก.\nบช.ด.\nบช.ตชด.\nบช.น.\nบช.บ.\nบช.ปส.\nบช.ภ.\nบช.ม.\nบชท.ป.\nบชน.ป.\nบชส.ป.\nบธ.ด.\nบธ.บ.\nบธ.ม.\nบนท.ป.\nบนอ.ป.\nบปช.ป.\nป.กท.\nป.กศ.\nป.กศ.สูง\nป.จ.\nป.จ.ว.\nป.ช.\nป.ธ.\nป.ป.\nป.ป.ก.\nป.ป.ช.\nป.ป.ป.\nป.ป.ร.\nป.ป.ส.\nป.พ.\nป.พ.พ.\nป.พย.\nป.ม.\nป.ม.ก.\nป.ม.ช.\nป.ม.ธ.\nป.ม.ศ.\nป.ม.อ.\nป.ร.ร.๔\nป.ร.ร.๕\nป.ร.ร.๖\nป.ล.\nป.ว.พ.\nป.วิ.อ.\nป.ส.ส.\nป.อ.\nป.อ.ร.ส.\nป.๑\nปม.วส.\nปอ.พ.\nผกก.ภ.\nผช.ผอ.\nผต.มท.\nผบ.ตร.\nผบ.ทบ.\nผบ.ทร.\nผบ.ทสส.\nผบ.ทอ.\nผบก.น.\nผบก.ป.\nผบก.ปค.\nผบก.ปม.\nผบก.ภ.\nผบช.ก.\nผบช.ตชด.\nผบช.น.\nผบช.ภ.\nผว.กทม.\nผอ.ปจ.\nพ.ก.ง.\nพ.กศ.\nพ.ข.ต.\nพ.ค.\nพ.ค.ช.\nพ.ค.ว.\nพ.ค.ศ.\nพ.จ.ต.\nพ.จ.ท.\nพ.จ.อ.\nพ.ช.\nพ.ช.ค.\nพ.ด.\nพ.ต.\nพ.ต.ต.\nพ.ต.ท.\nพ.ต.อ.\nพ.ต.อ.พิเศษ\nพ.ท.\nพ.บ.\nพ.ป.\nพ.ภ.ม.\nพ.ม.\nพ.ม.ช.\nพ.ย.\nพ.ร.ก.\nพ.ร.ฎ.\nพ.ร.ต.\nพ.ร.ธ.\nพ.ร.บ.\nพ.ศ.\nพ.ศ.บ.\nพ.ส.ร.\nพ.ส.ล.\nพ.อ.\nพ.อ.ต.\nพ.อ.ท.\nพ.อ.พิเศษ\nพ.อ.อ.\nพณ.ด.\nพณ.บ.\nพณ.ม.\nพธ.ด.\nพธ.บ.\nพธ.ม.\nพบ.ด.\nพบ.บ.\nพบ.ม.\nพย.ด.\nพย.บ.\nพย.ม.\nพล.จ.\nพล.ต.\nพล.ต.จ.\nพล.ต.ต.\nพล.ต.ท.\nพล.ต.อ.\nพล.ท.\nพล.ปตอ.\nพล.ม.\nพล.ม.๒\nพล.ร.จ.\nพล.ร.ต.\nพล.ร.ท.\nพล.ร.อ.\nพล.อ.\nพล.อ.จ.\nพล.อ.ต.\nพล.อ.ท.\nพล.อ.อ.\nพลา.ทร.\nพศ.ด.\nพศ.บ.\nพศ.ม.\nพอ.สว.\nภ.ง.ด.\nภ.ง.ด.๙\nภ.ด.\nภ.บ.\nภ.บ.ท.๕\nภ.ป.ร.\nภ.พ.\nภ.ม.\nภ.สถ.บ.\nม.ค.\nม.จ.\nม.ป.ท.\nม.ป.ป.\nม.ป.พ.\nม.ร.ว.\nม.ศ.\nม.อ.\nม.อ.ปัตตานี\nมิ.ย.\nมี.ค.\nยศ.ทบ.\nยศ.ทร.\nยศ.ทอ.\nร.ง.\nร.ด.\nร.ต.\nร.ต.ต.\nร.ต.ท.\nร.ต.อ.\nร.ท.\nร.น.\nร.บ.\nร.พ.\nร.ฟ.ล.\nร.ย.ล.\nร.ย.ส.ท.\nร.ล.\nร.ศ.\nร.ส.พ.\nร.อ.\nรป.ม.\nรร.จปร.\nรร.จอ.\nรร.ชท.\nรร.ตท.\nรร.นร.\nรร.นรต.\nรร.นอ.\nล.ญ.\nล.ว.\nลส.ชบ.\nว.ค.\nว.ฉ.\nว.ช.\nว.ด.ป.\nว.ป.ถ.\nวท.บ.\nศ.บ.\nศ.ป.ก.\nศ.ศ.ป.\nศฝร.ภ.\nศศ.บ.\nศษ.บ.\nศส.บ.\nส.ก.\nส.ก.ศ.ท.\nส.ค.\nส.ค.1\nส.ค.ร.\nส.ค.ส.\nส.ต.\nส.ต.ต.\nส.ต.ท.\nส.ต.อ.\nส.ท.\nส.ทร.\nส.ป.ช.\nส.ป.ส.ท.\nส.ป.อ.\nส.ร.\nส.ล.น.\nส.ว.\nส.ว.ท.\nส.ว.ส.ท.\nส.ส.\nส.ส.ท.\nส.ส.ร.\nส.ห.\nส.อ.\nสถ.บ.\nสนง.สสอ.\nสพ.ญ.\nสพ.บ.\nสว.จร.\nสว.ธร.\nสว.ส.\nสว.สป.\nสว.สส.\nสว.อก.\nสส.บ.\nสุ.จิ.ปุ.ลิ.\nห.ร.ม.\nอ.ก.ค.\nอ.ก.จ.\nอ.จ.\nอ.ช.พ.\nอ.ตร.\nอ.บ.\nอ.ส.ท.\nอ.ส.ม.ท.\nอ.ส.ย.\nอ.อ.ป.\nอส.รด.\nอุ.อา.ก.ส.\nฮ.จ.\nฮ.ท.\nฮ.ฝ.\nฮ.ล.\nฮ.ศ.\nเม.ย.\n\nกรีนิช\nกลันตัน\nกัลกัตตา\nกัวลาลัมเปอร์\nกัศมีร์\nกาฐมาณฑุ\nโกลกาตา\nควิเบก\nคอนเนตทิคัต\nคาบูล\nคุชราต\nคุนหมิง\nเคนตักกี\nเคนทักกี\nเคมบริดจ์\nแคชเมียร์\nแคนซัส\nแคนเบอร์รา\nแคโรไลนา\nแคลิฟอร์เนีย\nโคเปนเฮเกน\nโคลัมโบ\nโคโลราโด\nไครสต์เชิร์ช\nไคโร\nจาการ์ตา\nจำปาศักดิ์\nเจนไน\nเจนีวา\nเจ้อเจียง\nฉงชิ่ง\nเฉิงตู\nชานตง\nชิคาโก\nเชนไน\nเชอร์โนบิล\nซัปโปโร\nซานมารีโน\nซาบาห์\nซาราเยโว\nซาราวัก\nซิดนีย์\nซีอาน\nซีแอตเทิล\nซูริก\nซูริค\nเซเชลส์\nเซนได\nเซี่ยงไฮ้\nโซเฟีย\nโซล\nโซโลมอน\nไซ่ง่อน\nไซบีเรีย\nดัลลัส\nดาโคตา\nดานัง\nดีทรอยต์\nดูไบ\nเดนเวอร์\nเดลาแวร์\nเดียนเบียนฟู\nโดเวอร์\nโดฮา\nไดฟุกุ\nไดฟูกุ\nตรังกานู\nตริโปลี\nตูวาลู\nเตหะราน\nโตเกียว\nโตรอนโต\nทมิฬนาฑู\nทริโปลี\nทิเบต\nเทกซัส\nเท็กซัส\nเทนเนสซี\nเทลอาวีฟ\nแทสเมเนีย\nโทรอนโต\nไทเป\nธากา\nนางาซากิ\nนาริตะ\nนิวเจอร์ซีย์\nนิวเดลี\nนิวยอร์ก\nนิวยอร์ค\nนิวแฮมป์เชียร์\nเนบราสกา\nเนแบรสกา\nเนวาดา\nบรัสเซลส์\nบราซิเลีย\nบอมเบย์\nบอสตัน\nบังกาลอร์\nบังคาลอร์\nบูคาเรสต์\nบูดาเปสต์\nเบงกาซี\nเบนกาซี\nเบรุต\nเบลเกรด\nเบอร์ลิน\nแบกแดด\nปอยเปต\nปะลิส\nปะหัง\nปักกิ่ง\nปัญจาบ\nปัฏนา\nปารีส\nปีนัง\nเประ\nเปียงยาง\nพนมเปญ\nพระตะบอง\nพะโค\nพะสิม\nพาราณสี\nพิหารี\nเพนซิลวาเนีย\nเพนซิลเวเนีย\nฟรานซ์\nฟลอริดา\nฟิลาเดลเฟีย\nฟุกุชิมะ\nฟุกุชิมา\nฟุกุโอกะ\nฟูกูโอกะ\nแฟรงก์เฟิร์ต\nมอสโก\nมะนิลา\nมะละแหม่ง\nมัณฑะเลย์\nมัทราส\nมาดริด\nมิชิแกน\nมินนิโซตา\nมิยางิ\nมิลาน\nมิวนิก\nมิสซูรี\nมุมไบ\nเมน\nเมลเบิร์น\nเมาะตะมะ\nเมาะลำเลิง\nแมนจูเรีย\nแมนเชสเตอร์\nแมนฮัตตัน\nแมริแลนด์\nแมรีแลนด์\nแมสซาชูเซตส์\nยะไข่\nย่างกุ้ง\nยูทาห์\nยูนนาน\nเยรูซาเล็ม\nโยโกฮามา\nริยาด\nรีโอเดจาเนโร\nโรดไอแลนด์\nลอนดอน\nลอสแองเจลิส\nลาปาซ\nลาสเวกัส\nลิสบอน\nลุยเซียนา\nโลซาน\nโลซานน์\nวอชิงตัน\nวอร์ซอ\nวิสคอนซิน\nเวนิส\nเวลส์\nเวอร์จิเนีย\nเวอร์มอนต์\nเวียงจันทน์\nเวียนนา\nแวนคูเวอร์\nไวโอมิง\nสกอตแลนด์\nสก็อตแลนด์\nสตอกโฮล์ม\nสลังงอร์\nเสฉวน\nเสียมราฐ\nเสียมเรียบ\nหงสา\nหงสาวดี\nหนานไห่\nหลวงพระบาง\nหูเป่ย\nหูเป่ย์\nหูหนาน\nเหอเป่ย\nเหอเป่ย์\nเหอหนาน\nอชันตา\nอลาสกา\nอวันตี\nออริกอน\nออสโล\nอะแลสกา\nอัตตะปือ\nอัมมาน\nอัมสเตอร์ดัม\nอัสสัม\nอาบูดาบี\nอาร์คันซอ\nอินเดียนา\nอิบารากิ\nอิลลินอยส์\nอิสตันบูล\nอิสลามาบัด\nอุรุมชี\nอูลานบาตอร์\nเอดินบะระ\nเอเธนส์\nแอตแลนตา\nแอริโซนา\nแอลเจียร์\nโอคลาโฮมา\nโอค็อตสค์\nโอกินาวา\nโอซากา\nโอริสสา\nโอเรกอน\nโอไฮโอ\nไอดาโฮ\nไอโอวา\nฮอนโนลูลู\nฮานอย\nฮาเนดะ\nฮาราเร\nฮาวาย\nฮิโรชิมา\nฮุสตัน\nเฮลซิงกิ\n\nมกรา\nกุมภา\nมีนา\nเมษา\nพฤษภา\nมิถุนา\nกรกฎา\nสิงหา\nกันยา\nตุลา\nพฤศจิกา\nธันวา\nเอ\nบี\nซี\nดี\nอี\nเอฟ\nจี\nเอช\nไอ\nเจ\nเค\nแอล\nเอ็ม\nเอ็น\nโอ\nพี\nคิว\nอาร์\nเอส\nที\nยู\nวี\nดับเบิล\nดับบลิว\nเอ็กซ์\nเอ๊กซ์\nวาย\nแซด\nแอลฟา\nแอลฟ่า\nเบตา\nเบต้า\nแกมมา\nแกมม่า\nเดลตา\nเดลต้า\nโอเมกา\nโอเมก้า\nเมกะ\nกิกะ\nนาโน\nไมโคร\n\nกรรมาชน\nกรอบรูป\nกระดี๊กระด๊า\nกระบับ\nกราวนด์\nกรีน\nกรุ๊ป\nกฤษณ์\nกลาส\nก๊วน\nกษัตริยา\nกษัตริยาธิราช\nก่อนหน้า\nกะบับ\nกับดัก\nกัมมันตะ\nก๊าก\nก๋ากั่น\nกาญจน์\nกาญจนาภิเษก\nกามิกาเซ่\nการันตี\nกาหลิบ\nกิฟท์\nกิมจิ\nกีวี\nกึ๊ก\nกึ๋ย\nกุนซือ\nกุมภาพันธ์\nกู๋\nเกจิ\nเกมส์\nเกย์\nเกรด\nเกรย์\nเกสต์เฮาส์\nเก๊ะ\nเก๋ากี้\nเกิร์ล\nแกงค์\nแกรนด์\nแกสโซฮอล์\nแก๊สโซฮอล์\nโกเต็กซ์\nโกลด์\nโกะ\nโก๊ะ\nไกด์\nขั้นตอน\nเขวี้ยง\nคณาญาติ\nครัวซอง\nครัวซองต์\nคร่ำครวญ\nครีเอทีฟ\nครูเสด\nคลับ\nคลาสสิก\nคลิตอริส\nคลิป\nความหมาย\nควิก\nควีน\nคองเกรส\nคอนซูเมอร์\nคอนเซปต์\nคอนเซ็ปต์\nคอนโด\nคอนโดมิเนียม\nคอนเทนเนอร์\nคอนแทค\nคอนแท็ค\nคอนโทรล\nคอนเฟิร์ม\nคอปเตอร์\nคอมพ์\nคอมเพล็กซ์\nคอมมอนส์\nคอมเมนท์\nคอมเมนต์\nคอร์ป\nคอร์ปอเรชั่น\nคอร์รัปชัน\nคอร์รัปชั่น\nคอรัปชัน\nคอรัปชั่น\nคอร์ส\nคอลเล็กชั่น\nคอลัมน์\nคอลัมนิสต์\nคัตเอาต์\nคันคาก\nคันถธุระ\nคันธาระ\nคันยิ\nคัสตาร์ด\nคาราโอเกะ\nคีตกวี\nคีตปฏิภาณ\nคีตราชัน\nคาปูชิโน\nคามิคาเซ่\nคาเฟ่\nคาร์\nคาร์โก้\nคาราเมล\nคาแรกเตอร์\nคาแร็กเตอร์\nคาแรคเตอร์\nคาแร็คเตอร์\nคาวบอย\nคาสิโน\nคิกขุ\nคิวบิก\nคูลเลอร์\nเคบับ\nเครป\nเคลม\nเคลียร์\nเคลื่อนย้าย\nเคส\nเคอร์ฟิว\nแคชเชียร์\nแคทวอล์ค\nแคนดิเดต\nแคนตาลูป\nแคนยอน\nแคนู\nแคป\nแคมป์\nแคมปัส\nแคมเปญ\nแคร์\nแครกเกอร์\nแคร็กเกอร์\nแครอท\nแคสต์\nแคสติง\nแคสติ้ง\nโค้ก\nโค้ช\nโคโยตี\nโคโยตี้\nโครนา\nโคอะล่า\nโคอาลา\nโคอาล่า\nไคลแมกซ์\nไคลแม็กซ์\nงั้น\nง่าว\nงี้\nเง็ง\nโง่เขลา\nไง\nจตุคาม\nจ๊อกกี้\nจอหงวน\nจังโก้\nจัมโบ้\nจ๊าบ\nจารกรรม\nจารชน\nจิ๊ก\nจิ๊กโก๋\nจิ๊กซอว์\nจิตพิสัย\nจิตเภท\nจีดีพี\nจึ๊ก\nจุ๊ย\nจูน\nจูเนียร์\nเจ๊\nเจได\nเจ็ต\nเจล\nเจ๊าะแจ๊ะ\nเจี๊ยว\nแจ็กเก็ต\nแจ๊กเก็ต\nแจ็กพอต\nแจ็กพ็อต\nแจ๊กพอต\nแจ๊กพ็อต\nแจม\nแจ๊ส\nโจ๋\nฉลุย\nเฉิ่ม\nชนะเลิศ\nช็อค\nช็อต\nช็อป\nช็อปปิ้ง\nช็อปเปอร์\nชะโนด\nชัตเตอร์\nชัวร์\nชาร์จ\nชาร์ต\nชาร์ป\nชินบัญชร\nชิฟฟอน\nชีส\nชีอะห์\nเช็ก\nเช็งเม้ง\nเชฟ\nเชลียร์\nเชอร์รี่\nแชเชือน\nแช่แข็ง\nแชมป์\nแชมปิยอง\nแชมเปญ\nแชมเปี้ยน\nแชมพู\nโชว์รูม\nโชห่วย\nใช้งาน\nไชน่า\nซ้อ\nซอมบี้\nซะ\nซังเต\nซันตาคลอส\nซัพพลาย\nซัพพลายเออร์\nซัมเมอร์\nซากุระ\nซาดิสต์\nซาดิสม์\nซาตาน\nซานตาคลอส\nซาฟารี\nซาบะ\nซามูไร\nซาร์\nซาร์ดีน\nซาเล้ง\nซิง\nซิ่ง\nซิงเกิล\nซิตี\nซิตี้\nซินโดรม\nซิม\nซิ้ม\nซิมโฟนี\nซิมโฟนี่\nซิลเวอร์\nซี้\nซี้ซั้ว\nซีดาน\nซีน\nซีนีเพล็กซ์\nซีเนียร์\nซีร็อกซ์\nซีรีส์\nซีเรียส\nซีอีโอ\nซื่อบื้อ\nซุนหนี่\nซุปเปอร์\nซูชิ\nซูเปอร์\nซูม\nซูโม่\nซูเอี๋ย\nซูฮก\nเซ็กซ์\nเซ็กซี่\nเซ็กส์\nเซนเซอร์\nเซ็นเซอร์\nเซนเตอร์\nเซ็นเตอร์\nเซ็นทรัล\nเซนส์\nเซ่นไหว้\nเซฟตี้\nเซรามิก\nเซลส์\nเซลส์แมน\nเซอร์\nเซอร์ไพรส์\nเซอร์วิส\nเซาท์\nเซี้ยว\nแซ็ก\nแซกโซโฟน\nแซ็กโซโฟน\nแซนด์วิช\nแซมบ้า\nแซลมอน\nแซว\nโซเชียล\nโซน\nโซนี่\nโซลาร์\nโซโล\nโซโล่\nญาณทัสสนะ\nดยุก\nดยุค\nดร็อป\nดรัมเมเยอร์\nดรามา\nดราม่า\nดอกเตอร์\nด็อกเตอร์\nดัมพ์\nดั๊มพ์\nดาวน์\nดิกชันนารี\nดิสเครดิต\nดีกรี\nดีเจ\nดีไซน์\nดีไซน์เนอร์\nดีไซเนอร์\nดีเบต\nดีพาร์ตเมนต์\nดีพาร์ตเมนท์\nดีพาร์ทเมนต์\nดีพาร์ทเมนท์\nดีมานด์\nดีล\nดีลเลอร์\nดีเลย์\nเดชานุภาพ\nเดบิต\nเดโม\nเดย์\nเด้อ\nเดอะ\nเด๊ะ\nเดี้ยง\nเดี๊ยะ\nแดนซ์\nแดนเซอร์\nแดรี่\nโดนัท\nโดมิโน\nโดรายากิ\nไดเอ็ต\nตถตา\nตนเอง\nตรวจทาน\nตรวจสอบ\nตอกย้ำ\nต๊อง\nต่อยอด\nต่อรอง\nตะหงิด\nตังค์\nตันเถียน\nตัวตน\nตัวเอง\nตาปรือ\nต้าอ่วย\nติงต๊อง\nติ๋ม\nติ่มซำ\nติว\nติวเตอร์\nตี๋\nตื้บ\nตุ๊ก\nตุ๊กตุ๊ก\nตุ๊ด\nตุ๋ย\nตู้เซฟ\nเต๊ะ\nเตี๊ยม\nแตงกวา\nแตงโม\nแต๋ว\nโต๋เต๋\nโต๊ะจีน\nไตรมาส\nถ่ายทำ\nถูกต้อง\nทงคัตสึ\nทริป\nทรู\nทอม\nท็อป\nทอร์นาโด\nทอล์ค\nทักซิโด\nทันตกรรม\nทันตแพทยศาสตร์\nทับซ้อน\nทัวร์\nทัวร์นาเมนต์\nทัวร์นาเมนท์\nทัวริสต์\nทาเลนต์\nทาวน์\nทาวน์เฮาส์\nทำงาน\nทิป\nทิพยสมบัติ\nทิวลิป\nทีรามิสุ\nทีวี\nทูน่า\nเท็กซ์\nเทค\nเทคโน\nเทคโนแครต\nเทควันโด\nเทป\nเทรด\nเทรนด์\nเทรนเนอร์\nเทรลเลอร์\nเทรลเล่อร์\nเทเลกราฟ\nเทวบัญชา\nเทวบุตร\nเทวา\nเทวาธิราช\nเทโวโรหนะ\nเทอร์โบ\nเที่ยงคืน\nเที่ยงวัน\nเทียมทาน\nแทกติค\nแทคติค\nแทงกั๊ก\nแทงโก้\nโทมาฮอก\nโทมาฮอว์ก\nโทมาฮอว์ค\nโทร\nโทรโข่ง\nไทม์\nไทยแลนด์\nไทเฮา\nธรรมา\nธรรมาภิบาล\nธัมโม\nธีม\nธุรกรรม\nธุหร่ำ\nเธค\nนพมาศ\nนรีแพทย์\nน็อก\nน็อค\nน้องใหม่\nนอมินี\nนอร์ท\nน่ะ\nนางแบบ\nนาฏยศาลา\nนายแบบ\nนายพราน\nนินจา\nนิรันดร์\nนิว\nนิวส์\nนู้ด\nเนอะ\nเนิร์สเซอรี\nเนิร์สเซอรี่\nเนี้ยบ\nโนติส\nไนท์\nไนน์\nบรรพชน\nบร็อกโคลี\nบร็อคโคลี\nบรา\nบริกร\nบริวเวอรี่ส์\nบลอนด์\nบลูเบอร์รี\nบลูเบอร์รี่\nบ๊วย\nบอกซ์\nบ็อกซ์\nบ๊อกซ์\nบอดี้\nบอนด์\nบ๊อบ\nบอมบ์\nบ๋อย\nบอยคอต\nบอยคอตต์\nบอร์ด\nบังเกอร์\nบัตเตอร์\nบัลลาสต์\nบัส\nบาบูน\nบาร์บีคิว\nบาร์บี้\nบาลานซ์\nบิ๊ก\nบิล\nบึม\nบึ้ม\nบุญคุณ\nบุ๋น\nบุปผา\nบู๊\nบูชิโด\nบูติก\nบูติค\nบูม\nเบเกอรี่\nเบญจมบพิตร\nเบตาดีน\nเบนโตะ\nเบนโล\nเบบี้\nเบลอ\nเบอร์เกอร์\nเบอร์รี\nเบิร์ด\nเบิร์น\nแบ็กโฮ\nแบคโฮ\nแบด\nแบต\nแบนเนอร์\nแบรนด์\nแบล็ก\nแบล็ค\nไบโอ\nโบกี้\nโบตั๋น\nโบ้ย\nโบรกเกอร์\nโบรชัวร์\nโบว์\nโบว์ลิ่ง\nไบเบิล\nปฏิสัมพันธ์\nป๊อก\nปอดแหก\nป๊อป\nป๋อหลอ\nปักขคณนา\nปัจเจกชน\nปัจฉิมนิเทศ\nป๊า\nป๋า\nป่าไม้\nปาร์ตี้\nปาสกาล\nปาสคาล\nปาสเตอร์\nปิกอัพ\nปิ๊ง\nปิโตรเคมี\nปิยมิตร\nปึ้ก\nปูอัด\nเปโซ\nเป็นไง\nเปปเปอร์มินต์\nเปเปอร์\nเปราะบาง\nเป๊ะ\nเป่ายิงฉุบ\nเป่ายิ้งฉุบ\nเปียโน\nแป้ก\nแป๋ว\nแป๊ะเจี๊ยะ\nโปร\nโปรเจกต์\nโปรเจ็กต์\nโปรเจกเตอร์\nโปรเจ็กเตอร์\nโปรเจคท์\nโปรเจ็คท์\nโปรดักชั่น\nโปรดิวเซอร์\nโปรโมชั่น\nโปรโมต\nโปรโมเตอร์\nโปรโมท\nโปลิศ\nโปสเตอร์\nผลไม้\nผลักดัน\nผ้าห่ม\nผิดพลาด\nผู้นำ\nแผดเผา\nเฝอ\nพงษ์\nพริตตี้\nพรีเซนต์\nพรีเซ็นเตอร์\nพรีเมียม\nพรีเมียร์\nพฤหัส\nพล็อต\nพลาซ่า\nพลานุภาพ\nพ่อค้า\nพอเพียง\nพะเรอ\nพันธกิจ\nพันธุวิศวกรรม\nพาร์\nพาร์ตเนอร์\nพาร์ทเนอร์\nพาวเวอร์\nพาสเจอร์ไรส์\nพาสตา\nพาสต้า\nพาสปอร์ต\nพาเหรด\nพิซซ่า\nพีเรียด\nพุดดิ้ง\nพุทธภูมิ\nพุทธศตวรรษ\nพุทโธ\nพูล\nเพทนาการ\nเพนกวิน\nเพนตากอน\nเพรส\nเพรียวบาง\nเพลซ\nเพลท\nเพลย์บอย\nเพียบแปร้\nเพียว\nเพาเวอร์\nแพกเกจ\nแพ็ค\nแพตเทิร์น\nแพทเทิร์น\nแพทยสภา\nแพนงเชิญ\nแพนดา\nแพนด้า\nแพลน\nโพลล์\nโพลารอยด์\nโพสต์\nไพลิน\nฟยอร์ด\nฟรังก์\nฟรุต\nฟลอร์\nฟลุก\nฟลุค\nฟลุต\nฟลุท\nฟอยล์\nฟอร์ม\nฟันด์\nฟาวล์\nฟาสต์ฟู้ด\nฟินิกซ์\nฟิวเจอร์\nฟีด\nฟีเวอร์\nฟุตบาท\nเฟรช\nเฟรชชี่\nเฟรม\nเฟมินิสต์\nเฟส\nเฟอร์นิเจอร์\nเฟอร์รี่\nเฟิร์ม\nเฟี้ยวฟ้าว\nแฟกซ์\nแฟ็กซ์\nแฟนซี\nแฟนตาซี\nแฟ้บ\nแฟร์\nแฟรนไชส์\nแฟรี\nแฟรี่\nแฟลช\nแฟล็ต\nโฟน\nโฟม\nโฟล์ค\nไฟต์\nไฟแนนซ์\nไฟลต์\nไฟลท์\nภควัทคีตา\nภควัมบดี\nภควัมปติ\nภคันทลาพาธ\nภววิสัย\nภารตะ\nภูมิทัศน์\nม้ง\nมวลชน\nมยุราภิรมย์\nมลภาวะ\nมหภาค\nมหาอุปราชา\nมอคคา\nมอคค่า\nมอนสเตอร์\nม็อบ\nมอบตัว\nมอยส์เจอไรเซอร์\nมอลล์\nมะกัน\nมั้ง\nมัฟฟิน\nมั้ย\nม้านั่ง\nมาเฟีย\nมาม่า\nมายองเนส\nมายาคติ\nมาร์ก\nมาร์เก็ต\nมาร์เก็ตติ้ง\nมาร์ค\nมาร์จิน\nมาร์ช\nมาร์ต\nมาร์ท\nมาราธอน\nม้าหินอ่อน\nมินต์\nมินท์\nมินิ\nมิลค์\nมิวสิค\nมิสซัง\nมิสไซล์\nมิสเตอร์\nมือถือ\nมุมมอง\nเมคอัพ\nเมจิก\nเมจิค\nเมทัล\nเมเปิล\nเมาท์\nเมี่ยงคำ\nแมกกาซีน\nแม็กกาซีน\nแมคเคอเรล\nแม่ค้า\nแมชชีน\nแมชีน\nแมนชั่น\nแมมบ้า\nแมมโบ้\nโมจิ\nโมเดล\nโมเดิร์น\nโมเต็ล\nโมโนเรล\nโมหจริต\nไมค์\nไมเกรน\nยนตรกรรม\nยอมรับ\nยะเยือก\nยังไง\nยากูซ่า\nยาวี\nยิม\nยิว\nยุวทูต\nยูโทเปีย\nยูโร\nยูวี\nเยน\nเยลลี่\nเย้ว\nเยอบีรา\nเยอบีร่า\nเยอร์บีรา\nเยอร์บีร่า\nแยมโรล\nโยเกิร์ต\nโยโย่\nรวมมิตร\nร็อค\nร็อคเก็ต\nรองรับ\nรอมฎอน\nรอยัลตี้\nระโงก\nรันเวย์\nรัม\nรากหญ้า\nราชบัณฑิตยสถาน\nราชานุญาต\nราชานุสาวรีย์\nรามเทพ\nรามาธิบดี\nรามายณะ\nราเม็ง\nราเมน\nรายชื่อ\nราสเบอร์รี\nริกเตอร์\nริคเตอร์\nรีไซเคิล\nรีดไถ\nรีทัช\nรีเทิร์น\nรีไทร์\nรีแบรนด์\nรีพอร์ท\nรีโมต\nรีโมท\nรีวิว\nรีสอร์ต\nรีสอร์ท\nรีเสิร์ช\nรุมบ้า\nรุสโซ\nรูบิก\nรูบิค\nเรซิน\nเรซิ่น\nเรดิโอ\nเรต\nเรตติ้ง\nแรงใจ\nแรงดูด\nแรงผลัก\nแรลลี\nแรลลี่\nโรดแมป\nโรเนียว\nโรแมนติก\nโรแมนติค\nโรล\nโรลออน\nไรเฟิล\nล็อกเกอร์\nลอจิสติกส์\nล็อต\nล็อบบี้\nลอร์ด\nล้มเหลว\nละติน\nละอ่อน\nลาซานญ่า\nลาติน\nลาเต้\nลานีญา\nลามะ\nลิมิต\nลิมูซีน\nลิสต์\nลีก\nลีด\nลีดเดอร์\nลีเมอร์\nลีลาวดี\nลุค\nลูกชาย\nลูกสาว\nเลกเชอร์\nเลคเชอร์\nเลดี้\nเลสเบี้ยน\nเลิฟ\nแลนด์\nแล็บ\nโลโก้\nโลชั่น\nไลท์\nไลน์\nไลฟ์\nวนาราม\nวราราม\nวโรกาส\nว้อดก้า\nวอเตอร์\nวอฟเฟิล\nว้อย\nวอร์ม\nวอร์มอัพ\nวอร์รูม\nวอล์ก\nวอล์ค\nวอลซ์\nวอลนัต\nวอลนัท\nวอลล์\nว่ะ\nวันเวย์\nวัสสา\nวาซาบิ\nวาทกรรม\nวาทะ\nวานิลลา\nวานิลา\nวาฟเฟิล\nวาริชศาสตร์\nว้าว\nวัคค์\nวัจนะ\nวาไรตี้\nวิก\nวิดีโอ\nวิทย์\nวิน\nวิป\nวิปปิ้ง\nวิภัชภาค\nวิว\nวิลล์\nวิลเลจ\nวีเจ\nวีซ่า\nวีดิทัศน์\nวีน\nวีไอพี\nวืด\nเวณิกา\nเวเฟอร์\nเวสต์\nเวอร์\nเวิร์ก\nเวิร์กช็อป\nเวิร์ค\nเวิร์ลด์\nเวิลด์\nแวมไพร์\nไวกิ้ง\nไวเบรเตอร์\nไวอะกร้า\nไวอากร้า\nศากยบุตร\nศิรินทร์\nศิลปวัฒนธรรม\nศิลปากร\nศิวิไลซ์\nศึกษาศาสตร์\nสกรัม\nสกาย\nสกู๊ป\nสเกตช์\nสเก็ตช์\nสคริปต์\nสแควร์\nสงบสุข\nสจ๊วต\nสตรอเบอร์รี\nสตรอเบอรี\nสตรอว์เบอร์รี\nสตริง\nสต็อก\nสต๊อก\nสต็อค\nสต๊อค\nสตอรี\nสตาร์\nสตาร์ท\nสติกเกอร์\nสติ๊กเกอร์\nสตีล\nสตูดิโอ\nสเตชัน\nสเตชั่น\nสเตเดียม\nสเตนเลส\nสเต็ป\nสเตย์\nสเตริโอ\nสเตอริโอ\nสแตนดาร์ด\nสแตนเลส\nสโตน\nสโตร์\nสไตรค์\nสไตล์\nสถาปัตย์\nสไนเปอร์\nสปอต\nสป็อต\nสปอนเซอร์\nสปอร์ต\nสปา\nสปาย\nสปิริต\nสเปก\nสเปค\nสไปเดอร์\nสมณพราหมณ์\nสมาพันธ์\nสมิติเวช\nสโรชา\nสลัม\nสแล็ก\nสโลแกน\nสโลว์\nสไลด์\nสวีท\nสหรัฐ\nสหัชญาณ\nสหัสวรรษ\nสะกอม\nสะเด่า\nสะบึม\nสะบึมส์\nสะออน\nสังโฆ\nสะโหลสะเหล\nสันทนาการ\nสัมนา\nสามช่า\nสามแยก\nสารขัณฑ์\nสี่แยก\nสึนามิ\nสุนทรีย์\nสุริยยาตร\nสุริยยาตร์\nสุหนี่\nเสกสรรค์\nเสพติด\nเสือโคร่ง\nหงวน\nหน่อมแน้ม\nหมวย\nหมั่นโถว\nหม่านโถว\nหมายปอง\nหมิง\nหยวน\nหลวงตา\nหลวงปู่\nหลวงพี่\nหล่อฮังก้วย\nหลินจือ\nห่วย\nเห็นด้วย\nเหมย\nเห่ย\nเหี่ยวย่น\nแหม็บ\nแหวว\nโหงว\nโหงวเฮ้ง\nโหลน\nโหลยโท่ย\nไหง\nไหร่\nอพาร์ตเมนต์\nอพาร์ตเมนท์\nอพาร์ทเมนต์\nอพาร์ทเมนท์\nอมาตยาธิปไตย\nอยุติธรรม\nอริยสงฆ์\nอ่วม\nอวอร์ด\nออกแบบ\nออดิชั่น\nออดิทอเรียม\nออเดอร์\nออโต้\nออทิสติก\nอ่อนด้อย\nออฟ\nออยล์\nออร์แกน\nออร์แกนิก\nออร์แกนิค\nออร์เดอร์\nออรัล\nออสซี่\nอะ\nอัตลักษณ์\nอัตวิสัย\nอันเดอร์\nอันตรกิริยา\nอัลตรา\nอัลไซเมอร์\nอัลบัม\nอัลบั้ม\nอัลมอนด์\nอาข่า\nอาโนเนะ\nอาฟเตอร์\nอาร์ติสต์\nอาร์พีจี\nอาว์\nอาสวะ\nอิกัวนา\nอินดอร์\nอินดัสตรีส์\nอินเตอร์\nอิ่มแปร้\nอิมพีเรียล\nอิเล็กทริก\nอิเล็กทริค\nอิเลียด\nอิสรชน\nอิเหนา\nอิออน\nอีแต๋น\nอีโรติก\nอีเวนท์\nอีสต์\nอีสเตอร์\nอึ๊บ\nอึ้ม\nอึ๋ม\nอึมครึม\nอุด้ง\nอุตสาหการ\nอุเทน\nอุปการคุณ\nอุปทาน\nอุปนายก\nอุปนายิกา\nอุปสงค์\nอุปัทวเหตุ\nอุรังคธาตุ\nอูคูเลเล่\nอู้ฟู่\nเอ๋\nเอ็กซ์โป\nเอ็กซ์เพรส\nเอ็กโซเซต์\nเอ็กโซเซ่ต์\nเอเซีย\nเอ็นจีโอ\nเอ็นเตอร์เทน\nเอนทรานซ์\nเอ็นทรานซ์\nเอฟเฟ็กต์\nเอเยนต์\nเอลนีโญ\nเอสเปรสโซ\nเอสเพรสโซ\nเอ๋อ\nเอาต์\nเอาท์\nเอาท์ดอร์\nเอ๊าะ\nแอ็กชั่น\nแอ็คชั่น\nแอคทีฟ\nแอดมิชชั่น\nแอดมิสชัน\nแอนด์\nแอ๊บแบ๊ว\nแอปเปิล\nแอปเปิ้ล\nแอปพริคอท\nแอพพริคอท\nแอพริคอต\nแอร์\nแอโรบิก\nแอโรบิค\nแอลมอนด์\nแอสเตอร์\nโอเค\nโอเปอเรเตอร์\nโอเปร่า\nโอเพ่น\nโอ้ย\nโอยัวะ\nโอรสาธิราช\nโอเลี้ยง\nโอวัลติน\nโอเวอร์\nไอซ์\nไอซียู\nไอดอล\nไอเดีย\nไอติม\nฮวงจุ้ย\nฮ่องเต้\nฮองเฮา\nฮอต\nฮ็อต\nฮอตดอก\nฮ็อตด็อก\nฮันนีมูน\nฮัม\nฮัลโลวีน\nฮัลโหล\nฮากกา\nฮาร์ด\nฮาราคีรี\nฮาลาล\nฮาโลวีน\nฮิ\nฮิต\nฮิบรู\nฮิปโป\nฮิปฮอป\nฮีโร่\nฮูลาฮูป\nฮูล่าฮูป\nเฮฟวี\nเฮฟวี่\nเฮอร์ริเคน\nเฮีย\nแฮนด์\nแฮปปี้\nแฮมเบอร์เกอร์\nโฮป\nโฮม\nโฮลดิงส์\nโฮลวีต\nโฮสเตส\nไฮกุ\nไฮแจ็ค\nไฮโซ\nไฮเทค\nไฮบริด\nไฮเปอร์\nไฮไลต์\nไฮไลท์\nไฮเวย์\nไฮสคูล\nไฮเอนด์\n\nกรีซ\nกัมพูชา\nกัวเตมาลา\nกาตาร์\nกานา\nกาบอง\nกายอานา\nกินี\nเกรนาดีนส์\nเกรเนดา\nเกาหลี\nแกมเบีย\nโกตดิวัวร์\nคองโก\nคอโมโรส\nคอสตาริกา\nคาซัคสถาน\nคิตส์\nคิริบาตี\nคิริบาส\nคิวบา\nคีร์กีซสถาน\nคูเวต\nเคนยา\nเคปเวิร์ด\nเคย์แมน\nแคนาดา\nแคเมอรูน\nโครเอเชีย\nโคลอมเบีย\nจอร์เจีย\nจอร์แดน\nจาเมกา\nจิบูตี\nจีน\nชาด\nชิลี\nเช็ก\nซามัว\nซาอุ\nซิมบับเว\nซีเรีย\nซูดาน\nซูรินาเม\nเซนต์\nเซเนกัล\nเซอร์เบีย\nเซาตูเม\nเซียร์รา\nแซมเบีย\nโซมาเลีย\nโซเวียต\nไซปรัส\nญี่ปุ่น\nดารุสซาลาม\nเดนมาร์ก\nโดมินิกัน\nโดมินิกา\nตรินิแดด\nตองกา\nติมอร์\nตุรกี\nตูนิเซีย\nเติร์กเมนิสถาน\nโตโก\nโตเบโก\nไต้หวัน\nทาจิกิสถาน\nแทนซาเนีย\nนอร์เวย์\nนามิเบีย\nนาอูรู\nนิการากัว\nนิวซีแลนด์\nเนเธอร์แลนด์\nเนปาล\nเนวิส\nไนจีเรีย\nไนเจอร์\nบราซิล\nบริติช\nบริเตน\nบรูไน\nบอตสวานา\nบอสเนีย\nบังกลาเทศ\nบังคลาเทศ\nบัลแกเรีย\nบาร์บูดา\nบาร์เบโดส\nบาห์เรน\nบาฮามาส\nบิสเซา\nบุรุนดี\nบูร์กินาฟาโซ\nเบนิน\nเบลเยียม\nเบลารุส\nเบลีซ\nเบอร์มิวดา\nโบลิเวีย\nปรินซิปี\nปากีสถาน\nปานามา\nปาปัวนิวกินี\nปารากวัย\nปาเลสไตน์\nปาเลา\nเปรู\nเปอร์เซีย\nเปอร์โตริโก\nโปรตุเกส\nโปแลนด์\nฝรั่งเศส\nพม่า\nฟิจิ\nฟินแลนด์\nฟิลิปปินส์\nเฟรนช์\nภูฏาน\nภูฐาน\nมองโกเลีย\nมอนเตเนโกร\nมอนแทนา\nมอริเชียส\nมอริเตเนีย\nมอลโดวา\nมอลตา\nมัลดีฟส์\nมาเก๊า\nมาซิโดเนีย\nมาดากัสการ์\nมาร์แชลล์\nมาลาวี\nมาลี\nมาเลเซีย\nเม็กซิโก\nเมียนมาร์\nโมซัมบิก\nโมนาโก\nโมนาโค\nโมร็อกโก\nไมโครนีเซีย\nยูกันดา\nยูโกสลาเวีย\nยูเครน\nเยเมน\nเยอรมนี\nรวันดา\nรัสเซีย\nโรมาเนีย\nลักเซมเบิร์ก\nลัตเวีย\nลาว\nลิกเตนสไตน์\nลิทัวเนีย\nลิเบีย\nลีโอน\nลูเซีย\nเลโซโท\nเลบานอน\nเลสเต\nไลบีเรีย\nวาติกัน\nวานูอาตู\nวินเซนต์\nเวเนซุเอลา\nเวียดนาม\nศรีลังกา\nสเปน\nสโลวะเกีย\nสโลวัก\nสโลวีเนีย\nสวาซิแลนด์\nสวิตเซอร์แลนด์\nสวีเดน\nสหรัฐ\nสหราชอาณาจักร\nสิกขิม\nสิงคโปร์\nอเมริกา\nออสเตรเลีย\nออสเตรีย\nอันดอร์รา\nอัฟกานิสถาน\nอาเซอร์ไบจาน\nอาร์เจนตินา\nอาร์เมเนีย\nอาระเบีย\nอิเควทอเรียล\nอิตาลี\nอินเดีย\nอินโดนีเซีย\nอิรัก\nอิสราเอล\nอิหร่าน\nอียิปต์\nอุซเบกิสถาน\nอุรุกวัย\nเอกวาดอร์\nเอธิโอเปีย\nเอมิเรตส์\nเอริเทรีย\nเอลซัลวาดอร์\nเอสโตเนีย\nแองโกลา\nแอนติกา\nแอลจีเรีย\nแอลเบเนีย\nโอมาน\nไอซ์แลนด์\nไอร์แลนด์\nฮ่องกง\nฮอนดูรัส\nฮังการี\nเฮติ\nเฮอร์เซโกวีนา\n\nกระบี่\nกรุงเทพ\nกาญจนบุรี\nกาฬสินธุ์\nกำแพงเพชร\nขอนแก่น\nจันทบุรี\nฉะเชิงเทรา\nชลบุรี\nชัยนาท\nชัยภูมิ\nชุมพร\nเชียงราย\nเชียงใหม่\nตรัง\nตราด\nตาก\nนครนายก\nนครปฐม\nนครพนม\nนครราชสีมา\nนครศรีธรรมราช\nนครสวรรค์\nนนทบุรี\nนราธิวาส\nน่าน\nบึงกาฬ\nบุรีรัมย์\nปทุมธานี\nประจวบคีรีขันธ์\nปราจีนบุรี\nปัตตานี\nพะเยา\nพังงา\nพัทลุง\nพิจิตร\nพิษณุโลก\nเพชรบุรี\nเพชรบูรณ์\nแพร่\nภูเก็ต\nมหาสารคาม\nมุกดาหาร\nแม่ฮ่องสอน\nยโสธร\nยะลา\nร้อยเอ็ด\nระนอง\nระยอง\nราชบุรี\nลพบุรี\nลำปาง\nลำพูน\nเลย\nศรีสะเกษ\nสกลนคร\nสงขลา\nสตูล\nสมุทรปราการ\nสมุทรสงคราม\nสมุทรสาคร\nสระแก้ว\nสระบุรี\nสิงห์บุรี\nสุโขทัย\nสุพรรณบุรี\nสุราษฎร์\nสุราษฎร์ธานี\nสุรินทร์\nหนองคาย\nหนองบัวลำภู\nอยุธยา\nอ่างทอง\nอำนาจเจริญ\nอุดรธานี\nอุตรดิตถ์\nอุทัยธานี\nอุบลราชธานี\nกันทรลักษ์\nจตุจักร\nไชยา\nซีคอน\nดอนเมือง\nถลาง\nไทรโยค\nธนบุรี\nธัญบุรี\nบางกอก\nบางปะกง\nบางระจัน\nปะทิว\nปาย\nพญาไท\nพัฒน์พงษ์\nพัทยา\nพารากอน\nภูมิซรอล\nรัตนาธิเบศร์\nรังสิต\nลันตา\nลาดพร้าว\nวโรรส\nวิภาวดี\nสตึก\nสมุย\nสัตหีบ\nสิมิลัน\nสุขุมวิท\nสุไหง\nเสลภูมิ\nอังรีดูนังต์\nอ่างขาง\nอินทนนท์\nเอ็มโพเรียม\n\nคิวชู\nแคริบเบียน\nแคสเปียน\nดานูบ\nตะนาวศรี\nนอร์วีเจียน\nนิโคบาร์\nเนรัญชรา\nไนล์\nบอร์เนียว\nบอลติก\nเบงกอล\nปิง\nแปซิฟิก\nมะละกา\nมินดาเนา\nมิสซิสซิปปี\nเมดิเตอร์เรเนียน\nเมโสโปเตเมีย\nยมุนา\nยุโรป\nยูเรเชีย\nยูเรเซีย\nแยงซี\nแยงซีเกียง\nสแกนดิเนเวีย\nสะโตง\nสาละวิน\nสุมาตรา\nสุเอซ\nอะเมซอน\nอันดามัน\nอัลไต\nอาร์กติก\nอาหรับ\nอินโดจีน\nอิรวดี\nอิระวดี\nอีเจียน\nอุษาคเณย์\nอูราล\nเอเชีย\nเอเดรียติก\nเอเวอเรสต์\nแอตแลนติก\nแอนตาร์กติก\nแอนตาร์กติกา\nแอฟริกา\nโอลิมปัส\nไอโอเนียน\nฮวงโห\nฮอกไกโด\nฮอนชู\n\nกบิลพัสดุ์\nกุสินารา\nโกลิยะ\nโกสัมพี\nโคตรบอง\nโคตรบูรณ์\nตองอู\nทรอย\nทวารวดี\nทวาราวดี\nเทวทหะ\nไทรบุรี\nนาลันทา\nไบแซนไทน์\nปรัสเซีย\nปัลลวะ\nปาฏลีบุตร\nพุทธคยา\nมถุรา\nมายัน\nมิถิลา\nราชคฤห์\nล้านช้าง\nล้านนา\nลุมพินี\nวัชชี\nเวสาลี\nสารนาถ\nสาวัตถี\nหริภุญชัย\nอโยธยา\nออตโตมัน\nอังวะ\nอินทปัตถ์\nอุชเชนี\n\nกราฟิก\nกราฟิกส์\nกราฟิค\nกริด\nกิกะไบต์\nกูเกิล\nกูเกิ้ล\nเกตเวย์\nโกลบอล\nคลัสเตอร์\nคลาส\nคลิก\nคลิปอาร์ต\nคอนโซล\nคอนเทนต์\nคอมพิวติ้ง\nคอมไพล์\nคอมไพเลอร์\nคอมมูนิเคชั่น\nคอร์\nคีย์\nคีย์บอร์ด\nเครือข่าย\nเคอร์เซอร์\nเคอร์เนล\nแคช\nแคมฟรอก\nแคมฟร็อก\nแคร็ก\nโค้ด\nจาวา\nจีพีเอส\nชิป\nชิพ\nเชลล์\nแช็ต\nแชนเนล\nแชนแนล\nซ็อกเก็ต\nซอฟต์แวร์\nซอฟท์แวร์\nซอร์ส\nซัพพอร์ต\nซัพพอร์ท\nซีดี\nซีดีรอม\nซีเนอร์\nเซิร์ฟเวอร์\nโซลูชัน\nโซลูชั่น\nไซต์\nไซเบอร์\nทรานแซกชัน\nทรานแซกชั่น\nทรานแซ็กชัน\nทรานแซ็กชั่น\nทรานแซคชัน\nทรานแซคชั่น\nทรานแซ็คชัน\nทรานแซ็คชั่น\nทวิตเตอร์\nทวีต\nทัชแพด\nเทมเพลต\nเทอร์มินัล\nแท็ก\nแท็บ\nแทบเล็ต\nโทรจัน\nเน็ต\nเน็ตบุ๊ก\nเน็ตบุค\nเน็ตบุ๊ค\nเน็ตเวิร์ก\nเน็ตเวิร์ค\nโน้ตบุ๊ก\nโน้ตบุค\nโน้ตบุ๊ค\nดอส\nดาวน์เกรด\nดาวน์โหลด\nดิจิตอล\nดิจิทัล\nดีบั๊ก\nดีวีดี\nดีไวซ์\nเดเบียน\nเดลไฟ\nเดสก์ท็อป\nโดเมน\nไดรว์\nไดรเวอร์\nไดเรกทอรี\nไดโอด\nเทเลคอม\nบล็อกเกอร์\nบรอดแบนด์\nบราวเซอร์\nบลูทูท\nบลูทูธ\nบลูเรย์\nบั๊ก\nบัฟเฟอร์\nบิต\nบิท\nบูต\nเบราว์เซอร์\nแบนด์วิดท์\nไบต์\nไบนารี\nโปรแกรมเมอร์\nโปรเซส\nโปรเซสเซอร์\nโปรโตคอล\nพร็อกซี\nพอร์ต\nพอร์ท\nพาเนล\nพาร์ทิชัน\nพารามิเตอร์\nพาสเวิร์ด\nพิกเซล\nเพจ\nเพจเจอร์\nแพกเก็ต\nแพตช์\nแพลตฟอร์ม\nโพรเซส\nโพรเซสเซอร์\nโพรโทคอล\nไพธอน\nฟล็อปส์\nฟอนต์\nฟอร์แมต\nฟอร์เวิร์ด\nฟอรัม\nฟีเจอร์\nเฟซบุ๊ก\nเฟิร์มแวร์\nแฟล็ก\nโฟลเดอร์\nไฟร์ฟอกซ์\nไฟร์วอลล์\nไฟล์\nมอดูล\nมอนิเตอร์\nมัลติ\nมัลติทัช\nมัลติเพล็กซ์\nมัลแวร์\nมาสเตอร์\nมีเดีย\nเมนู\nเมมโมรี\nเมล\nเมาส์\nแมค\nโมดูล\nโมเด็ม\nโมบาย\nโมบายล์\nโมไบล์\nไมโครซอฟท์\nยูนิกซ์\nยูนิโคด\nยูนิโค้ด\nริงโทน\nรีเฟรช\nรีเลย์\nเราเตอร์\nเรียลไทม์\nลิงก์\nลินุกซ์\nลีนุกซ์\nลูป\nเลเยอร์\nแล็ปท็อป\nไลเซนส์\nไลบรารี\nวิกิ\nวิกิพีเดีย\nวินโดวส์\nวินโดว์ส\nเว็บ\nเวอร์ชวล\nเวอร์ชัน\nเวอร์ชั่น\nเวิร์กสเตชัน\nเวิร์กสเตชั่น\nเวิร์คสเตชัน\nเวิร์คสเตชั่น\nเวิร์ด\nเวิร์ม\nไวแมกซ์\nสกรีน\nสแกน\nสแกนเนอร์\nสแต็ก\nสนิฟเฟอร์\nสปายแวร์\nสเปซ\nสแปม\nสมาร์ท\nสล็อต\nเสิร์ช\nโหลด\nออนไลน์\nออปติก\nออปติคอล\nออปติคัล\nออฟไลน์\nออราเคิล\nอัพเกรด\nอัพเดต\nอัพโหลด\nอัปเกรด\nอัปเดต\nอัปโหลด\nอัลกอริทึม\nอาร์กิวเมนต์\nอินเตอร์เน็ต\nอินทิเกรเตอร์\nอินเทอร์เน็ต\nอินเทอร์เฟซ\nอินเทล\nอินพุต\nอินพุท\nอีเมล\nอีเมล์\nอูบุนตู\nอูบันตู\nเอนจิน\nเอ็นจิน\nเอาต์พุต\nเอาต์พุท\nเอาท์พุต\nเอาท์พุท\nแอนะล็อก\nแอนิเมชัน\nแอนิเมชั่น\nแอปพลิเคชัน\nแอปพลิเคชั่น\nแอพพลิเคชัน\nแอพพลิเคชั่น\nแอสเซมบลี\nแอสเซมเบลอร์\nโอเพน\nไอคอน\nไอซี\nไอพอด\nไอพ็อด\nไอแพด\nไอโฟน\nฮับ\nฮาร์ดดิสก์\nฮาร์ดแวร์\nแฮกเกอร์\nแฮ็กเกอร์\nแฮนด์เฮลด์\nโฮสต์\n\nกรีก\nกัณณาฑ\nกัศมีรี\nคันจิ\nคาตาคานะ\nคุชราตี\nคุรุมุขี\nซีริลลิก\nซูลู\nเซลติก\nเซิร์บ\nตากาล็อก\nเตลุคู\nเติร์ก\nทราวิฑ\nเบงกาลี\nปัญจาบี\nพินอิน\nมลยาฬัม\nมองโกล\nมาราฐี\nมาเลย์\nเม็กซิกัน\nแมนจู\nแมนดาริน\nไมถิลี\nเยอรมัน\nรัสเซียน\nสวาฮิลี\nสวิส\nสินธี\nอูรดู\nอัสสมี\nอารบิก\nอิตาเลียน\nอุยกูร์\nแอฟริกัน\nโอริยา\nไอริช\nฮันกึล\nฮินดี\nฮิรางานะ\nฮีบรู\n\nกรีนพีซ\nกรือเซะ\nกวนอิม\nกวนอู\nกัดดาฟี\nกัลยาณวัตร\nกัสสปะ\nกาลิเลโอ\nกินเนส\nกุมภกรรณ\nกูเตนเบิร์ก\nเกตส์\nเกษมณี\nโกณฑัญญะ\nขงเบ้ง\nคริสโตเฟอร์\nคองคอร์ด\nคอลเกต\nคานธี\nคาเบรียล\nคาร์ฟูร์\nคาร์สัน\nคาราบาว\nคาสิโอ\nคิริน\nคุนลุ้น\nคูโบต้า\nเครมลิน\nแคทรีนา\nโคตมะ\nโคตมี\nโคลัมบัส\nไคฟง\nไครสเลอร์\nง้อไบ๊\nจตุพร\nจ็อบส์\nจอห์น\nจิ้น\nจิม\nจิ๋ม\nจิว\nจุฬาภรณ์\nจุฬาลงกรณ์\nเจมส์\nแจ็กสัน\nโจเซฟ\nโจว\nชมัยมรุเชฐ\nชมัยมรุเชษฐ์\nชเวดากอง\nชาร์ลส์\nชินราช\nชินวัตร\nชุนชิว\nเช็ง\nเชตวัน\nเชฟรอน\nเชฟโรเลต\nเชลซี\nไชยานุชิต\nซ่ง\nซังฮี้\nซัดดัม\nซันซิล\nซัมซุง\nซัวเจ๋ง\nซินหัว\nซีซาร์\nซีแพค\nซูซาน\nซูซูกิ\nซูบารุ\nเซ็นทารา\nเซเวน\nเซเว่น\nโซฟิเทล\nโซยุซ\nโซยูซ\nณัฐวุฒิ\nดาร์ลี่\nดาวโจนส์\nดิสนีย์\nดีแทค\nดูปองท์\nเดโมแครต\nเดลล์\nเดลินิวส์\nเดวิด\nแดวู\nโดนัลด์\nโดราเอมอน\nโดเรมอน\nต๋อง\nตั๊กม้อ\nตากสิน\nตาเมือน\nตาลีบัน\nตูน\nเตมีย์\nโต๋\nโตชิบา\nโตโยต้า\nถังซัมจั๋ง\nถังซำจั๋ง\nทรพา\nทราเวล\nทรูมูฟ\nทีปังกร\nเทปโก\nเทพรัตน\nเทวทัต\nเทสโก้\nโทมัส\nไททานิก\nไททานิค\nไทยรัฐ\nธีออส\nนครินทรา\nนโปเลียน\nนพดล\nนราดูร\nนเรนทร\nนอสตราดามุส\nนาซา\nนาซ่า\nนาซี\nนาโต\nนาโต้\nนาลแก\nนิคอน\nนิโคลัส\nนิด้า\nนินเทนโด\nนิปปอน\nนิวตัน\nนิสสัน\nเนคเทค\nเนชั่น\nเนชันแนล\nเนชั่นแนล\nเนวิน\nเนสเล่\nเนสาด\nแนท\nแนสแดค\nโนเกีย\nโนเบล\nโนเวลล์\nโนโวเทล\nไนเม็กซ์\nบรอดเวย์\nบรัดเลย์\nบรู๊ซ\nบัลเมอร์\nบารัก\nบารัค\nบู๊ตึ๊ง\nเบญกาย\nเบนซ์\nเบ็นซ์\nเบนจามิน\nโบตัน\nไบโอเทค\nประชาธิปัตย์\nปวีณา\nปอเต็กตึ๊ง\nปอเต๊กตึ๊ง\nป่อเต็กตึ๊ง\nปัตตะโชติ\nปารุสก์\nปีเตอร์\nปูติน\nเป๊ปซี่\nเป้ย\nเปอร์โยต์\nเปาบุ้นจิ้น\nโปเกมอน\nโป๊ยก่าย\nพรหมทัต\nพลาโต\nพอลล่า\nพานาโซนิค\nพานาโซนิก\nพิทยานุกูล\nพิมพิสาร\nเพนแทกซ์\nเพลโต\nไพโอเนียร์\nฟรอยด์\nฟรังซิส\nฟรานซิส\nฟลอเรนซ์\nฟอร์ด\nฟิลิปส์\nฟูจิ\nแฟซ่า\nโฟร์โมสต์\nภูมิพล\nภูริทัต\nมงฟอร์ต\nมณโฑ\nมติชน\nมหิตลาธิเบศร\nมโหสถ\nมัจฉานุ\nมาร์กาเร็ต\nมาร์ติน\nมาสด้า\nมิตซูบิชิ\nมิราเคิล\nมุสโสลินี\nเม้ง\nเมจิ\nเมอร์ซีเดส\nเมอร์เซเดส\nแมกซ์เวลล์\nแมกไซไซ\nแมคอินทอช\nแมชีนเนอรี่\nโมคคัลลานะ\nโมโตโรลา\nโมโตโรล่า\nโมเนีย\nไมเคิล\nไมยราพณ์\nยโสธรา\nยะโฮวา\nยะโฮวาห์\nยามาฮ่า\nยาเวห์\nยาฮู\nยูนิเซฟ\nยูเนสโก\nยูไล\nเยโฮวาห์\nรอยเตอร์\nรอยัล\nรัชดา\nรัสปูติน\nราฟาเอล\nรามาวตาร\nราเมศวร\nราหุล\nริชาร์ด\nรีพับลิกัน\nรูนีย์\nเรนโบว์\nแรมโบ้\nโรตารี\nโรนัลโด\nโรนัลโด้\nโรบินสัน\nโรเบิร์ต\nล็อกซเล่ย์\nลิงคอล์น\nลิจฉวี\nลิไท\nลิไทย\nลินคอล์น\nลิเวอร์พูล\nเลโนโว\nเลียดก๊ก\nโลตัส\nวชิราลงกรณ์\nวลาดิเมียร์\nวอลล์สตรีท\nวาเลนไทน์\nวิกตอเรีย\nวิทยานุสรณ์\nวิทยายน\nวิมเบิลดัน\nวิลเลียม\nวีระ\nวุฒิชัย\nเวียดกง\nไวตามิลค์\nศกุนตลา\nศรีวิชัย\nศิริพงษ์\nศิริราช\nศุภชลาศัย\nสดกก๊อกธม\nสดายุ\nสตาลิน\nสตีฟ\nสแตนฟอร์ด\nสวรินทิรา\nสังกัจจายน์\nสาทิตย์\nสารีบุตร\nสิริกิติ์\nสิรินธร\nสิหิงค์\nสีวลี\nสีหนุ\nสีหมุนี\nสีหโมนี\nสุครีพ\nสุทโธทนะ\nสุเทพ\nสุนทราภรณ์\nสุนีย์\nสุรนารี\nสุรยุทธ์\nสุริยาสน์\nเส้าหลิน\nโสกราตีส\nโสภิต\nหนุมาน\nหลินฮุ่ย\nหลุยส์\nเห้งเจีย\nไหหม่า\nองคต\nองคุลิมาล\nอชาตศัตรู\nอดุลยเดช\nอพอลโล\nอริสโตเติล\nอริสมันต์\nอลิซาเบธ\nอ๋อม\nออร์คิด\nออสการ์\nอะพอลโล\nอับราฮัม\nอั้ม\nอัลกออิดะห์\nอัลคาเทล\nอัลจาซีราห์\nอัลเฟรด\nอัลเลาะห์\nอัสซุส\nอัสสชิ\nอัสสัมชัญ\nอาเซม\nอาเซ็ม\nอาเซียน\nอาฟต้า\nอาร์เซนอล\nอินทรชิต\nอินทราทิตย์\nอีซูซุ\nอีเลฟเวน\nอีเลฟเว่น\nอุณรุท\nอุบลรัตน์\nอุบาลี\nอุ๋ย\nเอกทัศน์\nเอเซอร์\nเอ็ดเวิร์ด\nเอดิสัน\nเอแบค\nเอลิซาเบธ\nเอสพลานาด\nเอสพลานาร์ด\nแอคคอร์\nแอคคอร์ด\nแองเจลิน่า\nแอตแลนติส\nแอน\nแอ๋ม\nแอมบาสซาเดอร์\nแอมบาสเดอร์\nแอมเวย์\nแอ๋ว\nโอดีสซีย์\nโอเดียน\nโอบามา\nโอรสาราม\nโอลิมเปีย\nโออิชิ\nไอน์สไตน์\nฮอนด้า\nฮอปกินส์\nฮอลลีวูด\nฮอลลีวู้ด\nฮานามิ\nฮามาส\nฮิตเลอร์\nฮิตาชิ\nฮุนเซน\nฮุนเซ็น\nฮุนได\nฮุสเซ็น\nเฮนรี\nเฮนรี่\nเฮเลน\nโฮจิมินห์\nโฮปเวลล์\nโฮเมอร์\n\nกลีเซอรีน\nกำทอน\nแกนีมีด\nครอส\nคริสตัล\nคลอโรพลาสต์\nคลอไรด์\nควอนตัม\nคอนดักเตอร์\nคอปเปอร์\nคอลลาเจน\nคอเลสเตอรอล\nคอสมิก\nคูลอมบ์\nเคอราติน\nแคโรทีน\nแคสสินี\nโครมาโทกราฟี\nไคโตซาน\nจีโนม\nจุลชีววิทยา\nชิคุนกุนยา\nซัลฟิวริก\nซัลเฟต\nซัลไฟด์\nซิงค์\nซิริอุส\nซิลิกา\nซิลิเกต\nซิลิโคน\nซีเทน\nซีเวิร์ต\nเซ็กเตอร์\nเซ็กเมนต์\nเซมิ\nโซนาร์\nไซบอร์ก\nดอปเปลอร์\nดอปเพลอร์\nไดนามิก\nไดนามิกส์\nไดนามิค\nไดนามิคส์\nไดออกไซด์\nทรานส์\nทามิฟลู\nเทฟลอน\nเทสโทสเตอโรน\nเทอร์โม\nแทนนิน\nไททัน\nไทฟอยด์\nไทรอยด์\nธาลัสซีเมีย\nเนกาตีฟ\nโนวา\nบอแรกซ์\nโบทอกซ์\nโบท็อกซ์\nไบโอติน\nปฏิยานุพันธ์\nโปรเจสเตอโรน\nพอลิเมอร์\nพันธุศาสตร์\nพัลซาร์\nพาร์กินสัน\nพาราเซตามอล\nพาราโบลา\nเพอร์ออกไซด์\nโพรเจสเทอโรน\nโพลาไรซ์\nโพลิเมอร์\nโพลีเอทิลีน\nไพรเมต\nฟลาโวนอยด์\nฟลูออเรสเซนซ์\nฟลูออไรด์\nฟอสซิล\nฟิชชัน\nฟิวชัน\nฟีโรโมน\nไฟเบอร์\nมอนอกไซด์\nมิราจ\nเมตริกซ์\nเมทริกซ์\nเมลานิน\nเมลามีน\nโมเมนตัม\nไมโตคอนเดรีย\nไมโทคอนเดรีย\nยีสต์\nยูริก\nยูเรีย\nรูมาตอยด์\nวีก้า\nเวกเตอร์\nเวก้า\nเวสิเคิล\nโวลต์\nสเกล\nสเกลาร์\nสเต็ม\nสเตียรอยด์\nสปีชีส์\nสเปิร์ม\nสัมพัทธภาพ\nสุริยจักรวาล\nออกเทน\nออโรรา\nออโรร่า\nอะคริลิก\nอะครีลิก\nอะซีติก\nอะซีโตน\nอะมิโน\nอะลูมินา\nอันโดรเมดา\nอัลคาไลน์\nอัลตราซาวด์\nอัลตราซาวนด์\nอัลลอยด์\nอินทิกรัล\nอินทิเกรต\nอีโบลา\nอีโบล่า\nเอ็กซ์โพเนนเชียล\nเอทานอล\nเอทิลีน\nเอนโทรปี\nเอสเตอร์\nเอสโตรเจน\nเอสโทรเจน\nแอนดรอยด์\nแอนแทร็กซ์\nแอมพลิจูด\nแอมโมเนียม\nแอลกอฮอลิซึม\nแอสพาร์แตม\nโอเซลทามิเวียร์\nฮับเบิล\nฮิวมัส\nไฮดรอกไซด์\nไฮดรอลิก\nไฮโดรลิก\nไฮเพอร์โบลา\n\nกงเต็ก\nกฎุมพี\nกฏ\nกฏหมาย\nกบฎ\nกราไฟต์\nก๊อปปี้\nกะทะ\nกังวาล\nกุฎฐัง\nกุฏุมพี\nฃวด\nคฑา\nคลินิค\nคลีนิก\nคลีนิค\nคาทอลิค\nคึ่นฉ่าย\nแคตตาล็อก\nโควต้า\nฅน\nจุมพฎ\nช็อคโกแลต\nแซ่ด\nดัทช์\nทนง\nทรมาณ\nทราก\nทะแยง\nทิฏฐิ\nบล็อค\nบ๊องแบ๊ว\nบัลเล่ต์\nเบรค\nแบงค์\nปรากฎ\nปัคคหะ\nปาฏิโมกข์\nปิติ\nปิรามิด\nโปรเตสแตนท์\nพนิช\nพยักเพยิด\nพริ้ว\nพลูโตเนียม\nพากษ์\nเฟิร์น\nยากี้\nเยภุยยสิกา\nรุสเซีย\nฤาษี\nล็อค\nล็อคเกอร์\nวันทยาหัตถ์\nวานิช\nวิญญาน\nวิศิษฐ์\nศรีษะ\nสเปกโทรสโคป\nสฤษฎ์\nสลิ่ม\nสัตตสดก\nสาราณียากร\nสุกี้\nสุกี้ยากี้\nสูญญากาศ\nหยอมแหยม\nหยอย\nหล่ะ\nอะดรีนาลีน\nอะหลั่ย\nอัตคัต\nอัฟริกา\nอานิสงค์\nอาฟริกา\nอิริยาบท\nอิเลคโทรนิคส์\nอีรุงตุงนัง\nอุตรายัน\nอุลตรา\nอุลตร้า\nโอกาศ\n\nกกหู\nกงการ\nกงจักร\nกฎเกณฑ์\nกฎบัตร\nกฎหมาย\nกฎหมู่\nกดขี่\nกดดัน\nก้นกบ\nก้นบึ้ง\nก้นปล่อง\nกนิษฐภคินี\nกนิษฐภาดา\nกรงเล็บ\nกรมการ\nกรมท่า\nกรมธรรม์\nกรมนา\nกรมวัง\nกรรมกร\nกรรมการ\nกรรมฐาน\nกรรมบถ\nกรรมพันธุ์\nกรรมวิธี\nกรรมสิทธิ์\nกระจกเงา\nกระจกตา\nกระจกนูน\nกระจกเว้า\nกระจอกชวา\nกระจอกเทศ\nกระจ้อยร่อย\nกระจับบก\nกระจับปิ้ง\nกระจับปี่\nกระโชกโฮกฮาก\nกระดานดำ\nกระดานหก\nกระดาษแก้ว\nกระดาษไข\nกระดาษทราย\nกระดาษสา\nกระดูกงู\nกระทาชาย\nกระเท่เร่\nกระบวนการ\nกระบองเพชร\nกระผีกริ้น\nกระยาทิพย์\nกระยาสารท\nกระยาหาร\nกระสอบทราย\nกระสุนปืน\nกระแสจิต\nกระแสน้ำ\nกระแสลม\nกรับคู่\nกรับพวง\nกรับเสภา\nกราดเกรี้ยว\nกราวรูด\nกริ่งเกรง\nกรีฑาสถาน\nกรีดกราย\nกรี๊ดกร๊าด\nกลไก\nกลบท\nกลเม็ด\nกลยุทธ์\nกลวิธี\nกลศาสตร์\nกลอักษร\nกลบเกลื่อน\nกลมกล่อม\nกลมกลืน\nกลมเกลียว\nกล้วยแขก\nกล้วยไม้\nกล่องเสียง\nกล่อมเกลา\nกล่อมเกลี้ยง\nกลัดกลุ้ม\nกลัดมัน\nกลั่นกรอง\nกลั่นแกล้ง\nกลับกลอก\nกลางคน\nกลางคัน\nกลางค่ำ\nกลางคืน\nกลางแจ้ง\nกลางแปลง\nกลางวัน\nกลาดเกลื่อน\nกล่าวขวัญ\nกล่าวโทษ\nกล่าวหา\nกล้ำกราย\nกล้ำกลืน\nกลิ้งกลอก\nกลิ้งเกลือก\nกลิ่นอาย\nกลียุค\nกวดขัน\nกวัดแกว่ง\nกวัดไกว\nกวีนิพนธ์\nก่อกวน\nก่อการ\nก่อตั้ง\nก่อสร้าง\nก่อหวอด\nก้อร่อก้อติก\nกองกลาง\nกองเกิน\nกองโจร\nกองทัพ\nกองทุน\nกองพล\nกองพัน\nกองฟอน\nกองร้อย\nกองหนุน\nกอบโกย\nกะเกณฑ์\nกะบังลม\nกักกัน\nกักขัง\nกักตัว\nกักตุน\nกัณฑ์เทศน์\nกัดฟัน\nกันชน\nกันท่า\nกันสาด\nกันเอง\nกับแกล้ม\nกับข้าว\nกับระเบิด\nกากเพชร\nกากหมู\nกาชาด\nกาญจนาภิเษก\nก้านคอ\nกาฝาก\nก้ามกราม\nกามกิจ\nกามคุณ\nกามเทพ\nกามโรค\nก้ามปู\nกายกรรม\nกายบริหาร\nกายภาพ\nกายวิภาค\nกายสิทธิ์\nก่ายกอง\nการคลัง\nการเงิน\nการบ้าน\nการเปรียญ\nการเมือง\nการเรือน\nการละเล่น\nกาลกิริยา\nกาลเทศะ\nก้าวก่าย\nก้าวร้าว\nก้าวหน้า\nกาสาวพัสตร์\nกาฬพฤกษ์\nกาฬโรค\nกำปั้น\nกำมือ\nกำแพงขาว\nกำลังใจ\nกำลังม้า\nกี่งอำเภอ\nกิจกรรม\nกิจการ\nกิจวัตร\nกิจจะลักษณะ\nกิตติคุณ\nกิตติศัพท์\nกินขาด\nกินใจ\nกินดอง\nกินโต๊ะ\nกินแบ่ง\nกินเปล่า\nกินเลี้ยง\nกินเส้น\nกินแหนง\nกิโลกรัม\nกิโลเมตร\nกิโลลิตร\nกิโลเฮิรตซ์\nกีดกัน\nกีดกั้น\nกีดขวาง\nกึกก้อง\nกึกกัก\nกึกกือ\nกึ่งกลาง\nกุกกัก\nกุ๊กกิ๊ก\nกุ้งฝอย\nกุ้งมังกร\nกุ้งแห้ง\nกุ้งเต้น\nกุ้งยิง\nกุญแจผี\nกุญแจมือ\nกุญแจเลื่อน\nกุญแจเสียง\nกุลธิดา\nกุลบุตร\nกุลสตรี\nกู้ยืม\nเก้งก้าง\nเก็บกวาด\nเก็บเกี่ยว\nเก็บงำ\nเก็บตก\nเกรงกลัว\nเกรงใจ\nเกรงขาม\nเกรียงไกร\nเกรียมกรม\nเกรี้ยวกราด\nเกล็ดเลือด\nเกลี้ยกล่อม\nเกลี้ยงเกลา\nเกลือกกลั้ว\nเกลือกกลิ้ง\nเกลื่อนกล่น\nเกลื่อนกลาด\nเกศธาตุ\nเกษตรกร\nเกษตรกรรม\nเกษตรศาสตร์\nเกษมสันต์\nเกษียรสมุทร\nเก้อเขิน\nเกาะแกะ\nเกี่ยงงอน\nเกียจคร้าน\nเกียรติคุณ\nเกียรตินิยม\nเกียรติประวัติ\nเกียรติภูมิ\nเกียรติยศ\nเกียรติศักดิ์\nเกียรติมุข\nเกี่ยวข้อง\nเกี่ยวดอง\nเกี่ยวพัน\nเกี่ยวโยง\nเกี้ยวพาน\nเกี้ยวพาราสี\nแก่แดด\nแก้ขัด\nแก้ไข\nแก้ตัว\nแก้เผ็ด\nแก้ลำ\nแกงคั่ว\nแกงจืด\nแกงบวด\nแกงป่า\nแกงเผ็ด\nแกงส้ม\nแก่งแย่ง\nแก่นแก้ว\nแก่นสาร\nแก้วตา\nแก้วหู\nแกว่งกวัด\nแกว่งไกว\nแกะรอย\nโก้เก๋\nโกรธเกรี้ยว\nโกรธขึ้ง\nไก่เขี่ย\nไก่ชน\nไก่บ้าน\nไก่ป่า\nไก่ฟ้า\nไกล่เกลี่ย\nขจัดขจาย\nขนเพชร\nขนสัตว์\nขนหนู\nขนส่ง\nขนมจีน\nขบขัน\nขบวนการ\nข่มขี่\nข่มขู่\nข่มขืน\nข่มเหง\nขมหิน\nขมิ้นชัน\nขมุบขมิบ\nขยะแขยง\nขยักขย่อน\nขยักขย้อน\nขยับขยาย\nขยับเขยื้อน\nขวดโหล\nขวยเขิน\nขวัญใจ\nขวัญตา\nขวัญอ่อน\nขวากหนาม\nขวางโลก\nของ้าว\nขอสับ\nขอขมา\nขอทาน\nขอโทษ\nขอร้อง\nขออภัย\nข้อเขียน\nข้อความ\nข้อเท็จจริง\nของกลาง\nของขวัญ\nของแข็ง\nของชำ\nของลับ\nของเล่น\nของว่าง\nของเหลว\nของไหล\nของไหว้\nข้องใจ\nข้องแวะ\nขอบข่าย\nขอบเขต\nขอบคุณ\nขอบใจ\nขอบพระคุณ\nข้อมูล\nข้อแม้\nข้อหา\nข้อสังเกต\nขัดข้อง\nขัดขืน\nขัดเขิน\nขัดจังหวะ\nขัดดอก\nขัดแตะ\nขัดยอก\nขัดแย้ง\nขัดสมาธิ\nขันหมาก\nขันอาสา\nขับขี่\nขับเคี่ยว\nขั้วโลก\nขาจร\nขาประจำ\nข้าราชการ\nข้าศึก\nข้าหลวง\nขาดแคลน\nขาดใจ\nขาดตอน\nขาดตัว\nขาดทุน\nขาดเหลือ\nขายหน้า\nข่าวกรอง\nข่าวคราว\nข่าวล่า\nข่าวลือ\nข่าวสาร\nข้าวเกรียบ\nข้าวแกง\nข้าวของ\nข้าวจี่\nข้าวเจ้า\nข้าวซอย\nข้าวต้ม\nข้าวตอก\nข้าวตัง\nข้าวแตน\nข้าวทิพย์\nข้าวบิณฑ์\nข้าวเปลือก\nข้าวโพด\nข้าวฟ่าง\nข้าวเม่า\nข้าวสวย\nข้าวสาร\nข้าวเหนียว\nข้าวหมาก\nข้าวหลาม\nขี้เกียจ\nขี้ข้า\nขี้ครอก\nขี้คร้าน\nขี้คุก\nขี้ไคล\nขี้เซา\nขีดขั้น\nขีดคร่อม\nขีดคั่น\nขีดฆ่า\nขี้ตา\nขี้ตืด\nขี้เถ้า\nขี้ทูด\nขี้ปะติ๋ว\nขี้ผึ้ง\nขี้มูก\nขี้ยา\nขี้แย\nขี้ริ้ว\nขี้เรื้อน\nขี้เล็บ\nขี้หู\nขี้หน้า\nขี้เหนียว\nขี้เหล็ก\nขี้เหร่\nขึงขัง\nขึงพืด\nขึ้งโกรธ\nขึ้นใจ\nขึ้นชื่อ\nขึ้นมือ\nขืนใจ\nขื่นขม\nขุดคุ้ย\nขุนทอง\nขุนนาง\nขุนพล\nขุนศึก\nขุนหลวง\nขูดรีด\nเข็มกลัด\nเข็มขัด\nเข้มข้น\nเข้มแข็ง\nเข้มงวด\nเข็มทิศ\nเข็มหมุด\nเข้าขา\nเข้าเค้า\nเข้าใจ\nเข้าชื่อ\nเข้าตัว\nเข้าถึง\nเข้าทรง\nเข้าท่า\nเข้าที\nเข้าเนื้อ\nเข้าเล่ม\nเขียวเสวย\nเขียวหวาน\nแขกเต้า\nแข็งกร้าว\nแข็งกล้า\nแข็งแกร่ง\nแข็งข้อ\nแข็งขัน\nแข่งขัน\nแข็งใจ\nแข็งตัว\nแข็งเมือง\nแข็งแรง\nแขวนลอย\nโขกสับ\nโขยกเขยก\nไขข้อ\nไขควง\nไขมัน\nไข่มุก\nไขว่ห้าง\nไขสันหลัง\nไขสือ\nไข่เค็ม\nไข่เยี่ยวม้า\nไข่หงส์\nไข่เหี้ย\nคงกระพัน\nคงตัว\nคงทน\nคงที่\nคชลักษณ์\nคชสาร\nคชสีห์\nคชราช\nคณิตศาสตร์\nคดเคี้ยว\nคติธรรม\nคติพจน์\nคนกลาง\nคนไข้\nคนใช้\nคนทรง\nคบไฟ\nคบเพลิง\nคบค้า\nคบคิด\nคบหา\nคมคาย\nครบครัน\nครบถ้วน\nครอบครอง\nครอบคลุม\nครอบครัว\nครอบงำ\nครอบจักรวาล\nคริสตกาล\nคริสตจักร\nคริสต์มาส\nคริสต์ศตวรรษ\nคริสต์ศักราช\nคริสตัง\nคริสเตียน\nครุกรรม\nครุภัณฑ์\nครุศาสตร์\nครุฑพ่าห์\nครุ่นคิด\nคลอเคลีย\nคล่องแคล่ว\nคล่องตัว\nคล่องมือ\nคลั่งไคล้\nคลาคล่ำ\nคลาไคล\nคลาดเคลื่อน\nคลาดแคล้ว\nคลี่คลาย\nคลึงเคล้น\nคลึงเคล้า\nคลื่นไส้\nคลื่นเหียน\nคลุกคลี\nคลุกคลาน\nคลุมเครือ\nคลุมโปง\nคลุ้มคลั่ง\nควงสว่าน\nควบคุม\nควบคู่\nควบแน่น\nควันหลง\nความคิด\nความหลัง\nความเห็น\nคอหอย\nคอห่าน\nคอแห้ง\nค่อนขอด\nค่อนแคะ\nค้อนควัก\nคั่งค้าง\nคั่งแค้น\nคัดค้าน\nคัดง้าง\nคัดท้าย\nคัดเลือก\nคันจาม\nคันฉ่อง\nคันฉาย\nคันชัก\nคันชั่ง\nคันไถ\nคันนา\nคันเร่ง\nคับขัน\nคับคั่ง\nคับแค้น\nคับแคบ\nคางทูม\nคางหมู\nค้างคืน\nค้างปี\nคาดคั้น\nคาดเชือก\nคาดโทษ\nคาดหมาย\nคานหาม\nคาบเกี่ยว\nคาบศิลา\nคาบสมุทร\nคำขาด\nคำนำ\nคิดค้น\nคืนดี\nคืนตัว\nคุกเข่า\nคุณค่า\nคุณชาย\nคุณธรรม\nคุณนาย\nคุณภาพ\nคุณลักษณะ\nคุณวุฒิ\nคุณศัพท์\nคุณสมบัติ\nคุณหญิง\nคุณากร\nคุณูปการ\nคุโณปการ\nคุมเชิง\nคุ้มกัน\nคุยเขื่อง\nคุยโต\nคุ้ยเขี่ย\nคุ้มครอง\nคู่กรณี\nคู่กัด\nคู่ขา\nคู่แข่ง\nคู่ครอง\nคู่ควร\nคู่คิด\nคู่คี่\nคู่ใจ\nคู่ชีพ\nคู่ชีวิต\nคู่บารมี\nคู่บุญ\nคู่ปรปักษ์\nคู่ปรับ\nคู่ผสม\nคู่มือ\nคู่รัก\nคู่ลำดับ\nคู่สาย\nคู่หมั้น\nคู่หู\nคู่อริ\nคู่อาฆาต\nเคมีภัณฑ์\nเคยตัว\nเคร่งขรึม\nเคร่งครัด\nเคร่งเครียด\nเครดิตฟองซิเอร์\nเครื่องกล\nเครื่องกัณฑ์\nเครื่องแกง\nเครื่องเขิน\nเครื่องครัว\nเครื่องเคียง\nเครื่องเงิน\nเครื่องจักร\nเครื่องเซ่น\nเครื่องดนตรี\nเครื่องต้น\nเครื่องทุ่นแรง\nเครื่องเทศ\nเครื่องใน\nเครื่องบิน\nเครื่องบูชา\nเครื่องแบบ\nเครื่องประดับ\nเครื่องปรุง\nเครื่องปรุงรส\nเครื่องมือ\nเครื่องยนต์\nเครื่องร่อน\nเครื่องราง\nเครื่องเรือน\nเครื่องล่าง\nเครื่องเล่น\nเครื่องสาย\nเครื่องสำอาง\nเครื่องสุกำศพ\nเครื่องหมาย\nเครือรัฐ\nเคลียคลอ\nเคลื่อนที่\nเคลื่อนไหว\nเคลือบแคลง\nเคลือบแฝง\nเคลือบฟัน\nเคว้งคว้าง\nเคหสถาน\nเค้าโครง\nเคียดแค้น\nเคี่ยวเข็ญ\nเคี้ยวเอื้อง\nเคืองขุ่น\nโคนม\nโคบาล\nโคมูตร\nโคมลอย\nโครงการ\nโครงเรื่อง\nโครงงาน\nโครงสร้าง\nโครมคราม\nโคลงเคลง\nฆ้องกระแต\nฆ้องชัย\nฆ้องวง\nฆ้องหุ่ย\nฆ้องเหม่ง\nฆ้องโหม่ง\nฆาตกร\nฆาตกรรม\nฆานประสาท\nงงงวย\nงงงัน\nงดเว้น\nงบดุล\nงบประมาณ\nงมโข่ง\nงมงาย\nง่วงงุน\nง่วงเหงา\nงอหาย\nง้องอน\nงอนง้อ\nงอมแงม\nงาช้าง\nง่าเงย\nงานการ\nง่ายดาย\nงึมงำ\nเงินเดือน\nเงินตรา\nเงินยวง\nเงียบกริบ\nเงียบเชียบ\nเงียบเหงา\nเงื่องหงอย\nเงื่อนไข\nเงื่อนงำ\nเงื่อนเวลา\nเงื้อมมือ\nแง่งอน\nจงใจ\nจงรัก\nจดจ่อ\nจดจำ\nจดหมาย\nจดหมายเหตุ\nจรจัด\nจรรยาบรรณ\nจริงจัง\nจริงใจ\nจอมขวัญ\nจอมใจ\nจอมทัพ\nจอมปลวก\nจอมพล\nจ๊ะเอ๋\nจักสาน\nจักรพรรดิ\nจักรภพ\nจักรยาน\nจักรยานยนต์\nจักรราศี\nจักรวรรดิ\nจักรวรรดินิยม\nจักรวาล\nจังหนับ\nจัดการ\nจัดจ้าน\nจัดเจน\nจัดแจง\nจัดตั้ง\nจัดสรร\nจับกุม\nจับจด\nจับเจ่า\nจ่าหน้า\nจาตุทสี\nจาตุมหาราช\nจาตุมหาราชิก\nจาตุมหาราชิกา\nจาตุรงคสันนิบาต\nจาตุรราชการ\nจานเชิง\nจานบิน\nจานผี\nจานเสียง\nจาบจ้วง\nจำเป็น\nจำพรรษา\nจำวัด\nจ้ำจี้จ้ำไช\nจำเลาะตา\nจิงโจ้น้ำ\nจิตใจ\nจิตตภาวนา\nจิตตัง\nจิตตานุปัสสนา\nจิตนิยม\nจิตบำบัด\nจิตแพทย์\nจิตวิสัย\nจิตรกร\nจิตรกรรม\nจิตรลดา\nจิตวิทยา\nจิตเวช\nจิตเวชศาสตร์\nจินตกวี\nจินตนา\nจินตนาการ\nจินตภาพ\nจุฑามณี\nจุฑามาศ\nจุฑารัตน์\nจุนเจือ\nจุ้นจ้าน\nจุลชีพ\nจุลชีวัน\nจุลชีวิน\nจุลทรรศน์\nจุลภาค\nจุลวรรค\nจุลศักราช\nจุลสาร\nจุลินทรีย์\nจุฬามณี\nจุฬาลักษณ์\nเจตคติ\nเจตจำนง\nเจตนารมณ์\nเจตภูต\nเจริญพร\nเจ้ากรม\nเจ้ากรรม\nเจ้าของ\nเจ้าขา\nเจ้าข้า\nเจ้าคณะ\nเจ้าค่ะ\nเจ้าจอม\nเจ้าชู้\nเจ้าตัว\nเจ้าถิ่น\nเจ้าท่า\nเจ้าที่\nเจ้าทุกข์\nเจ้านาย\nเจ้าเนื้อ\nเจ้าบ้าน\nเจ้าบ่าว\nเจ้าประคุณ\nเจ้าประคู้น\nเจ้าพนักงาน\nเจ้าพระคุณ\nเจ้าพระยา\nเจ้าพ่อ\nเจ้าพายุ\nเจ้าฟ้า\nเจ้าภาพ\nเจ้ามือ\nเจ้าแม่\nเจ้าเรือน\nเจ้าสังกัด\nเจ้าสัว\nเจ้าสาว\nเจ้าหน้าที่\nเจ้าหนี้\nเจ้าอาวาส\nเจาะจง\nเจือจาง\nเจือจาน\nเจือปน\nเจื้อยแจ้ว\nแจกจ่าย\nแจ่มแจ้ง\nแจ่มใส\nโจงกระเบน\nโจมตี\nโจรกรรม\nโจรสลัด\nใจความ\nใจคอ\nฉกฉวย\nฉกชิง\nฉลองได\nฉ้อฉล\nฉัตรมงคล\nฉันทลักษณ์\nฉายาลักษณ์\nฉิบหาย\nฉุกเฉิน\nฉุกละหุก\nฉุนเฉียว\nฉุปศาสตร์\nเฉไฉ\nเฉยเมย\nเฉาโฉด\nเฉิดฉัน\nเฉิดฉาย\nเฉิดฉิน\nเฉียบขาด\nเฉียบพลัน\nเฉียบแหลม\nเฉื่อยชา\nแฉะแบะ\nโฉดเฉา\nโฉมงาม\nโฉมฉาย\nโฉมเฉลา\nโฉมตรู\nโฉมยง\nโฉมศรี\nโฉมหน้า\nชดช้อย\nชดเชย\nชดใช้\nชนบท\nชนินทร์\nชนกกรรม\nชนมพรรษา\nชนมายุ\nชมเชย\nชมพูทวีป\nชมพูนท\nชมพูนุท\nชราธรรม\nชราภาพ\nชลจร\nชลธาร\nชลธี\nชลนัยน์\nชลนา\nชลเนตร\nชลประทาน\nชลมารค\nชลาธาร\nชลาลัย\nชลาศัย\nชลาสินธุ์\nชโลทร\nช่วงชิง\nช่วงใช้\nชวนชม\nชวนหัว\nช่วยเหลือ\nช่อฟ้า\nช่อม่วง\nชอกช้ำ\nช่องเขา\nช่องแคบ\nช่องไฟ\nช่องว่าง\nช้องนาง\nชอบกล\nชอบใจ\nชอบธรรม\nชอบพอ\nชักโครก\nชักเงา\nชักจูง\nชักชวน\nชักนำ\nชักเนื้อ\nชักพระ\nชักเย่อ\nชักใย\nชั่งใจ\nชังฆวิหาร\nชัดเจน\nชั้นเชิง\nชั่วคน\nชั่วคราว\nชั่วช้า\nชั่วโมง\nชั่วแล่น\nชาเย็น\nช้านาน\nช่างเครื่อง\nช่างฝีมือ\nช่างฟิต\nช่างไฟ\nช้างน้ำ\nช้างเผือก\nช้างพลาย\nช้างพัง\nช้างสาร\nช้างสีดอ\nชาติธรรม\nชาตินิยม\nชาติพันธุ์\nชาติพันธุ์วิทยา\nชาติภูมิ\nชานชาลา\nชายชาตรี\nชายคา\nชายฝั่ง\nชายทะเล\nชาวเล\nชาววัง\nช้ำใจ\nช้ำชอก\nชิงชัง\nชิงพลบ\nชินชา\nชินบุตร\nชิ้นเอก\nชิมลาง\nชีเปลือย\nชี้ขาด\nชี้แจง\nชี้นำ\nชี้แนะ\nชี้ฟ้า\nชีพจร\nชีพิตักษัย\nชื่นชม\nชื่นบาน\nชื่นมื่น\nชื่อย่อ\nชื่อรอง\nชื่อเล่น\nชื่อเสียง\nชุกชุม\nชุติมา\nชุบตัว\nชุบเลี้ยง\nชุมชน\nชุมทาง\nชุมสาย\nชุ่มใจ\nชุ่มชื่น\nชุ่มชื้น\nชุมนุมชน\nชูชีพ\nชูโรง\nชู้สาว\nเชยชม\nเชลยศักดิ์\nเชลยศึก\nเช่าซื้อ\nเช้าตรู่\nเช้ามืด\nเชิงกราน\nเชิงกล\nเชิงชั้น\nเชิงชาย\nเชิงซ้อน\nเชิงเดียว\nเชิงเดี่ยว\nเชิงตะกอน\nเชิงเทิน\nเชิงมุม\nเชิดชู\nเชิงอรรถ\nเชี่ยนหมาก\nเชี่ยวชาญ\nเชื่องช้า\nเชื่อใจ\nเชื่อถือ\nเชื่อฟัง\nเชื่อมือ\nเชื้อชาติ\nเชื้อเพลิง\nเชื้อไฟ\nเชื้อโรค\nเชื้อสาย\nเชื้อเชิญ\nเชื่องช้า\nเชือนแช\nเชื่อวัน\nแช่เย็น\nแช่อิ่ม\nแช่มช้อย\nแช่มชื่น\nโชกโชน\nโชติช่วง\nโชติรส\nใช้สอย\nซบเซา\nซมซาน\nซวนเซ\nซอกซอน\nซอกแซก\nซ่องสุม\nซ่องเสพ\nซ่องแซ่ง\nซ่อนรูป\nซ่อนเร้น\nซ่อนหา\nซ่อนกลิ่น\nซ่อนทราย\nซ่อมแซม\nซักค้าน\nซักซ้อม\nซักไซ้\nซักฟอก\nซักแห้ง\nซังกะตาย\nซังตาย\nซัดเซ\nซัดทอด\nซับซ้อน\nซับใน\nซับพระพักตร์\nซากศพ\nซ่านเซ็น\nซ้ำซ้อน\nซ้ำซาก\nซ้ำเติม\nซ้ำร้าย\nซี่โครง\nซีดเซียว\nซึมกะทือ\nซึมซาบ\nซึมเซา\nซึมทราบ\nซึมเศร้า\nซื่อตรง\nซื่อสัตย์\nซื้อขาย\nซุกซน\nซุกซ่อน\nซุบซิบ\nซู่ซ่า\nเซซัง\nเซ่อซ่า\nแซ่ซ้อง\nโซดาไฟ\nญาณทัสนะ\nญาณวิทยา\nญาณศาสตร์\nญาติกา\nฐานราก\nดกดื่น\nดงดิบ\nดลใจ\nดลบันดาล\nดวงแก้ว\nดวงใจ\nดวงเดือน\nดวงตรา\nดวงตา\nดวงสมร\nดอกจัน\nดอกจิก\nดอกบัว\nดอกเบี้ย\nดอกฟ้า\nดอกไม้\nดอกยาง\nดอกเล็บ\nดอกทอง\nดอกสร้อย\nดองยา\nดักคอ\nดักฟัง\nดังนั้น\nดังนี้\nดังหนึ่ง\nดั้งเดิม\nดัดจริต\nดัดแปลง\nดันทุรัง\nดับขันธ์\nดับจิต\nดับชีพ\nด่าทอ\nด่างทับทิม\nด่างพร้อย\nดาดฟ้า\nดาราศาสตร์\nดาลเดือด\nดาวกระจาย\nดาวเคราะห์\nดาวตก\nดาวเทียม\nดาวรุ่ง\nดาวเรือง\nดาวฤกษ์\nดาวหาง\nดาวเหนือ\nดาษดื่น\nดินขาว\nดินดาน\nดินดำ\nดินประสิว\nดินปืน\nดินระเบิด\nดินสอ\nดินสอพอง\nดิ้นรน\nดิบดี\nดีเกลือ\nดีใจ\nดีซ่าน\nดีดัก\nดีเดือด\nดีฝ่อ\nดีดดิ้น\nดึกดำบรรพ์\nดึกดื่น\nดึงดัน\nดึงดูด\nดื่มด่ำ\nดื้อด้าน\nดื้อดึง\nดื้อแพ่ง\nดื้อยา\nดื้อรั้น\nดุดัน\nดุเดือด\nดุร้าย\nดุลการค้า\nดุลพินิจ\nดุลภาค\nดุลยพินิจ\nดุลยภาพ\nดุษฎีนิพนธ์\nดุษฎีบัณฑิต\nดุษณีภาพ\nดูแคลน\nดูถูก\nดูดาย\nดูเบา\nดูแล\nดูหมิ่น\nดูเหมือน\nดูดดื่ม\nเด็ดขาด\nเด็ดดวง\nเด็ดเดี่ยว\nเดนตาย\nเดาสวด\nเดาสุ่ม\nเดินทาง\nเดินสะพัด\nเดินสาย\nเดินเหิน\nเดิมพัน\nเดียงสา\nเดียดฉันท์\nเดียวกัน\nเดียวดาย\nเดี๋ยวเดียว\nเดี๋ยวนี้\nเดือดดาล\nเดือดร้อน\nเดือนมืด\nเดือนหงาย\nแดดาล\nแดดิ้น\nแดกดัน\nโด่เด่\nโด่งดัง\nโดดเดี่ยว\nโดยสาร\nได้การ\nได้แก่\nได้ใจ\nได้ที\nได้ยิน\nได้เสีย\nตกเขียว\nตกค้าง\nตกใจ\nตกต่ำ\nตกแต่ง\nตกทอด\nตกฟาก\nตกมัน\nตกยาก\nตกลง\nตกหล่น\nต้นขั้ว\nต้นคิด\nต้นฉบับ\nต้นตอ\nต้นตำรับ\nต้นทุน\nต้นแบบ\nต้นเพลิง\nต้นมือ\nต้นไม้\nต้นร่าง\nต้นเรื่อง\nต้นสังกัด\nต้นหน\nต้นเหตุ\nตบตา\nตบแต่ง\nตบแผละ\nตบมือ\nต้มข่า\nต้มโคล้ง\nต้มยำ\nต้มส้ม\nตรมตรอม\nตรรกวิทยา\nตรรกศาสตร์\nตรวจการ\nตรวจการณ์\nตรวจตรา\nตระบัดสัตย์\nตรัสรู้\nตราตั้ง\nตราบาป\nตรายาง\nตราสาร\nตริตรอง\nตรีกฏุก\nตรีกาย\nตรีโกณ\nตรีโกณมิติ\nตรีคูณ\nตรีทูต\nตรีปิฎก\nตรีภพ\nตรีมูรติ\nตรึกตรอง\nตรึงตรา\nตรุษจีน\nตฤณชาติ\nตฤณมัย\nตลกบาตร\nตลบตะแลง\nตลบหลัง\nตลาดนัด\nตลาดน้ำ\nตลาดมืด\nตลาดสด\nต่อตี\nต่อเติม\nต่อว่า\nต่อสู้\nต่อกร\nต่อต้าน\nต่อแย้ง\nต้องการ\nต้องโทษ\nต้องหา\nต้อนรับ\nตอบโต้\nตอบแทน\nต่อยหอย\nตะพาบน้ำ\nตักตวง\nตักบาตร\nตั้งเข็ม\nตั้งไข่\nตั้งเค้า\nตั้งแง่\nตั้งใจ\nตั้งต้น\nตั้งแต่\nตั้งท้อง\nตัดขาด\nตัดใจ\nตัดเชือก\nตัดตอน\nตัดทอน\nตัดบท\nตัดพ้อ\nตัดรอน\nตัดสิน\nตับเต่า\nตับแลบ\nตับอ่อน\nตัวกลาง\nตัวการ\nตัวเก็ง\nตัวดี\nตัวตั้ง\nตัวเต็ง\nตัวถัง\nตัวแทน\nตัวประกอบ\nตัวประกัน\nตัวแปร\nตัวผู้\nตัวพิมพ์\nตัวเมีย\nตัวยืน\nตัวเลข\nตัวอย่าง\nตั๋วเงิน\nตั๋วแลกเงิน\nตากล้อง\nตาไก่\nตาข่าย\nตาชั่ง\nตาตุ่ม\nตาทวด\nตาปลา\nตาราง\nต่างหาก\nต้านทาน\nตามใจ\nตายใจ\nตายซาก\nตายด้าน\nตายตัว\nตายทั้งกลม\nตายห่า\nตายโหง\nตาลปัตร\nต่ำช้า\nต่ำต้อย\nตำส้ม\nติเตียน\nติณชาติ\nติดขัด\nติดใจ\nติดต่อ\nติดตั้ง\nติดตาม\nติดตื้น\nติดพัน\nติดลม\nติดอ่าง\nตีเกลียว\nตีขลุม\nตีความ\nตีคู่\nตีจาก\nตีตื้น\nตีแผ่\nตีรวน\nตีลังกา\nตีวง\nตีเสมอ\nตีนกา\nตีนคู้\nตีนจก\nตีนตะขาบ\nตีนผี\nตีนเหยียด\nตึกแถว\nตึกระฟ้า\nตึงเครียด\nตึงตัง\nตื้นตัน\nตื่นตัว\nตื่นตูม\nตื่นเต้น\nตุ๊ต๊ะ\nตุ้บตั้บ\nตุ้มหู\nตุลาการ\nตุลาคม\nตู้นิรภัย\nตูมตาม\nตู้เสบียง\nเตโชธาตุ\nเตร็ดเตร่\nเต้นรำ\nเตาแก๊ส\nเตาผิง\nเตาฟู่\nเตาไฟ\nเตารีด\nเตาสูบ\nเต่าทอง\nเต้ารับ\nเต้าส่วน\nเต้าเสียบ\nเต้าหู้ยี้\nแต่ละ\nแตกคอ\nแตกคอก\nแตกฉาน\nแตกดับ\nแตกตื่น\nแตกพาน\nแตกแยก\nแตกร้าว\nแตกหัก\nแต่งงาน\nแต่งตั้ง\nแต้มคู\nแต้มต่อ\nแตรงอน\nแตรเดี่ยว\nแตรฝรั่ง\nแตรฟันฟาร์\nแตรวง\nโต้ตอบ\nโต้เถียง\nโต้แย้ง\nโต๊ะหมู่\nโต๊ะอิหม่าม\nใต้ถุน\nไต้ก๋ง\nไต่คู้\nไต่เต้า\nไต่ถาม\nไต้ฝุ่น\nไตรจักร\nไตรจีวร\nไตรตรึงษ์\nไตรทวาร\nไตรปิฎก\nไตรเพท\nไตรภพ\nไตรภูมิ\nไตรภาคี\nไตรยางศ์\nไตรรงค์\nไตรรัตน์\nไตรลักษณ์\nไตรโลก\nไตรสรณคมน์\nไตรสิกขา\nไต่สวน\nถกเถียง\nถดถอย\nถนัดถนี่\nถนิมสร้อย\nถมถืด\nถมเถ\nถมไป\nถลากไถล\nถ้วนถี่\nถ้วยฟู\nถ่องแท้\nถอดถอน\nถ้อยคำ\nถ้อยแถลง\nถากถาง\nถ่านไฟฉาย\nถ่านหิน\nถามไถ่\nถ่ายทอด\nถ่ายทุกข์\nถ่ายเท\nถาวรวัตถุ\nถ้ำมอง\nถี่ถ้วน\nถึงใจ\nถูกใจ\nถูกชะตา\nเถรวาท\nเถ้าแก่\nเถ้าแก่เนี้ย\nแถมพก\nแถลงการณ์\nไถ่ถอน\nไถ่ถาม\nทดแทน\nทดรอง\nทดลอง\nทดสอบ\nทนทาน\nทนายความ\nทบทวน\nทแยงมุม\nทรงกลด\nทรงเครื่อง\nทรงเจ้า\nทรัพย์สิน\nทรามชม\nทรามเชย\nทรามวัย\nทรามสงวน\nทรามสวาท\nทรุดโทรม\nทฤษฎีบท\nท้วงติง\nท่วมท้น\nทวาทศ\nทวาทศมาส\nทวาบรยุค\nทวารบาล\nทวิบถ\nทวิบท\nทวิบาท\nทวิภาค\nทวิภาคี\nทวีคูณ\nทศกัณฐ์\nทศชาติ\nทศทิศ\nทศนิยม\nทศพร\nทศพล\nทศพิธราชธรรม\nทศมาส\nทศวรรษ\nท่อไอเสีย\nท้อถอย\nท้อแท้\nทองขาว\nทองคำ\nทองคำขาว\nทองคำเปลว\nทองเค\nทองแดง\nทองบรอนซ์\nทองม้วน\nทองย้อย\nทองสัมฤทธิ์\nทองหยอด\nทองหยิบ\nทองเหลือง\nทองเอก\nท่องเที่ยว\nท้องตรา\nท้องถิ่น\nท้องที่\nท้องน้อย\nท้องร่อง\nท้องเรื่อง\nทอดมัน\nทอดทิ้ง\nทอดน่อง\nทอดยอด\nทอดหุ่ย\nทอยกอง\nทะเบียนบ้าน\nทะเลทราย\nทะเลสาบ\nทะเลหลวง\nทักขิณาวัฏ\nทักท้วง\nทักทาย\nทักษิณาวรรต\nทักษิณาทาน\nทักษิณานุประทาน\nทั้งกลม\nทั้งคน\nทั้งดุ้น\nทั้งที\nทั้งนั้น\nทั้งนี้\nทั้งปวง\nทั้งผอง\nทั้งเพ\nทั้งมวล\nทั้งสิ้น\nทั้งหมด\nทั้งหลาย\nทัณฑ์บน\nทัดทาน\nทัดเทียม\nทันควัน\nทันใจ\nทันใด\nทันตา\nทันสมัย\nทันที\nทับถม\nทับทรวง\nทับศัพท์\nทั่วถึง\nทั่วไป\nท่าทาง\nท่าที\nท้าทาย\nทางการ\nทางข้าม\nทางด่วน\nทางเท้า\nทางโท\nทางใน\nทางผ่าน\nทางม้าลาย\nทางหลวง\nทางออก\nทางเอก\nทานกัณฑ์\nทานตะวัน\nท่านชาย\nทานบารมี\nท่านผู้หญิง\nท่านหญิง\nทาบทาม\nท้ายทอย\nทารุณกรรม\nทำคลอด\nทำใจ\nทำซ้ำ\nทำท่า\nทำที\nทำแท้ง\nทำโทษ\nทำบาป\nทำบุญ\nทำพิษ\nทำฟัน\nทำร้าย\nทำวัตร\nทำสาว\nทำเสน่ห์\nทำหมัน\nทำให้\nทิ้งขว้าง\nทิ้งทวน\nทิ้งท้าย\nทินกร\nทิพจักขุ\nทิพโสต\nทิพยจักษุ\nทิพยญาณ\nทิพยเนตร\nทิพยรส\nทิพากร\nทิ่มตำ\nทิ่มแทง\nทิวากร\nทิวากาล\nทิศทาง\nทีเด็ด\nทีท่า\nทีนี้\nทีหลัง\nทีฆนิกาย\nทีฆสระ\nที่ดิน\nที่นอน\nที่นั่ง\nที่ปรึกษา\nที่พึ่ง\nที่มั่น\nที่ราบ\nที่ว่าการ\nที่สุด\nที่หมาย\nที่ไหน\nทุกที\nทุกเมื่อ\nทุกข์สุข\nทุนทรัพย์\nทุนนิยม\nทุนรอน\nทุนสำรอง\nทุ่มเถียง\nทุ่มเท\nทูนหัว\nทูลกระหม่อม\nเทกระจาด\nเทครัว\nเทพเจ้า\nเทพดา\nเทพธิดา\nเทพนม\nเทพนิยม\nเทพนิยาย\nเทพบุตร\nเทพสังหรณ์\nเทศกาล\nเทศนาโวหาร\nเทศบัญญัติ\nเทศบาล\nเทศมนตรี\nเทห์ฟากฟ้า\nเท่ากับ\nเท่าใด\nเท่าตัว\nเท่าทัน\nเท่าทุน\nเท่าเทียม\nเท่านั้น\nเท่าไร\nเท้าช้าง\nเทิดทูน\nเที่ยงตรง\nเที่ยงแท้\nเที่ยงธรรม\nเทียนชนวน\nเทียนพรรษา\nเทียบเคียง\nเทียบเท่า\nเทือกเขา\nเทือกเถา\nแท็งก์น้ำ\nแท่นพิมพ์\nแท่นมณฑล\nแท่นหมึก\nแทรกซอน\nแทรกซ้อน\nแทรกซึม\nแทรกแซง\nแทะโลม\nไทยดำ\nไทยทาน\nไทยธรรม\nไทยน้อย\nไทยใหญ่\nธงชัย\nธงชาติ\nธงทิว\nธรณีวิทยา\nธรณีสงฆ์\nธรรมกาย\nธรรมการ\nธรรมเกษตร\nธรรมขันธ์\nธรรมคุณ\nธรรมจรรยา\nธรรมจริยา\nธรรมจักร\nธรรมจักษุ\nธรรมจาคะ\nธรรมจารี\nธรรมชาติ\nธรรมดา\nธรรมเนียม\nธรรมราชา\nธรรมศาสตร์\nธรรมสภา\nธรรมสังเวช\nธัญพืช\nธารพระกร\nธีรภาพ\nธีรราช\nนกเขา\nนกต่อ\nนกยูง\nนกรู้\nนกหวีด\nนครบาล\nนครรัฐ\nนงคราญ\nนงนุช\nนงพะงา\nนงเยาว์\nนงราม\nนงลักษณ์\nนบนอบ\nนพเก้า\nนพคุณ\nนพเคราะห์\nนพปฎล\nนพพล\nนพรัตน์\nนพศก\nนพศูล\nนมข้น\nนมผง\nนมไม้\nนมนาน\nนมหนู\nนมแมว\nนรีเวช\nนรีเวชวิทยา\nนวดฟั้น\nนวยนาด\nนวลระหง\nนวลลออ\nนวลละออง\nนวลจันทร์\nนอกครู\nนอกคอก\nนอกจาก\nนอกใจ\nนอกชาน\nนอกรีต\nนอกเหนือ\nนองเนือง\nนองเลือด\nนอนก้น\nนอนใจ\nนอบนบ\nนอนเล่น\nนอบน้อม\nน้อมนำ\nน้อยใจ\nน้อยหน้า\nนักการ\nนักการเมือง\nนักกีฬา\nนักข่าว\nนักท่องเที่ยว\nนักเทศน์\nนักโทษ\nนักธรรม\nนักบวช\nนักบิน\nนักบุญ\nนักปราชญ์\nนักพรต\nนักรบ\nนักเรียน\nนักเลง\nนักวิชาการ\nนักศึกษา\nนักสิทธิ์\nนักสืบ\nนักหนา\nนั่งเทียน\nนั่งร้าน\nนัดแนะ\nนัดหมาย\nนั่นแหละ\nนั่นเอง\nนับถือ\nนับประสา\nนัยน์ตา\nนาดำ\nนาปรัง\nนาปี\nนาสวน\nนาหว่าน\nนาคบาศ\nนาคปรก\nนาคราช\nนางกวัก\nนางกำนัล\nนางงาม\nนางใน\nนางบำเรอ\nนางแบบ\nนางพญา\nนางฟ้า\nนางไม้\nนางโลม\nนางสาว\nนางห้าม\nนางเอก\nนาฏกรรม\nนาฏดนตรี\nนาฏศิลป์\nนานนม\nน่านน้ำ\nน่านฟ้า\nนามกร\nนามธรรม\nนามไธย\nนามบัตร\nนามปากกา\nนามแฝง\nนามสกุล\nนามสงเคราะห์\nนามสมญา\nนายทะเบียน\nนายท่า\nนายท้าย\nนายทุน\nนายประกัน\nนายหน้า\nนายอำเภอ\nนารายณ์หัตถ์\nนารีผล\nนาวิกโยธิน\nนำจับ\nนำพา\nนำทาง\nนำร่อง\nนำสืบ\nนำแสดง\nน้ำกรด\nน้ำกาม\nน้ำเกลือ\nน้ำข้าว\nน้ำแข็ง\nน้ำแข็งไส\nน้ำแข็งแห้ง\nน้ำครำ\nน้ำคร่ำ\nน้ำค้าง\nน้ำค้างแข็ง\nน้ำคาวปลา\nน้ำคำ\nน้ำเค็ม\nน้ำเคย\nน้ำเงิน\nน้ำเงี้ยว\nน้ำจัณฑ์\nน้ำจิ้ม\nน้ำใจ\nน้ำเชื้อ\nน้ำเชื่อม\nน้ำซาวข้าว\nน้ำดอกไม้\nน้ำดี\nน้ำตก\nน้ำตา\nน้ำตาล\nน้ำท่า\nน้ำนม\nน้ำนวล\nน้ำบาดาล\nน้ำประสานทอง\nน้ำประปา\nน้ำปลา\nน้ำป่า\nน้ำผึ้ง\nน้ำพริก\nน้ำพริกเผา\nน้ำพี้\nน้ำพุ\nน้ำมนต์\nน้ำมนตร์\nน้ำมัน\nน้ำมือ\nน้ำมูก\nน้ำเมา\nน้ำย่อย\nน้ำยา\nน้ำรัก\nน้ำแร่\nน้ำลาย\nน้ำเลี้ยง\nน้ำสต๊อก\nน้ำส้ม\nน้ำส้มสายชู\nน้ำสังข์\nน้ำสาบาน\nน้ำเสียง\nน้ำหนวก\nน้ำหนอง\nน้ำหนัก\nน้ำหน้า\nน้ำหนึ่ง\nน้ำหมึก\nน้ำหอม\nน้ำเหลือง\nน้ำอบ\nน้ำอ้อย\nน้ำอัดลม\nนิ่งเฉย\nนิจศีล\nนิดเดียว\nนิดหน่อย\nนิติกร\nนิติกรรม\nนิติธรรม\nนิตินัย\nนิติบัญญัติ\nนิติบุคคล\nนิติภาวะ\nนิติวิทยาศาสตร์\nนิติเวช\nนิติเวชศาสตร์\nนิติศาสตร์\nนิเทศศาสตร์\nนิ่มนวล\nนิรุกติศาสตร์\nนิเวศวิทยา\nนิศากร\nนิศากาล\nนิศาชล\nนิศารัตน์\nนี่แน่ะ\nนี่แหละ\nนี่เอง\nนึกคิด\nนุงถุง\nนุ่งห่ม\nนุ่มนวล\nนุ่มนิ่ม\nเนตรนารี\nเนติบัณฑิต\nเนยเทียม\nเนยใส\nเนิ่นนาน\nเนิบนาบ\nเนื้อความ\nเนื้อคู่\nเนื้อเค็ม\nเนื้องอก\nเนื้อตัว\nเนื้อตาย\nเนื้อที่\nเนื้อแท้\nเนื้อเปื่อย\nเนื้อผ้า\nเนื้อเพลง\nเนื้อไม้\nเนื้อเยื่อ\nเนื้อร้อง\nเนื้อร้าย\nเนื้อเรื่อง\nเนื้อหา\nเนืองนอง\nเนืองนิตย์\nเนืองแน่น\nแน่ใจ\nแน่ชัด\nแน่แท้\nแน่นอน\nแน่นิ่ง\nแน่แน่ว\nแน่นแฟ้น\nแน่นหนา\nแนบเนียน\nแนบแน่น\nแนวคิด\nแนวทาง\nแนวโน้ม\nแนวป่า\nแนวรบ\nแนวร่วม\nแนวหน้า\nแนวหลัง\nแน่วแน่\nแนะนำ\nแนะแนว\nโน้มน้าว\nในหลวง\nบกพร่อง\nบงกช\nบงการ\nบดบัง\nบทกลอน\nบทกวี\nบทความ\nบทคัดย่อ\nบทเฉพาะกาล\nบทนำ\nบทบัญญัติ\nบทบาท\nบทประพันธ์\nบทเพลง\nบทร้อง\nบทเรียน\nบทลงโทษ\nบทสนทนา\nบทอัศจรรย์\nบทจร\nบทบงสุ์\nบทมาลย์\nบทรัช\nบทเรศ\nบทวลัญช์\nบนบาน\nบรมครู\nบรมธาตุ\nบรมบพิตร\nบรมวงศานุวงศ์\nบรมอัฐิ\nบรรณพิภพ\nบรรณศาลา\nบรรณาการ\nบรรณาธิการ\nบรรณานุกรม\nบรรณารักษ์\nบรรณารักษศาสตร์\nบรรดามี\nบรรดาศักดิ์\nบรรทัดฐาน\nบรรพบุรุษ\nบรรลัยกัลป์\nบรรลัยจักร\nบริคณห์สนธิ\nบวงสรวง\nบ่วงบาศ\nบ้วนพระโอษฐ์\nบ่อเกิด\nบอกกล่าว\nบอกบท\nบอกบุญ\nบอกใบ้\nบอกปัด\nบ้องกัญชา\nบ้องตื้น\nบ้องไฟ\nบ้องหู\nบอดสี\nบ่อนทำลาย\nบอบช้ำ\nบอบบาง\nบอบแบบ\nบังโกลน\nบังโคลน\nบังใบ\nบั้งไฟ\nบังคับการ\nบังคับบัญชา\nบัญชาการ\nบัณฑุกัมพล\nบัดดล\nบัดเดี๋ยว\nบัดนั้น\nบัดนี้\nบัดสีบัดเถลิง\nบัตรเครดิต\nบัตรพลี\nบัตรสนเท่ห์\nบัตรสินเชื่อ\nบั่นทอน\nบั้นท้าย\nบั้นปลาย\nบั้นพระองค์\nบั้นเอว\nบันไดลิง\nบันไดเลื่อน\nบันเทิงคดี\nบัวลอย\nบัวบก\nบ้าจี้\nบ้าดีเดือด\nบ้าน้ำลาย\nบ้าบิ่น\nบ้าระห่ำ\nบ้าเลือด\nบ้าหอบฟาง\nบากบั่น\nบากหน้า\nบางตา\nบางเบา\nบางที\nบาดเจ็บ\nบาดแผล\nบาดหมาง\nบาตรใหญ่\nบาทบงกช\nบาทบงสุ์\nบาทบริจาริกา\nบาทวิถี\nบานเกล็ด\nบานตะเกียง\nบานตะไท\nบานเบอะ\nบานปลาย\nบานแผละ\nบานพับ\nบ้านจัดสรร\nบ้านช่อง\nบ้านนอก\nบ้านพัก\nบ้านเมือง\nบ้านรับรอง\nบ้านเรือน\nบาปกรรม\nบายศรี\nบ่ายเบี่ยง\nบ่ายหน้า\nบ่าวไพร่\nบิดเบี้ยว\nบิดเบือน\nบิดพลิ้ว\nบี้แบน\nบีบคั้น\nบีบรัด\nบึ้งตึง\nบึ้งบูด\nบุกบั่น\nบุกเบิก\nบุกรุก\nบุคลิกภาพ\nบุคลิกลักษณะ\nบุญธรรม\nบุญนิธิ\nบุญฤทธิ์\nบุบสลาย\nบุ้ยใบ้\nบุรุษเพศ\nบุหงารำไป\nบู้บี้\nบูชายัญ\nบูดบึ้ง\nบูดเบี้ยว\nเบาความ\nเบาใจ\nเบาบาง\nเบาปัญญา\nเบามือ\nเบาแรง\nเบาสมอง\nเบาหวาน\nเบาโหวง\nเบ้าตา\nเบาะแส\nเบิกความ\nเบิกบาน\nเบี้ยล่าง\nเบี้ยเลี้ยง\nเบี้ยหวัด\nเบี่ยงบ่าย\nเบียดบัง\nเบียดเบียน\nเบียดเสียด\nเบื้องต้น\nเบื้องบน\nเบื้องหน้า\nเบื้องหลัง\nแบกะดิน\nแบเบาะ\nแบ่งเบา\nแบ่งปัน\nแบ่งแยก\nแบบฉบับ\nแบบแปลน\nแบบแผน\nแบบฝึกหัด\nแบบพิมพ์\nแบบสอบถาม\nแบบอย่าง\nแบะแฉะ\nแบะท่า\nโบแดง\nโบราณคดี\nโบราณวัตถุ\nโบราณสถาน\nใบขับขี่\nใบจอง\nใบตอง\nใบแทรก\nใบบอก\nใบบุญ\nใบเบิกทาง\nใบปลิว\nใบพัด\nใบโพ\nใบไม้\nใบระกา\nใบรับรอง\nใบลา\nใบเลี้ยง\nใบสั่ง\nใบสำคัญ\nใบสุทธิ\nใบเสร็จ\nใบหน้า\nใบอนุญาต\nใบระกา\nปกครอง\nปกคลุม\nปกป้อง\nปกปิด\nปฏิบัติการ\nปฏิบัติบูชา\nปฐพีวิทยา\nปฐมฌาน\nปฐมทัศน์\nปฐมเทศนา\nปฐมนิเทศ\nปฐมพยาบาล\nปฐมยาม\nปฐมฤกษ์\nปฐมวัย\nปฐมสมโพธิ\nปนเป\nป่นปี้\nปมเขื่อง\nปมเด่น\nปมด้อย\nปรนเปรอ\nปรบไก่\nปรบมือ\nปรสิตวิทยา\nประโปรย\nประพรม\nประกันชีวิต\nประกันภัย\nประจักษ์พยาน\nประจัญบาน\nประจันหน้า\nประจำการ\nประจำเดือน\nประจำเมือง\nประจำยาม\nประชดประชัน\nประชากร\nประชากรศาสตร์\nประชาคม\nประชาชน\nประชาราษฎร์\nประชาชาติ\nประชาชี\nประชาทัณฑ์\nประชาบาล\nประชาพิจารณ์\nประชาภิบาล\nประชามติ\nประชาสงเคราะห์\nประชาสัมพันธ์\nประดับประดา\nประดามี\nประดาน้ำ\nประเดี๋ยวเดียว\nประเดี๋ยวนี้\nประทับใจ\nประทุษร้าย\nประเทศราช\nประพาสต้น\nประเพณีนิยม\nประลัยกัลป์\nประวัติการณ์\nประวัติศาสตร์\nประสบการณ์\nประสบการณ์นิยม\nประสาทการ\nประสูติการ\nประสูติกาล\nประเส\nปรับทุกข์\nปรับโทษ\nปรับปรุง\nปรากฏการณ์\nปราดเปรียว\nปราดเปรื่อง\nปราบปราม\nปริญญาบัตร\nปรัยัติธรรม\nปรุโปร่ง\nปลงใจ\nปลงตก\nปลดทุกข์\nปลดปลง\nปลดปล่อย\nปลดเปลื้อง\nปลดระวาง\nปลดแอก\nปล้นสะดม\nปลอกกระสุน\nปลอกคอ\nปลอดโปร่ง\nปลอดภัย\nปลอมปน\nปลอมแปลง\nปลอบโยน\nปล่อยใจ\nปล่อยตัว\nปล่อยปละ\nปลั๊กไฟ\nปลากริม\nปลาเค็ม\nปลาจ่อม\nปลาเจ่า\nปลาแดก\nปลาตู้\nปลาทอง\nปลาร้า\nปลาส้ม\nปลาดาว\nปลาบิน\nปลาฝา\nปลาวาฬ\nปลาหมึก\nปลาบปลื้ม\nปลายข้าว\nปลายแถว\nปลายทาง\nปลิ้นปลอก\nปลิ้นปล้อน\nปลีกตัว\nปลีกย่อย\nปลุกใจ\nปลุกปล้ำ\nปลุกปั่น\nปลุกระดม\nปลุกเสก\nปลูกฝัง\nปลูกสร้าง\nปวดถ่วง\nปวดมวน\nปวดร้าว\nป่วนปั่น\nป่วยการ\nปอกลอก\nป้องกัน\nปักใจ\nปักดำ\nปักหลัก\nปัจเจกบุคคล\nปัจเจกพุทธะ\nปัจเจกโพธิ\nปัจฉิมชน\nปัจฉิมทิศ\nปัจฉิมภาค\nปัจฉิมยาม\nปัจฉิมลิขิต\nปัจฉิมวัย\nปัจฉิมวาจา\nปัญญาชน\nปัญญาวิมุติ\nปัญญาอ่อน\nปัดเป่า\nปันส่วน\nปั่นป่วน\nปั่นแปะ\nปั่นหัว\nปั้นจิ้ม\nปั้นเจ๋อ\nปั้นปึ่ง\nปั้นสิบ\nปั๊มน้ำมัน\nป่าช้า\nป่าชายเลน\nป่าดง\nป่าดงดิบ\nป่าดิบ\nป่าเถื่อน\nป่าเบญจพรรณ\nป่าละเมาะ\nปากกา\nปากขอ\nปากแข็ง\nปากคอ\nปากคำ\nปากคีบ\nปากจัด\nปากน้ำ\nปากเปล่า\nปากเสียง\nปานกลาง\nป่านนี้\nป้านลม\nป้ายสี\nป่าวร้อง\nปิดฉาก\nปิดบัง\nปิตุฆาต\nปิตุภูมิ\nปีมะโว้\nปีแสง\nปี่กลาง\nปี่ไฉน\nปี่ชวา\nปี่นอก\nปี่ใน\nปี่พาทย์\nปี่อ้อ\nปีกกา\nปีนเกลียว\nปีนป่าย\nปึกแผ่น\nปึงปัง\nปืนกล\nปืนครก\nปืนพก\nปืนยา\nปืนยาว\nปืนลม\nปืนเล็ก\nปืนเล็กยาว\nปืนสั้น\nปืนใหญ่\nปุบปับ\nปุ๊บปั๊บ\nปุ่มเปือก\nปุยฝ้าย\nปุ๋ยคอก\nปุ๋ยเคมี\nปุ๋ยวิทยาศาสตร์\nปุ๋ยหมัก\nปุ๋ยอินทรีย์\nปูจ๋า\nปูเสฉวน\nปู่เจ้า\nปู่ทวด\nปูนขาว\nปูนซีเมนต์\nปูนดิบ\nปูนแดง\nปูนปลาสเตอร์\nปูนปั้น\nเป็ดเทศ\nเป็ดน้ำ\nเป็นกลาง\nเป็นใจ\nเป็นต้น\nเป็นต่อ\nเป็นรอง\nเป็นไร\nเป็นลม\nเป็นห่วง\nเป็นอยู่\nเปรมปรีดิ์\nเปรอะเปื้อน\nเปรียบเทียบ\nเปรียบเปรย\nเปรี้ยวปาก\nเปรี้ยวหวาน\nเปรื่องปราด\nเปลญวน\nเปล่งปลั่ง\nเปล่าดาย\nเปล่าเปลี่ยว\nเปลี่ยนใจ\nเปลี่ยนตัว\nเปลี่ยนแปลง\nเปลี่ยนมือ\nเปลี่ยนหน้า\nเป๋อเหลอ\nเปะปะ\nเป่ากบ\nเป้านิ่ง\nเป้าหมาย\nเปิดฉาก\nเปิดเปิง\nเปิดโปง\nเปิดผนึก\nเปิดเผย\nเปียกปูน\nแป้งสาลี\nแป้งนวล\nแป้งเปียก\nแป้งมัน\nแป้งฝุ่น\nแป้งร่ำ\nแป้งสิงคโปร์\nแป้งหมี่\nแปดปน\nแปดเปื้อน\nแปรปรวน\nแปรผัน\nแปรพักตร์\nแปรรูป\nแปรอักษร\nแปลกปลอม\nแปะโป้ง\nโป้ปด\nโปร่งแสง\nโปร่งใส\nโปรดปราน\nโปรยทาน\nโปรยปราย\nโปโลน้ำ\nผกผัน\nผกากรอง\nผงขาว\nผงชูรส\nผงซักฟอก\nผงฟู\nผดุงครรภ์\nผมไฟ\nผลพลอยได้\nผลลัพธ์\nผลัดเปลี่ยน\nผลิตผล\nผลิตภัณฑ์\nผลุบโผล่\nผสมเทียม\nผสมผสาน\nผสมผเส\nผสมพันธุ์\nผสมโรง\nผสมเสร็จ\nผ่องแผ้ว\nผ่องใส\nผ่อนคลาย\nผ่อนชำระ\nผ่อนปรน\nผ่อนผัน\nผ่อนส่ง\nผอมโซ\nผอมแห้ง\nผักชี\nผักตบชวา\nผักบุ้ง\nผังเมือง\nผัดผ่อน\nผันแปร\nผันผวน\nผ่าตัด\nผ่าเผย\nผ่าหมาก\nผ่าเหล่า\nผ้าขนหนู\nผ้าขาวม้า\nผ้าขี้ริ้ว\nผ้าเช็ดตัว\nผ้าเช็ดปาก\nผ้าเช็ดมือ\nผ้าเช็ดหน้า\nผ้าดิบ\nผ้าต่วน\nผ้าไตร\nผ้าถุง\nผ้าแถบ\nผ้านวม\nผ้านุ่ง\nผ้าใบ\nผ้าป่า\nผ้าป่าน\nผ้าผ่อน\nผ้าพันคอ\nผ้าพันแผล\nผ้าแพร\nผ้าโพกหัว\nผ้ามัดหมี่\nผ้ายาง\nผ้าลูกไม้\nผ้าเหลือง\nผ้าอนามัย\nผ้าอ้อม\nผาดโผน\nผาติกรรม\nผิดหวัง\nผิวเผิน\nผิวพรรณ\nผิวหนัง\nผีกระสือ\nผีกระหัง\nผีกองกอย\nผีโขมด\nผีดิบ\nผีตองเหลือง\nผีถ้วยแก้ว\nผีแถน\nผีทะเล\nผีบุญ\nผีปอบ\nผีพุ่งไต้\nผีฟ้า\nผีเรือน\nผีสาง\nผีเสื้อ\nผีห่า\nผึ่งผาย\nผุดผ่อง\nผุดผาด\nผู้คน\nผู้คุม\nผู้จัดการ\nผู้ชาย\nผู้เชี่ยวชาญ\nผู้ดี\nผู้โดยสาร\nผู้ต้องขัง\nผู้ต้องหา\nผู้แทน\nผู้น้อย\nผู้บริโภค\nผู้บังคับบัญชา\nผู้ปกครอง\nผู้ประกอบการ\nผู้ป่วย\nผู้พิพากษา\nผู้เยาว์\nผู้ร้าย\nผู้วิเศษ\nผู้สื่อข่าว\nผู้เสียหาย\nผู้หญิง\nผู้ใหญ่\nผู้ใหญ่บ้าน\nผูกขวัญ\nผูกขาด\nผูกพัน\nผูกมัด\nเผชิญหน้า\nเผด็จการ\nเผด็จศึก\nเผยแผ่\nเผยแพร่\nเผละผละ\nเผ่าพันธุ์\nเผื่อแผ่\nแผงลอย\nแผนการ\nแผนงาน\nแผนที่\nแผนผัง\nแผนภาพ\nแผนภูมิ\nแผ่นดิน\nแผ่นเสียง\nแผ้วพาน\nโผงผาง\nฝนทอง\nฝอยทอง\nฝักแค\nฝักบัว\nฝักฝ่าย\nฝักใฝ่\nฝังใจ\nฝังหัว\nฝาชี\nฝาแฝด\nฝาละมี\nฝ่าพระบาท\nฝ่าฝืน\nฝ่าฟัน\nฝ้าฟาง\nฝากตัว\nฝากฝัง\nฝีดาษ\nฝีมะม่วง\nฝีจักร\nฝีเท้า\nฝีปาก\nฝีพาย\nฝีมือ\nฝีเย็บ\nฝึกงาน\nฝึกปรือ\nฝึกฝน\nฝึกสอน\nฝึกหัด\nฝืดเคือง\nใฝ่ฝัน\nพงพี\nพงศ์พันธุ์\nพญาโศก\nพญาไฟ\nพบปะ\nพบพาน\nพรสวรรค์\nพรมคด\nพรมแดน\nพรมมิ\nพรรคพวก\nพรรณราย\nพรวดพราด\nพรหมชาติ\nพรหมลิขิต\nพรหมโลก\nพรหมวิหาร\nพร้อมใจ\nพร้อมพรั่ง\nพร้อมเพรียง\nพร้อมมูล\nพร้อมสรรพ\nพร้อมหน้า\nพระครู\nพระคุณ\nพระเคราะห์\nพระเครื่อง\nพระเจ้า\nพระเจ้าอยู่หัว\nพระชายา\nพระทัย\nพระนาง\nพระนางเจ้า\nพระเป็นเจ้า\nพระผู้เป็นเจ้า\nพระพิมพ์\nพระพุทธเจ้า\nพระพุทธองค์\nพระภูมิ\nพระยา\nพระรอง\nพระสนม\nพระสนมเอก\nพระองค์\nพระองค์เจ้า\nพระเอก\nพรั่งพร้อม\nพรั่งพรู\nพรั่นพรึง\nพร่างพราว\nพรายน้ำ\nพรายแพรว\nพราวแพรว\nพร่ำพลอด\nพร่ำเพรื่อ\nพร่ำเพ้อ\nพริกไทย\nพริ้งพราย\nพริ้งเพรา\nพริ้งเพริศ\nพริบตา\nพริ้มพราย\nพริ้มเพรา\nพรุ่งนี้\nพฤติกรรม\nพฤติการณ์\nพฤตินัย\nพลการ\nพลขับ\nพลความ\nพลเมือง\nพลรบ\nพลร่ม\nพลเรือน\nพลโลก\nพลศึกษา\nพลบค่ำ\nพลอดรัก\nพลังงาน\nพลังเงียบ\nพลังจิต\nพลั้งปาก\nพลั้งเผลอ\nพลั้งพลาด\nพลัดถิ่น\nพลัดพราก\nพลาดท่า\nพลาดพลั้ง\nพลิกแพลง\nพลีกรรม\nพลุ่งพล่าน\nพวกพ้อง\nพวงมาลัย\nพวงมาลา\nพวงหรีด\nพวงคราม\nพวงชมพู\nพวงแสด\nพ่วงพี\nพวยน้ำ\nพวยพุ่ง\nพสกนิกร\nพหุคูณ\nพหุภาคี\nพหูพจน์\nพหูสูต\nพอควร\nพอใจ\nพอใช้\nพอใช้ได้\nพอดี\nพอตัว\nพอทำเนา\nพอประมาณ\nพอเพียง\nพอแรง\nพอสมควร\nพอเหมาะ\nพ่อขุน\nพ่อครัว\nพ่อตา\nพ่อบ้าน\nพ่อพันธุ์\nพ่อม่าย\nพ่อเมือง\nพ่อเลี้ยง\nพ่อสื่อ\nพอกพูน\nพ้องพาน\nพักผ่อน\nพักพิง\nพักฟื้น\nพักร้อน\nพักแรม\nพัดยศ\nพัดลม\nพันพัว\nพับฐาน\nพับเพียบ\nพัวพัน\nพาซื่อ\nพาดพิง\nพิณพาทย์\nพิธีกร\nพิธีกรรม\nพิธีการ\nพิธีรีตอง\nพิธีสาร\nพินัยกรรม\nพิมพ์เขียว\nพิมพ์ใจ\nพิมพ์ดีด\nพิษสง\nพี่น้อง\nพี่เบิ้ม\nพี่เลี้ยง\nพึงใจ\nพึงพอใจ\nพึ่งพา\nพึ่งพิง\nพืชพันธุ์\nพืชมงคล\nพื้นฐาน\nพื้นที่\nพื้นบ้าน\nพื้นเพ\nพื้นเมือง\nพื้นเสีย\nพุพอง\nพุทธกาล\nพุทธคุณ\nพุทธจักร\nพุทธเจดีย์\nพุทธฎีกา\nพุทธปฏิมา\nพุทธปฏิมากร\nพุทธมามกะ\nพุทธศักราช\nพุทธศาสนิกชน\nพุทธองค์\nพุทธชาด\nพุทธรักษา\nพุ่มพวง\nพุ่มไม้\nพู่กัน\nพูดจา\nเพ่งเล็ง\nเพดานบิน\nเพดานปาก\nเพริศพราย\nเพริศพริ้ง\nเพริศแพร้ว\nเพรียกพร้อง\nเพรียวลม\nเพลงเชิด\nเพลงยาว\nเพลิงกัลป์\nเพลินใจ\nเพลินตา\nเพลี่ยงพล้ำ\nเพ้อฝัน\nเพาะกาย\nเพาะชำ\nเพาะปลูก\nเพิกถอน\nเพิกเฉย\nเพิ่มเติม\nเพิ่มพูน\nเพียงตา\nเพียงพอ\nเพียบแประ\nเพียบพร้อม\nเพื่อนเกลอ\nเพื่อนตาย\nเพื่อนบ้าน\nเพื่อนฝูง\nเพื่อนยาก\nแพ้ท้อง\nแพร่หลาย\nแพร่งพราย\nแพรวพราว\nโพธิญาณ\nโพธิบัลลังก์\nโพธิสมภาร\nโพธิสัตว์\nโพ้นทะเล\nโพยภัย\nไพ่ตาย\nไพ่ป๊อก\nไพรวัน\nไพรสณฑ์\nไพรสัณฑ์\nไพร่พล\nไพร่ฟ้า\nไพร่สม\nไพร่ส่วย\nไพร่หลวง\nฟกช้ำ\nฟองเต้าหู้\nฟองน้ำ\nฟองมัน\nฟ้องกลับ\nฟ้องร้อง\nฟอนเฟะ\nฟักทอง\nฟัดเฟียด\nฟันดาบ\nฟันฝ่า\nฟันแท้\nฟันน้ำนม\nฟันปลา\nฟันฟาง\nฟันเฟือง\nฟันม้า\nฟันเลื่อย\nฟันหนู\nฟั่นเฝือ\nฟั่นเฟือน\nฟื้นตัว\nฟื้นฝอย\nฟื้นฟู\nฟุ้งซ่าน\nฟุ้งเฟ้อ\nฟุ้งเฟื่อง\nฟุตบอล\nฟูฟ่อง\nฟูเฟื่อง\nฟูมฟัก\nฟูมฟาย\nเฟะฟะ\nเฟื่องฟ้า\nเฟื่องฟุ้ง\nเฟื่องฟู\nไฟฉาย\nไฟแช็ก\nไฟธาตุ\nไฟฟ้า\nภัตกิจ\nภาคทัณฑ์\nภาคพื้น\nภาคเรียน\nภาคภูมิ\nภาพถ่าย\nภาพนิ่ง\nภาพประกอบ\nภาพพจน์\nภาพยนตร์\nภาพลวงตา\nภาพลักษณ์\nภายนอก\nภายใน\nภายหน้า\nภายหลัง\nภารกิจ\nภารธุระ\nภารโรง\nภารตวิทยา\nภาษาศาสตร์\nภาสกร\nภิญโญภาพ\nภินชาติ\nภูธร\nภูธเรศ\nภูบาล\nภูเบศ\nภูเบศวร์\nภูเขา\nภูเขาไฟ\nภูผา\nภูตคาม\nภูตบดี\nภูตรูป\nภูเตศวร\nภูมินทร์\nภูมิบาล\nภูมิประเทศ\nภูมิภาค\nภูมิรัฐศาสตร์\nภูมิลำเนา\nภูมิศาสตร์\nภูมิอากาศ\nภูมิธรรม\nภูมิปัญญา\nภูมิรู้\nภูมิใจ\nภูมิฐาน\nภูมิคุ้มกัน\nภูมิแพ้\nภูษาโยง\nเภทภัย\nเภสัชกร\nเภสัชกรรม\nเภสัชวิทยา\nเภสัชศาสตร์\nโภคทรัพย์\nโภคภัณฑ์\nโภชนากร\nโภชนาการ\nมกุฎราชกุมาร\nมงคลแฝด\nมงคลสูตร\nมงคลหัตถี\nมณเฑียรบาล\nมดดำ\nมดแดง\nมดเท็จ\nมดยอบ\nมดลูก\nมธุปายาส\nมธุรส\nมนเทียรบาล\nมนุษย์กบ\nมโนกรรม\nมโนคติ\nมโนทุจริต\nมโนธรรม\nมโนภาพ\nมโนมัย\nมโนรถ\nมโนรมย์\nมโนสุจริต\nมรรคนายก\nมรรคผล\nมฤคชาติ\nมฤคทายวัน\nมฤคราช\nมลทิน\nมลพิษ\nมลสาร\nมวกเหล็ก\nม้วนหน้า\nมวยไทย\nมวยปล้ำ\nมวยล้ม\nมวยวัด\nมวยสากล\nมวยหมู่\nมวลสาร\nมอคราม\nมอซอ\nมอหมึก\nมองเมียง\nมอบตัว\nมอบหมาย\nมอมเมา\nมะขามเทศ\nมะขามป้อม\nมะขามเปียก\nมะเขือเทศ\nมะเขือพวง\nมะพร้าวแก้ว\nมักคุ้น\nมักจี่\nมักง่าย\nมักน้อย\nมักมาก\nมักใหญ่\nมั่งคั่ง\nมั่งมี\nมัจจุราช\nมัชฌิมนิกาย\nมัชฌิมประเทศ\nมัชฌิมยาม\nมัชฌิมวัย\nมัดจำ\nมัดหมี่\nมัธยมกาล\nมัธยมศึกษา\nมันแกว\nมันเทศ\nมันฝรั่ง\nมันเปลว\nมันสมอง\nมั่นคง\nมั่นใจ\nมั่นหมาย\nมั่นเหมาะ\nมัวเมา\nมัวหมอง\nมั่วสุม\nม้าเทศ\nม้าน้ำ\nม้ามืด\nม้าเร็ว\nม้าล่อ\nม้าลาย\nมากมาย\nมาตรการ\nมาตรฐาน\nมาตราส่วน\nมาตุคาม\nมาตุฆาต\nมาตุภูมิ\nม่านตา\nม่านบังตา\nมายากร\nมายากล\nมายาการ\nมายาวี\nมารผจญ\nมารวิชัย\nมารสังคม\nมารหัวขน\nมาลาการ\nมิ่งขวัญ\nมิ่งมิตร\nมิจฉาจาร\nมิจฉาชีพ\nมิดชิด\nมิดเมี้ยน\nมิดหมี\nมิตรจิต\nมิตรภาพ\nมิตรสหาย\nมิน่า\nมีหน้า\nมีดโกน\nมีดดาบ\nมีดโต้\nมีดพก\nมีดพับ\nมีดสั้น\nมึนงง\nมึนชา\nมึนตึง\nมึนเมา\nมืดครึ้ม\nมืดมน\nมืดมัว\nมือจับ\nมือดี\nมือเติบ\nมือปืน\nมือเปล่า\nมือมืด\nมือสอง\nมือเสือ\nมือหนึ่ง\nมือใหม่\nมุกตลก\nมุขปาฐะ\nมุขมนตรี\nมุ่งมั่น\nมุ่งมาด\nมุ่งหน้า\nมุ่งหมาย\nมุ่งหวัง\nมุ้งลวด\nมุ้งสายบัว\nมุมก้ม\nมุมกลับ\nมุมเงย\nมุมฉาก\nมุมตรง\nมุมป้าน\nมุมมืด\nมุมแย้ง\nมุมสะท้อน\nมุมหักเห\nมุมแหลม\nมุสาวาท\nมูกเลือด\nมูกมัน\nมูกหลวง\nมูนดิน\nมูลฐาน\nมูลนาย\nมูลนิธิ\nมูลเหตุ\nมูลค่า\nมูลฝอย\nเม็ดเงิน\nเม็ดเลือด\nเม็ดโลหิต\nเม่นทะเล\nเมรุมาศ\nเมรุราช\nเมล์อากาศ\nเมาดิบ\nเมามัน\nเมามัว\nเมามาย\nเมินเฉย\nเมียน้อย\nเมียหลวง\nเมียงมอง\nเมี่ยงลาว\nเมี่ยงส้ม\nเมื่อกี้\nเมื่อตะกี้\nเมื่อใด\nเมื่อไร\nเมื่อไหร่\nเมื่อนั้น\nเมืองขึ้น\nเมืองท่า\nเมืองนอก\nเมืองหลวง\nเมื่อยขบ\nเมื่อยล้า\nแม่กอง\nแม่กุญแจ\nแม่คุณ\nแม่งาน\nแม่เจ้า\nแม่ชี\nแม่ทัพ\nแม่นม\nแม่น้ำ\nแม่บท\nแม่บ้าน\nแม่เบี้ย\nแม่พระ\nแม่พิมพ์\nแม่เพลง\nแม่มด\nแม่ม่าย\nแม่ไม้\nแม่ยก\nแม่ยาย\nแม่ร้าง\nแม่เรือน\nแม่แรง\nแม่เล้า\nแม่เลี้ยง\nแม่สี\nแม่สื่อ\nแม่เหล็ก\nแมงมุม\nแม่นยำ\nแมลงช้าง\nแมลงวัน\nแมลงปอ\nแมลงภู่\nแมลงเม่า\nแมวเซา\nแมวน้ำ\nแมวป่า\nแมวมอง\nไม้กลัด\nไม้กวาด\nไม้กางเขน\nไม้เกาหลัง\nไม้ขีดไฟ\nไม้จิ้มฟัน\nไม้เด็ด\nไม้ตาย\nไม้ตีพริก\nไม้ที\nไม้เท้า\nไม้บรรทัด\nไม้เมตร\nไม้ระแนง\nไม้เรียว\nไม้หมอน\nไม้อัด\nไม้จัตวา\nไม้ตรี\nไม้ไต่คู้\nไม้โท\nไม้ผัด\nไม้มลาย\nไม้ม้วน\nไม้ยมก\nไม้หน้า\nไม้หันอากาศ\nไม้เอก\nยกกลีบ\nยกครู\nยกเครื่อง\nยกเค้า\nยกทรง\nยกฟ้อง\nยกเมฆ\nยกยอ\nยกย่อง\nยกเลิก\nยกเว้น\nย่นย่อ\nยมทูต\nยมบาล\nยมราช\nยมโลก\nยวดยิ่ง\nยวดยาน\nยวนยี\nยวบยาบ\nย่อท้อ\nย่อส่วน\nย่อหน้า\nย่อหย่อน\nยอกย้อน\nยองใย\nย่องเบา\nย่องแย่ง\nยอดเยี่ยม\nยอดอก\nย้อนยอก\nย้อนรอย\nย้อนศร\nย้อนแสง\nย้อนหลัง\nยอบแยบ\nยอมความ\nย่อมเยา\nย่อยยับ\nยักยอก\nยักย้าย\nยักเยื้อง\nยัญกรรม\nยัญพิธี\nยัดเยียด\nยับเยิน\nยับยั้ง\nยั่วยวน\nยั่วยุ\nยั่วเย้า\nยากวาด\nยากันยุง\nยาเขียว\nยาใจ\nยาฉุน\nยาชา\nยาซัด\nยาดอง\nยาแดง\nยาถ่าย\nยาธาตุ\nยานัตถุ์\nยาเบื่อ\nยาโป๊\nยาแฝด\nยาพิษ\nยาระบาย\nยาสลบ\nยาสั่ง\nยาสีฟัน\nยาสูบ\nยาเส้น\nยาเสพติด\nยาหม่อง\nยาเหลือง\nย่าทวด\nย่านาง\nยากแค้น\nยากจน\nยากเย็น\nยากไร้\nยางนอก\nยางใน\nยางมะตอย\nยางมะตูม\nยางลบ\nยางสน\nยางอาย\nย่างกราย\nย่างเยื้อง\nย่างสด\nย่างสามขุม\nย่างเหยียบ\nยานเกราะ\nยานพาหนะ\nยานอวกาศ\nยานคาง\nยายทวด\nยาวเฟื้อย\nยาวยืด\nยาวเหยียด\nยำทวาย\nยำใหญ่\nยำเกรง\nยำเยง\nย่ำต๊อก\nย่ำยี\nย่ำแย่\nยิงเป้า\nยิ่งนัก\nยิ่งยวด\nยิ่งใหญ่\nยินดี\nยินยอม\nยินร้าย\nยิ้มกริ่ม\nยิ้มแฉ่ง\nยิ้มแต้\nยิ้มแป้น\nยิ้มเผล่\nยิ้มเยาะ\nยิ้มแย้ม\nยียวน\nยึดครอง\nยึดถือ\nยึดมั่น\nยึดเหนี่ยว\nยืดยาด\nยืดยาว\nยืดเยื้อ\nยืดหยุ่น\nยืดอก\nยืนกราน\nยืนต้น\nยืนพื้น\nยืนยง\nยืนยัน\nยืนหยัด\nยื้อยุด\nยุยง\nยุแยง\nยุแหย่\nยุคลบาท\nยุคเข็ญ\nยุคทอง\nยุคมืด\nยุ่งขิง\nยุ่งยาก\nยุ่งเหยิง\nยุติธรรม\nยุทธการ\nยุทธนาวี\nยุทธปัจจัย\nยุทธภัณฑ์\nยุทธภูมิ\nยุทธวิธี\nยุทธศาสตร์\nยุทธหัตถี\nยุทธนาการ\nยุทธนาธิการ\nยุบยับ\nยุบยิบ\nยุพราช\nยู่ยี่\nเย็นเจี๊ยบ\nเย็นใจ\nเย็นฉ่ำ\nเย็นเฉียบ\nเย็นชา\nเย็นชืด\nเย็นตา\nเย็นเยียบ\nเย็นเยือก\nเย็นวาบ\nเย็นวูบ\nเย็บกี่\nเย็บจักร\nเย็บด้าย\nเย้ยหยัน\nเย้าหยอก\nเยาะเย้ย\nเยี่ยมกราย\nเยี่ยมเยียน\nเยี่ยมเยือน\nเยี่ยมยอด\nเยื่อเคย\nเยื่อใย\nเยือกเย็น\nเยื้องกราย\nเยื้องยัก\nเยื้องย่าง\nแยกย้าย\nแยกแยะ\nแย่งชิง\nแยบคาย\nแยบยล\nแย้มพราย\nแย้มยิ้ม\nแย้มสรวล\nโยเย\nโย้เย้\nโยกโคลง\nโยกย้าย\nโยกโย้\nโยนกลอง\nใยหิน\nรกชัฏ\nรกร้าง\nรกเรี้ยว\nรกเรื้อ\nรกราก\nรงควัตถุ\nรชนีกร\nรถกระบะ\nรถเก๋ง\nรถเข็น\nรถแข่ง\nรถจักร\nรถจี๊ป\nรถตู้\nรถทัวร์\nรถบรรทุก\nรถพ่วง\nรถพยาบาล\nรถไฟ\nรถไฟฟ้า\nรถม้า\nรถเมล์\nรถยนต์\nรถราง\nรถลาก\nรถสปอร์ต\nรถสิบล้อ\nรบกวน\nรบรา\nรบเร้า\nรมดำ\nร่มเกล้า\nร่มชูชีพ\nร่มเย็น\nร่มรื่น\nร่วงโรย\nรวงผึ้ง\nรวงรัง\nรวดเร็ว\nรวนเร\nรวบยอด\nรวบรวม\nรวบรัด\nรวมพล\nรวมหัว\nร่วมใจ\nร่วมเพศ\nร่วมมือ\nร่วมรัก\nร่วมสมัย\nรวยริน\nรวยรื่น\nรสชาติ\nรสนิยม\nรองท้อง\nรองเท้า\nรองพื้น\nร่องน้ำ\nร่องรอย\nร้องขอ\nร้องทุกข์\nร้องเรียน\nร้องห่ม\nร้องไห้\nรองช้ำ\nรองทรง\nรอดชีวิต\nรอดตัว\nรอดตาย\nรอนแรม\nร่อนเร่\nร้อนใจ\nร้อนตัว\nร้อนรน\nร้อนรุ่ม\nร้อนวิชา\nร้อนอาสน์\nรอบจัด\nรอบเดือน\nรอบรู้\nรอยร้าว\nร่อยหรอ\nร้อยละ\nร้อยกรอง\nร้อยแก้ว\nร้อยหวาย\nระนาดทุ้ม\nระนาดเอก\nระเบิดขวด\nระเบิดมือ\nระเบียบการ\nรักใคร่\nรักษาการ\nรักษาการณ์\nรังไข่\nรังแตน\nรังนก\nรังผึ้ง\nรังเพลิง\nรังมด\nรังสรรค์\nรังสฤษฏ์\nรั้งรอ\nรังสีแพทย์\nรังสีวิทยา\nรัชกาล\nรัชทายาท\nรัชนีกร\nรัฐธรรมนูญ\nรัฐบาล\nรัฐบุรุษ\nรัฐประศาสน์\nรัฐประหาร\nรัฐพิธี\nรัฐมนตรี\nรัฐวิสาหกิจ\nรัฐศาสตร์\nรัฐสภา\nรัดกุม\nรัดเกล้า\nรัดตัว\nรัดประคด\nรัดรึง\nรัดรูป\nรัตติกาล\nรับขวัญ\nรับจ้าง\nรับช่วง\nรับใช้\nรับซื้อ\nรับทราบ\nรับประกัน\nรับประทาน\nรับปาก\nรับผิด\nรับผิดชอบ\nรับฟ้อง\nรับฟัง\nรับมือ\nรับรอง\nรับรู้\nรับสมัคร\nรับสั่ง\nรับหน้า\nรับเหมา\nรั่วไหล\nรามือ\nร่าเริง\nรากแก้ว\nรากขวัญ\nรากฐาน\nรากฟัน\nรากศัพท์\nรากเหง้า\nร่างกาย\nร่างแห\nร้างรา\nราชกรณียกิจ\nราชการ\nราชกิจ\nราชครู\nราชฐาน\nราชทัณฑ์\nราชทินนาม\nราชทูต\nราชธานี\nราชนาวี\nราชบัณฑิต\nราชบัลลังก์\nราชบาตร\nราชบุตร\nราชปะแตน\nราชภัฏ\nราชมัล\nราชยาน\nราชรถ\nราชลัญจกร\nราชเลขาธิการ\nราชเลขานุการ\nราชวงศ์\nราชวัติ\nราชสกุล\nราชสมบัติ\nราชสาส์น\nราชหัตถเลขา\nราชองครักษ์\nราชโองการ\nราชาคณะ\nราชาศัพท์\nราชินีนาถ\nร้านชำ\nร้านรวง\nราบคาบ\nราบรื่น\nราบเรียบ\nรายการ\nรายงาน\nรายจ่าย\nรายได้\nรายทาง\nรายรับ\nรายล้อม\nรายละเอียด\nรายวิชา\nร่ายยาว\nร่ายรำ\nร้ายกาจ\nร้ายแรง\nราวนม\nราวป่า\nร้าวฉาน\nร้าวราน\nรำพัด\nรำแพน\nรำวง\nร่ำไป\nร่ำร้อง\nร่ำเรียน\nร่ำไร\nร่ำลา\nร่ำไห้\nริเริ่ม\nริอ่าน\nริมฝีปาก\nริ้วรอย\nรีบร้อน\nรีบรุด\nรีบเร่ง\nรื่นรมย์\nรื่นเริง\nรื้อถอน\nรื้อฟื้น\nรุกฆาต\nรุกราน\nรุกล้ำ\nรุกไล่\nรุ่งขึ้น\nรุ่งแจ้ง\nรุ่งเช้า\nรุ่งเรือง\nรุ่งโรจน์\nรุ่งสว่าง\nรุ่งสาง\nรุ่งอรุณ\nรุจิเรข\nรุดหน้า\nรุนแรง\nรุมเร้า\nรุมล้อม\nรุ่มรวย\nรุ่มร้อน\nรุ่ยร่าย\nรู้แกว\nรู้ความ\nรู้คุณ\nรู้งาน\nรู้จัก\nรู้แจ้ง\nรู้ใจ\nรู้เชิง\nรู้ตัว\nรู้ทัน\nรู้เท่า\nรู้เรื่อง\nรู้สำนึก\nรู้สึก\nรู้เห็น\nรูปการณ์\nรูปโฉม\nรูปฌาน\nรูปถ่าย\nรูปทรง\nรูปธรรม\nรูปแบบ\nรูปพรรณ\nรูปพรหม\nรูปภพ\nรูปภาพ\nรูปร่าง\nรูปสมบัติ\nเร่ร่อน\nเร่งด่วน\nเร่งมือ\nเร่งรัด\nเร่งรีบ\nเร่งเร้า\nเร้นลับ\nเร่อร่า\nเร่าร้อน\nเราะราย\nเราะร้าย\nเริงใจ\nเริงรมย์\nเริดร้าง\nเริ่มต้น\nเริ่มแรก\nเรี่ยราด\nเรี่ยไร\nเรียกคืน\nเรียกตัว\nเรียกร้อง\nเรียกหา\nเรียบร้อย\nเรียงความ\nเรียงตัว\nเรียงเบอร์\nเรียงพิมพ์\nเรียงเม็ด\nเรียงราย\nเรียนรู้\nเรียบร้อย\nเรียบเรียง\nเรียบวุธ\nเรี่ยมเร้\nเรี่ยวแรง\nเรือกลไฟ\nเรือกอและ\nเรือกำปั่น\nเรือจ้าง\nเรือดำน้ำ\nเรือโดยสาร\nเรือตรวจการณ์\nเรือตังเก\nเรือธง\nเรือนำร่อง\nเรือบด\nเรือบิน\nเรือใบ\nเรือประมง\nเรือพ่วง\nเรือพิฆาต\nเรือยนต์\nเรือยาว\nเรือโยง\nเรือรบ\nเรือลากจูง\nเรือสำปั้น\nเรือสำเภา\nเรือหลวง\nเรือหางยาว\nเรืออีโปง\nเรือเอี้ยมจุ๊น\nเรื้อรัง\nเรือกสวน\nเรืองนาม\nเรืองรอง\nเรืองแสง\nเรื่องราว\nเรื่องสั้น\nเรือนแก้ว\nเรือนจำ\nเรือนเบี้ย\nเรือนแพ\nเรือนหอ\nเรื่อยเจื้อย\nเรื่อยเฉื่อย\nเรื่อยเปื่อย\nแรเงา\nแรกนา\nแรกนาขวัญ\nแรงงาน\nแรงดึงดูด\nแรงเทียน\nแรงม้า\nแรงเหวี่ยง\nแรมรอน\nแรมรา\nแรมโรย\nโรคจิต\nโรงครัว\nโรงงาน\nโรงเจ\nโรงเตี๊ยม\nโรงทาน\nโรงนา\nโรงพยาบาล\nโรงพัก\nโรงพิมพ์\nโรงเรียน\nโรงเรือน\nโรงแรม\nโรงเลี้ยง\nโรงเลื่อย\nโรงสี\nโรงสีข้าว\nโรงอาหาร\nโรมรัน\nโรยรา\nฤชากร\nฤดูกาล\nลงขัน\nลงแขก\nลงคอ\nลงตัว\nลงท้าย\nลงทุน\nลงโทษ\nลงพุง\nลงมือ\nลงรัก\nลงรอย\nลงแรง\nลงโรง\nลงเอย\nลดตัว\nลดละ\nลดเลี้ยว\nลดหย่อน\nลดหลั่น\nลนลาน\nล้นพ้น\nล้นหลาม\nล้นเหลือ\nลบล้าง\nลบเลือน\nลบหลู่\nลมกรด\nลมค้า\nลมงวง\nลมแดด\nลมทะเล\nลมบก\nลมบน\nลมบ้าหมู\nลมปราณ\nลมปาก\nลมพิษ\nลมว่าว\nลมเสีย\nลมหนาว\nลมหายใจ\nล่มจม\nล่มสลาย\nล้มละลาย\nล้มลุก\nล้มเลิก\nล่วงเกิน\nล่วงรู้\nล่วงละเมิด\nล่วงลับ\nล่วงล้ำ\nล่วงเลย\nล่วงหน้า\nลวดลาย\nลวดสปริง\nลวดหนาม\nล้วนแล้ว\nลหุโทษ\nล่อลวง\nล่อหลอก\nล่อแหลม\nล้อต๊อก\nล้อเลื่อน\nล้อเล่น\nล้อเลียน\nล้อหลอก\nลองเชิง\nลองดี\nลองภูมิ\nล่องหน\nลอดช่อง\nล่อนจ้อน\nลอบกัด\nล้อมวง\nลอยแก้ว\nลอยชาย\nลอยตัว\nลอยนวล\nลอยแพ\nลอยลำ\nละทิ้ง\nละเลย\nละเว้น\nละครนอก\nละครใน\nละครเพลง\nละครร้อง\nละครรำ\nละครลิง\nละครสัตว์\nละเอียดอ่อน\nลักไก่\nลักพา\nลักเพศ\nลักยิ้ม\nลักลอบ\nลักลั่น\nลักหลับ\nลัดเลาะ\nลับตา\nลับแล\nลับหลัง\nลาออก\nล่าช้า\nล่าทัพ\nล้าสมัย\nล้าหลัง\nลากข้าง\nล้างบาง\nล้างผลาญ\nลาดเขา\nลาดตระเวน\nลาดเท\nลาดยาง\nลานบิน\nลาภปาก\nลาภลอย\nลามปาม\nลามเลีย\nลายคราม\nลายเซ็น\nลายแทง\nลายน้ำ\nลายพร้อย\nลายมือ\nลายลักษณ์\nลายเส้น\nลำกล้อง\nลำแข้ง\nลำธาร\nลำแสง\nลำไส้\nลำตัด\nลำนำ\nล่ำสัน\nล้ำยุค\nล้ำสมัย\nล้ำลึก\nล้ำเลิศ\nล้ำเส้น\nล้ำหน้า\nลิงจุ่น\nลิงลม\nลิงโลด\nลิดรอน\nลิ้นไก่\nลิ้นชัก\nลิ้นปี่\nลิ้นควาย\nลิ้นงูเห่า\nลิ้นหมา\nลิบลับ\nลิบลิ่ว\nลิ่มเลือด\nลี้ภัย\nลี้ลับ\nลึกซึ้ง\nลึกลับ\nลึกล้ำ\nลืมตน\nลืมต้น\nลืมตัว\nลืมตา\nลืมเลือน\nลือชา\nลือชื่อ\nลือลั่น\nลุล่วง\nลุกลน\nลุกลาม\nลุกลี้ลุกลน\nลุกฮือ\nลุ่มน้ำ\nลุ่มลึก\nลุ่มหลง\nลุ่ทาง\nลูกกรง\nลูกกรอก\nลูกกรุง\nลูกกลอน\nลูกกลิ้ง\nลูกกวาด\nลูกกะจ๊อก\nลูกกุญแจ\nลูกเกด\nลูกแก้ว\nลูกขนไก่\nลูกข่าง\nลูกขุน\nลูกเขย\nลูกครึ่ง\nลูกคลื่น\nลูกความ\nลูกคอ\nลูกค้า\nลูกคิด\nลูกคู่\nลูกจ้าง\nลูกช้าง\nลูกชิด\nลูกชิ้น\nลูกชุบ\nลูกซอง\nลูกโซ่\nลูกดอก\nลูกดิ่ง\nลูกตะกั่ว\nลูกตุ้ม\nลูกเต้า\nลูกเต๋า\nลูกถ้วย\nลูกทุ่ง\nลูกเธอ\nลูกน้อง\nลูกน้ำ\nลูกนิมิต\nลูกบอล\nลูกบ้าน\nลูกบาศก์\nลูกบิด\nลูกเบี้ยว\nลูกประคบ\nลูกประคำ\nลูกปัด\nลูกปืน\nลูกโป่ง\nลูกผสม\nลูกผู้ชาย\nลูกผู้หญิง\nลูกพรรค\nลูกพี่\nลูกฟูก\nลูกไฟ\nลูกมือ\nลูกโม่\nลูกไม้\nลูกยาเธอ\nลูกรอก\nลูกรัง\nลูกเรือ\nลูกล้อ\nลูกลอย\nลูกเล่น\nลูกเลี้ยง\nลูกโลก\nลูกวัด\nลูกศร\nลูกศิษย์\nลูกสมุน\nลูกสะใภ้\nลูกสูบ\nลูกเสือ\nลูกหนัง\nลูกหนี้\nลูกหนู\nลูกหมาก\nลูกหลง\nลูกหลาน\nลูกหาบ\nลูกหิน\nลูกเห็บ\nลูกเหม็น\nลูกแหง่\nลูกอม\nลูกหม้อ\nลูบคม\nลูบคลำ\nลูบไล้\nเล็กน้อย\nเลขคณิต\nเลขผา\nเลขหมาย\nเล็ดลอด\nเล่นงาน\nเล่นแง่\nเล่นชู้\nเล่นตัว\nเล่นลิ้น\nเล่นหัว\nเลนส์นูน\nเลนส์เว้า\nเล็บครุฑ\nเลยเถิด\nเลศนัย\nเล่ห์กล\nเล่ห์เหลี่ยม\nเลอโฉม\nเลอมาน\nเลอเลิศ\nเลอสรวง\nเล่อล่า\nเลอะเลือน\nเล่าเรียน\nเล่าลือ\nเลาะลัด\nเลิกรา\nเลิกร้าง\nเลิกล้ม\nเลิศเลอ\nเลี้ยงชีพ\nเลี้ยงดู\nเลี้ยงต้อย\nเลียบเคียง\nเลี้ยวลด\nเลือกตั้ง\nเลือกเฟ้น\nเลือกสรร\nเลื่องลือ\nเลือดกำเดา\nเลือดเนื้อ\nเลือดฝาด\nเลือดเย็น\nเลือดร้อน\nเลือดหมู\nเลือดอุ่น\nเลือนราง\nเลื่อนเปื้อน\nเลื่อนลอย\nเลื่อมพราย\nเลื่อมใส\nเลื่อยฉลุ\nเลื่อยลันดา\nเลื่อยวงเดือน\nเลื้อยคลาน\nแลเหลียว\nแลกเปลี่ยน\nแล้วกัน\nและเล็ม\nโล่งใจ\nโล่งโถง\nโล่งอก\nโลดเต้น\nโลดโผน\nโลดลิ่ว\nโลดแล่น\nไล่ที่\nไล่เบี้ย\nไล่เลี่ย\nไล่เลียง\nไล่หลัง\nไล่ออก\nวกวน\nวงกบ\nวงกลม\nวงการ\nวงแขน\nวงเงิน\nวงจร\nวงนอก\nวงใน\nวงรี\nวงเล็บ\nวงเวียน\nวงแหวน\nวงศ์วาน\nวจีกรรม\nวจีเภท\nวจีภาค\nวนเวียน\nวอดวาย\nว็อบแว็บ\nวังวน\nวังหน้า\nวังหลวง\nวังหลัง\nวัดราษฎร์\nวัดวา\nวัดหลวง\nวัดผล\nวัดพื้น\nวัตถุนิยม\nวัตถุประสงค์\nวัตรปฏิบัติ\nวันโกน\nวันพระ\nวันเพ็ญ\nวัยรุ่น\nวัยวุฒิ\nว่ากล่าว\nว่าจ้าง\nว่าด้วย\nว่าที่\nวางก้าม\nวางใจ\nวางตัว\nวางตา\nวางโต\nวางท่า\nวางมวย\nวางมาด\nวางมือ\nวางวาย\nว่างเปล่า\nว่างเว้น\nวาดเขียน\nว่านเครือ\nวาบหวาม\nวายชนม์\nวายปราณ\nวายวาง\nวายวอด\nวายร้าย\nวายุภักษ์\nวาววับ\nวาววาม\nวาวแวว\nวาวแสง\nวิกฤตการณ์\nวิกฤติการณ์\nวิกฤตกาล\nวิกฤติกาล\nวิกลจริต\nวิงเวียน\nวิ่งเต้น\nวิ่งผลัด\nวิ่งรอก\nวิ่งราว\nวิจิตรศิลป์\nวิชาการ\nวิชาชีพ\nวิชาธร\nวิญญูชน\nวิดพื้น\nวิตกจริต\nวิถีทาง\nวิทยากร\nวิทยากล\nวิทยาการ\nวิทยาเขต\nวิทยาทาน\nวิทยาธร\nวิทยานิพนธ์\nวิทยาศาสตร์\nวิเทศสัมพันธ์\nวิธีการ\nวินัยธร\nวินัยปิฎก\nวินาศกรรม\nวินาศภัย\nวินาศสันตะโร\nวิภัชพยากรณ์\nวิภัชวาที\nวิไลวรรณ\nวิสัญญีแพทย์\nวิสัญญีภาพ\nวิสัญญีวิทยา\nวุฒิบัตร\nวุฒิสภา\nวุฒิสมาชิก\nวุ่นวาย\nวุ้นเส้น\nวูบวาบ\nเวจกุฎี\nเวจมรรค\nเวชกรรม\nเวชภัณฑ์\nเวชศาสตร์\nเวทมนตร์\nเวนคืน\nเวรกรรม\nเวฬุการ\nเวฬุวัน\nเว้าวอน\nเวิ้งว้าง\nเวียงวัง\nเวียนเทียน\nแว้งกัด\nแวดล้อม\nแวดวง\nแว่นขยาย\nแว่นแคว้น\nแว่นตา\nแวบวับ\nแววตา\nแวววาม\nแวววาว\nแวะเวียน\nโวยวาย\nไวไฟ\nไว้ใจ\nไว้ชื่อ\nไว้ตัว\nไว้ทุกข์\nไว้ลาย\nไว้หน้า\nไว้อาลัย\nศนิวาร\nศอกกลับ\nศอกกำ\nศอกกำมา\nศักดิ์ศรี\nศักดิ์สิทธิ์\nศารทวิษุวัติ\nศาลแขวง\nศาลจังหวัด\nศาลชั้นต้น\nศาลฎีกา\nศาลเตี้ย\nศาลทหาร\nศาลปกครอง\nศาลพระภูมิ\nศาลเพียงตา\nศาลแพ่ง\nศาลรัฐธรรมนูญ\nศาลแรงงาน\nศาลล้มละลาย\nศาลโลก\nศาลสูง\nศาลสูงสุด\nศาลอาญา\nศาลอุทธรณ์\nศาลากลาง\nศาลาดิน\nศาลาราย\nศาลาวัด\nศิลาฤกษ์\nศิลาแลง\nศิษย์เก่า\nศิษย์เอก\nศีลจุ่ม\nศีลธรรม\nศีลวัต\nศีลอด\nศูนย์กลาง\nศูนย์การค้า\nศูนย์ถ่วง\nศูนย์สูตร\nศูนย์หน้า\nเศร้าใจ\nเศร้าโศก\nเศร้าสร้อย\nเศร้าสลด\nเศร้าหมอง\nเศวตฉัตร\nเศษเกิน\nเศษซ้อน\nเศษวรรค\nเศษส่วน\nเศษเหล็ก\nโศกนาฏกรรม\nโศกศัลย์\nโศกเศร้า\nโศกสลด\nสกลโลก\nส่งเดช\nส่งท้าย\nส่งเสริม\nส่งเสีย\nส่งเสียง\nสงบเงียบ\nสงบเสงี่ยม\nสง่างาม\nสง่าราศี\nสดชื่น\nสดใส\nสตรีเพศ\nสติปัญญา\nสถลมารค\nสถานกงสุล\nสถานที่\nสถานทูต\nสถานการณ์\nสถานภาพ\nสถิติศาสตร์\nสนตะพาย\nสนใจ\nส้นตีน\nสนธิสัญญา\nสนนราคา\nสนับแข้ง\nสนับเพลา\nสนับมือ\nสนามบิน\nสนามเพลาะ\nสนิทสนม\nสนิมขุม\nสนิมสร้อย\nสนุกสนาน\nสบประมาท\nสบายใจ\nสภาพธรรม\nสมควร\nสมจริง\nสมใจ\nสมนัย\nสมน้ำหน้า\nสมประกอบ\nสมส่วน\nสมหวัง\nสมคบ\nสมทบ\nสมยอม\nสมรัก\nสมรู้\nสมสู่\nส้มฉุน\nส้มตำ\nส้มลิ้ม\nส้มกุ้ง\nส้มเช้า\nสมญานาม\nสมมติฐาน\nสมมุติฐาน\nสมมติเทพ\nสมรภูมิ\nสมัครใจ\nสมัยนิยม\nสมุทรศาสตร์\nสมุทรเสนา\nสยดสยอง\nสยองขวัญ\nสยามรัฐ\nสรรหา\nสรวมชีพ\nสรวลเส\nสร้อยเศร้า\nสร้างสรรค์\nสร้างเสริม\nสลดใจ\nสลบไสล\nสละสลวย\nสลาเหิน\nสลากภัต\nสวนครัว\nสวนป่า\nสวนสนุก\nสวนหย่อม\nส่วนกลาง\nส่วนเกิน\nส่วนตัว\nส่วนบุญ\nส่วนแบ่ง\nส่วนประกอบ\nส่วนพระองค์\nส่วนผสม\nส่วนรวม\nส่วนร่วม\nส่วนลด\nส่วนสัด\nสวมกอด\nสวมเขา\nสวมรอย\nสวยมภู\nสว่างไสว\nสวามิภักดิ์\nสวิงสวาย\nสสารนิยม\nส่อเสียด\nสอดคล้อง\nสอดแทรก\nสอดแนม\nสอบถาม\nสอบทาน\nสอบไล่\nสอบสวน\nส้อมเสียง\nสะสวย\nสะแกวัลย์\nสะแกแสง\nสะใจ\nสะเด็ดยาด\nสะเทือนใจ\nสะบัดช่อ\nสั่งสม\nสั่งสอน\nสั่งเสีย\nสังเกตการณ์\nสังคมนิยม\nสังคมวิทยา\nสังคมศาสตร์\nสังคมศึกษา\nสังคมสงเคราะห์\nสัญญาบัตร\nสัดส่วน\nสัตการ\nสัตบุรุษ\nสัตบริภัณฑ์\nสัตภัณฑ์\nสัตมหาสถาน\nสัตโลหะ\nสันเขา\nสันดอน\nสันหลัง\nสั่นเทา\nสั่นเทิ้ม\nสันติบาล\nสันติภาพ\nสันติวิธี\nสันติสุข\nสับเปลี่ยน\nสับสน\nสับหลีก\nสับหว่าง\nสัมมาคารวะ\nสัมมาชีพ\nส่าเหล้า\nสากกะเบือ\nสาทิสลักษณ์\nสาธุการ\nสาธุชน\nสาบเสือ\nสาปสรร\nสาปแช่ง\nสาปส่ง\nสามง่าม\nสามล้อ\nสามเหลี่ยม\nสามเวท\nสามัญชน\nสามัญสำนึก\nสายดิ่ง\nสายดิน\nสายตรวจ\nสายน้ำ\nสายบัว\nสายพาน\nสายฟ้า\nสายยาง\nสายยู\nสายใย\nสายรก\nสายรุ้ง\nสายล่อฟ้า\nสายลับ\nสายเลือด\nสายโลหิต\nสายวัด\nสายส่ง\nสายสวาท\nสายสะดือ\nสายสะพาย\nสายสัมพันธ์\nสายสิญจน์\nสายสืบ\nสายไหม\nสายอากาศ\nสายตา\nสายหยุด\nสารตรา\nสารประกอบ\nสารละลาย\nสารส้ม\nสารหนู\nสารทฤดู\nสาวใช้\nสาวน้อย\nสาวใหญ่\nสำนักงาน\nสำนักพิมพ์\nสำนักสงฆ์\nสำมะโนครัว\nสำเร็จรูป\nสิกขาบท\nสิงสถิต\nสิงสู่\nสิ่งก่อสร้าง\nสิ่งของ\nสิ่งปฏิกูล\nสิ่งพิมพ์\nสิ่งแวดล้อม\nสิ่งศักดิ์สิทธิ์\nสิทธิกร\nสิทธิ์ขาด\nสิทธิชัย\nสิทธิโชค\nสิทธิบัตร\nสินค้า\nสินจ้าง\nสินเชื่อ\nสินไถ่\nสินทรัพย์\nสินน้ำใจ\nสินบน\nสินแร่\nสินสมรส\nสินสอด\nสินไหม\nสิ้นเชิง\nสิ้นสุด\nสีผึ้ง\nสีลม\nสีชอล์ก\nสีถ่าน\nสีเทียน\nสีน้ำ\nสีน้ำมัน\nสีโปสเตอร์\nสีฝุ่น\nสี่เหลี่ยม\nสีหน้า\nสึกหรอ\nสืบทอด\nสืบค้น\nสืบสวน\nสืบสาว\nสืบเสาะ\nสื่อผสม\nสื่อมวลชน\nสื่อสาร\nสุกงอม\nสุกดิบ\nสุกปลั่ง\nสุกใส\nสุขนาฏกรรม\nสุขภัณฑ์\nสุขภาพ\nสุขลักษณะ\nสุขวิทยา\nสุขศาลา\nสุขศึกษา\nสุดท้าย\nสุตกวี\nสุนทรพจน์\nสุภาพชน\nสู่ขอ\nสู่รู้\nสู่สม\nสูงส่ง\nสูญเปล่า\nสูญสิ้น\nสูญเสีย\nสูญหาย\nเสสรวล\nเสแสร้ง\nเสกสรร\nเสถียรภาพ\nเส้นชัย\nเส้นตรง\nเส้นตาย\nเส้นทาง\nเส้นใย\nเส้นรุ้ง\nเส้นเลือด\nเส้นแวง\nเส้นสาย\nเส้นเสียง\nเส้นหมี่\nเส้นเอ็น\nเสบียงกรัง\nเสมอภาค\nเสมอหน้า\nเสมอเหมือน\nเสมียนตรา\nเสร็จสรรพ\nเสร็จสิ้น\nเสริมส่ง\nเสริมสร้าง\nเสริมสวย\nเสรีไทย\nเสรีธรรม\nเสรีนิยม\nเสรีภาพ\nเสาเข็ม\nเสาธง\nเสียใจ\nเสียเชิง\nเสียดาย\nเสียที\nเสียเที่ยว\nเสียเปรียบ\nเสียเปล่า\nเสียรู้\nเสียแรง\nเสียสละ\nเสียหลัก\nเสียหาย\nเสี่ยงทาย\nเสียดแทง\nเสียดแทรก\nเสียดสี\nเสี้ยนศึก\nเสี้ยนหนาม\nเสี้ยมสอน\nเสียวซ่าน\nเสียวไส้\nเสือดาว\nเสือดำ\nเสือปลา\nเสือป่า\nเสือไฟ\nเสื่อกก\nเสื่อกระจูด\nเสื่อน้ำมัน\nเสื่อลำแพน\nเสื้อกล้าม\nเสื้อกั๊ก\nเสื้อเกราะ\nเสื้อครุย\nเสื้อแสง\nเสื้อเมือง\nเสือกคลาน\nเสือกสน\nเสือกไส\nเสื่อมคลาย\nเสื่อมถอย\nเสื่อมทราม\nเสื่อมโทรม\nเสื่อมสลาย\nเสื่อมสูญ\nเสื่อมเสีย\nเสือหมอบ\nแสกหน้า\nแสดงออก\nแสเถา\nแสนกล\nแสนรู้\nแสร้งว่า\nใส่ความ\nใส่ไคล้\nใส่ใจ\nใส่ไฟ\nไส้กรอก\nไส้ไก่\nไส้ติ่ง\nไส้ศึก\nไส้อั่ว\nไส้เดือน\nไส้ตัน\nไสยเวท\nไสยศาสตร์\nหกล้ม\nหงส์หยก\nหงอนไก่\nหงอยก๋อย\nหงอยเหงา\nหงายท้อง\nหงายหลัง\nหงำเหงอะ\nหงำเหงือก\nหดหาย\nหดหู่\nหนทาง\nหนวกหู\nหน่วงเหนี่ยว\nหน่วยก้าน\nหน่อไม้\nหนองใน\nหนองแซง\nหนักข้อ\nหนักใจ\nหนักแน่น\nหนักหน่วง\nหนักหนา\nหนังกลับ\nหนังตะลุง\nหนังเรียด\nหนังสด\nหนังใหญ่\nหนังสือพิมพ์\nหนาแน่น\nหน้ากระดาน\nหน้ากาก\nหน้ากาฬ\nหน้าแข้ง\nหน้าจั่ว\nหน้าฉาน\nหน้าตัก\nหน้าตา\nหน้าต่าง\nหน้าท้อง\nหน้าทับ\nหน้าที่\nหน้าที่นั่ง\nหน้าบัน\nหน้าปัด\nหน้าผา\nหน้าผาก\nหน้าม้า\nหน้ามุข\nหน้าไม้\nหน้าเลือด\nหน้าอก\nหนามเตย\nหน่ายหนี\nหน่ายแหนง\nหนาวเหน็บ\nหนำใจ\nหนี้สิน\nหนี้ส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น\nกณิกนันต์\nกณิการ์\nกด\nกตเวทิตา\nกตเวที\nกตัญชลี\nกตัญญุตา\nกตัญญู\nกตาธิการ\nกตาภินิหาร\nกติกา\nกถา\nกถิกาจารย์\nกทลี\nกน\nก่น\nก้น\nกนก\nกนิษฐ์\nกนิษฐา\nกบ\nกบฏ\nกบดาน\nกบทู\nกบาล\nกบินทร์\nกบิล\nกบี่\nกบูร\nกเบนทร์\nกม\nก้ม\nกมณฑลาภิเษก\nกมณฑโลทก\nกมล\nกมลา\nกมลาศ\nกมลาสน์\nกมเลศ\nกมัณฑลุ\nกมุท\nกร\nกรกฎ\nกรกฎาคม\nกรกฏ\nกรง\nกรชกาย\nกรณฑ์\nกรณิการ์\nกรณี\nกรณีย์\nกรณียกิจ\nกรณียะ\nกรด\nกรน\nกรบ\nกรบูร\nกรพินธุ์\nกรม\nกรรกฎ\nกรรกศ\nกรรเกด\nกรรไกร\nกรรเจียก\nกรรชิง\nกรรเชียง\nกรรโชก\nกรรฐ์\nกรรฐา\nกรรณ\nกรรณา\nกรรณิกา\nกรรณิการ์\nกรรดิ\nกรรดิก\nกรรดึก\nกรรตุ\nกรรไตร\nกรรทบ\nกรรแทก\nกรรบิด\nกรรบูร\nกรรภิรมย์\nกรรม\nกรรม์\nกรรม์ภิรมย์\nกรรมชวาต\nกรรมัชวาต\nกรรมาชีพ\nกรรมาธิการ\nกรรมาร\nกรรษก\nกรรสะ\nกรรแสง\nกรวด\nกรวบ\nกรวม\nกร้วม\nกรวย\nกรวิก\nกรสาปน์\nกรสุทธิ์\nกรอ\nกร้อ\nกรอก\nกร็อกกร๋อย\nกรอกแกรก\nกรอง\nกรองกรอย\nกรอด\nกร่อน\nกรอบ\nกรอม\nกร่อย\nกระ\nกระกร\nกระกรุ่น\nกระกลับกลอก\nกระกี้\nกระเกรอก\nกระเกริก\nกระเกริ่น\nกระคน\nกระคาย\nกระงกกระเงิ่น\nกระง่องกระแง่ง\nกระง่อนกระแง่น\nกระเง้ากระงอด\nกระโงก\nกระจก\nกระจง\nกระจร\nกระจอก\nกระจองหง่อง\nกระจ๋องหง่อง\nกระจองอแง\nกระจ้อน\nกระจอนหู\nกระจ้อย\nกระจ๋อหวอ\nกระจะ\nกระจัก\nกระจัง\nกระจัด\nกระจับ\nกระจ่า\nกระจ่าง\nกระจาด\nกระจาน\nกระจาบ\nกระจาม\nกระจาย\nกระจาว\nกระจิก\nกระจิ๋ง\nกระจิด\nกระจิบ\nกระจิ๋ม\nกระจิริด\nกระจิ๋ว\nกระจี้\nกระจี๋\nกระจุก\nกระจุ๋งกระจิ๋ง\nกระจุบ\nกระจุ๊บ\nกระจุ๋มกระจิ๋ม\nกระจุย\nกระจู้\nกระจู๋กระจี๋\nกระจูด\nกระเจอะกระเจิง\nกระเจา\nกระเจ่า\nกระเจ้า\nกระเจาะ\nกระเจิง\nกระเจิดกระเจิง\nกระเจี้ยง\nกระเจี๊ยบ\nกระเจียว\nกระเจี๊ยว\nกระแจะ\nกระโจน\nกระโจม\nกระฉอก\nกระฉ่อน\nกระฉับกระเฉง\nกระฉิ่ง\nกระฉีก\nกระฉูด\nกระเฉก\nกระเฉด\nกระแฉก\nกระโฉกกระเฉก\nกระโฉม\nกระชดกระช้อย\nกระชอน\nกระชอมดอก\nกระช้อย\nกระชัง\nกระชั้น\nกระชับ\nกระชาก\nกระชาย\nกระชิง\nกระชิด\nกระชุ\nกระชุก\nกระชุ่มกระชวย\nกระเชอ\nกระเชา\nกระเช้า\nกระเชียง\nกระแชง\nกระแชะ\nกระโชก\nกระซ่องกระแซ่ง\nกระซับ\nกระซาบ\nกระซิก\nกระซิบ\nกระซี้\nกระซุง\nกระซุบกระซิบ\nกระซุ้ม\nกระซู่\nกระเซ็น\nกระเซอ\nกระเซอะกระเซอ\nกระเซอะกระเซิง\nกระเซ้า\nกระเซิง\nกระแซ\nกระแซะ\nกระโซกระเซ\nกระฎี\nกระฎุมพี\nกระดก\nกระด้ง\nกระดนโด่\nกระดวง\nกระดวน\nกระด้วมกระเดี้ยม\nกระดอ\nกระดอง\nกระดองหาย\nกระดอน\nกระดอม\nกระดักกระเดี้ย\nกระดังงัว\nกระดังงา\nกระดาก\nกระด้าง\nกระดางลาง\nกระดาด\nกระดาดขาว\nกระดาน\nกระดานพน\nกระดาษ\nกระดำกระด่าง\nกระดิก\nกระดิ่ง\nกระดิ้ง\nกระดิบ\nกระดี่\nกระดี้กระเดียม\nกระดึง\nกระดืบ\nกระดุ\nกระดุกกระดิก\nกระดุ้งกระดิ้ง\nกระดุบ\nกระดุบกระดิบ\nกระดุม\nกระดูก\nกระเดก\nกระเด้ง\nกระเด็น\nกระเด้า\nกระเดาะ\nกระเดิด\nกระเดี้ย\nกระเดียด\nกระเดือก\nกระเดื่อง\nกระแด็ก\nกระแด้ง\nกระแด้แร่\nกระแด่ว\nกระแดะ\nกระโดก\nกระโดง\nกระโดด\nกระโดน\nกระได\nกระตรับ\nกระตราก\nกระตรุด\nกระตรุม\nกระต้วมกระเตี้ยม\nกระต้อ\nกระต่องกระแต่ง\nกระต๊อบ\nกระต้อยตีวิด\nกระตัก\nกระตั้ว\nกระต่าย\nกระติก\nกระติ๊ด\nกระติบ\nกระตือรือร้น\nกระตุก\nกระตุ้งกระติ้ง\nกระตุ่น\nกระตุ้น\nกระตูบ\nกระเตง\nกระเต็น\nกระเตอะ\nกระเตาะ\nกระเตาะกระแตะ\nกระเตื้อง\nกระแต\nกระแตแต้แว้ด\nกระโตกกระตาก\nกระโตน\nกระถด\nกระถอบ\nกระถั่ว\nกระถาง\nกระถิก\nกระถิน\nกระเถิบ\nกระโถน\nกระทก\nกระทง\nกระทบ\nกระทรวง\nกระทอก\nกระท้อน\nกระท่อนกระแท่น\nกระท่อม\nกระท้อมกระแท้ม\nกระทะ\nกระทั่ง\nกระทั้น\nกระทา\nกระทาย\nกระทาสี\nกระทาหอง\nกระทำ\nกระทิกกระทวย\nกระทิง\nกระทึง\nกระทืบ\nกระทุ\nกระทุง\nกระทุ้ง\nกระทุ่ม\nกระทู้\nกระเท่\nกระเทียบ\nกระเทียม\nกระเทือน\nกระเทื้อม\nกระแทก\nกระแท่น\nกระแทะ\nกระไทชาย\nกระน่อง\nกระนั้น\nกระนี้\nกระแนะกระแหน\nกระโน้น\nกระไน\nกระบก\nกระบม\nกระบวน\nกระบวย\nกระบวร\nกระบอก\nกระบอง\nกระบะ\nกระบัด\nกระบั้วกระเบี้ย\nกระบ่า\nกระบ้า\nกระบาก\nกระบาย\nกระบาล\nกระบิ\nกระบิ้ง\nกระบิด\nกระบิล\nกระบี่\nกระบือ\nกระบุง\nกระบุ่มกระบ่าม\nกระบู้กระบี้\nกระบูน\nกระบูร\nกระเบง\nกระเบญ\nกระเบ็ดกระบวน\nกระเบน\nกระเบา\nกระเบิก\nกระเบียด\nกระเบียน\nกระเบื้อง\nกระแบก\nกระแบะ\nกระโบม\nกระปมกระปำ\nกระปมกระเปา\nกระปรอก\nกระปรอกว่าว\nกระปรี้กระเปร่า\nกระปอก\nกระป้อกระแป้\nกระป่อง\nกระป๋อง\nกระปอดกระแปด\nกระป๋อหลอ\nกระปั้วกระเปี้ย\nกระป่ำ\nกระปุก\nกระปุ๊กลุก\nกระปุ่ม\nกระปุ่มกระป่ำ\nกระปุ่มกระปิ่ม\nกระเป๋า\nกระเปาะ\nกระโปก\nกระโปรง\nกระผม\nกระผลีกระผลาม\nกระผาน\nกระผีก\nกระพรวน\nกระพริ้ม\nกระพอก\nกระพอง\nกระพ้อม\nกระพัก\nกระพัง\nกระพังเหิร\nกระพังโหม\nกระพัด\nกระพัตร\nกระพัน\nกระพั่น\nกระพา\nกระพาก\nกระพี้\nกระพือ\nกระพุ้ง\nกระพุ่ม\nกระเพาะ\nกระเพิง\nกระเพื่อม\nกระแพ้ง\nกระฟัดกระเฟียด\nกระฟูมกระฟาย\nกระมล\nกระมอบ\nกระมอมกระแมม\nกระมัง\nกระมัน\nกระมิดกระเมี้ยน\nกระมุท\nกระเมาะ\nกระย่อง\nกระย่องกระแย่ง\nกระย่อน\nกระย่อม\nกระยา\nกระยาง\nกระยาจก\nกระยาหงัน\nกระยิก\nกระยิ้มกระย่อง\nกระยึกกระยือ\nกระยืดกระยาด\nกระเย้อกระแหย่ง\nกระรอก\nกระเรียน\nกระโรกน้ำข้าว\nกระโรกใหญ่\nกระไร\nกระลด\nกระลบ\nกระลอก\nกระลอม\nกระละหล่ำ\nกระลัด\nกระลับ\nกระลัมพร\nกระลา\nกระลำ\nกระลำพัก\nกระลำพุก\nกระลิง\nกระลี\nกระลุมพาง\nกระลุมพุก\nกระลุมพู\nกระลูน\nกระลู่น์\nกระเล็น\nกระเลียด\nกระเลือก\nกระโลง\nกระวน\nกระวัด\nกระวาด\nกระวาน\nกระวาย\nกระวิน\nกระวี\nกระวีกระวาด\nกระวูดกระวาด\nกระเวน\nกระเวยกระวาย\nกระแวน\nกระโวยกระวาย\nกระษัย\nกระษาปณ์\nกระสง\nกระสบ\nกระสม\nกระสรวล\nกระสร้อย\nกระสวน\nกระสวย\nกระสอบ\nกระสะ\nกระสัง\nกระสัน\nกระสับกระส่าย\nกระสา\nกระสานติ์\nกระสาบ\nกระสาย\nกระสือ\nกระสุงกระสิง\nกระส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ทิง\nกากะเยีย\nกากี\nกาง\nก้าง\nกางเกง\nกางเขน\nก๊าซ\nกาซะลอง\nกาญจนา\nกาฐ\nกาด\nก๊าด\nกาน\nก่าน\nก้าน\nก๊าน\nกานดา\nกานต์\nกานน\nก้านพร้าว\nกานพลู\nกาน้า\nกาบ\nก้าบ\nกาบู\nกาพย์\nกาเฟอีน\nกาแฟ\nกาม\nก้าม\nกามารมณ์\nกามินี\nกาเมสุมิจฉาจาร\nกาย\nก่าย\nกาเยน\nการ\nการณ์\nการ์ด\nการ์ตูน\nการบูร\nการย์\nการวิก\nการเวก\nการะเกด\nการะบุหนิง\nการัณย์\nการันต์\nการางหัวขวาน\nการิตการก\nการิตวาจก\nการุญ\nการุณย์\nกาเรการ่อน\nกาล\nกาลกรรณี\nกาลกิณี\nกาลจักร\nกาลัญญุตา\nกาลัญญู\nกาลัด\nกาลานุกาล\nกาลิก\nกาลี\nกาแล\nกาแล็กซี\nกาแล็กโทส\nกาว\nก้าว\nกาววาว\nกาวาง\nกาแวน\nกาศิก\nกาษฐะ\nกาษา\nกาสร\nกาสะ\nกาสา\nกาสาร\nกาสาวะ\nกาสิโน\nกาหล\nกาหลง\nกาหลา\nกาเหว่า\nกาไหล่\nกาฬ\nกาฬาวก\nกาฮัง\nกำ\nก่ำ\nกำกวม\nกำกัด\nกำกับ\nก้ำกึ่ง\nกำกูน\nก้ำเกิน\nกำเกียง\nกำคูน\nกำจร\nกำจัด\nกำจาย\nกำชับ\nกำชำ\nกำซาบ\nกำซำ\nกำด้น\nกำดัด\nกำดาล\nกำเดา\nกำธร\nกำนล\nกำนัน\nกำนัล\nกำเนิด\nกำบัง\nก่ำบึ้ง\nกำเบ้อ\nกำปอ\nกำปั่น\nกำผลา\nกำพง\nกำพด\nกำพต\nกำพร้า\nกำพราก\nกำพวด\nกำพอง\nกำพืด\nกำพุด\nกำพู\nกำเพลิง\nกำแพง\nกำภู\nกำมลาศน์\nกำมเลศ\nกำมะถัน\nกำมะลอ\nกำมะหยี่\nกำมะหริด\nกำมังละการ\nกำมังวิลิต\nกำมัชพล\nกำยาน\nกำยำ\nกำรอ\nกำราบ\nกำราล\nกำเริบ\nกำไร\nกำลัง\nกำลุง\nกำเลา\nกำไล\nกำสรด\nกำสรวล\nกำหนด\nกำหนัด\nกำเหน็จ\nกำแหง\nกิก\nกิ๊ก\nกิ่ง\nกิ้งก่า\nกิ้งกือ\nกิ้งโครง\nกิจ\nกิจจะ\nกิจจา\nกิดาการ\nกิดาหยัน\nกิตติ\nกิตติมศักดิ์\nกิน\nกินนร\nกินปลี\nกินเปี้ยว\nกินริน\nกินรี\nกิ๊บ\nกิมตึ๋ง\nกิมิชาติ\nกิมิวิทยา\nกิโมโน\nกิโยตีน\nกิระ\nกิริณี\nกิรินท\nกิริเนศวร\nกิริยา\nกิเลน\nกิเลส\nกิโล\nกิโลมกะ\nกิ่ว\nกิ๋ว\nกี\nกี่\nกี้\nกี๊\nกี๋\nกีฏวิทยา\nกีด\nกีตาร์\nกีบ\nกีรติ\nกีฬา\nกึก\nกึง\nกึ่ง\nกึ๋น\nกุ\nกุก\nกุ๊ก\nกุกกุฏ\nกุกกุร\nกุกรรม\nกุ้ง\nกุงอน\nกุงาน\nกุโงก\nกุจี\nกุญแจ\nกุญชร\nกุฎ\nกุฎา\nกุฎี\nกุฎุมพี\nกุฏฐัง\nกุฏิ\nกุณฑ์\nกุณฑล\nกุณฑี\nกุณโฑ\nกุณาล\nกุณี\nกุด\nกุดัง\nกุดั่น\nกุดา\nกุทัณฑ์\nกุน\nกุ๊น\nกุนเชียง\nกุนที\nกุโนกามอ\nกุบ\nกุบกับ\nกุม\nกุ่ม\nกุมฝอย\nกุมภ์\nกุมภนิยา\nกุมภัณฑ์\nกุมภา\nกุมภิล\nกุมภีล์\nกุมเหง\nกุมาร\nกุมารา\nกุมารี\nกุมุท\nกุย\nกุ๊ย\nกุ๋ย\nกุยช่าย\nกุยเฮง\nกุรระ\nกุรุพินท์\nกุเรา\nกุล\nกุลา\nกุลาหล\nกุลี\nกุลีกุจอ\nกุเลา\nกุแล\nกุเวร\nกุศราช\nกุศล\nกุศโลบาย\nกุสุม\nกุสุมภ์\nกุสุมา\nกุสุมาลย์\nกุสุมิตลดาเวลลิตา\nกุหนี\nกุหนุง\nกุหร่า\nกุหล่า\nกุหลาบ\nกุแหละ\nกู\nกู่\nกู้\nกู๊ก\nกูฏ\nกูฏา\nกูณฑ์\nกูด\nกูบ\nกูปรี\nกูรมะ\nกูรมาวตาร\nเก\nเก้\nเก๊\nเก๋\nเกก\nเก๊ก\nเกกมะเหรก\nเก๊กฮวย\nเก้กัง\nเก็ง\nเก่ง\nเก้ง\nเก๋ง\nเกงกอย\nเก่งกาจ\nเกงเขง\nเก๋งเคง\nเก็จ\nเกจิอาจารย์\nเกชา\nเกณฑ์\nเกด\nเก็ด\nเกตุ\nเกน\nเก็บ\nเกม\nเกย\nเกยูร\nเกรง\nเกร็ง\nเกร็ด\nเกรน\nเกร่อ\nเกรอะ\nเกราะ\nเกริก\nเกริน\nเกริ่น\nเกรียก\nเกรียง\nเกรียด\nเกรียน\nเกรียบ\nเกรียม\nเกรียว\nเกรี้ยว\nเกเร\nเกล็ด\nเกลศ\nเกลอ\nเกลา\nเกล้า\nเกลาะ\nเกลี่ย\nเกลี้ย\nเกลียง\nเกลี้ยง\nเกลียด\nเกลียว\nเกลือ\nเกลื้อ\nเกลือก\nเกลื่อน\nเกลื้อน\nเกวัฏ\nเกวียน\nเกศ\nเกศว\nเกศวะ\nเกศา\nเกศินี\nเกศี\nเกษตร\nเกษม\nเกษียณ\nเกษียน\nเกษียร\nเกส\nเกสร\nเกสรี\nเกสา\nเกสี\nเก้อ\nเกอิชา\nเกะ\nเกะกะ\nเกา\nเก่า\nเก้า\nเก๋า\nเกาต์\nเกาทัณฑ์\nเกาบิล\nเกาลัด\nเกาลิน\nเกาไศย\nเกาหลี\nเกาเหลา\nเกาเหลียง\nเก้าอี้\nเกาะ\nเกิ้ง\nเกิด\nเกิน\nเกิบ\nเกีย\nเกียกกาย\nเกียง\nเกี่ยง\nเกี๋ยง\nเกียจ\nเกียด\nเกียน\nเกี้ยมไฉ่\nเกี้ยมอี๋\nเกียร์\nเกียรติ\nเกียรติ์\nเกี่ยว\nเกี้ยว\nเกี๊ยว\nเกี๊ยะ\nเกื้อ\nเกือก\nเกื้อกูล\nเกือบ\nแก\nแก่\nแก้\nแกง\nแก่ง\nแก้ง\nแก๊ง\nแกงได\nแกงแนง\nแกโดลิเนียม\nแกน\nแก่น\nแก๊ป\nแกม\nแก้ม\nแกมมา\nแกรก\nแกร่ง\nแกร็น\nแกรนิต\nแกรไฟต์\nแกร่ว\nแกระ\nแกล\nแกล้ง\nแกลน\nแกลบ\nแกล้ม\nแกลลอน\nแกลเลียม\nแกล้ว\nแกละ\nแกแล\nแกว\nแก้ว\nแกว่ง\nแก๊ส\nแกะ\nโก\nโก่\nโก้\nโก๋\nโกก\nโกกนุท\nโกกิลา\nโกโก้\nโกง\nโก่ง\nโกงกาง\nโก้งเก้ง\nโกงโก้\nโก้งโค้ง\nโกเชาว์\nโกญจนาท\nโกญจา\nโกฏิ\nโกฐ\nโกฐาส\nโกณะ\nโกดัง\nโกทัณฑ์\nโกน\nโก่น\nโก๋น\nโกมล\nโกมุท\nโกเมน\nโกเมศ\nโกย\nโกรก\nโกรกกราก\nโกรง\nโกร่ง\nโกร่งกร่าง\nโกรงเกรง\nโกร๋งเกร๋ง\nโกรญจ\nโกรต๋น\nโกรธ\nโกรธา\nโกร๋น\nโกรม\nโกรย\nโกรศ\nโกโรโกเต\nโกโรโกโรก\nโกโรโกโส\nโกลน\nโกลาหล\nโกไล\nโกวิท\nโกศ\nโกศล\nโกษม\nโกสน\nโกสัช\nโกสินทร์\nโกสีย์\nโกสุม\nโกไสย\nโกหก\nใกล้\nไก\nไก่\nไก๊\nไก๋\nไก่กอม\nไกพัล\nไกร\nไกรพ\nไกรลาส\nไกรศร\nไกรศรี\nไกรสร\nไกรสรี\nไกรสิทธิ\nไกล\nไกล่\nไกลาส\nไกว\nไกวัล\nขงจื๊อ\nขจร\nขจรจบ\nขจัด\nขจ่าง\nขจาย\nขจาว\nขจิต\nขจี\nขจุย\nขเจา\nขณะ\nขด\nขน\nข้น\nขนง\nขนด\nขนบ\nขนม\nขนอง\nขนอน\nขนอบ\nขนัด\nขนัน\nขนาก\nขนาง\nขนาด\nขนาน\nขนาบ\nขนาย\nขนำ\nขนิษฐ\nขนิษฐา\nขนุน\nขนุนนก\nขบ\nขบถ\nขบวน\nขบวร\nขม\nข่ม\nขมงโกรย\nขมวด\nขมวน\nขมอง\nขม่อม\nขมัง\nขมับ\nขมา\nขม้ำ\nขมิ้น\nขมิบ\nขมีขมัน\nขมึง\nขมึงทึง\nขมุ\nขมุกขมัว\nขมุบ\nขโมย\nขยด\nขยม\nขย่ม\nขยอก\nขยอง\nขย่อน\nขย้อน\nขยะ\nขยัก\nขยัน\nขยั้น\nขยับ\nขยาด\nขยาย\nขยำ\nขย้ำ\nขยิก\nขยิบ\nขยิ่ม\nขยี้\nขยุกขยิก\nขยุกขยุย\nขยุบ\nขยุบขยิบ\nขยุม\nขยุ้ม\nขยุย\nขรม\nขรรค์\nขรัว\nขริบ\nขรี\nขรึม\nขรุขระ\nขลบ\nขล้อ\nขลัง\nขลับ\nขลาด\nขลาย\nขลิบ\nขลุก\nขลุกขลัก\nขลุกขลิก\nขลุบ\nขลุม\nขลุ่ย\nขลู\nขลู่\nขวง\nข่วง\nขวด\nข่วน\nขวนขวาย\nขวบ\nขวย\nขวักไขว่\nขวัญ\nขวั้น\nขวับ\nขวับเขวียว\nขวา\nขวาก\nขวาง\nขว้าง\nขวาด\nขวาน\nขวายขวน\nขวาว\nขว้าว\nขวิด\nขอ\nข่อ\nข้อ\nของ\nข้อง\nขอด\nขอน\nข้อน\nขอบ\nขอม\nข่อย\nข้อย\nข่อยหยอง\nขะแจะ\nขะเน็ด\nขะมอมขะแมม\nขะมักเขม้น\nขะมุกขะมอม\nขะยิก\nขะยุก\nขะเย้อแขย่ง\nขัค\nขัง\nขังขอก\nขัช\nขัณฑสกร\nขัณฑสีมา\nขัด\nขัดมอน\nขัตติยมานะ\nขัน\nขั้น\nขันติ\nขันตี\nขันโตก\nขันที\nขันธ์\nขันธาวาร\nขับ\nขัว\nขั้ว\nขา\nข่า\nข้า\nขาก\nขาก๊วย\nขาง\nข่าง\nข้าง\nขาณุ\nขาด\nขาทนียะ\nขาน\nขาบ\nข้าพเจ้า\nขาม\nข่าม\nข้าม\nขาย\nข่าย\nขาล\nขาว\nข่าว\nข้าว\nข้าวอังกุลี\nขำ\nขิก\nขิง\nขิงแกลง\nขิงแครง\nขิด\nขิปสัทโท\nขิม\nขี่\nขี้\nขี้เข็บ\nขีณาสพ\nขีด\nขี้ตังนี\nขีปนาวุธ\nขี้ยอก\nขีระ\nขึง\nขึ้ง\nขึ้น\nขึ้นฉ่าย\nขืน\nขื่น\nขื่อ\nขุก\nขุด\nขุน\nขุ่น\nขุนเพ็ด\nขุม\nขุย\nขู่\nขูด\nเข\nเข้\nเขก\nเข็ง\nเข่ง\nเขจร\nเข็ญ\nเข็ด\nเขดา\nเขต\nเขน\nเข็น\nเข่น\nเขนง\nเขน็ด\nเขนย\nเขบ็จขบวน\nเขบ็ต\nเขม\nเข็ม\nเข้ม\nเข้มขาบ\nเขม็ง\nเขม็ดแขม่\nเขม่น\nเขม้น\nเขม้นขะมัก\nเขมร\nเขมา\nเขม่า\nเขมือบ\nเขย\nเขยก\nเขย่ง\nเขย้อแขย่ง\nเขย่า\nเขยิน\nเขยิบ\nเขยื้อน\nเขรอะ\nเขลง\nเขลอะ\nเขละ\nเขลา\nเขลาะ\nเขว\nเขษม\nเขฬะ\nเขะขะ\nเขา\nเข่า\nเข้า\nเขิง\nเขิน\nเขิบ\nเขี่ย\nเขียง\nเขียด\nเขียดตะปาด\nเขียน\nเขี่ยน\nเขียม\nเขียว\nเขี้ยว\nเขียะ\nเขือ\nเขือง\nเขื่อง\nเขื่อน\nเขือม\nแข\nแข้\nแขก\nแข็ง\nแข่ง\nแข้ง\nแขน\nแข่น\nแข้น\nแขนง\nแขม\nแขม็บ\nแขม่ว\nแขยง\nแขย่ง\nแขวก\nแขวง\nแขวน\nแขวะ\nโข\nโขก\nโขง\nโข่ง\nโขด\nโขดง\nโขน\nโขนง\nโขม\nโขมง\nโขมด\nโขยก\nโขยง\nโขย่ง\nโขยด\nโขลก\nโขลง\nโขลน\nโขษม\nไข\nไข่\nไข้\nไขว่\nไขว้\nคคนะ\nคคนัมพร\nคคนางค์\nคคนานต์\nคง\nคงคา\nคงไคย\nคช\nคชาชาติ\nคชาชีพ\nคชาธาร\nคชาภรณ์\nคณนา\nคณบดี\nคณะ\nคณาจารย์\nคณาธิการ\nคณาธิปไตย\nคณานับ\nคณิกา\nคณิต\nคเณศ\nคด\nคดี\nคติ\nคทา\nคน\nค้น\nคนทา\nคนทิสอ\nคนที\nคนโท\nคนธ์\nคันธ์\nคนธรรพ์\nคเนจร\nคบ\nคม\nคมน์\nคมนาการ\nคมนาคม\nคมิกภัต\nครก\nครบ\nครรชิต\nครรภ\nครรภ์\nครรลอง\nครรโลง\nครรไล\nครวญ\nครวี\nครหา\nครอก\nครอง\nครองแครง\nคร่อเงาะ\nคร่อเทียน\nครอบ\nคร่อม\nคระเมิม\nคระแลง\nคระไล\nคระแวง\nคระหน\nคระหวน\nคระหาย\nคระโหย\nครั่ง\nครั้ง\nครัดเคร่ง\nครัน\nครั่น\nครั้น\nครับ\nครัว\nครา\nคร่า\nคราก\nคราง\nคราญ\nคราด\nคร้าน\nคราบ\nคราม\nคร้าม\nครามครัน\nคราว\nคร่าว\nคราส\nครำ\nคร่ำ\nคร่ำเคร่ง\nคริปทอน\nคริสต์\nครีบ\nครีม\nครีษมายัน\nครึ\nครึกครื้น\nครึกโครม\nครึ่ง\nครึ่ด\nครึน\nครึ้ม\nครืด\nครืน\nครื้น\nครืนครั่น\nครื้นครั่น\nครื้นครึก\nครื้นเครง\nครือ\nครุ\nครุคระ\nครุฑ\nครุ่น\nครุมเครือ\nครุย\nครุวนา\nครู\nครู่\nครูด\nคฤโฆษ\nคฤนถ์\nคฤหบดี\nคฤหัสถ์\nคฤหาสน์\nคลวง\nคลอ\nคล้อ\nคลอก\nคลอง\nคล่อง\nคล้อง\nคลอด\nคลอน\nคล้อย\nคลอรีน\nคลอโรฟอร์ม\nคลอโรฟีลล์\nคละ\nคละคลุ้ง\nคลัก\nคลั่ก\nคลัง\nคลั่ง\nคลัตช์\nคลับคล้าย\nคลับคลา\nคลา\nคล้า\nคลางแคลง\nคลาด\nคลาน\nคลาย\nคล้าย\nคล้ายคลึง\nคล่าว\nคลำ\nคล่ำ\nคล้ำ\nคลิ้งโคลง\nคลิด\nคลินิก\nคลี\nคลี่\nคลึง\nคลื่น\nคลุก\nคลุ้ง\nคลุบ\nคลุม\nคลุ่ม\nคลุ้ม\nควง\nควณ\nควน\nควบ\nควย\nควร\nควัก\nควั่ก\nควั่งคว้าง\nควัน\nควั่น\nคว้า\nควาก\nคว้าง\nควาญ\nควาน\nคว้าน\nความ\nควาย\nคว่าว\nคว่ำ\nควินิน\nควิวคว่าง\nคหกรรม\nคหกรรมศาสตร์\nคหบดี\nคหัฐ\nคอ\nค้อ\nคอก\nค็อกคัส\nค็อกเทล\nคอเคซอยด์\nค่องอ้อย\nคอด\nคอแดง\nคอน\nค่อน\nค้อน\nคอนกรีต\nคอนเดนเซอร์\nคอนแวนต์\nคอนเสิร์ต\nคอม\nค่อม\nค้อม\nคอมพิวเตอร์\nคอมมานโด\nคอมมิวนิสต์\nคอย\nค่อย\nค้อย\nคอยล์\nคอร์ด\nคอแลน\nคอสติกโซดา\nคะ\nค่ะ\nคะไขว่\nคะค้อย\nคะคาน\nคะนน\nคะนอง\nคะน้า\nคะนึง\nคะเน\nคะเนงร้าย\nคะเน็ด\nคะแนน\nคะมำ\nคะยั้นคะยอ\nคะเยอ\nคัก\nคั่ก\nคัคนะ\nคัคนัมพร\nคัคนางค์\nคัคนานต์\nคั่ง\nคังไคย\nคัจฉ\nคัณฑมาลา\nคัณฑสูตร\nคัด\nคัดเค้า\nคัดมอน\nคัดเม็ง\nคัทลียา\nคัน\nคั่น\nคั้น\nคันถรจนาจารย์\nคันธกุฎี\nคันธมาทน์\nคันธารราษฎร์\nคับ\nคับค้อน\nคับคา\nคับแค\nคัพภ์\nคัมภีร์\nคัมภีรภาพ\nคัล\nคั่ว\nคา\nค่า\nค้า\nค่าคบ\nคาง\nค่าง\nค้าง\nคางคก\nค้างคาว\nคาด\nคาถา\nคาทอลิก\nคาน\nค้าน\nคาบ\nคาพยุต\nคาม\nคามวาสี\nคามณีย์\nคามภีร์\nคาย\nค่าย\nคาร์บอน\nคาร์บอเนต\nคาร์บอลิก\nคาร์บูเรเตอร์\nคาร์โบรันดัม\nคาร์โบไฮเดรต\nคารม\nคารวะ\nคาราเต้\nคาราวาน\nคาว\nค่าว\nค้าว\nคาวตอง\nคาวี\nคาวุต\nคาส\nคำ\nค่ำ\nค้ำ\nคำนวณ\nคำนวร\nคำนับ\nคำนัล\nคำนึง\nคำนูณ\nคำฝอย\nคำเพลิง\nคำรน\nคำรบ\nคำราม\nคำแสด\nคำแหง\nคำโอง\nคิก\nคิง\nคิด\nคิมหันต์\nคิรี\nคิลาน\nคิลานะ\nคิว\nคิ้ว\nคี่\nคีต\nคีบ\nคีม\nคีรี\nคีรีบูน\nคึก\nคึ่ก\nคึกคัก\nคืน\nคืบ\nคือ\nคุ\nคุก\nคุกกี้\nคุกคาม\nคุกพาทย์\nคุ้ง\nคุณ\nคุด\nคุดทะราด\nคุต\nคุตติ\nคุ่น\nคุ้น\nคุป\nคุปต์\nคุปติ\nคุม\nคุ่ม\nคุ้ม\nคุย\nคุ้ย\nคุยหฐาน\nคุยหประเทศ\nคุรุ\nคุลา\nคุลิก่า\nคุลีการ\nคุหา\nคู\nคู่\nคู้\nคูณ\nคูถ\nคูน\nคูปอง\nคูเรียม\nคูหา\nเค้ก\nเค้เก้\nเค้ง\nเคณฑะ\nเคด\nเค็ด\nเคน\nเค้น\nเคเบิล\nเค็ม\nเคมี\nเคย\nเครง\nเคร่ง\nเครงครา\nเครงครำ\nเครดิต\nเครน\nเครา\nเคร่า\nเคราหณี\nเคราะห์\nเครียด\nเครียว\nเครือ\nเครื่อง\nเคล้ง\nเคล็ด\nเคล้น\nเคล้า\nเคล่าคล่อง\nเคลิบเคลิ้ม\nเคลิ้ม\nเคลีย\nเคลื่อน\nเคลือบ\nเคว้ง\nเคหะ\nเคหา\nเคอะ\nเค้า\nเคาน์เตอร์\nเคารพ\nเคาะ\nเคาะแคะ\nเคียง\nเคียด\nเคียน\nเคียม\nเคี่ยม\nเคียร\nเคียว\nเคี่ยว\nเคี้ยว\nเคือง\nแค\nแค่\nแค้\nแคแล\nแคดเมียม\nแค็ตตาล็อก\nแคแตร\nแคโทด\nแคน\nแค่น\nแค้น\nแคบ\nแคบหมู\nแคปซูล\nแคม\nแคร่\nแครก\nแครง\nแคระ\nแคลคูลัส\nแคลง\nแคลเซียม\nแคลน\nแคล้ว\nแคล่วคล่อง\nแคลอรี\nแคลิฟอร์เนียม\nแคว\nแควก\nแคว้น\nแคแสด\nแคะ\nโค\nโคก\nโคเคน\nโค่ง\nโค้ง\nโคจร\nโคเซแคนต์\nโคไซน์\nโคตร\nโคแทนเจนต์\nโคธา\nโคน\nโค่น\nโคบอลต์\nโคปผกะ\nโคม\nโคม่า\nโครก\nโครกคราก\nโครง\nโคร่ง\nโคร่งคร่าง\nโครม\nโครเมียม\nโครโมโซม\nโคราช\nโครำ\nโคล\nโคลง\nโคลน\nโควตา\nโคออร์ดิเนต\nใคร\nใคร่\nใคร่ครวญ\nไค\nไค้\nไคร้\nไคร้เครือ\nไคล\nไคล้\nฆนะ\nฆราวาส\nฆ้อง\nฆ่า\nฆาต\nฆาน\nฆานินทรีย์\nเฆี่ยน\nโฆรวิส\nโฆษก\nโฆษณา\nโฆษะ\nโฆษ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nเฉ่ง\nเฉด\nเฉท\nเฉนียน\nเฉพาะ\nเฉย\nเฉลย\nเฉลว\nเฉลา\nเฉลิม\nเฉลี่ย\nเฉลียง\nเฉลี่ยง\nเฉลียบ\nเฉลียว\nเฉวียง\nเฉวียน\nเฉอะแฉะ\nเฉา\nเฉาก๊วย\nเฉาฮื้อ\nเฉาะ\nเฉิด\nเฉิบ\nเฉียง\nเฉียงพร้านางแอ\nเฉียด\nเฉียบ\nเฉียว\nเฉี่ยว\nเฉือน\nเฉื่อย\nแฉ\nแฉ่\nแฉก\nแฉง\nแฉ่ง\nแฉลบ\nแฉล้ม\nแฉะ\nโฉ\nโฉ่\nโฉเก\nโฉ่งฉ่าง\nโฉงเฉง\nโฉด\nโฉนด\nโฉบ\nโฉเบ๊\nโฉม\nโฉลก\nไฉน\nไฉไล\nชก\nชคัตตรัย\nชง\nชงโค\nชงฆ์\nชงฆา\nชงโลง\nชฎา\nชฎามังษี\nชฎามังสี\nชฎิล\nชด\nชน\nชนก\nชนนี\nชนม์\nชนวน\nชนะ\nชนัก\nชนา\nชนาง\nชนิด\nชเนตตี\nชบา\nชม\nชมดชม้อย\nชมนาด\nชมพู\nชมพู่\nชมรม\nชม้อย\nชม้าย\nชไม\nชยา\nชโย\nชรทึง\nชรริน\nชรอุ่ม\nชระล้ำ\nชระลุ\nชระอาบ\nชระเอม\nชรัด\nชรา\nชล\nชโลง\nชโลม\nช่วง\nชวด\nชวน\nชวย\nช่วย\nชวร\nชวลิต\nชวา\nชวาล\nชวาลา\nช่อ\nชอก\nช็อก\nช็อกโกเลต\nช็อกโกแลต\nชอง\nช่อง\nช้อง\nชองระอา\nชอน\nช่อน\nช้อน\nชอบ\nชอม\nช้อย\nชอล์ก\nชอ่ำ\nชอุ่ม\nชะ\nชะคราม\nชะงอก\nชะง่อน\nชะงัก\nชะงัด\nชะง้ำ\nชะงุ้ม\nชะเง้อ\nชะเงื้อม\nชะแง้\nชะโงก\nชะฉ่า\nชะช่อง\nชะชะ\nชะช้า\nชะโด\nชะตา\nชะต้า\nชะนี\nชะเนาะ\nชะเนียง\nชะพลู\nชะเพลิง\nชะมด\nชะมบ\nชะมวง\nชะมัง\nชะมัด\nชะแม่\nชะรอย\nชะลอ\nชะลอม\nชะล่า\nชะลาน\nชะลิน\nชะลูด\nชะเลง\nชะเลย\nชะแล็ก\nชะแลง\nชะวาก\nชะวาด\nชะเวิกชะวาก\nชะแวง\nชะอม\nชะอ้อน\nชะเอม\nชะโอน\nชัก\nชักคราม\nชักช้า\nชัง\nชั่ง\nชังคา\nชังฆ\nชัชวาล\nชัฏ\nชัด\nชัดช้า\nชัน\nชั้น\nชันกาด\nชันชี\nชันตุ\nชันนะตุ\nชันนุ\nชันโรง\nชันษา\nชันสูตร\nชัปนะ\nชัพ\nชัมพูนท\nชัย\nชัยพฤกษ์\nชัยภูมิ\nชัลลุกา\nชั่ว\nชั้ว\nชัวชม\nชา\nช้า\nชาคระ\nชาคริต\nชาคริยานุโยค\nช่าง\nช้าง\nช้าช่อน\nชาญ\nชาด\nชาดก\nชาต\nชาตบุษย์\nชาตรี\nชาตะ\nชาตา\nชาติ\nชาน\nชานุ\nช้าปี่\nชาปีไหน\nช้าแป้น\nช้าพลู\nชาม\nชามพูนท\nชามาดร\nชามาดา\nชามาตุ\nชาย\nชายา\nชาล\nชาลา\nชาลินี\nช้าเลือด\nชาว\nชาวี\nชำ\nช่ำ\nช้ำ\nชำงัด\nชำงาย\nช่ำชอง\nชำนะ\nชำนัญ\nชำนัน\nชำนาญ\nชำนิ\nชำเนียร\nชำมะนาด\nชำมะเลียง\nชำร่วย\nชำระ\nช้ำรั่ว\nชำรุด\nชำเรา\nชำเราะ\nชำแรก\nชำแระ\nชำเลือง\nชำแหละ\nชิ\nชิง\nชิ่ง\nชิงชัน\nชิงช้า\nชิงช้าชาลี\nชิงชี่\nชิงฮื้อ\nชิชะ\nชิชิ\nชิณณะ\nชิด\nชิเดนทรีย์\nชิต\nชิตินทรีย์\nชิน\nชิ้น\nชินโต\nชิโนรส\nชิม\nชิมแปนซี\nชิยา\nชิรณะ\nชิระ\nชิวหา\nชิสา\nชี\nชี่\nชี้\nชีปะขาว\nชีผะขาว\nชีผ้าขาว\nชีพ\nชีฟอง\nชีรณ\nชีระ\nชีวเคมี\nชีวประวัติ\nชีวภาพ\nชีววิทยา\nชีวะ\nชีวัน\nชีวา\nชีวาตม์\nชีวาลัย\nชีวิต\nชีวิตักษัย\nชีวิน\nชีวี\nชืด\nชื่น\nชื้น\nชื่อ\nชุก\nชุกชี\nชุ้ง\nชุณห\nชุด\nชุติ\nชุน\nชุบ\nชุม\nชุ่ม\nชุมนุม\nชุมพร\nชุมพา\nชุมแพรก\nชุมรุม\nชุมแสง\nชุมเห็ด\nชุ่ย\nชุลมุน\nชุลี\nชุษณะ\nชู\nชู้\nเช็ค\nเช้ง\nเช้งวับ\nเชงเลง\nเช็ด\nเชน\nเช่น\nเชย\nเชลง\nเชลย\nเชลแล็ก\nเชลียง\nเชวง\nเชษฐะ\nเชษฐา\nเชอ\nเช่า\nเช้า\nเชาว์\nเชาวน์\nเชิง\nเชิญ\nเชิด\nเชิ้ต\nเชียง\nเชี่ยน\nเชียบ\nเชี่ยม\nเชียร\nเชียร์\nเชียว\nเชี่ยว\nเชื่อ\nเชื้อ\nเชือก\nเชื่อง\nเชือด\nเชือน\nเชื่อม\nแช\nแช่\nแช่ง\nแชงมา\nแชบ๊วย\nแช่ม\nแชร์\nแชล่ม\nแชสซี\nแชะ\nโชก\nโชค\nโชงโลง\nโชดก\nโชดึก\nโชต\nโชตก\nโชติ\nโชติก\nโชน\nโชมโรม\nโชย\nโชยงการ\nโชยชาย\nโชยติส\nโชว์\nใช่\nใช้\nไช\nไชนะ\nไชย\nไชโย\nซก\nซ่ก\nซงดำ\nซ่งฮื้อ\nซด\nซน\nซ้น\nซบ\nซม\nซวดเซ\nซวน\nซวย\nซอ\nซอก\nซอง\nซ่อง\nซ้อง\nซองแมว\nซ้องแมว\nซอน\nซ่อน\nซ้อน\nซอม\nซ่อม\nซ้อม\nซอมซ่อ\nซอย\nซอส\nซัก\nซักส้าว\nซัง\nซั้ง\nซัด\nซับ\nซัลฟา\nซั้ว\nซา\nซ่า\nซาก\nซาง\nซ่าง\nซาด\nซาน\nซ่าน\nซาบซ่าน\nซาบซึ้ง\nซ่าโบะ\nซาแมเรียม\nซ้าย\nซาลาเปา\nซาว\nซ่าหริ่ม\nซำ\nซ้ำ\nซิ\nซี\nซิก\nซิกข์\nซิกซี้\nซิกแซ็ก\nซิการ์\nซิงโคนา\nซิ่น\nซินนามิก\nซินแส\nซิบ\nซิป\nซิฟิลิส\nซิลิคอน\nซิว\nซี่\nซีก\nซีเซียม\nซีด\nซี้ด\nซีนอน\nซีป่าย\nซีเมนต์\nซีเรียม\nซีลีเนียม\nซีอิ๊ว\nซึก\nซึง\nซึ่ง\nซึ้ง\nซึม\nซื่อ\nซื้อ\nซุก\nซุง\nซุน\nซุบ\nซุป\nซุ่ม\nซุ้ม\nซุ่มซ่าม\nซุย\nซู่\nซูโครส\nซูด\nซู้ด\nซูดซาด\nซูบ\nเซ\nเซ็ก\nเซแคนต์\nเซ็ง\nเซ่ง\nเซ้ง\nเซ็งแซ่\nเซต\nเซน\nเซ็น\nเซ่น\nเซนติกรัม\nเซนติเกรด\nเซนติเมตร\nเซนติลิตร\nเซปักตะกร้อ\nเซราะ\nเซรุ่ม\nเซลเซียส\nเซลล์\nเซลลูลอยด์\nเซลลูโลส\nเซ่อ\nเซอร์โคเนียม\nเซอะ\nเซา\nเซ้าซี้\nเซาะ\nเซิง\nเซิ้ง\nเซียน\nเซียบ\nเซียมซี\nเซียว\nเซี่ยว\nเซี่ยวกาง\nเซื่อง\nแซ\nแซ่\nแซ็กคาริน\nแซง\nแซงแซว\nแซด\nแซบ\nแซม\nแซยิด\nแซ่ว\nแซะ\nโซ\nโซ่\nโซก\nโซ่ง\nโซงโขดง\nโซเซ\nโซดา\nโซเดียม\nโซม\nโซรม\nโซลา\nไซ\nไซ้\nไซเกิล\nไซโคลน\nไซน์\nไซยาไนด์\nไซร้\nไซเรน\nไซโล\nฌาน\nฌาปน\nฌาปนกิจ\nฌาปนสถาน\nเฌอ\nเฌอเอม\nญวน\nญัตติ\nญาณ\nญาติ\nญานาซะฮ์\nญิบ\nญี่ปุ่น\nเญยธรรม\nไญยธรรม\nฎีกา\nฐกัด\nฐากูร\nฐาน\nฐานะ\nฐานันดร\nฐานานุกรม\nฐานานุรูป\nฐานานุศักดิ์\nฐานียะ\nฐาปน\nฐาปนา\nฐายี\nฐิต\nฐิติ\nฑาหก\nฑาหะ\nเฒ่า\nณรงค์\nเณร\nดก\nดง\nด้ง\nด้น\nดนโด่\nดนตรี\nดนัย\nดนุ\nดนู\nดบัสวิน\nดบัสวี\nดม\nดรงค์\nดรณี\nดรรชนี\nดราฟต์\nดรุณ\nดรุณี\nดล\nดวง\nด้วง\nดวด\nด่วน\nด้วน\nด้วย\nดอก\nดอง\nด่อง\nด้อง\nดองฉาย\nดองดึง\nดอด\nดอน\nด่อน\nดอม\nด้อม\nดอย\nด้อย\nดอลลาร์\nดะ\nดะโต๊ะ\nดะหมัง\nดัก\nดักดาน\nดักแด้\nดัง\nดั่ง\nดั้ง\nดัชนี\nดัด\nดัตช์\nดัน\nดั้น\nดับ\nดัมพ์\nดั้วเดี้ย\nดัสกร\nดา\nด่า\nดาก\nด่าง\nด้าง\nดาด\nดาน\nด่าน\nด้าน\nดาบ\nดาบส\nดาม\nด้าม\nด้ามจิ้ว\nดามพ์\nดาย\nด้าย\nดารกะ\nดารณี\nดารดาษ\nดาระ\nดารา\nดาล\nดาลัด\nดาว\nด่าว\nด้าว\nดาวดึงส์\nดาวบส\nดาษ\nดาษดา\nดำ\nด่ำ\nด้ำ\nดำกล\nดำเกิง\nดำแคง\nดำดง\nดำนาณ\nดำเนิน\nดำบล\nดำรง\nดำรวจ\nดำรัส\nดำริ\nดำรี\nดำรู\nดำฤษณา\nดำเลิง\nดิก\nดิ่ง\nดิฉัน\nดิฐ\nดิตถ์\nดิถี\nดิน\nดิ้น\nดิบ\nดิรัจฉาน\nดิลก\nดิ่ว\nดิ้ว\nดิ้วเดี้ยว\nดิษฐ์\nดิสโพรเซียม\nดี\nดีเซล\nดีด\nดีดีที\nดีบุก\nดีปลี\nดีเปรสชัน\nดีหมี\nดีหลี\nดึก\nดึง\nดึ่ง\nดึ่ม\nดื่น\nดื่ม\nดือ\nดื้อ\nดุ\nดุก\nดุกดิก\nดุกทะเล\nดุ้ง\nดุ้งดิ้ง\nดุจ\nดุด\nดุน\nดุ้น\nดุบ\nดุม\nดุ่ม\nดุ่ย\nดุรงค์\nดุริยะ\nดุริยางค์\nดุริยางคศาสตร์\nดุริยางคศิลป์\nดุล\nดุษฎี\nดุษณี\nดุษณีภาพ\nดุษิต\nดุสิต\nดุเหว่า\nดู\nดูกค่าง\nดูกร\nดูด\nดูรา\nดูแล\nเด\nเด่\nเดก\nเด็ก\nเดกซ์โทรส\nเดคากรัม\nเดคาเมตร\nเดคาลิตร\nเด้ง\nเด็จ\nเดช\nเดชน์\nเดชนะ\nเดชะ\nเดโช\nเดซิกรัม\nเดซิเมตร\nเดซิลิตร\nเด็ด\nเดน\nเด่น\nเดนมาร์ก\nเดรัจฉาน\nเด๋อ\nเด๋อด๋า\nเดา\nเด้า\nเดาะ\nเดิน\nเดิ่น\nเดิม\nเดียง\nเดียด\nเดียรดาษ\nเดียรถีย์\nเดียรัจฉาน\nเดียว\nเดี่ยว\nเดี๋ยว\nเดียะ\nเดื่อ\nเดือก\nเดื่อง\nเดือด\nเดือน\nเดือย\nแด\nแด่\nแดก\nแด็ก\nแดกงา\nแดกแด้\nแดง\nแดด\nแดน\nแด่น\nแด่ว\nแดะ\nแดะแด๋\nโด\nโด่\nโดกเดก\nโด่ง\nโดด\nโดน\nโดม\nโดมร\nโดย\nโดรณ\nใด\nได\nได้\nไดแซ็กคาไรด์\nไดนาโม\nไดนาไมต์\nไดโนเสาร์\nไดเรกตริกซ์\nตก\nต๊กโต\nตง\nต๋ง\nตงฉิน\nตงิด\nตงุ่น\nตด\nตติย\nตถาคต\nตน\nต้น\nตนัย\nตนุ\nตบ\nตบะ\nตปนียะ\nตม\nต้ม\nตมูก\nตยาคี\nตรง\nตรณี\nตรม\nตรรก\nตรรกะ\nตรลบ\nตรลอด\nตรลาด\nตรวจ\nตรวน\nตรอก\nตรอง\nตรอมใจ\nตรอมตรม\nตระ\nตระกล\nตระกวน\nตระกอง\nตระการ\nตระกูล\nตระคัร\nตระเตรียม\nตระนาว\nตระบก\nตระบอก\nตระบอง\nตระบัด\nตระบัน\nตระเบ็ง\nตระแบก\nตระแบง\nตระโบม\nตระพอง\nตระพัง\nตระลาการ\nตระวัน\nตระเว็ด\nตระเวน\nตระสัก\nตระหง่าน\nตระหนก\nตระหนัก\nตระหน่ำ\nตระหนี่\nตรัง\nตรังค์\nตรับ\nตรับฟัง\nตรัย\nตรัยตรึงศ์\nตรัส\nตรัสสา\nตรา\nตรากตรำ\nตราชู\nตราบ\nตราสัง\nตรำ\nตริ\nตริว\nตรี\nตรีปวาย\nตรีพิธพรรณ\nตรียัมปวาย\nตรึก\nตรึง\nตรุ\nตรุณ\nตรุณะ\nตรุษ\nตรู\nตรู่\nตฤณ\nตฤตีย\nตฤษณา\nตลก\nตลบ\nตลอด\nตลับ\nตลาด\nตลิ่ง\nตลึง\nตวง\nต่วน\nต้วมเตี้ยม\nตวัก\nตวัด\nตวาด\nตอ\nตอม่อ\nต่อ\nต้อ\nตอก\nต๊อก\nต๊อกต๋อย\nตอง\nต้อง\nตองกราย\nต้องเต\nตองแตก\nต่องแต่ง\nตองเหลือง\nตอด\nตอน\nต้อน\nตอบ\nตอเบา\nตอม\nต่อม\nต๋อม\nต่อย\nต้อย\nต้อยตริ่ง\nต้อยติ่ง\nต้อยตีวิด\nตอแย\nตอร์ปิโด\nต่อไส้\nตอแหล\nตะ\nตะกรน\nตะกร้อ\nตะกรัน\nตะกรับ\nตะกร้า\nตะกราม\nตะกรุด\nตะกรุม\nตะกรุมตะกราม\nตะกละ\nตะกลาม\nตะกวด\nตะกอ\nตะกอน\nตะกัง\nตะกั่ว\nตะกาง\nตะกาด\nตะกาย\nตะกาว\nตะกุกตะกัก\nตะกุย\nตะกู\nตะกูด\nตะเกียกตะกาย\nตะเกียง\nตะเกียบ\nตะแก\nตะแก่\nตะแกรง\nตะโก\nตะโก้\nตะโกก\nตะโกน\nตะโกรง\nตะโกรม\nตะไกร\nตะขบ\nตะขอ\nตะขาบ\nตะขิดตะขวง\nตะเข้\nตะเข็บ\nตะโขง\nตะคร้อ\nตะครอง\nตะครั่นตะครอ\nตะคร้ำ\nตะคริว\nตะคิว\nตะครุบ\nตะคอก\nตะคัน\nตะค้า\nตะคาก\nตะค้าน\nตะคุ่ม\nตะเครียว\nตะเคียว\nตะเคียน\nตะแคง\nตะไคร่\nตะไคร้\nตะเฆ่\nตะนอย\nตะนาว\nตะบม\nตะบอง\nตะบอย\nตะบัน\nตะบิ้ง\nตะบิด\nตะบิดตะบอย\nตะบี้ตะบัน\nตะบึง\nตะบูน\nตะเบ็ง\nตะเบ็งมาน\nตะเบ๊ะ\nตะแบก\nตะแบง\nตะโบม\nตะไบ\nตะปบ\nตะปลิง\nตะปิ้ง\nตะปุ่มตะป่ำ\nตะปู\nตะพง\nตะพด\nตะพอง\nตะพัก\nตะพัง\nตะพัด\nตะพั้น\nตะพาก\nตะพาน\nตะพาบ\nตะพาย\nตะพึด\nตะพึดตะพือ\nตะพุ่น\nตะเพรา\nตะเพิง\nตะเพิด\nตะเพียน\nตะโพก\nตะโพง\nตะโพน\nตะเภา\nตะใภ้\nตะม่อ\nตะมอย\nตะรังกะนู\nตะรังตังกวาง\nตะรังตังช้าง\nตะราง\nตะลอง\nตะลอน\nตะล่อม\nตะละ\nตะลาน\nตะลิงปลิง\nตะลิบ\nตะลีตะลาน\nตะลึง\nตะลึงพรึงเพริด\nตะลุง\nตะลุ่ม\nตะลุ่มนก\nตะลุมบอน\nตะลุ่มโปง\nตะลุมพอ\nตะลุมพุก\nตะลุย\nตะเลง\nตะแลงแกง\nตะไล\nตะวัน\nตะเวน\nตะหลิว\nตะหลุก\nตะหลุง\nตะแหง่ว\nตะแหมะแขะ\nตะโหงก\nตัก\nตักกะ\nตักเตือน\nตั๊กแตน\nตักษัย\nตัง\nตั่ง\nตั้ง\nตังเก\nตังฉ่าย\nตังเม\nตังวาย\nตังโอ๋\nตัจฉก\nตัจฉนี\nตัณฑุล\nตัณหา\nตัด\nตัน\nตันตระ\nตันติ\nตันหยง\nตับ\nตับปิ้ง\nตัว\nตั๋ว\nตัวจี๊ด\nตัวตืด\nตั้วโผ\nตั้วเหี่ย\nตา\nตาก\nตากวาง\nต่าง\nตาด\nตาน\nต่าน\nต้าน\nตานนกกด\nตานี\nตาบ\nตาม\nตามะแน\nตามิน\nตาย\nตาราไต\nตาล\nตาลุ\nต๋าว\nตาเสือ\nตาหนู\nตาฬ\nตำ\nต่ำ\nตำนาน\nตำบล\nตำแบ\nตำแย\nตำรวจ\nตำรับ\nตำรา\nตำรุ\nตำลึง\nตำเสา\nตำหนัก\nตำหนิ\nตำแหน่ง\nติ\nติก\nติ๊ก\nติกะ\nติกาหรัง\nติง\nติ่ง\nติ๋ง\nติ่งตั่ง\nติ๋งต่าง\nติงส\nติงสติ\nติณ\nติด\nติตติกะ\nติตติร\nติตถ\nติตถะ\nติถี\nติมิงคละ\nติรัจฉาน\nติลก\nติละ\nติ้ว\nตี\nตี่\nตีน\nตีบ\nตีรถะ\nตีระ\nตึ\nตึก\nตึ้ก\nตึ้กตั้ก\nตึง\nตึดตื๋อ\nตึ๊ดตื๋อ\nตืด\nตื่น\nตื้น\nตื้อ\nตื๊อ\nตื๋อ\nตุ\nตุ๊\nตุ๊กแก\nตุ๊กตา\nตุ๊กต่ำ\nตุกติก\nตุ๊กติ๊ก\nตุ๊กตุ่น\nตุ๊กตุ๋ย\nตุง\nตุ้งก่า\nตุ้งติ้ง\nตุ๊ดตู่\nตุน\nตุ่น\nตุ๋น\nตุนาหงัน\nตุบ\nตุ้บ\nตุ๊บป่อง\nตุปัดตุป่อง\nตุปัดตุเป๋\nตุ่ม\nตุ้ม\nตุ๋ม\nตุ้มกว้าว\nตุมกา\nตุ้มแซะ\nตุมตัง\nตุ้มเต๋น\nตุ้มปี่\nตุมพะ\nตุ่ย\nตุ้ย\nตุ๊ย\nตุ๋ยตุ่ย\nตุรคะ\nตุรงค์\nตุล\nตุลา\nตุหรัดตุเหร่\nตู\nตู่\nตู้\nตูก\nตูด\nตูบ\nตูม\nเต๊ก\nเต็ง\nเต่ง\nเตช\nเตโช\nเต้น\nเต็นท์\nเต็ม\nเตย\nเตร่\nเตร็ด\nเตรตา\nเตรียม\nเตรียมตรม\nเตละ\nเตลิด\nเตว็ด\nเต่อ\nเตอะ\nเตะ\nเตา\nเต่า\nเต้า\nเต๋า\nเต่าเกียด\nเต้าเจี้ยว\nเต้าทึง\nเต้าหู้\nเต้าฮวย\nเต๊าะ\nเตาะแตะ\nเติ่ง\nเติบ\nเติม\nเตี้ย\nเตียง\nเตียน\nเตียบ\nเตี๋ยม\nเตียรถ์\nเตียว\nเตี่ยว\nเตือน\nแต่\nแต้\nแตก\nแตง\nแต่ง\nแตงเม\nแต้จิ๋ว\nแตด\nแต๊ดแต๋\nแตน\nแต้ม\nแตร\nแตระ\nแต้ว\nแต้วแร้ว\nแต้วแล้ว\nแต่ว่า\nแต้แว้ด\nแตะ\nโต\nโต้\nโตก\nโต่ง\nโต้ง\nโตงเตง\nโตฎก\nโต๊ด\nโตน\nโตนด\nโต้โผ\nโตมร\nโตย\nโตรก\nโต๊ะ\nใต้\nไต\nไต่\nไต้\nไต๋\nไตร\nไตรกิศยา\nไตรดายุค\nไตร่ตรอง\nไตรย\nไต้หวัน\nถก\nถกล\nถงาด\nถด\nถนน\nถนอม\nถนัด\nถนัน\nถนำ\nถนิม\nถม\nถ่ม\nถมอ\nถมึงทึง\nถลก\nถลกบาตร\nถลน\nถล่ม\nถลอก\nถลัน\nถลา\nถลาก\nถลาย\nถลำ\nถลึงตา\nถลุง\nถ่วง\nถ้วน\nถ้วย\nถวัล\nถวัลย์\nถวาย\nถวิน\nถวิล\nถ่อ\nถ้อ\nถอก\nถอง\nถ่อง\nถ้อง\nถอด\nถอน\nถอบ\nถอบแถบ\nถ่อม\nถอย\nถ่อย\nถ้อย\nถะ\nถะถั่น\nถะมัดถะแมง\nถัก\nถัง\nถั่ง\nถัด\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นยา\nบิลเลียด\nบิวเรตต์\nบิสมัท\nบี้\nบีฑา\nบีตา\nบีบ\nบีเยศ\nบึก\nบึกบึน\nบึง\nบึ่ง\nบึ้ง\nบุ\nบุก\nบุคคล\nบุคลากร\nบุคลาธิษฐาน\nบุคลิก\nบุง\nบุ่ง\nบุ้ง\nบุ้งกี๋\nบุญ\nบุญญาธิการ\nบุญญานุภาพ\nบุญญาภินิหาร\nบุญญาภิสังขาร\nบุณฑริก\nบุณมี\nบุณย์\nบุตร\nบุตรี\nบุถุชน\nบุทคล\nบุนนาค\nบุบ\nบุบบิบ\nบุปผชาติ\nบุพกรรม\nบุพการี\nบุพกิจ\nบุพชาติ\nบุพทักษิณ\nบุพนิมิต\nบุพบท\nบุพพาจารย์\nบุพเพสันนิวาส\nบุพโพ\nบุ๋ม\nบุ่มบ่าม\nบุ้ย\nบุรณะ\nบุรพทิศ\nบุรพบท\nบุรพาจารย์\nบูรพาจารย์\nบุระ\nบุราณ\nบุรินทร์\nบุริมทิศ\nบุริมพรรษา\nบุริมสิทธิ\nบุรี\nบุรุษ\nบุโรทั่ง\nบุษกร\nบุษบก\nบุษบง\nบุษบัน\nบุษบา\nบุษบามินตรา\nบุษปราค\nบุษปะ\nบุษย์\nบุษย์น้ำทอง\nบุษยมาส\nบุษยะ\nบุษราคัม\nบุหงัน\nบุหงา\nบุหรง\nบุหรี่\nบุหลัน\nบู่\nบู้\nบูชนียสถาน\nบูชา\nบูชิต\nบูด\nบูดู\nบูร\nบูรณ์\nบูรณภาพ\nบูรณมี\nบูรณะ\nบูรณาการ\nบูรพ์\nบูรพะ\nบูรพา\nเบ้\nเบ่ง\nเบ๊จี๋\nเบญกานี\nเบญจกัลยาณี\nเบญจกามคุณ\nเบญจขันธ์\nเบญจดุริยางค์\nเบญจธรรม\nเบญจบรรพต\nเบญจพรรณ\nเบญจเพส\nเบญจมาศ\nเบญจรงค์\nเบญจศก\nเบญจศีล\nเบญจะ\nเบญจา\nเบญจางค์\nเบญจางคประดิษฐ์\nเบญญา\nเบญพาด\nเบ็ด\nเบ็ดเตล็ด\nเบ็ดเสร็จ\nเบน\nเบนซิน\nเบรก\nเบริลเลียม\nเบส\nเบ้อ\nเบอร์\nเบอร์คีเลียม\nเบ้อเร่อ\nเบ้อเร่อเท่อ\nเบ้อเริ่ม\nเบ้อเริ่มเทิ่ม\nเบอะ\nเบอะบะ\nเบะ\nเบา\nเบ้า\nเบาราณ\nเบาะ\nเบิก\nเบิ่ง\nเบี้ย\nเบี่ยง\nเบียด\nเบียน\nเบียร์\nเบี้ยว\nเบือ\nเบื่อ\nเบื้อ\nเบื้อง\nเบือน\nแบ\nแบ้\nแบก\nแบคทีเรีย\nแบ่ง\nแบงก์\nแบดมินตัน\nแบตเตอรี่\nแบน\nแบนโจ\nแบบ\nแบ็บ\nแบเรียม\nแบหลา\nแบะ\nโบ\nโบ้\nโบ๋\nโบก\nโบกขรณี\nโบกขรพรรษ\nโบชุก\nโบต\nโบนัส\nโบ๊เบ๊\nโบย\nโบรมีน\nโบรอน\nโบราณ\nโบสถ์\nใบ\nใบ้\nไบ่\nปก\nปกติ\nปกรณ์\nปกรณัม\nปกิณกะ\nปกีรณัม\nปโกฏิ\nปง\nป่ง\nปงช้าง\nปฎล\nปฏัก\nปฏิกรณ์\nปฏิกรรม\nปฏิการะ\nปฏิกิริยา\nปฏิกูล\nปฏิคม\nปฏิคหิต\nปฏิคาหก\nปฏิฆะ\nปฏิชีวนะ\nปฏิญญา\nปฏิญาณ\nปฏิทิน\nปฏิบถ\nปฏิบัติ\nปฏิปทา\nปฏิปักษ์\nปฏิปัน\nปฏิปุจฉาพยากรณ์\nปฏิปุจฉาวาที\nปฏิพัทธ์\nปฏิพากย์\nปฏิภาค\nปฏิภาณ\nปฏิมา\nปฏิมากร\nปฏิยุทธ์\nปฏิรพ\nปฏิรูป\nปฏิโลม\nปฏิวัติ\nปฏิวาต\nปฏิวาท\nปฏิเวธ\nปฏิสนธิ\nปฏิสวะ\nปฏิสังขรณ์\nปฏิสันถาร\nปฏิสัมภิทา\nปฏิเสธ\nปฐพี\nปฐม\nปฐวี\nปณต\nปณาม\nปณิธาน\nปณิธิ\nปณีต\nปด\nปดิวรัดา\nปติ\nปถพี\nปถมัง\nปถวี\nปทัฏฐาน\nปทัสถาน\nปทานุกรม\nปทีป\nปทุม\nปน\nป่น\nปนัดดา\nปปัญจะ\nปม\nปรนัย\nปรปักษ์\nปรโลก\nปรวาที\nปรก\nปรกติ\nปรง\nปรตยักษ์\nปรน\nปรนนิบัติ\nปรนิมมิตวสวัตดี\nปรบ\nปรปักษ์\nปรมัตถ์\nปรมาจารย์\nปรมาณู\nปรมาภิไธย\nปรมาภิเษก\nปรมินทร์\nบรเมนทร์\nปรเมศวร์\nปรเมษฐ์\nปรวด\nปรวนแปร\nปรศุ\nปรสิต\nปร๋อ\nปรองดอง\nปรอด\nปรอท\nปรอย\nประ\nประกบ\nประกฤต\nประกฤติ\nประกล\nประกวด\nประกวดประขัน\nประกอบ\nประกัน\nประกับ\nประกาย\nประกายพรึก\nประการ\nประกาศ\nประกาศนียบัตร\nประกาศิต\nประกำ\nประกิด\nประกิต\nประคด\nประคนธรรพ\nประคนธรรพ์\nประคบ\nประคบประหงม\nประคอง\nประคับประคอง\nประคัลภ์\nประคำ\nประคิ่น\nประคุณ\nประเคน\nประเคราะห์\nประโคน\nประโคนธรรพ\nประโคนธรรพ์\nประโคม\nประจง\nประจญ\nประจบ\nประจบประแจง\nประจวบ\nประจ๋อประแจ๋\nประจักษ์\nประจักษนิยม\nประจัญ\nประจัน\nประจาก\nประจาค\nประจาน\nประจำ\nประจิม\nประจิ้มประเจ๋อ\nประจุ\nประจุคมน์\nประจุบัน\nประเจก\nประเจิด\nประเจิดประเจ้อ\nประเจียด\nประแจ\nประชด\nประชน\nประชวม\nประชวร\nประชัน\nประชา\nประชาธิปไตย\nประชิด\nประชี\nประชุม\nประเชิญ\nประณต\nประณม\nประณาม\nประณิธาน\nประณิธิ\nประณีต\nประณุท\nประดง\nประดน\nประดวน\nประดอน\nประดอย\nประดัก\nประดักประเดิด\nประดัง\nประดับ\nประดา\nประดาก\nประดาป\nประดาษ\nประดิชญา\nประดิดประดอย\nประดิทิน\nประดิษฐ์\nประดิษฐกรรม\nประดิษฐาน\nประดุง\nประดุจ\nประดู่\nประเด\nประเด็น\nประเดิม\nประเดียง\nประเดี๋ยว\nประเดี๋ยวประด๋าว\nประแดง\nประแดะ\nประโดง\nประโดย\nประตง\nประตัก\nประตาป\nประติชญา\nประติญาณ\nประติทิน\nประติมากร\nประติมากรรม\nประติรพ\nประตู\nประถม\nประถมจินดา\nประทม\nประท้วง\nประทวน\nประทักษ์\nประทักษิณ\nประทัง\nประทัด\nประทับ\nประทาน\nประทาย\nประทาศี\nประทิน\nประทิ่น\nประทีป\nประทุฐ\nประทุน\nประทุษ\nประทุษฐ์\nประเทศ\nประเทา\nประเทียด\nประเทียบ\nประเทือง\nประธาน\nประธานาธิบดี\nประนม\nประนอ\nประนอม\nประนัง\nประนัปดา\nประนีประนอม\nประปราน\nประปราย\nประปา\nประเปรี้ยง\nประเปรียว\nประพจน์\nประพนธ์\nประพรม\nประพฤติ\nประพฤทธิ์\nประพัด\nประพัทธ์\nประพันธ์\nประพาต\nประพาส\nประพาสมหรณพ\nประพาฬ\nประพิณ\nประพิมพ์ประพาย\nประพุทธ์\nประเพณี\nประโพธ\nประไพ\nประไพร\nประภพ\nประภัสสร\nประภา\nประภาคาร\nประภาพ\nประภาษ\nประภาส\nประเภท\nประมง\nประมวล\nประมาณ\nประมาท\nประมุข\nประมุท\nประมูล\nประเมิน\nประโมง\nประโมทย์\nประยงค์\nประยุกต์\nประยุทธ์\nประยุร\nประยูร\nประโยค\nประโยชน์\nประโรหิต\nประลมพ์\nประลอง\nประลัย\nประลาต\nประลาย\nประลุ\nประเล่ห์\nประเล้าประโลม\nประโลม\nประวรรต\nประวรรตน์\nประวัติ\nประวาล\nประวาลปัทม์\nประวาส\nประวิง\nประวิช\nประวิตร\nประวิน\nประวีณ\nประเวณี\nประเวศ\nประเวศน์\nประศม\nประศาสน์\nประศุ\nประสก\nประสงค์\nประสบ\nประสพ\nประสม\nประสะ\nประสัก\nประสันนาการ\nประสัยห์\nประสา\nประสาท\nประสาธน์\nประสาน\nประสาร\nประสิทธิ์\nประสิทธิผล\nประสิทธิภาพ\nประสีประสา\nประสูต\nประสูติ\nประเสบัน\nประเสบันอากง\nประเสริฐ\nประหนึ่ง\nประหม่า\nประหยัด\nประหลาด\nประหล่ำ\nประหวัด\nประหวั่น\nประหัตประหาร\nประหาณ\nประหาร\nประเหล\nประเหส\nประไหมสุหรี\nประอบ\nประอร\nปรัก\nปรักปรำ\nปรักมะ\nปรัง\nปรัชญา\nปรัตถจริยา\nปรัตยุบัน\nปรัน\nปรับ\nปรัมปรา\nปรัศจิม\nปรัศนา\nปรัศนี\nปรัศว์\nปรัสสบท\nปร่า\nปรากฏ\nปรากรม\nปรากฤต\nปราการ\nปราง\nปรางค์\nปราจีน\nปราชญ์\nปราชญา\nปราชัย\nปราณ\nปราณี\nปราด\nปราน\nปรานี\nปราบ\nปราบดาภิเษก\nปราปต์\nปราม\nปรามาส\nปราโมช\nปราโมทย์\nปราย\nปรารถนา\nปรารภ\nปรารมภ์\nปราศ\nปราศจาก\nปราศรัย\nปราษณี\nปราษาณ\nปราสัย\nปราสาท\nปรำ\nปริ\nปริก\nปริกขาร\nปริกรรม\nปริกัป\nปริคณห์\nปริจาค\nปริจาริกา\nปริเฉท\nปริชน\nปริซึม\nปริญญา\nปริณาม\nปริณายก\nปริต\nปริตตะ\nปริตโตทก\nปริตร\nปริทรรศน์\nปริทัยหัคคี\nปริทัศน์\nปริเทพ\nปริเทวะ\nปรินิพพาน\nปริบ\nปริบท\nปริปันถ์\nปริพนธ์\nปริพัตร\nปริพันธ์\nปริพาชก\nปริภัณฑ์\nปริภาษ\nปริภูมิ\nปริโภค\nปริ่ม\nปริมณฑล\nปริมาณ\nปริมาตร\nปริยัติ\nปริยานุช\nปริยาย\nปริเยศ\nปริโยสาน\nปริวรรต\nปริวัตร\nปริวาร\nปริวาส\nปริวิตก\nปริเวณ\nปริศนา\nปริษัท\nปริสัญญู\nปริสุทธิ\nปริหาน\nปริหาร\nปรี่\nปรีชญา\nปรีชา\nปรี๊ด\nปรีดา\nปรีดิ\nปรีดิ์\nปรีดี\nปรีติ\nปรียะ\nปรียา\nปรึก\nปรึกษา\nปรึง\nปรือ\nปรื๋อ\nปรุ\nปรุง\nปรู\nปรู๋\nปรูด\nปรู๊ด\nปรู๊ดปร๊าด\nปรู๊ฟ\nปฤงคพ\nปฤจฉา\nปฤษฎางค์\nปฤษฐ\nปลก\nปลกเปลี้ย\nปลง\nปล่ง\nปลด\nปล้น\nปลวก\nปลอก\nปล่อง\nปล้อง\nปลอด\nปล้อน\nปลอบ\nปลอม\nปล่อย\nปละ\nปลัก\nปลั๊ก\nปลัง\nปลั่ง\nปลัด\nปลัดขิก\nปลา\nปลาต\nปลาบ\nปลาย\nปลาสเตอร์\nปลาสนาการ\nปล้ำ\nปลิง\nปลิด\nปลิ้น\nปลิโพธ\nปลิม\nปลิ่ม\nปลิว\nปลี\nปลีก\nปลื้ม\nปลุก\nปลูก\nปวกเปียก\nปวง\nป่วง\nปวด\nป่วน\nป้วน\nป้วนเปี้ยน\nป่วย\nปวัตน์\nปวารณา\nปวาล\nปวาส\nปวาฬ\nปวิช\nปวิตร\nปวิธ\nปวิเวก\nปวีณ\nปวุติ\nปเวณี\nปเวส\nปเวสน์\nปศุ\nปสันนะ\nปสันนาการ\nปสาท\nปสาน\nปสาสน์\nปสุ\nปสุต\nปสูติ\nปหังสนะ\nปหาน\nปหาร\nปหาส\nปอ\nป้อ\nป๋อ\nปอก\nปอง\nป่อง\nป้อง\nปอด\nปอน\nป้อน\nปอนด์\nปอเนาะ\nปอบ\nป้อแป้\nปอม\nป้อม\nป๋อม\nปอย\nป้อย\nป้อยอ\nปะ\nปะกน\nปะกัง\nปะการัง\nปะกำ\nปะขาว\nปะงาบ\nปะตาปา\nปะตาระกาหลา\nปะติดปะต่อ\nปะติยาน\nปะทะ\nปะทะปะทัง\nปะทุ\nปะทุน\nปะปน\nปะมง\nปะราลี\nปะรำ\nปะไร\nปะลอม\nปะเลง\nปะแล่ม\nปะโลง\nปะวะหล่ำ\nปะหงับ\nปะหนัน\nปะหัง\nปะเหลาะ\nปัก\nปักข์\nปักเป้า\nปักษ์\nปักษา\nปักษิน\nปักษี\nปัง\nปั๋ง\nปังสุ์\nปังสุกุล\nปัจจัตตะ\nปัจจันต์\nปัจจันตคาม\nปัจจันตชนบท\nปัจจันตประเทศ\nปัจจัย\nปัจจามิตร\nปัจจุคมน์\nปัจจุทธรณ์\nปัจจุบัน\nปัจจุสมัย\nปัจเจก\nปัจโจปการกิจ\nปัจฉา\nปัจฉิม\nปัจถรณ์\nปัจนึก\nปัจยาการ\nปัจเวกขณ์\nปัชชุน\nปัญจนที\nปัญจวัคคีย์\nปัญจก\nปัญจกะ\nปัญจมี\nปัญจวีสติ\nปัญญัติ\nปัญญา\nปัญหา\nปัฏ\nปัฏนะ\nปัฐยาวัต\nปัณฑรหัตถี\nปัณณะ\nปัณณาส\nปัณณาสก์\nปัณรสี\nปัณหิ\nปัด\nปัตคาด\nปัตตะ\nปัตตานึก\nปัตตานุโมทนา\nปัตตาเวีย\nปัตติ\nปัตติก\nปัตถร\nปัตถะ\nปัตนิ\nปัตนี\nปัตหล่า\nปัถพี\nปัถวี\nปัทม์\nปัทมะ\nปัทมาสน์\nปัน\nปั่น\nปั้น\nปั้นจั่น\nปันจุเหร็จ\nปั้นลม\nปั้นหยา\nปั้นเหน่ง\nปับ\nปั๊บ\nปัปผาสะ\nปัพพาชนียกรรม\nปัพภาระ\nปั๊ม\nปัยกา\nปัยยิกา\nปัวเปีย\nปัศจิม\nปัศตัน\nปัสสาวะ\nปัสสาสะ\nปา\nป่า\nป้า\nปาก\nปาง\nป้าง\nปาจรีย์\nปาจิตตีย์\nปาจีน\nปาฏลิ\nปาฏิบท\nปาฏิบุคลิก\nปาฏิโภค\nปาฏิหาริย์\nปาฐก\nปาฐกถา\nปาฐะ\nปาณกชาติ\nปาณะ\nปาณาติบาต\nปาณิ\nปาณี\nปาด\nปาติโมกข์\nปาตี\nปาเต๊ะ\nปาท่องโก๋\nปาทังกา\nปาทาน\nปาทุกา\nปาน\nป่าน\nป้าน\nปานะ\nปานียะ\nป้าบ\nป๊าบ\nปาพจน์\nปาม\nปาโมกข์\nป่าย\nป้าย\nปายาส\nปาร์เกต์\nปารมี\nปารเมศ\nปาราชิก\nปาริฉัตร\nปาริชาต\nปารุสกวัน\nปาล\nปาล์ม\nปาลิไลยก์\nปาลี\nปาว\nป่าว\nป๊าว\nปาวาร\nปาษาณ\nปาส\nปาสาณ\nปาสาทิกะ\nปาหนัน\nปาหี่\nปำ\nป้ำ\nป้ำเป๋อ\nปิกนิก\nปิ้ง\nปิงคละ\nปิงปอง\nปิฎก\nปิฏฐะ\nปิฐิ\nปิณฑะ\nปิด\nปิตตะ\nปิตา\nปิตามหัยกา\nปิตามหัยยิกา\nปิตุ\nปิตุจฉา\nปิตุภูมิ\nปิตุลา\nปิโตรเลียม\nปิ่น\nปิ่นแก้ว\nปิ่นโต\nปิปผลี\nปิ่ม\nปิ้ม\nปิยะ\nปิยังคุ\nปิโยรส\nปิลันธน์\nปิ๋ว\nปิศาจ\nปิหกะ\nปี\nปี่\nปี้\nปี๋\nปีก\nปีฐะ\nปี๊ด\nปีติ\nปีน\nปีบ\nปี๊บ\nปีศาจ\nปีฬกะ\nปึก\nปึง\nปึ่ง\nปึ๋ง\nปึ้ด\nปึมปื้อ\nปืน\nปื้น\nปือ\nปื้อ\nปื๋อ\nปุ\nปุ๊\nปุก\nปุกปุย\nปุคละ\nปุ้งกี๋\nปุงควะ\nปุงลิงค์\nปุงลึงค์\nปุจฉา\nปุฏะ\nปุณฑริก\nปุด\nปุตตะ\nปุถุชน\nปุนนาค\nปุนภพ\nปุนัพสุ\nปุบ\nปุ๊บ\nปุปผะ\nปุปะ\nปุพพะ\nปุ่ม\nปุ่มป่ำ\nปุ้ม\nปุ๋ม\nปุย\nปุ้ย\nปุ๋ย\nปุรณะ\nปุระ\nปุราณ\nปุราณะ\nปุริมพรรษา\nปุเรจาริก\nปุโรหิต\nปุลลิงค์\nปุลลึงค์\nปุลินท์\nปุโลปุเล\nปุษยะ\nปุสสะ\nปู\nปู่\nปูชกะ\nปูชนียบุคคล\nปูชนียวัตถุ\nปูชนียะ\nปูชา\nปูชิต\nปูด\nปูน\nปูม\nปู้ยี่ปู้ยำ\nปูระ\nปูลู\nเป้\nเป๋\nเปก\nเป๊ก\nเป่ง\nเป้ง\nเป๋ง\nเป็ด\nเปตพลี\nเปตอง\nเป็น\nเปยยาล\nเปร็ง\nเปรต\nเปรม\nเปรย\nเปรอ\nเปรอะ\nเปราะ\nเปรียง\nเปรี้ยง\nเปรี้ยงปร้าง\nเปรียญ\nเปรียบ\nเปรี่ยม\nเปรียว\nเปรี้ยว\nเปรียะ\nเปรี๊ยะ\nเปรื่อง\nเปรื้อย\nเปล\nเปล่ง\nเปลว\nเปลา\nเปล่า\nเปล้า\nเปลาะ\nเปลี้ย\nเปลี่ยน\nเปลี่ยม\nเปลี่ยว\nเปลือก\nเปลือง\nเปลื้อง\nเปลือย\nเปศะ\nเปศัส\nเปสการ\nเปสละ\nเปสุญวาท\nเป๋อ\nเป้อเย้อ\nเปอร์\nเปอร์เซ็นต์\nเปะ\nเปา\nเป่า\nเป้า\nเป๊า\nเป๋า\nเป๋าฮื้อ\nเปาะ\nเป๊าะ\nเปาะเปี๊ยะ\nเปาะแปะ\nเปิก\nเปิง\nเปิงมาง\nเปิด\nเปิ่น\nเปิบ\nเปิ๊บ\nเปีย\nเปียก\nเปี๊ยก\nเปี๊ยบ\nเปี่ยม\nเปี้ยว\nเปี๊ยว\nเปียะ\nเปี๊ยะ\nเปือก\nเปื้อน\nเปื่อย\nแป\nแป้ง\nแป๋ง\nแปด\nแป๊ด\nแปทู\nแป้น\nแป๊น\nแปบ\nแป๊บ\nแปม\nแปร\nแปร๋\nแปรก\nแปรง\nแปร่ง\nแปร๊ด\nแปร้น\nแปร๋น\nแปรปรวน\nแประ\nแปล\nแปล้\nแปลก\nแปลง\nแปลน\nแปลบ\nแปล๊บ\nแป้ว\nแป๊ว\nแปะ\nแป๊ะ\nแป๊ะซะ\nโป\nโป่\nโป้\nโป๊\nโปก\nโป๊ก\nโป๊กเกอร์\nโปกขรณี\nโปกขรพรรษ\nโปเก\nโปง\nโป่ง\nโป้ง\nโป่งข่าม\nโปงลาง\nโป่งวิด\nโปฐบท\nโปฐปทมาส\nโปดก\nโปตถกะ\nโปน\nโป๊ป\nโป๊ยเซียน\nโปรแกรม\nโปรง\nโปร่ง\nโปรด\nโปรตอน\nโปรตีน\nโปรเตสแตนต์\nโปรแทรกเตอร์\nโปรย\nโปล่ง\nโปลิโอ\nโปโล\nโปสก\nโปสต์การ์ด\nโปะ\nโป๊ะ\nโป๊ะจ้าย\nไป\nไป่\nไป๋\nไปยาล\nไปรษณีย์\nไปรษณียบัตร\nไปรษณียภัณฑ์\nไปรษณียากร\nไปล่\nไปศาจ\nผก\nผกา\nผกาย\nผคม\nผง\nผงก\nผงม\nผงะ\nผงาด\nผง่าน\nผจง\nผจญ\nผจัญ\nผจาน\nผชุม\nผณิน\nผณินทร\nผณิศวร\nผด\nผดุง\nผเดิน\nผทม\nผนวก\nผนวช\nผนัง\nผนิด\nผนึก\nผม\nผยอง\nผรณาปีติ\nผรสุ\nผริต\nผรุสวาท\nผล\nผลคุน\nผลคุนี\nผล็อย\nผละ\nผลัก\nผลัด\nผลับ\nผลัวะ\nผลา\nผลาญ\nผลานิสงส์\nผลาผล\nผลาหาร\nผลิ\nผลิกะ\nผลิต\nผลิน\nผลี\nผลีผลาม\nผลึก\nผลึ่ง\nผลือ\nผลุ\nผลุง\nผลุด\nผลุน\nผลุนผลัน\nผลุบ\nผลุบผลับ\nผลุย\nผลู\nผวน\nผวย\nผวา\nผสม\nผสาน\nผอง\nผ่อง\nผ่อน\nผอบ\nผอม\nผ็อย\nผอูน\nผะ\nผะดา\nผะสา\nผัก\nผัคคุณ\nผัคคุณี\nผัง\nผัด\nผัน\nผับ\nผัว\nผัวะ\nผัสสะ\nผัสสาหาร\nผา\nผ่า\nผ้า\nผาก\nผาง\nผ่าง\nผาณิต\nผาด\nผาติ\nผ่าน\nผาม\nผาย\nผ่ายผอม\nผาล\nผาลคุน\nผาลา\nผ่าว\nผาสุก\nผ้าฮาด\nผำ\nผ้ำ\nผิ\nผิง\nผิด\nผิตะ\nผิน\nผิว\nผี\nผี้ว์\nผึง\nผึ่ง\nผึ้ง\nผึ้งรวง\nผืน\nผื่น\nผุ\nผุด\nผุยผง\nผุสราคา\nผู้\nผูก\nเผ\nเผง\nเผชิญ\nเผ็ด\nเผด็จ\nเผดิม\nเผดียง\nเผ่น\nเผนิก\nเผย\nเผยอ\nเผยิบ\nเผยิบผยาบ\nเผล\nเผล่\nเผล็ด\nเผลอ\nเผลอไผล\nเผละ\nเผลาะ\nเผลาะแผละ\nเผลียง\nเผอเรอ\nเผอิญ\nเผอิล\nเผะ\nเผา\nเผ่า\nเผ้า\nเผาะ\nเผิ้ง\nเผิน\nเผียน\nเผือ\nเผื่อ\nเผือก\nเผือด\nเผือน\nเผื่อน\nแผ่\nแผก\nแผง\nแผด\nแผน\nแผ่น\nแผนก\nแผล\nแผลง\nแผล็บ\nแผล็ว\nแผละ\nแผ่ว\nแผ้ว\nโผ\nโผง\nโผฏฐัพพะ\nโผน\nโผเผ\nโผย\nโผล่\nโผลกเผลก\nโผละ\nโผอน\nโผะ\nไผ\nไผ่\nไผท\nฝน\nฝรั่ง\nฝรั่งเศส\nฝ่อ\nฝอย\nฝัก\nฝัง\nฝั่ง\nฝัด\nฝัน\nฝา\nฝ่า\nฝ้า\nฝาก\nฝาง\nฝาด\nฝาน\nฝาย\nฝ่าย\nฝ้าย\nฝิ่น\nฝี\nฝี่\nฝีก\nฝึก\nฝืด\nฝืน\nฝุ่น\nฝูง\nเฝ้า\nเฝือ\nเฝือก\nเฝือง\nเฝื่อน\nแฝก\nแฝง\nแฝด\nใฝ่\nไฝ\nพก\nพกุล\nพง\nพงศ์\nพงศกร\nพงศธร\nพงศา\nพงศาวดาร\nพจน์\nพจนา\nพจนานุกรม\nพจนารถ\nพจนีย์\nพจมาน\nพจี\nพชระ\nพญา\nพญาลอ\nพณิช\nพณิชย์\nพดด้วง\nพธู\nพนันดร\nพนาดร\nพนาดอน\nพนาราม\nพนาลัย\nพนาลี\nพนาวาส\nพนาเวศ\nพนาศรม\nพนาสณฑ์\nพนาสัณฑ์\nพเนจร\nพ่น\nพ้น\nพนม\nพนอง\nพนอม\nพนัก\nพนักงาน\nพนัง\nพนัน\nพนัส\nพนา\nพนาย\nพนิดา\nพนิต\nพบ\nพม่า\nพยนต์\nพยศ\nพยัก\nพยักพเยิด\nพยัคฆ์\nพยัคฆา\nพยัคฆิน\nพยัคฆี\nพยัชน์\nพยัญชนะ\nพยัต\nพยับ\nพยากรณ์\nพยาฆร์\nพยางค์\nพยาธิ\nพยาน\nพยาบาท\nพยาบาล\nพยาม\nพยามะ\nพยายาม\nพยุ\nพยุง\nพยุหยาตรา\nพยุหโยธา\nพยุหเสนา\nพยุหะ\nพยู่ห์\nพเยีย\nพร\nพรต\nพรม\nพรรค\nพรรค์\nพรรคานต์\nพรรณ\nพรรณนา\nพรรดึก\nพรรลาย\nพรรษ\nพรรษา\nพรรเอิญ\nพรวด\nพรวน\nพรหม\nพรหมจรรย์\nพรหมจาริณี\nพรหมจารี\nพรหมา\nพรหมาสตร์\nพรหมินทร์\nพรอก\nพร่อง\nพร้อง\nพรอด\nพร้อม\nพร้อมพรัก\nพร่อย\nพร้อย\nพระ\nพระนอม\nพระนาด\nพระฮาม\nพรักพร้อม\nพรั่ง\nพรัด\nพรั่น\nพรับ\nพร่า\nพร้า\nพราก\nพราง\nพร่าง\nพราด\nพราน\nพราย\nพราว\nพราหมณ์\nพราหมณะ\nพราหมณี\nพราหมี\nพรำ\nพร่ำ\nพริก\nพริ้ง\nพริบ\nพริ้ม\nพรึง\nพรึน\nพรึบ\nพรึ่บ\nพรืด\nพรุ\nพรุ่ง\nพรุน\nพรู\nพรูด\nพฤกษ์\nพฤกษชาติ\nพฤกษเทวดา\nพฤกษราช\nพฤกษศาสตร์\nพฤกษา\nพฤฒ\nพฤฒา\nพฤฒาจารย์\nพฤฒิ\nพฤต\nพฤติ\nพฤทธ์\nพฤทธิ์\nพฤนต์\nพฤนท์\nพฤศจิก\nพฤศจิกายน\nพฤษภ\nพฤษภาคม\nพฤหัสบดี\nพล\nพละ\nพลากร\nพลาดิศัย\nพลาธิการ\nพลานามัย\nพลบ\nพลวก\nพลวง\nพลวัต\nพลศาสตร์\nพลอ\nพล้อ\nพลอง\nพลอด\nพลอน\nพลอมแพลม\nพลอย\nพล่อย\nพลั่ก\nพลัง\nพลั่ง\nพลั้ง\nพลัด\nพลัน\nพลับ\nพลับพลา\nพลับพลึง\nพลั่ว\nพล่า\nพลาง\nพลาญ\nพลาด\nพล่าน\nพลาม\nพล่าม\nพลาย\nพลาสติก\nพลาสมา\nพลาหก\nพลำ\nพล้ำ\nพลำภัง\nพลิก\nพลิพัท\nพลิ้ว\nพลี\nพลุ\nพลุก\nพลุ่ก\nพลุกพล่าน\nพลุ่ง\nพลุ้ย\nพลู\nพลูโต\nพลูโทเนียม\nพวก\nพวง\nพ่วง\nพวน\nพวย\nพสก\nพสุ\nพสุธา\nพสุสงกรานต์\nพหล\nพหุ\nพหุล\nพหู\nพอ\nพ่อ\nพ้อ\nพอก\nพอง\nพ้อง\nพอน\nพ้อม\nพอโลเนียม\nพะ\nพะงา\nพะงาบ\nพะจง\nพะทำมะรง\nพะนอ\nพะเน้าพะนอ\nพะเนิน\nพะเนียง\nพะแนง\nพะพาน\nพะพิง\nพะเพิง\nพะยอม\nพ่ะย่ะค่ะ\nพะยูง\nพะยูน\nพะเยิบ\nพะเยิบพะยาบ\nพะรุงพะรัง\nพะโล้\nพะไล\nพะวง\nพะวักพะวน\nพะวา\nพะว้าพะวัง\nพะอง\nพะอากพะอำ\nพะอืดพะอม\nพัก\nพักตร์\nพักตรา\nพักร\nพัง\nพังกา\nพังงา\nพังผืด\nพังพวย\nพังพอน\nพังพาน\nพังพาบ\nพังเพย\nพัช\nพัชนี\nพัชระ\nพัญจน์\nพัฒนะ\nพัฒนา\nพัฒนากร\nพัฒนาการ\nพัด\nพัดชา\nพัดดึงส์\nพัตร\nพัทธ์\nพัทธยา\nพัทธสีมา\nพัทร\nพัน\nพันตู\nพันทาง\nพันธ์\nพันธะ\nพันธกรณี\nพันธบัตร\nพันธมิตร\nพันธนะ\nพันธนาคาร\nพันธนาการ\nพันธุ์\nพันธุกรรม\nพันลึก\nพันลือ\nพันเลิศ\nพันเอิญ\nพับ\nพัลลภ\nพัลวัน\nพัว\nพัวะ\nพัศดี\nพัสดุ\nพัสตร์\nพัสถาน\nพา\nพาก\nพากเพียร\nพากย์\nพาง\nพ่าง\nพาชี\nพาณิช\nพาณิชย์\nพาณิชยกรรม\nพาณิชยการ\nพาณิชยศาสตร์\nพาณิชยศิลป์\nพาณินี\nพาณี\nพาณีนี\nพาด\nพาท\nพาทย์\nพาธ\nพาธา\nพาน\nพ่าน\nพานร\nพานรินทร์\nพาม\nพาย\nพ่าย\nพายม้า\nพายัพ\nพายุ\nพาร์เซก\nพารณ\nพารา\nพาราฟิน\nพาล\nพาลา\nพาลี\nพาลุก\nพาโล\nพาไล\nพาส\nพาสน์\nพาสนา\nพาสุกรี\nพ่าห์\nพาหนะ\nพาหะ\nพาหา\nพาหิรกะ\nพาหิระ\nพาหุ\nพาหุรัด\nพาหุสัจจะ\nพาเหียร\nพาฬ\nพำ\nพำนัก\nพำพึม\nพำลา\nพิกล\nพิกสิต\nพิกัด\nพิกัติ\nพิกัน\nพิการ\nพิกุล\nพิเคราะห์\nพิฆน์\nพิฆเนศ\nพิฆเนศวร\nพิฆาต\nพิง\nพิจัย\nพิจาร\nพิจารณ์\nพิจารณา\nพิจิก\nพิจิต\nพิจิตร\nพิชญ์\nพิชัย\nพิชาน\nพิชิต\nพิเชฐ\nพิเชียร\nพิฑูรย์\nพิณ\nพิดทูล\nพิดาน\nพิโดร\nพิตร\nพิถี\nพิถีพิถัน\nพิทย\nพิทย์\nพิทยา\nพิทยาคม\nพิทยาคาร\nพิทยาธร\nพิทยาลัย\nพิทักษ์\nพิทูร\nพิเทศ\nพิธาน\nพิธี\nพิธุ\nพินทุ\nพินอบพิเทา\nพินัย\nพินาศ\nพินิจ\nพินิต\nพินิศ\nพินิศจัย\nพิเนต\nพิบัติ\nพิบุล\nพิบูล\nพิปริต\nพิปลาส\nพิพรรธ\nพิพรรธน์\nพิพักพิพ่วน\nพิพัฒ\nพิพัฒน์\nพิพากษ์\nพิพากษา\nพิพาท\nพิพิธ\nพิพิธภัณฑ์\nพิพิธภัณฑสถาน\nพิภพ\nพิภัช\nพิภาค\nพิภูษณะ\nพิเภก\nพิมปะการัง\nพิมพ์\nพิมพการัง\nพิมพา\nพิมพาภรณ์\nพิมล\nพิมเสน\nพิมาน\nพิมุข\nพิโมกข์\nพิโมกษ์\nพิโยกพิเกน\nพิโยค\nพิรอด\nพิระ\nพิรากล\nพิราบ\nพิราม\nพิราลัย\nพิริยะ\nพิรี้พิไร\nพิรุณ\nพิรุธ\nพิรุฬห์\nพิเรนทร์\nพิเราะ\nพิโรธ\nพิไร\nพิลังกาสา\nพิลาป\nพิลาส\nพิลิปดา\nพิลึก\nพิลึกกึกกือ\nพิลึกพิลั่น\nพิโลน\nพิไล\nพิศ\nพิศวง\nพิศวาส\nพิศาล\nพิศุทธ์\nพิศุทธิ์\nพิเศษ\nพิษ\nพิษฐาน\nพิษนาศน์\nพิสดาร\nพิสมร\nพิสมัย\nพิสัง\nพิสัช\nพิสัย\nพิสิฐ\nพิสุทธิ์\nพิสูจน์\nพิหค\nพิหาร\nพิฬาร\nพี\nพี่\nพี้\nพีชคณิต\nพีระมิด\nพึง\nพึ่ง\nพึ่บ\nพึ่บพั่บ\nพึม\nพึมพำ\nพืช\nพืด\nพื้น\nพุ\nพุก\nพุกาม\nพุง\nพุ่ง\nพุงดอ\nพุฒ\nพุฒิ\nพุด\nพุดตาน\nพุทธ\nพุทธะ\nพุทธังกูร\nพุทธางกูร\nพุทธันดร\nพุทธาภิเษก\nพุทธาวาส\nพุทธิ\nพุทโธ่\nพุทรา\nพุธ\nพุ่ม\nพุมเรียง\nพุ้ย\nพู\nพูพอน\nพู่\nพูด\nพูน\nพู้น\nพู่ระหง\nเพ\nเพ็ก\nเพกา\nเพคะ\nเพ็ง\nเพ่ง\nเพ็จ\nเพชฉลูกรรม\nเพชฌฆาต\nเพชร\nเพชรดา\nเพชรปาณี\nเพชรฤกษ์\nเพชรายุธ\nเพชรกลับ\nเพชรสังฆาต\nเพชรหลีก\nเพชรหึง\nเพ็ญ\nเพฑูริย์\nเพณี\nเพ็ดทูล\nเพดาน\nเพท\nเพทนา\nเพทาย\nเพทุบาย\nเพโทบาย\nเพ่นพ่าน\nเพนียด\nเพไนย\nเพ้ย\nเพรง\nเพรซีโอดิเมียม\nเพรา\nเพราะ\nเพริด\nเพริศ\nเพรียก\nเพรียง\nเพรียบ\nเพรี้ยม\nเพรียว\nเพรื่อ\nเพรือง\nเพล\nเพลง\nเพล็ด\nเพล้โพล้\nเพลา\nเพลาะ\nเพลิง\nเพลิดเพลิน\nเพลิน\nเพลีย\nเพลี้ย\nเพลี่ยง\nเพศ\nเพส\nเพสลาด\nเพ่อ\nเพ้อ\nเพ้อเจ้อ\nเพอิญ\nเพะ\nเพา\nเพาะ\nเพิก\nเพิง\nเพิ่ง\nเพิดเพ้ย\nเพิ่ม\nเพี้ย\nเพียง\nเพี้ยง\nเพียงออ\nเพี้ยน\nเพียบ\nเพียร\nเพื่อ\nเพื่อน\nแพ\nแพ้\nแพง\nแพ่ง\nแพงพวย\nแพทย์\nแพทยศาสตร์\nแพน\nแพ่น\nแพนก\nแพนงเชิง\nแพร\nแพร่\nแพรก\nแพร่ง\nแพรว\nแพร้ว\nแพลง\nแพลทินัม\nแพล็บ\nแพลม\nแพลเลเดียม\nแพละ\nแพละโลม\nแพว\nแพ้ว\nแพศย์\nแพศยา\nแพะ\nโพ\nโพก\nโพกพาย\nโพง\nโพงพาง\nโพชฌงค์\nโพซิตรอน\nโพด\nโพทะเล\nโพแทสเซียม\nโพธ\nโพธิ\nโพธิ์\nโพน\nโพ้น\nโพนทะนา\nโพบาย\nโพย\nโพยก๊วน\nโพยม\nโพรก\nโพรง\nโพรโทแอกทิเนียม\nโพรมีเทียม\nโพระดก\nโพล่\nโพลง\nโพล่ง\nโพล้ง\nโพลน\nโพล้เพล้\nโพละ\nโพสพ\nไพ\nไพ่\nไพจิตร\nไพชน\nไพชยนต์\nไพฑูรย์\nไพที\nไพบูลย์\nไพพรรณ\nไพร\nไพร่\nไพรจิตร\nไพรชน\nไพรชยนต์\nไพรฑูรย์\nไพรที\nไพรบูลย์\nไพรเราะ\nไพรัช\nไพรำ\nไพริน\nไพรินทร์\nไพรี\nไพเราะ\nไพโรจน์\nไพล\nไพล่\nไพศาขะ\nไพศาล\nไพเศษ\nไพสพ\nไพสิฐ\nไพหาร\nฟก\nฟ้ง\nฟรักโทส\nฟรี\nฟลูออรีน\nฟ่อ\nฟ้อ\nฟอก\nฟอง\nฟ่อง\nฟ้อง\nฟอด\nฟอน\nฟ่อน\nฟ้อน\nฟ้อแฟ้\nฟอร์มาลดีไฮด์\nฟอร์มาลิน\nฟอสฟอรัส\nฟอสเฟต\nฟัก\nฟักฟุ้น\nฟัง\nฟังก์ชัน\nฟัด\nฟัน\nฟั่น\nฟั้น\nฟ้า\nฟาก\nฟาง\nฟ่าง\nฟาด\nฟาทอม\nฟาน\nฟ่าม\nฟาย\nฟาร์ม\nฟาสซิสต์\nฟิด\nฟิต\nฟิบ\nฟิล์ม\nฟิวส์\nฟิสิกส์\nฟี่\nฟี้\nฟืดฟาด\nฟืน\nฟื้น\nฟืม\nฟุ\nฟุ้ง\nฟุต\nฟุน\nฟุบ\nฟุ่บ\nฟุ่มเฟือย\nฟุลสแก๊ป\nฟู\nฟู่\nฟูก\nฟูด\nฟูม\nเฟ็ด\nเฟ้น\nเฟลด์สปาร์\nเฟ้อ\nเฟอร์เมียม\nเฟอะ\nเฟอะฟะ\nเฟะ\nเฟะฟะ\nเฟิน\nเฟี้ยม\nเฟี้ยว\nเฟือ\nเฟื้อ\nเฟือง\nเฟื่อง\nเฟื้อง\nเฟือน\nเฟือย\nเฟื้อย\nแฟ่\nแฟง\nแฟชั่น\nแฟน\nแฟบ\nแฟ้ม\nแฟรนเซียม\nแฟลกซ์\nแฟลต\nแฟะ\nโฟกัส\nไฟ\nภควดี\nภควัต\nภควันต์\nภควัม\nภควา\nภควาน\nภคะ\nภคันทลา\nภคินี\nภณะ\nภณิดา\nภพ\nภมร\nภมริน\nภมรี\nภมุกา\nภยันตราย\nภยาคติ\nภระ\nภรณี\nภรต\nภรรดร\nภรรดา\nภรรยา\nภระมร\nภระมรี\nภราดร\nภราดรภาพ\nภราดา\nภริยา\nภฤศ\nภวะ\nภวตัณหา\nภวนะ\nภวังค์\nภวังคจิต\nภักดี\nภักตะ\nภักติ\nภักษ์\nภักษา\nภักษาหาร\nภัค\nภัคน์\nภังคะ\nภังคี\nภัจ\nภัณฑ์\nภัณฑาคาร\nภัณฑาคาริก\nภัณฑารักษ์\nภัณฑนะ\nภัณฑู\nภัต\nภัตตาคาร\nภัตตาหาร\nภัตร\nภัทระ\nภัทรกัป\nภัพ\nภัย\nภัสดา\nภัสตรา\nภัสมะ\nภัสสร\nภา\nภาค\nภาคย์\nภาคยานุวัติ\nภาคินี\nภาคิไนย\nภาคี\nภาคียะ\nภาชนะ\nภาชี\nภาณ\nภาณวาร\nภาณกะ\nภาณี\nภาณุ\nภาดร\nภาดา\nภาตระ\nภาตา\nภาตุ\nภาติกะ\nภาติยะ\nภาพ\nภาพย์\nภาม\nภาย\nภาร\nภาระ\nภารดี\nภารต\nภารตี\nภารยทรัพย์\nภารยา\nภารา\nภาวนา\nภาวะ\nภาษ\nภาษณ์\nภาษา\nภาษิต\nภาษี\nภาส\nภาสน์\nภาสวร\nภาสา\nภาสุระ\nภิกขา\nภิกขาจาร\nภิกขุ\nภิกขุนี\nภิกษา\nภิกษาจาร\nภิกษาหาร\nภิกษุ\nภิกษุณี\nภิงคาร\nภิญโญ\nภิตติ\nภินท์\nภินทนาการ\nภิยโย\nภิรมย์\nภิรมย์สุรางค์\nภิษัช\nภิสัก\nภีตะ\nภีมะ\nภีรุ\nภุกต์\nภุขัน\nภุช\nภุชงค์\nภุต\nภุมมะ\nภุมรัตน์\nภุมวาร\nภุมรา\nภุมริน\nภุมรี\nภุมเรศ\nภู\nภู่\nภูต\nภูติ\nภูม\nภูมิ\nภูมี\nภูริ\nภูรี\nภูวดล\nภูวนาถ\nภูวเนตร\nภูวไนย\nภูษา\nภูษิต\nเภกะ\nเภตรา\nเภท\nเภทุบาย\nเภรี\nเภสัช\nโภค\nโภคะ\nโภคิน\nโภคี\nโภไคย\nโภไคศวรรย์\nโภช\nโภชย์\nโภชก\nโภชนะ\nโภชนา\nโภชนาหาร\nโภชนียะ\nไภริน\nไภรี\nไภษัชคุรุ\nไภษัชย์\nมกร\nมกราคม\nมกุฎ\nมคธ\nมฆวัน\nมฆะ\nมฆา\nม่ง\nมงกุฎ\nมงโกรย\nมงคล\nมงคลวาร\nมณฑ์\nมณฑก\nมณฑนะ\nมณฑป\nมณฑล\nมณฑา\nมณฑารพ\nมณฑิระ\nมณเฑียร\nมณี\nมด\nมตะ\nมตกภัต\nมติ\nมทนะ\nมทะ\nมธุ\nมธุกร\nมธุการี\nมธุลีห์\nมธุระ\nมธุรพจน์\nมน\nมนินทรีย์\nม่น\nมนต์\nมนตร์\nมนตรี\nมนท์\nมนทิราลัย\nมนเทียร\nมนสิการ\nมนัส\nมนัสวี\nมนินทรีย์\nมนิมนา\nมนิลา\nมนุญ\nมนุษย์\nมนุษยชาติ\nมนุษยธรรม\nมนุษย์มนา\nมนุษยโลก\nมนุษยศาสตร์\nมนุษยสัมพันธ์\nมนุสาร\nมนู\nมนูสาร\nมโน\nมโนช\nมโนชญ์\nมโนราห์\nมโนสาเร่\nมโนห์รา\nมมังการ\nมยุรฉัตร\nมยุระ\nมยุรา\nมยุรี\nมยุเรศ\nมยูร\nมรกต\nมรคา\nมรฑป\nมรณ์\nมรณะ\nมรณกรรม\nมรณบัตร\nมรณภัย\nมรณภาพ\nมรดก\nมรรค\nมรรคา\nมรรตัย\nมรรยาท\nมรรษ\nมรสุม\nมริจ\nมริยาท\nมรีจิ\nมรุต\nมฤค\nมฤคย์\nมฤคศิระ\nมฤคศิรมาส\nมฤคเศียร\nมฤคินทร์\nมฤเคนทร์\nมฤดก\nมฤต\nมฤตยู\nมฤทุ\nมล\nมละ\nมลัก\nมลังเมลือง\nมล้าง\nมลาย\nมลายู\nมวก\nม่วง\nมวน\nม่วน\nม้วน\nม้วนต้วน\nมวย\nม้วย\nมวล\nมหกรรม\nมหรณพ\nมหรรณพ\nมหรสพ\nมหัจฉริยะ\nมหัต\nมหัทธนะ\nมหันต์\nมหันตโทษ\nมหัพภาค\nมหัศจรรย์\nมหา\nมหากฐิน\nมหากาฬ\nมหาขันธกะ\nมหาจักร\nมหาชน\nมหาชัย\nมหาชาติ\nมหาโชตรัต\nมหาดไทย\nมหาดเล็ก\nมหาตมะ\nมหาไถ่\nมหาเทพ\nมหาเทพี\nมหาเทวี\nมหาธาตุ\nมหานิกาย\nมหานิล\nมหาบพิตร\nมหาบัณฑิต\nมหาพน\nมหาพรหม\nมหาภารตะ\nมหาภิเนษกรมณ์\nมหาภูต\nมหาเมฆ\nมหายาน\nมหายุค\nมหาราช\nมหาฤกษ์\nมหาละลวย\nมหาละลาย\nมหาวงศ์\nมหาวรรค\nมหาวิทยาลัย\nมหาศักราช\nมหาศาล\nมหาสงกรานต์\nมหาสดมภ์\nมหาสดำ\nมหาสมุทร\nมหาสาวก\nมหาหงส์\nมหาหิงคุ์\nมหาอำนาจ\nมหาอุจ\nมหาอุด\nมหาอุปรากร\nมหาอุปราช\nมหิ\nมหิดล\nมหิธร\nมหิป\nมหิงส์\nมหิทธิ\nมหินท์\nมหิมา\nมหิศร\nมหิศวร\nมหิษ\nมหิษี\nมหึมา\nมเหยงค์\nมเหศ\nมเหศวร\nมเหศักดิ์\nมเหสักข์\nมเหสิ\nมเหสี\nมเหาฬาร\nมโหรสพ\nมโหระทึก\nมโหรี\nมโหฬาร\nมไหศวรรย์\nมอ\nมอง\nมองโกลอยด์\nมองโกเลีย\nมองคร่อ\nมอญ\nมอด\nม่อต้อ\nมอเตอร์\nมอเตอร์ไซค์\nมอน\nม่อน\nมอบ\nมอม\nมอมแมม\nม่อย\nมอร์ฟีน\nมอระกู่\nมอลโทส\nม่อลอกม่อแลก\nม่อห้อม\nม่อฮ่อม\nมะ\nมะกรูด\nมะกล่ำ\nมะกอก\nมะก่อง\nมะกะโรนี\nมะกา\nมะเกลือ\nมะเกี๋ยง\nมะข่วง\nมะขวิด\nมะขาม\nมะเขือ\nมะแข่น\nมะคังแดง\nมะค่า\nมะคำไก่\nมะคำดีควาย\nมะงั่ว\nมะงุมมะงาหรา\nมะซัก\nมะซาง\nมะดัน\nมะดีหวี\nมะดูก\nมะเดหวี\nมะเดื่อ\nมะต้อง\nมะตะบะ\nมะตาด\nมะตาหะรี\nมะตึ่ง\nมะตื๋น\nมะตูม\nมะแตก\nมะโต\nมะนาว\nมะปราง\nมะปริง\nมะฝ่อ\nมะพร้าว\nมะพลับ\nมะพูด\nมะแพน\nมะแพร้ว\nมะเฟือง\nมะแฟน\nมะไฟ\nมะม่วง\nมะม่าว\nมะมี่\nมะมื่น\nมะมุด\nมะเมอ\nมะเมีย\nมะเมื่อย\nมะแม\nมะยง\nมะยม\nมะระ\nมะริด\nมะรืน\nมะรุม\nมะรุมมะตุ้ม\nมะเร็ง\nมะเรื่อง\nมะโรง\nมะลอกมะแลก\nมะละกอ\nมะลิ\nมะลื่น\nมะลืมดำ\nมะลุลี\nมะแว้ง\nมะสัง\nมะเส็ง\nมะหวด\nมะหะหมัด\nมะหาด\nมะหิ่ง\nมะเหงก\nมะอึก\nมะฮอกกานี\nมัก\nมักกะโรนี\nมักกะลีผล\nมักกะสัน\nมักขะ\nมั่กขั้ก\nมักขิกา\nมัค\nมัคคะ\nมัคคุเทศก์\nมัคนายก\nมัฆวาน\nมั่ง\nมังกง\nมังกร\nมังกุ\nมังคละ\nมังค่า\nมังคุด\nมังตาน\nมังสวิรัติ\nมังสะ\nมังสี\nมัจจะ\nมัจจุ\nมัจฉริยะ\nมัจฉรี\nมัจฉะ\nมัจฉา\nมัชชะ\nมัชวิรัติ\nมัชชาระ\nมัชฌันติกสมัย\nมัชฌิม\nมัชฌิมา\nมัญจา\nมัญชิษฐา\nมัญชุ\nมัญชุสา\nมัญชูสา\nมัญเชฏฐะ\nมัฏฐะ\nมัณฑนศิลป์\nมัณฑนา\nมัด\nมัตตะ\nมัตตัญญู\nมัตตา\nมัตติกา\nมัตถกะ\nมัตถลุงค์\nมัตสยะ\nมัตสยา\nมัตสระ\nมัตสริน\nมัททวะ\nมัทนะ\nมัทยะ\nมัธยฐาน\nมัธยม\nมัธยันห์\nมัธยัสถ์\nมัน\nมั่น\nมันตา\nมันถะ\nมันทิระ\nมันทิราลัย\nมับ\nมั้ม\nมัมมี่\nมัย\nมัลละ\nมัลลิกา\nมัว\nมัวซัว\nมั่ว\nมัศยา\nมัสตุ\nมัสตาร์ด\nมัสมั่น\nมัสยิด\nมัสรู่\nมัสลิน\nมัสสุ\nมา\nม้า\nมาก\nมาคสิระ\nมาฆบูชา\nมาฆะ\nม้าง\nมางสะ\nมาณพ\nมาณวิกา\nมาด\nมาดา\nมาตงค์\nมาตร\nมาตรา\nมาตฤ\nมาตังคะ\nมาตา\nมาตามหัยกะ\nมาตามหัยกา\nมาตามหัยยิกา\nมาติกะ\nมาติกา\nมาตุ\nมาตุจฉา\nมาตุรงค์\nมาตุเรศ\nมาตุละ\nมาตุลา\nมาตุลานี\nมาทะ\nมาธยมิก\nมาธยมิกะ\nมาธุระ\nมาธุสร\nมาธูระ\nมาน\nม่าน\nม้าน\nมานพ\nมานะ\nมานัต\nมานัส\nมานิต\nมานี\nมานุษ\nมานุษยวิทยา\nมาโนชญ์\nมาบ\nมาภา\nม้าม\nม่าย\nมายา\nมาร\nมาราธิราช\nมารค\nมารดร\nมารดา\nมารยา\nมารยาท\nมารศรี\nมารษา\nมาริต\nมารุต\nมาลย์\nมาลัย\nมาลา\nมาลาตี\nมาลาเรีย\nมาลินี\nมาลี\nมาลุต\nมาศ\nมาส\nมาสก\nมาห์\nม่าห์\nมาหิส\nม่าเหมี่ยว\nมาฬก\nมิ\nมิค\nมิคสัญญี\nมิ่ง\nมิจฉา\nมิด\nมิตร\nมิติ\nมิเตอร์\nมิถยา\nมิถุน\nมิถุนายน\nมิทธะ\nมินตรา\nมินตา\nมินหม้อ\nมิ่ม\nมิ้ม\nมิไย\nมิรันตี\nมิลลิกรัม\nมิลลิบาร์\nมิลลิเมตร\nมิลลิลิตร\nมิลักขะ\nมิลักขู\nมิส\nมิสกรี\nมิสกวัน\nมิสซา\nมี\nมี่\nมีด\nมีเทน\nมีน\nมีนาคม\nมี่สั้ว\nมึง\nมึน\nมืด\nมืน\nมื่น\nมือ\nมื้อ\nมุ\nมุก\nมุกดา\nมุกดาหาร\nมุกุระ\nมุข\nมุขเด็จ\nมุขยประโยค\nมุโขโลกนะ\nมุคคะ\nมุง\nมุ่ง\nมุ้ง\nมุจฉา\nมุจนะ\nมุจลินท์\nมุญจนะ\nมุญชะ\nมุฐิ\nมุณฑกะ\nมุณฑะ\nมุด\nมุตกิด\nมุตฆาต\nมุตตะ\nมุตตา\nมุตติ\nมุตะ\nมุติ\nมุททา\nมุทธชะ\nมุทธา\nมุทธาภิเษก\nมุทรา\nมุทริกา\nมุทะลุ\nมุทา\nมุทิกา\nมุทิงค์\nมุทิตา\nมุทุ\nมุทุตา\nมุ่น\nมุนิ\nมุนินทร์\nมุนี\nมุบ\nมุบมิบ\nมุม\nมุ้ม\nมุ่ย\nมุรธา\nมุรธาภิเษก\nมุสละ\nมุสลิม\nมุสา\nมุสิก\nมุหงิด\nมุหน่าย\nมุหุต\nมุฮัมมัด\nมูก\nมูเซอ\nมูตร\nมู่ทู่\nมูน\nมูมมาม\nมูรติ\nมูรธา\nมูรธาภิเษก\nมูล\nมูละ\nมูลา\nมูลิกากร\nมู่ลี่\nมู่เล่\nมูสัง\nมูสิก\nมูสิกะ\nมูสิกทันต์\nเม\nเม็ก\nเมกะเฮิรตซ์\nเมขลา\nเมฆ\nเมฆา\nเมฆินทร์\nเมฆี\nเม็ง\nเม็ด\nเมตตา\nเมตไตรย\nเมตร\nเมตริก\nเมตริกตัน\nเมถุน\nเมท\nเมโท\nเมทนี\nเมทินี\nเมทนีดล\nเมทานอล\nเมทิลแอลกอฮอล์\nเมธ\nเมธา\nเมธาวี\nเมธี\nเมน\nเม่น\nเม้น\nเมนเดลีเวียม\nเมนทอล\nเม้ม\nเมรัย\nเมริเดียน\nเมรุ\nเมล์\nเมล็ด\nเมลือง\nเมษ\nเมษายน\nเมห์\nเมหนะ\nเมหะ\nเมะ\nเมา\nเม่า\nเม้า\nเมารี\nเมาลี\nเมาฬี\nเมาะ\nเมิง\nเมิน\nเมิล\nเมีย\nเมียง\nเมี่ยง\nเมี้ยน\nเมือ\nเมื้อ\nเมื่อ\nเมือก\nเมือง\nเมือบ\nเมื่อย\nแม่\nแม้\nแมก\nแมกนีเซียม\nแมง\nแมงกะพรุน\nแมงกานิน\nแมงกานีส\nแมงคา\nแมงคาเรือง\nแมงช้าง\nแมงดา\nแมงลัก\nแม่ตะงาว\nแมน\nแม่น\nแม้น\nแมลง\nแมลบ\nแมว\nแม้ว\nแมะ\nโม\nโม่\nโม้\nโมก\nโมกข์\nโมกษะ\nโมฆกรรม\nโมฆสัญญา\nโมฆะ\nโมฆียกรรม\nโมฆียะ\nโมง\nโม่ง\nโมงครุ่ม\nโมทนา\nโมโนแซ็กคาไรด์\nโมไนย\nโมเม\nโมเมนต์\nโมเย\nโมรา\nโมรี\nโมเรส\nโมลิบดีนัม\nโมลี\nโมเลกุล\nโมเสก\nโมเสส\nโม่ห์\nโมหะ\nโมหันธ์\nโมหาคติ\nโมโห\nไม่\nไม้\nไมกา\nไมครอน\nไมโครกรัม\nไมโครฟิล์ม\nไมโครโฟน\nไมโครมิเตอร์\nไมโครเมตร\nไมโครลิตร\nไมโครเวฟ\nไมตรี\nไมยราบ\nไมล์\nยก\nยกกระบัตร\nยกนะ\nยง\nยงโย่\nยชุรเวท\nยติ\nยติภังค์\nยถากรรม\nยถาภูตญาณ\nย่น\nยนต์\nยนตร์\nยม\nยมก\nยมโดย\nยมนา\nยมล\nยมะ\nยรรยง\nยล\nยวง\nยวด\nยวน\nยวบ\nย้วย\nยวรยาตร\nยศ\nยโส\nยอ\nย่อ\nยอก\nย็อกแย็ก\nยอง\nย่อง\nย้อง\nยอด\nยอน\nย้อน\nยอบ\nยอม\nย่อม\nย้อม\nย่อย\nย้อย\nย้อแย้\nยะ\nย่ะ\nยะยอบ\nยะยับ\nยัก\nยักข์\nยักขินี\nยักษ์\nยักษา\nยักษิณี\nยักษี\nยัง\nยั้ง\nยั่งยืน\nยัชโญปวีต\nยัญ\nยัญญะ\nยัด\nยัติภังค์\nยัน\nยั่น\nยันต์\nยันตร\nยันตร์\nยันตรกรรม\nยั่นตะนี\nยับ\nยั่ว\nยั้ว\nยั้วเยี้ย\nยัวรยาตร\nยัวะ\nยัษฏิ\nยา\nย่า\nยาก\nยาคุ\nยาคู\nยาง\nย่าง\nยางพารา\nยาจก\nยาจนา\nยาไฉน\nยาด\nยาดา\nยาตร\nยาตรา\nยาน\nย่าน\nย่านพาโหม\nยานมาศ\nยานุมาศ\nยานี\nยาม\nย่าม\nยามะ\nยามักการ\nยามา\nยาย\nย้าย\nยายี\nยาว\nย้าว\nยาวกาลิก\nยาวชีวิก\nยาสูบ\nย่าหยา\nยาหยี\nยำ\nย่ำ\nย้ำ\nยำเยีย\nยิก\nยิง\nยิ่ง\nยิฏฐะ\nยิน\nยิบ\nยิบหยี\nยิปซัม\nยิปซี\nยิ้ม\nยิมนาสติก\nยิหวา\nยี\nยี่\nยี้\nยี่ก่า\nยี่เก\nยี่เข่ง\nยี่โถ\nยีน\nยี่โป้\nยี่ภู่\nยีราฟ\nยี่สก\nยี่สง\nยี่สน\nยี่สาน\nยี่สุ่น\nยี่หระ\nยี่หร่า\nยี่ห้อ\nยี่หุบ\nยึกยัก\nยึกยือ\nยึด\nยืด\nยืน\nยื่น\nยืม\nยื้อ\nยุ\nยุกกระบัตร\nยุกดิ\nยุกติ\nยุกติธรรม\nยุกต์\nยุค\nยุคนธร\nยุคล\nยุคันต์\nยุคันธร\nยุคุนธร\nยุง\nยุ่ง\nยุ้ง\nยุด\nยุต\nยุติ\nยุทธ\nยุทธ์\nยุทธนา\nยุทโธปกรณ์\nยุบ\nยุ่บ\nยุ่บยั่บ\nยุบล\nยุพดี\nยุพเรศ\nยุพา\nยุพาน\nยุพาพาล\nยุพาพิน\nยุ่มย่าม\nยุ่ย\nยุ้ย\nยุรยาตร\nยูรยาตร\nยุวชน\nยุวดี\nยุวราช\nยุวา\nยุวาน\nยู\nยู่\nยูง\nยูโด\nยูถะ\nยูถิกา\nยูริก\nยูเรนัส\nยูเรเนียม\nยูโรเพียม\nเย\nเย้\nเยง\nเยซู\nเย็ด\nเย็น\nเย็นตาโฟ\nเย็นเตาโฟ\nเย็บ\nเย้ย\nเยอ\nเย่อ\nเยอรมัน\nเยอว\nเย่อหยิ่ง\nเยอะ\nเยอะแยะ\nเยา\nเย้า\nเยาว์\nเยาวชน\nเยาวมาลย์\nเยาวยอด\nเยาวราช\nเยาวเรศ\nเยาวลักษณ์\nเยาวพา\nเยาวพาณี\nเยาวพาน\nเยาะ\nเยิง\nเยิน\nเยิ่น\nเยิ่นเย้อ\nเยินยอ\nเยิบ\nเยิบยาบ\nเยิ้ม\nเยีย\nเยี่ยง\nเยี่ยงอย่าง\nเยียงผา\nเยียดยัด\nเยียน\nเยียบ\nเยี่ยม\nเยียรบับ\nเยียรยง\nเยียว\nเยี่ยว\nเยียวยา\nเยือ\nเยื่อ\nเยื้อ\nเยือก\nเยือง\nเยื่อง\nเยื้อง\nเยือน\nเยื้อน\nแย่\nแย้\nแยก\nแยง\nแย่ง\nแย้ง\nแยงแย่\nแยงแย้\nแยบ\nแย็บ\nแยม\nแย้ม\nแยแส\nแยะ\nโย\nโย้\nโยก\nโยกเยก\nโยคาพจร\nโยคาวจร\nโยคเกณฑ์\nโยคยะ\nโยคะ\nโยคิน\nโยคี\nโยง\nโย่ง\nโย่งเย่ง\nโยงโย่\nโยชน์\nโยชนา\nโยถิกะ\nโยทะกา\nโยธวาทิต\nโยธา\nโยธิน\nโยน\nโยนก\nโยนิโส\nโยนี\nโยม\nโยโส\nใย\nไย\nไย่\nไยดี\nไยไพ\nรก\nรง\nรงค์\nรงควัตถุ\nรงรอง\nรจนา\nรจเรข\nรจเลข\nรจิต\nรชตะ\nรชนิ\nรชนี\nรชะ\nรณรงค์\nรด\nรดี\nรตนะ\nรตะ\nรติ\nรถ\nรน\nร่น\nรบ\nรบาญ\nรพี\nรม\nร่ม\nรมณี\nรมณีย์\nรมณียสถาน\nรมย์\nรมเยศ\nรยางค์\nรวก\nรวง\nร่วง\nรวด\nรวน\nรวนเร\nร่วน\nรวบ\nรวม\nร่วม\nรวย\nรวิ\nรวิวาร\nรวี\nรศนา\nรส\nรสนา\nรสสุคนธ์\nรสายนเวท\nรสิก\nรหัท\nรหัส\nรโห\nรโหฐาน\nรอ\nร่อ\nรอก\nรอง\nร่อง\nร้อง\nรองเง็ง\nร่องแร่ง\nรอด\nรอน\nร่อน\nร้อน\nรอบ\nรอบคอบ\nรอม\nรอมชอม\nรอมร่อ\nรอย\nร่อย\nร้อย\nร่อแร่\nระ\nระกะ\nระกา\nระกำ\nระเกะระกะ\nระคน\nระคาง\nระคาย\nระแคะ\nระฆัง\nระงม\nระงับ\nระแง้\nระโงกหิน\nระชวย\nระดม\nระดะ\nระดับ\nระดา\nระด่าว\nระดู\nระเด่น\nระเดียง\nระแด\nระตู\nระทก\nระทด\nระทม\nระทวย\nระทา\nระทึก\nระแทะ\nระนาด\nระนาบ\nระนาม\nระนาว\nระเนน\nระเนระนาด\nระเนียด\nระแนง\nระแนะ\nระบบ\nระบม\nระบอบ\nระบัด\nระบับ\nระบาด\nระบาย\nระบำ\nระบิล\nระบือ\nระบุ\nระเบง\nระเบ็ง\nระเบิด\nระเบียง\nระเบียน\nระเบียบ\nระแบบ\nระมัดระวัง\nระมาด\nระเมียร\nระย่อ\nระย่อม\nระยะ\nระยั้ง\nระยับ\nระย้า\nระยาบ\nระยำ\nระยิบระยับ\nระโยง\nระโยงระยาง\nระรวย\nระรอง\nระร่อน\nระรัว\nระราน\nระร่าย\nระริก\nระรี่\nระรึง\nระรื่น\nระรื้น\nระเร้ง\nระเริง\nระเรียง\nระเรื่อย\nระแรง\nระลวง\nระลอก\nระลึก\nระวัง\nระวาง\nระวาย\nระวิง\nระแวง\nระแวดระวัง\nระไว\nระส่ำระสาย\nระหกระเหิน\nระหง\nระหวย\nระหว่าง\nระหองระแหง\nระหัด\nระหาย\nระเห็จ\nระเหย\nระเหระหน\nระเหหน\nระเหิด\nระเหินระหก\nระแหง\nระโหย\nระอา\nระอิดระอา\nระอุ\nรัก\nรักข์\nรักขิต\nรักตะ\nรักบี้\nรักเร่\nรักแร้\nรักษ์\nรักษา\nรัง\nรั้ง\nรังเกียจ\nรังแก\nรังค์\nรังควาน\nรังแค\nรังรอง\nรังวัด\nรังสิ\nรังสี\nรังสิมันตุ์\nรังสิมา\nรัจฉา\nรัช\nรัชชูปการ\nรัชมังคลาภิเษก\nรัชชุ\nรัชฎาภิเษก\nรัชดาภิเษก\nรัชนะ\nรัชนี\nรัญจวน\nรัฏฐาภิปาลโนบาย\nรัฐ\nรัฐประศาสโนบาย\nรัฐประศาสนศาสตร์\nรัด\nรัต\nรัตกัมพล\nรัตมณี\nรัตคน\nรัตจันทน์\nรัตตัญญู\nรัตติ\nรัตน์\nรัตนะ\nรัตนโกสินทร์\nรัตนโกสินทรศก\nรัตนชาติ\nรัตนตรัย\nรัตนบัลลังก์\nรัตนวราภรณ์\nรัตนสิงหาสน์\nรัตนา\nรัตนากร\nรัตนาภรณ์\nรัตนาวลี\nรัตมา\nรัถ\nรัถยา\nรัทเทอร์ฟอร์เดียม\nรัน\nรั้น\nรันทด\nรันทวย\nรับ\nรัมณียสถาน\nรัมภา\nรัมมี่\nรัมย์\nรัย\nรัว\nรั่ว\nรั้ว\nรัศมิมัต\nรัศมิมาน\nรัศมี\nรัษฎากร\nรัสเซีย\nรัสสะ\nรัสสระ\nรา\nร่า\nร้า\nราก\nรากษส\nรากสาด\nราคะ\nราคจริต\nราคา\nราคิน\nราคี\nราง\nร่าง\nร้าง\nรางจืด\nรางชาง\nรางวัล\nราช\nราชกิจจานุเบกษา\nราชนิกุล\nราชวโรงการ\nราชญี\nราชดัด\nราชพฤกษ์\nราชมาณพ\nราชมาษ\nราชมาส\nราชย์\nราชสีห์\nราชะ\nราชัน\nราชันย์\nราชัย\nราชา\nราชาธิปไตย\nราชาธิราช\nราชาภิเษก\nราชายตนะ\nราชาวดี\nราชี\nราชินิกุล\nราชินีกุล\nราชินี\nราชินูปถัมภ์\nราชูปถัมภ์\nราชูปโภค\nราเชน\nราเชนทร์\nราเชนทรยาน\nราโชวาท\nราไชศวรรย์\nราญ\nราญรอน\nราด\nราต\nราตร\nราตรี\nราน\nร่าน\nร้าน\nราบ\nราพณ์\nราพณาสูร\nราม\nรามเกียรติ์\nรามสูร\nรามัญ\nรามา\nราย\nร่าย\nร้าย\nราว\nร้าว\nราวี\nราศี\nราษฎร\nราษฎร์\nราษตรี\nราษราตรี\nราหุ\nราหู\nรำ\nร่ำ\nรำคาญ\nรำงับ\nรำจวน\nรำบาญ\nรำพัน\nรำพาย\nรำพึง\nรำเพย\nรำไพ\nรำมะนา\nรำมะนาด\nรำมะร่อ\nร่ำรวย\nร่ำร่ำ\nรำไร\nรำลึก\nรำหัด\nรำหัส\nริ\nริก\nริดสีดวง\nริน\nริ้น\nริบ\nริบบิ้น\nริบรี่\nริบหรี่\nริปุ\nริปู\nริม\nริ้ว\nริษยา\nรี\nรี่\nรี้พล\nรีด\nรีดักชัน\nรีต\nรีเนียม\nรีบ\nรีม\nรีรอ\nรี้ริก\nรึง\nรึ้ง\nรื่น\nรื้น\nรื้อ\nรุ\nรุก\nรุกข์\nรุกขชาติ\nรุกขเทวดา\nรุกขมูล\nรุกขา\nรุกรุย\nรุ่ง\nรุ้ง\nรุงรัง\nรุ่งริ่ง\nรุจ\nรุจา\nรุจนะ\nรุจิ\nรุจี\nรุจิระ\nรุจิรา\nรุด\nรุต\nรุทธ์\nรุทระ\nรุธิร\nรุธิระ\nรุเธียร\nรุน\nรุ่น\nรุบรู่\nรุม\nรุ่ม\nรุ่มร่าม\nรุย\nรุ่ย\nรุรุ\nรุหะ\nรู\nรู่\nรู้\nรูจี\nรูด\nรูทีเนียม\nรูบิเดียม\nรูป\nรูปิยะ\nรูปี\nรูเล็ตต์\nเร่\nเรข\nเรขา\nเรขาคณิต\nเร็ง\nเร่ง\nเร้ง\nเรณุ\nเรณู\nเรดอน\nเรดาร์\nเรเดียม\nเร้น\nเรรวน\nเรไร\nเร็ว\nเร่ว\nเรวดี\nเรอ\nเร่อ\nเรา\nเร่า\nเร้า\nเราะ\nเริง\nเริด\nเริม\nเริ่ม\nเริ้ม\nเริศร้าง\nเรี่ย\nเรี้ย\nเรียก\nเรียง\nเรียด\nเรียน\nเรียบ\nเรียม\nเรี่ยม\nเรียว\nเรี่ยว\nเรี้ยวรก\nเรือ\nเรื่อ\nเรื้อ\nเรือก\nเรือง\nเรื่อง\nเรื้อง\nเรืองรอง\nเรือด\nเรือน\nเรื้อน\nเรื่อย\nแร\nแร่\nแรก\nแร็กเกต\nแรง\nแร่ง\nแร้ง\nแรด\nแร้นแค้น\nแรม\nแร้ว\nแระ\nโร\nโร่\nโรค\nโรคา\nโรคาพาธ\nโรง\nโรจ\nโรจน์\nโรเดียม\nโรตี\nโรท\nโรธ\nโรม\nโรมัน\nโรเมอร์\nโรย\nโรเร\nโรหิณี\nโรหิต\nไร\nไร่\nไร้\nไรย์\nฤกษ์\nฤกษณะ\nฤคเวท\nฤชา\nฤชุ\nฤณ\nฤดี\nฤดียา\nฤดู\nฤต\nฤติยา\nฤตุ\nฤทธา\nฤทธิ์\nฤทัย\nฤษภ\nฤษยา\nฤษี\nฤๅ\nฤๅดี\nฤๅทัย\nฤๅษี\nฤๅสาย\nลก\nล่ก\nลฆุ\nลง\nล่ง\nลงกา\nล้งเล้ง\nลด\nลดา\nลดาวัลย์\nลน\nล้น\nลบ\nลบอง\nลพ\nลพุช\nลม\nล่ม\nล้ม\nลมาด\nลรรลุง\nลลนา\nลลิต\nลวก\nลวง\nล่วง\nล้วง\nลวณะ\nลวด\nล้วน\nลวนลาม\nลวนะ\nล่วม\nลวะ\nลวิตร\nลหุ\nลหุกาบัติ\nล่อ\nล้อ\nลอก\nล็อก\nล็อกเกต\nลอกแลก\nลอการิทึม\nลอง\nล่อง\nลองกอง\nลองจิจูด\nลองไน\nลอด\nลอตเตอรี่\nลอน\nล่อน\nลอบ\nลอม\nล้อม\nลอมชอม\nลอมพอก\nลอย\nล่อย\nล่อแล่\nลอว์เรนเซียม\nลออ\nละ\nล่ะ\nละคร\nละติจูด\nละบม\nละบอง\nละบือ\nละเบ็ง\nละโบม\nละม่อม\nละมั่ง\nละมาน\nละม้าย\nละมุ\nละมุด\nละมุน\nละเมอ\nละเมาะ\nละเมิด\nละเมียด\nละแมะ\nละโมก\nละโมบ\nละไม\nละลวย\nละลอก\nละล้า\nละล้าละลัง\nละลาน\nละลาบละล้วง\nละลาย\nละล้าว\nละล่ำละลัก\nละลิบ\nละลุม\nละเลง\nละเล้า\nละเลาะ\nละเลิง\nละเลียด\nละเลียบ\nละไล้\nละว้า\nละวาด\nละเวง\nละแวก\nละโว้\nละหมาด\nละห้อย\nละหาน\nละหาร\nละหุ่ง\nละเหย\nละเหี่ย\nละอง\nละออง\nละอาย\nละเอียด\nละแอน\nลัก\nลักขณะ\nลักขณา\nลักขะ\nลักขี\nลักจั่น\nลักปิดลักเปิด\nลักษณ์\nลักษณนาม\nลักษณะ\nลักษณาการ\nลักษมณ์\nลักษมาณา\nลักษมี\nลักษะ\nลัคคะ\nลัคน์\nลัคนา\nลัง\nลั่ง\nลังกา\nลังคี\nลังถึง\nลังลอง\nลังเล\nลังสาด\nลัชชา\nลัชชี\nลัญจ์\nลัญจกร\nลัญฉกร\nลัญฉน์\nลัฐิ\nลัฐิกา\nลัด\nลัดา\nลัทธ์\nลัทธิ\nลัน\nลั่น\nลันเต\nลันเตา\nลันไต\nลั่นทม\nลันโทม\nลับ\nลัพธ์\nลัพธิ\nลัภ\nลัภนะ\nลัภย์\nลัมพ์\nลัย\nลา\nล่า\nล้า\nลาก\nลาง\nล่าง\nล้าง\nลางลิง\nลางสาด\nลาช\nลาชะ\nลาชา\nลาญ\nลาด\nลาดเลา\nล้าต้า\nล่าเตียง\nลาน\nล่าน\nล้าน\nลาบ\nลาพอน\nลาภ\nลาม\nล่าม\nลามก\nลาย\nล้าย\nลายสือ\nลาลา\nลาว\nลาวัณย์\nลาวา\nลำ\nล่ำ\nล้ำ\nลำเข็ญ\nลำแข\nลำเค็ญ\nลำเจียก\nลำดวน\nลำดับ\nลำเนา\nลำบอง\nลำบาก\nลำปำ\nลำพวน\nลำพอง\nลำพัง\nลำพู\nลำเพ็ญ\nลำเพา\nลำแพน\nลำโพง\nลำไพ่\nลำภุขัน\nลำมะลอก\nลำยอง\nลำไย\nลำลอง\nล่ำลา\nลำลาบ\nลำลึก\nลำเลาะ\nลำเลิก\nลำเลียง\nลำเวียง\nลำเอียก\nลำเอียง\nลิ\nลิกขา\nลิกไนต์\nลิกู\nลิเก\nลิขนะ\nลิขสิทธิ์\nลิขิต\nลิง\nลิงค์\nลิด\nลิต\nลิตมัส\nลิตร\nลิเทียม\nลิ่น\nลิ้น\nลินจง\nลิ้นจี่\nลินลา\nลินสีด\nลิ่นฮื้อ\nลินิน\nลิบ\nลิปดา\nลิปสติก\nลิปิ\nลิฟต์\nลิเภา\nลิ่ม\nลิ้ม\nลิมป์\nลิมปนะ\nลิลิต\nลิว\nลิ่ว\nลิสง\nลี\nลี่\nลี้\nลีซอ\nลีบ\nลีลา\nลีลาศ\nลีฬหา\nลึก\nลึงค์\nลืด\nลื่น\nลื้น\nลืบ\nลืม\nลือ\nลื่อ\nลื้อ\nลุ\nลุก\nลุง\nลุ้ง\nลุ่น\nลุ้น\nลุพธ์\nลุ่ม\nลุมพี\nลุมพู\nลุย\nลุ่ย\nลุ้ย\nลู่\nลูก\nลูกระมาศ\nลูกเอ็น\nลูขะ\nลูทีเชียม\nลูบ\nเลก\nเล็ก\nเลข\nเลขา\nเลขาธิการ\nเลขานุการ\nเล็ง\nเล้ง\nเล่งฮื้อ\nเลเซอร์\nเลฑฑุ\nเลณฑุ\nเลณะ\nเล็ด\nเลน\nเล็น\nเล่น\nเลนส์\nเล็บ\nเลบง\nเลปกร\nเลปน์\nเลเป\nเลเพ\nเล็ม\nเล่ม\nเลย\nเลว\nเลวง\nเลวูโลส\nเลศ\nเลษฏุ\nเล่ห์\nเล่ห์กระเท่ห์\nเลหลัง\nเลหะ\nเลอ\nเล่อ\nเลอะ\nเลอะเทอะ\nเละ\nเละเทะ\nเลา\nเล่า\nเล้า\nเลากัย\nเล้าโลม\nเลาะ\nเลิก\nเลิ่กลั่ก\nเลิง\nเลิ้ง\nเลินเล่อ\nเลิศ\nเลีย\nเลียง\nเลี่ยง\nเลี้ยง\nเลียงผา\nเลียงฝ้าย\nเลียงมัน\nเลียน\nเลี่ยน\nเลียนไฟ\nเลียบ\nเลี่ยม\nเลียว\nเลี้ยว\nเลือก\nเลือง\nเลื่อง\nเลือด\nเลือน\nเลื่อน\nเลื่อม\nเลื่อย\nเลื้อย\nเลื่อยล้า\nแล\nแล่\nแล้\nแลก\nแล็กเกอร์\nแล็กโทส\nแลง\nแล่ง\nแล้ง\nแลน\nแล่น\nแลนทานัม\nแลบ\nแล้ว\nและ\nโล่\nโล้\nโลก\nโลกเชษฐ์\nโลกธรรม\nโลกธาตุ\nโลกนาถ\nโลกบาล\nโลกย์\nโลกัย\nโลกวัชชะ\nโลกวิทู\nโลกัตถจริยา\nโลกันตร์\nโลกา\nโลกาธิบดี\nโลกาธิปไตย\nโลกานุวัตร\nโลกาภิวัตน์\nโลกามิส\nโลกายัต\nโลกาวินาศ\nโลกิยะ\nโลกีย์\nโลกียวัตร\nโลกียวิสัย\nโลกียสุข\nโลกุตระ\nโลกุตรธรรม\nโลกุตรภูมิ\nโลง\nโล่ง\nโล้ง\nโล่งโจ้ง\nโล่งโต้ง\nโล้งโต้ง\nโลจนะ\nโลณะ\nโลด\nโล่ติ๊น\nโลโต\nโลท\nโลน\nโล้น\nโลภ\nโลม\nโลมเล้า\nโลมะ\nโลมา\nโลลุป\nโลเล\nโลโล\nโลโล้\nโลหะ\nโลหกุมภี\nโลหัช\nโลหิต\nไล่\nไล้\nไลย\nไลลา\nไล่เลี่ย\nฦๅ\nฦๅชา\nฦๅสาย\nวก\nวง\nวงก์\nวงกต\nวงศ์\nวงศกร\nวงศา\nวงษ์\nวจนะ\nวจี\nวชิระ\nวชิรปาณี\nวชิรหัตถ์\nวชิราวุธ\nวฏะ\nวฏาการ\nวณิช\nวณิชชา\nวณิชย์\nวณิชยา\nวณิพก\nวดี\nวทนะ\nวทัญญุตา\nวทัญญู\nวธุกา\nวธู\nวน\nวนศาสตร์\nวนสณฑ์\nวนสัณฑ์\nวนอุทยาน\nวนัส\nวนัสบดี\nวนา\nวนาดร\nวนาดอน\nวนานต์\nวนาลัย\nวนาลี\nวนาวาส\nวนาศรม\nวนาสณฑ์\nวนาสัณฑ์\nวนิดา\nวนิพก\nวเนจร\nวโนทยาน\nวยัคฆ์\nวยากรณ์\nวรดนู\nวรทาน\nวรมหาวิหาร\nวรงค์\nวรณะ\nวรรค\nวรรคย์\nวรรช\nวรรชย์\nวรรณะ\nวรรณกรรม\nวรรณคดี\nวรรณยุกต์\nวรรณยุต\nวรรณศิลป์\nวรรณนา\nวรรณพฤติ\nวรรณึก\nวรรธกะ\nวรรธนะ\nวรรษ\nวรรษา\nวรวิหาร\nวรัญญู\nวรางคณา\nวรางคนา\nวราห์\nวราหะ\nวรุณ\nวโรดม\nวฤก\nวลัช\nวลัญช์\nวลัญชน์\nวลัย\nวลาหก\nวลี\nวศค\nวศะ\nวศิน\nวสนะ\nวสภะ\nวสละ\nวสวัดดี\nวสวัตตี\nวสะ\nวสันต์\nวสันตดิลก\nวสันตฤดู\nวสันตวิษุวัต\nวสา\nวสี\nวสุ\nวสุธา\nวสุนธรา\nวสุมดี\nวหะ\nวอ\nวอก\nวอกแวก\nว่องไว\nวอด\nวอน\nว่อน\nว็อบ\nวอมแวม\nวอลเลย์บอล\nวอแว\nวะ\nวัก\nวักกะ\nวัคคีย์\nวัคคุ\nวัคซีน\nวัง\nวังก์\nวังชา\nวังเวง\nวังศะ\nวังสะ\nวัจจะ\nวัจกุฎี\nวัจฉละ\nวัจน์\nวัช\nวัชชะ\nวัชพืช\nวัชฌ์\nวัชระ\nวัชรปาณี\nวัชรยาน\nวัชรอาสน์\nวัชราสน์\nวัชรินทร์\nวัชรี\nวัชเรนทร์\nวัฏ\nวัฏฏะ\nวัฏจักร\nวัฏทุกข์\nวัฏสงสาร\nวัฏกะ\nวัฏฏิ\nวัฒกะ\nวัฒกี\nวัฒนธรรม\nวัฒนะ\nวัฒนา\nวัณ\nวัณโรค\nวัณฏ์\nวัณณะ\nวัณนา\nวัด\nวัต\nวัตต์\nวัตตา\nวัตถ์\nวัตถาภรณ์\nวัตถาลังการ\nวัตถุ\nวัตนะ\nวัตร\nวัตสดร\nวัตสะ\nวัติ\nวัทน์\nวัน\nวันต์\nวันทนา\nวันทนาการ\nวันทนีย์\nวันทยหัตถ์\nวันทยาวุธ\nวันทา\nวันทิ\nวับ\nวับวาบ\nวับวาม\nวับแวบ\nวับแวม\nวัปปะ\nวัมมิกะ\nวัย\nวัลก์\nวัลคุ\nวัลย์\nวัลลภ\nวัลลี\nวัว\nวัสสะ\nวัสโสทก\nวัสดุ\nวัสตร์\nวัสน์\nวัสนะ\nวัสสานะ\nวัสสานฤดู\nวา\nว่า\nว้า\nว้าเหว่\nวาก\nว้าก\nวากยสัมพันธ์\nวากยะ\nวาง\nว่าง\nว้าง\nวาจก\nวาจา\nวาจาไปยะ\nวาจาล\nวาชเปยะ\nวาณิช\nวาณิชกะ\nวาณิชย์\nวาณี\nวาด\nวาต\nวาตะ\nวาตภัย\nวาท\nวาทศาสตร์\nวาทศิลป์\nวาทกะ\nวาทนะ\nวาทย์\nวาทยกร\nวาทิต\nวาทิน\nวาที\nวาน\nวานซืน\nว่าน\nวานร\nวานรินทร์\nวาเนเดียม\nวาบ\nวาปี\nวาม\nวามน\nวามนาวตาร\nวามะ\nวาย\nว่าย\nว้าย\nวายะ\nวาโย\nวายามะ\nวายุ\nวายุกูล\nวาร\nวาระ\nวารสาร\nวารสารศาสตร์\nวาริ\nวารี\nวาริช\nวารีช\nวาริท\nวาริธร\nวารุณ\nวารุณี\nวาล\nวาลวีชนี\nวาล์ว\nวาลิกา\nวาลุกา\nวาว\nว่าว\nว้าว่อน\nว้าวุ่น\nวาสนะ\nวาสนา\nวาสพ\nวาสะ\nวาสิน\nวาสี\nวาสุกรี\nวาสุกี\nวาสุเทพ\nวาหนะ\nวาหะ\nวาหินี\nวาฬ\nวิกขัมภ์\nวิกขัมภนะ\nวิกเขป\nวิกรม\nวิกรัย\nวิกรานต์\nวิกฤต\nวิกฤติ\nวิกล\nวิกสิต\nวิกัต\nวิกัติ\nวิกัติการก\nวิกัป\nวิกัย\nวิการ\nวิกาล\nวิกาลโภชน์\nวิคหะ\nวิเคราะห์\nวิฆเนศ\nวิฆเนศวร\nวิฆาต\nวิง\nวิ่ง\nวิ่งเปี้ยว\nวิงวอน\nวิจฉิกะ\nวิจล\nวิจักขณ์\nวิจักษ์\nวิจักษณ์\nวิจัย\nวิจาร\nวิจารณ์\nวิจารณญาณ\nวิจิ\nวิจิกิจฉา\nวิจิต\nวิจิตร\nวิจิน\nวิจุณ\nวิจุรณ\nวิชชา\nวิชชุ\nวิชชุดา\nวิชชุตา\nวิชชุลดา\nวิชญะ\nวิชน\nวิชนี\nวิชย\nวิชัย\nวิชา\nวิชานนะ\nวิชิต\nวิเชียร\nวิญญัตติ\nวิญญาณ\nวิญญาณกทรัพย์\nวิญญู\nวิฑูรย์\nวิด\nวิตก\nวิตถาร\nวิตามิน\nวิถี\nวิทธะ\nวิทยฐานะ\nวิทยา\nวิทยาคม\nวิทยาคาร\nวิทยาลัย\nวิทยุ\nวิทยุต\nวิทวัส\nวิทัตถิ\nวิทัศน์\nวิทารณ์\nวิทิต\nวิทู\nวิทูร\nวิเทศ\nวิเทโศบาย\nวิธ\nวิธวา\nวิธาน\nวิธี\nวิธุระ\nวิธู\nวิธูปนะ\nวิ่น\nวินตกะ\nวินัย\nวินาที\nวินายก\nวินาศ\nวินิจ\nวินิจฉัย\nวินิต\nวินิบาต\nวินิปาติก\nวิเนต\nวิบัติ\nวิบาก\nวิบุล\nวิบุลย์\nวิบูล\nวิบูลย์\nวิปการ\nวิปฏิสาร\nวิปโยค\nวิประโยค\nวิปริต\nวิปลาส\nวิปวาส\nวิปักษ์\nวิปัสสก\nวิปัสสนา\nวิปัสสนายานิก\nวิพากษ์\nวิพิธทัศนา\nวิพุธ\nวิภว\nวิภวตัณหา\nวิภังค์\nวิภัช\nวิภัตติ\nวิภา\nวิภาค\nวิภาช\nวิภาดา\nวิภาวี\nวิภาษ\nวิภาส\nวิภู\nวิภูษณะ\nวิภูษา\nวิภูษิต\nวิมน\nวิมล\nวิมลัก\nวิมังสา\nวิมัติ\nวิมาน\nวิมุข\nวิมุต\nวิมุตติ\nวิเมลือง\nวิโมกข์\nวิโยค\nวิระ\nวิรงรอง\nวิรังรอง\nวิรัช\nวิรัต\nวิรัติ\nวิราคะ\nวิราม\nวิริยภาพ\nวิริยะ\nวิรุธ\nวิรุฬห์\nวิรุฬหก\nวิรูป\nวิรูปักษ์\nวิเรนทร์\nวิโรจ\nวิโรจน์\nวิโรฒ\nวิโรธ\nวิลันดา\nวิลัย\nวิลาด\nวิลาศ\nวิลาป\nวิลาวัณย์\nวิลาส\nวิลาสินี\nวิลิปดา\nวิลิศมาหรา\nวิเลป\nวิเลปนะ\nวิโลกนะ\nวิโลม\nวิไล\nวิไลวรรณ\nวิวรณ์\nวิวรรธน์\nวิวัฏ\nวิวัฒน์\nวิวัฒนาการ\nวิวัน\nวิวาท\nวิวาห์\nวิวาหมงคล\nวิวาหะ\nวิวิต\nวิวิธ\nวิเวก\nวิศรุต\nวิศว\nวิศวกร\nวิศวกรรม\nวิศวกรรมศาสตร์\nวิศัลย์\nวิศาขบูชา\nวิศาขา\nวิศาล\nวิศิษฏ์\nวิศุทธ์\nวิศุทธิ์\nวิเศษ\nวิเศษณ์\nวิษณุ\nวิษณุกรรม\nวิษธร\nวิษัย\nวิษาณ\nวิษุวัต\nวิสกี้\nวิสรรชนีย์\nวิสฤต\nวิสสุกรรม\nวิสัชนา\nวิสัญญี\nวิสัย\nวิสัยทัศน์\nวิสาขบูชา\nวิสาขะ\nวิสาขา\nวิสามัญ\nวิสามานยนาม\nวิสาร\nวิสารทะ\nวิสาล\nวิสาสะ\nวิสาหกิจ\nวิสิฐ\nวิสุงคามสีมา\nวิสุทธ์\nวิสุทธิ์\nวิสูตร\nวิเสท\nวิหค\nวิหลั่น\nวิหาร\nวิหิงสา\nวิเหสา\nวิฬาร\nวิฬาร์\nวี\nวีจิ\nวีชนี\nวีณา\nวี้ด\nวีรกรรม\nวีรชน\nวีรบุรุษ\nวีรสตรี\nวี่วัน\nวี่แวว\nวีสะ\nวุ้ง\nวุฐิ\nวุฒ\nวุฒิ\nวุด\nวุ่น\nวุ้น\nวุบ\nวุ้ย\nวุลแฟรม\nวู้\nวูดวาด\nวูบ\nวู่วาม\nเว้\nเวค\nเวคิน\nเวคี\nเวจ\nเวช\nเวชยันต์\nเวฐน์\nเวณิ\nเวณิก\nเวณุ\nเวตน์\nเวตร\nเวตาล\nเวท\nเวทคู\nเวทนา\nเวทย์\nเวทัลละ\nเวทางค์\nเวทางคศาสตร์\nเวทานต์\nเวทานตะ\nเวทิ\nเวที\nเวธะ\nเวน\nเว้น\nเวนไตย\nเวไนย\nเวมัต\nเว้ย\nเวยยากรณะ\nเวร\nเวรมณี\nเวรี\nเวโรจน์\nเวลา\nเวเลนซี\nเวศม์\nเวศย์\nเวศยา\nเวสน์\nเวสภู\nเวสม์\nเวสวัณ\nเวสสะ\nเวสสันดร\nเวสสุกรรม\nเวสสุวัณ\nเวสารัช\nเวสิยา\nเวหน\nเวหะ\nเวหา\nเวหาส\nเวฬุ\nเวฬุริยะ\nเว่อ\nเว้า\nเวิก\nเวิ้ง\nเวี่ย\nเวียง\nเวียด\nเวียดนาม\nเวียน\nเวียร\nเวี่ยว\nแว้\nแวง\nแว้ง\nแวด\nแว้ด\nแวน\nแว่น\nแวนดา\nแวบ\nแว็บ\nแวม\nแว็ม\nแวว\nแว่ว\nแวะ\nโว\nโว่\nโวการ\nโว่ง\nโวทาน\nโวย\nโว้ย\nโว้เว้\nโวหาร\nไว\nไว้\nไวกูณฐ์\nไวฑูรย์\nไวทย์\nไวน์\nไวพจน์\nไวยากรณ์\nไวยาวัจกร\nไวยาวัจมัย\nไวรัส\nไววรรณ\nไวษณพ\nไวโอลิน\nศก\nศกุน\nศกุนต์\nศกุนิ\nศกุนี\nศจี\nศตะ\nศตภิษัช\nศตวรรษ\nศตพรรษ\nศตกะ\nศนิ\nศพ\nศมนะ\nศมะ\nศยาม\nศยามล\nศร\nศรายุธ\nศราวรณ์\nศรรกรา\nศรวณะ\nศรวณีย์\nศรวิษฐา\nศรัณย์\nศรัณยู\nศรัท\nศรัทธา\nศรัย\nศราทธ์\nศราทธพรต\nศราพก\nศราวก\nศราวณะ\nศรี\nศรีตรัง\nศรุติ\nศฤคาล\nศฤงค์\nศฤงคาร\nศฤงคาริน\nศฤงคารี\nศลิษฏ์\nศลิษา\nศวะ\nศวัส\nศวา\nศวาน\nศศะ\nศศธร\nศศพินทุ์\nศศลักษณ์\nศศิ\nศศิน\nศศี\nศศิขัณฑ์\nศศิธร\nศศิมณฑล\nศศิวิมล\nศอ\nศอก\nศักดา\nศักดิ\nศักดิ์\nศักดินา\nศักติ\nศักย\nศักยภาพ\nศักย์\nศักยะ\nศักร\nศักรินทร์\nศักเรนทร์\nศักราช\nศังกร\nศัตรู\nศันสนะ\nศันสนีย์\nศัพท์\nศัยยา\nศัล\nศัลย์\nศัลยกรรม\nศัลยแพทย์\nศัลยศาสตร์\nศัสดร\nศัสตร\nศัสตรศาสตร์\nศัสตรา\nศัสตราวุธ\nศากตะ\nศากย\nศากยะ\nศากยพุทธ\nศากยมุนี\nศาฎก\nศาณ\nศานต์\nศานติ\nศาป\nศารท\nศารทูล\nศาริกา\nศาล\nศาลา\nศาศวัต\nศาสดา\nศาสตร์\nศาสตรา\nศาสตราจารย์\nศาสนา\nศาสนกิจ\nศาสนจักร\nศาสนธรรม\nศาสนบุคคล\nศาสนพิธี\nศาสนวัตถุ\nศาสนศาสตร์\nศาสนสถาน\nศาสนสมบัติ\nศาสนิกชน\nศาสนีย์\nศาสนูปถัมภก\nศาสน์\nศิกษก\nศิการ\nศิขร\nศิขริน\nศิขรี\nศิขัณฑ์\nศิคาล\nศิงขร\nศิงขริน\nศิตะ\nศิถี\nศิพิระ\nศิระ\nศิรประภา\nศิราภรณ์\nศิโรรัตน์\nศิโรเวฐน์\nศิรา\nศิรามพุช\nศิโรราบ\nศิลป\nศิลป์\nศิลปะ\nศิลปกร\nศิลปกรรม\nศิลปกิจ\nศิลปวัตถุ\nศิลปวิทยา\nศิลปศาสตร์\nศิลปศึกษา\nศิลปหัตถกรรม\nศิลปิน\nศิลปี\nศิลา\nศิวะ\nศิวโมกข์\nศิวลึงค์\nศิวเวท\nศิวาลัย\nศิศีระ\nศิษฎิ\nศิษฏ์\nศิษย์\nศิษยานุศิษย์\nศีขร\nศีต\nศีตกาล\nศีรษะ\nศีล\nศึก\nศึกษา\nศึกษาธิการ\nศึกษานิเทศก์\nศุกร์\nศุกรวรรณ\nศุกรวาร\nศุกระ\nศุกล\nศุกลปักษ์\nศุจิ\nศุทธะ\nศุทธิ\nศุนะ\nศุนิ\nศุภกร\nศุภเคราะห์\nศุภนิมิต\nศุภมัสดุ\nศุภมาตรา\nศุภมาส\nศุภอักษร\nศุภางค์\nศูกร\nศุลกากร\nศุลการักษ์\nศุลี\nศุษิร\nศูทร\nศูนย์\nศูนยวาท\nศูละ\nศูลิน\nเศรณี\nเศรษฐ\nเศรษฐ์\nเศรษฐกิจ\nเศรษฐศาสตร์\nเศรษฐี\nเศร้า\nเศลษ\nเศวต\nเศวตร\nเศวตัมพร\nเศษ\nเศาจ\nเศาร์\nเศารยะ\nเศิก\nเศียร\nโศก\nโศกา\nโศกาดูร\nโศกาลัย\nโศกี\nโศจิ\nโศธนะ\nโศภน\nโศภะ\nโศภา\nโศภิต\nโศภิน\nโศภิษฐ์\nโศภี\nโศรดา\nโศรตร\nโศลก\nไศล\nไศวะ\nษมา\nษัฏ\nษัฑ\nษัณ\nษัษ\nษัษฐะ\nษัษฐี\nโษฑศัน\nสก\nสกวาที\nสกฏะ\nสกทาคามิผล\nสกิทาคามิผล\nสกทาคามิมรรค\nสกิทาคามิมรรค\nสกทาคามี\nสกิทาคามี\nสกนธ์\nสกปรก\nสกรณีย์\nสกรรจ์\nสกรรมกริยา\nสกล\nสกลมหาสังฆปริณายก\nสกัด\nสกา\nสกาว\nสกี\nสกุณ\nสกุณา\nสกุณี\nสกุน\nสกุนต์\nสกุล\nสเกต\nสแกนเดียม\nสขะ\nสง\nส่ง\nสงกร\nสงกรานต์\nสงกา\nสงค์\nสงคร\nสงคราม\nสงเคราะห์\nสงฆ์\nสงบ\nสงวน\nส่งสการ\nสงสัย\nสงสาร\nสงสารวัฏ\nสงัด\nสง่า\nสฐะ\nสณฑ์\nสด\nสดมภ์\nสดับ\nสดับปกรณ์\nสดำ\nสดุดี\nสตะ\nสตน\nสตภิสชะ\nสตรอนเชียม\nสตริกนิน\nสตรี\nสตัฟฟ์\nสตัมภ์\nสตางค์\nสติ\nสติปัฏฐาน\nสตี\nสตู\nสตูป\nสเต๊ก\nสถบดี\nสถล\nสถวีระ\nสถาน\nสถานะ\nสถานี\nสถาบัน\nสถาปนา\nสถาปนิก\nสถาปัตยกรรม\nสถาปัตยกรรมศาสตร์\nสถาปัตยเรขา\nสถาปัตยเวท\nสถาพร\nสถาวร\nสถิต\nสถิตยศาสตร์\nสถิติ\nสถิร\nสถีรวาท\nสถุล\nสถูป\nสทิง\nสทึง\nสทุม\nสธนะ\nสาธุสะ\nสน\nส้น\nสนทนา\nสนทรรศ\nสนทรรศน์\nสนเทศ\nสนเท่ห์\nสนธยา\nสนธิ\nสนน\nสนม\nสนวน\nสนอง\nสนอบ\nสนอม\nสนะ\nสนัด\nสนั่น\nสนับ\nสนับทึบ\nสนับสนุน\nสนาน\nสนาม\nสนายุ\nสนิกะ\nสนิท\nสนิธ\nสนิม\nสนุก\nสนุกเกอร์\nสนุข\nสนุต\nสนุ่น\nสบ\nสบง\nสบถ\nสบัน\nสบาย\nสบู่\nสไบ\nสปริง\nสปอร์\nสปาเกตตี\nสเปกตรัม\nสเปกโทรสโกป\nสไปริลลัม\nสพาบ\nสภา\nสภาพ\nสภาวการณ์\nสภาวะ\nสม\nสมการ\nสมจารี\nสมดุล\nสมมูล\nส้ม\nสมญา\nสมณะ\nสมณบริขาร\nสมณศักดิ์\nสมณสารูป\nสมเด็จ\nสมถะ\nสมถยานิก\nสมถวิปัสสนา\nสมนาคุณ\nสมบัติ\nสมบุกสมบัน\nสมบูรณ์\nสมบูรณาญาสิทธิราชย์\nสมประดี\nสมปฤดี\nสมปฤๅดี\nส้มป่อย\nสมปัก\nสมผุส\nสมพง\nสมพงศ์\nสมพล\nสมพัตสร\nสมพาส\nสมเพช\nสมโพธน์\nสมโพธิ\nสมภพ\nสมภาร\nสมโภค\nสมโภช\nสมมต\nสมมติ\nสมมุติ\nสมมาตร\nส้มมือ\nสมโมท\nสมโยค\nสมร\nสมรด\nสมรรถ\nสมรรถนะ\nสมรรถภาพ\nสมรส\nสมฤดี\nสมฤติ\nสมวายะ\nสมเสร็จ\nสมอ\nสมอง\nสมะ\nสมัคร\nสมังคี\nสมัช\nสมัชชา\nสมัญญา\nสมัต\nสมัน\nสมันต์\nสมัย\nสมา\nสมาคม\nสมาจาร\nสมาชิก\nสมาทาน\nสมาธิ\nสมาน\nสมานฉันท์\nสมาบัติ\nสมาพันธรัฐ\nสมาส\nสม่ำเสมอ\nสมิง\nสมิต\nสมิติ\nสมิทธ์\nสมิทธิ\nสมี\nสมุก\nสมุจจัย\nสมุจเฉท\nสมุฏฐาน\nสมุด\nสมุทร\nสมุทรโคดม\nสมุทัย\nสมุน\nสมุนไพร\nสมุลแว้ง\nสมุห\nสมุห์\nสมุหกลาโหม\nสมุหเทศาภิบาล\nสมุหนาม\nสมุหนายก\nสโมธาน\nสโมสร\nสยด\nสยนะ\nสยบ\nสยมพร\nสยมภู\nสยอง\nสยอน\nสยัมวรา\nสยาม\nสยามานุสติ\nสยามินทร์\nสยาย\nสยิว\nสยิ้ว\nสยุมพร\nสยุมภู\nสร\nสรง\nสร่ง\nสรณะ\nสรณคมน์\nสรณาคมน์\nสรณตรัย\nสรตะ\nสรทะ\nสรนุก\nสรเนาะ\nสรไน\nสรเพชญ\nสรภะ\nสรภัญญะ\nสรภู\nสรม\nสรร\nสรรค์\nสรรพ\nสรรพคุณ\nสรรพนาม\nสรรพสามิต\nสรรพัชญ\nสรรพากร\nสรรพางค์\nสรรเพชญ\nสรรเพชุดา\nสรรเสริญ\nสรลอน\nสรเลข\nสรวง\nสรวม\nสรวล\nสรเสริญ\nสร้อย\nสระ\nสระกอ\nสระท้อน\nสระพรั่ง\nสระอาด\nสรั่ง\nสรัสวดี\nสร่าง\nสร้าง\nสราญ\nสรี้\nสรีระ\nสรีรกิจ\nสรีรธาตุ\nสรีรวิทยา\nสรีรศาสตร์\nสรีรังคาร\nสรีรางคาร\nสรุป\nสโรช\nสโรชะ\nสฤก\nสฤต\nสฤษฎิ\nสฤษฎี\nสฤษฏ์\nสฤษดิ์\nสลด\nสลบ\nสลวน\nสลวย\nสลอด\nสลอน\nสลอย\nสละ\nสลัก\nสลัด\nสลัดได\nสลับ\nสลัว\nสลา\nสลาก\nสลาง\nสล้าง\nสลาด\nสลาตัน\nสลาบ\nสลาย\nสลิด\nสลิล\nสลึก\nสลึง\nสลุต\nสลุบ\nสลุมพร\nสแลง\nสวการย์\nสวภาพ\nสวราชย์\nสวก\nส้วง\nสวด\nสวน\nสวนะ\nสวนาการ\nส่วน\nสวนิต\nสวบ\nสวม\nส้วม\nสวย\nส่วย\nส้วย\nสวยม\nสวรรค\nสวรรค์\nสวรรคต\nสวรรคาลัย\nสวรรยา\nสวระ\nสวะ\nสวัสดิ\nสวัสดิ์\nสวัสดิการ\nสวัสดิภาพ\nสวัสดิมงคล\nสวัสดี\nสวัสติ\nสวาตี\nสวัสติกะ\nสวา\nสวาปาม\nสวาคตะ\nสวาง\nสว่าง\nสวาด\nสวาดิ\nสวาท\nสว่าน\nสว้าน\nสวาบ\nสวามิ\nสวามี\nสวามินี\nสวาย\nสวาสดิ์\nสวาหะ\nสวิง\nสวิญญาณกทรัพย์\nสวิตช์\nสสาร\nสสุระ\nสสุรี\nสหกรณ์\nสหการ\nสหจร\nสหชาต\nสหชาติ\nสหธรรม\nสหธรรมิก\nสหประชาชาติ\nสหพันธ์\nสหพันธรัฐ\nสหภาพ\nสหศึกษา\nสหัช\nสหัมบดี\nสหัส\nสหัสสะ\nสหัสธารา\nสหัสนัยน์\nสหัสเนตร\nสหัสรังสี\nสหัสา\nสหาย\nสอ\nส่อ\nสอง\nส่อง\nส้อง\nสอด\nสอน\nส่อน\nสอบ\nสอพลอ\nส้อม\nสอย\nสะ\nสะกด\nสะกอ\nสะกาง\nสะการะ\nสะกิด\nสะกิดสะเกา\nสะเก็ด\nสะแก\nสะคร้อ\nสะคราญ\nสะค้าน\nสะเงาะสะแงะ\nสะดม\nสะดวก\nสะดิ้ง\nสะดึง\nสะดือ\nสะดุ้ง\nสะดุด\nสะเด็ด\nสะเดา\nสะเดาะ\nสะตอ\nสะตาหมัน\nสะตึ\nสะตือ\nสะตุ\nสะเต๊ะ\nสะโตก\nสะทก\nสะท้อน\nสะท้าน\nสะทึก\nสะเทิน\nสะเทิ้น\nสะเทือน\nสะเทื้อน\nสะบะ\nสะบัก\nสะบักสะบอม\nสะบัด\nสะบัดสะบิ้ง\nสะบั้น\nสะบันงา\nสะบ้า\nสะบู\nสะแบง\nสะเปะสะปะ\nสะพรั่ง\nสะพรึงกลัว\nสะพรึบ\nสะพรึ่บ\nสะพัก\nสะพัง\nสะพัด\nสะพั้น\nสะพาน\nสะพาย\nสะเพร่า\nสะโพก\nสะเภา\nสะใภ้\nสะโมง\nสะระตะ\nสะระแหน่\nสะลาง\nสะลาบ\nสะลึมสะลือ\nสะวี้ดสะว้าด\nสะสม\nสะสวย\nสะสาง\nสะเหล่อ\nสะอาง\nสะอาด\nสะอ้าน\nสะอิ้ง\nสะอิดสะเอียน\nสะอึก\nสะอื้น\nสะเอ้ง\nสะเอว\nสะเออะ\nสะโอดสะอง\nสะไอ\nสัก\nสักกะ\nสักยะ\nสักกัจจะ\nสักกายทิฐิ\nสักการ\nสักการะ\nสักขี\nสักวา\nสักหลาด\nสัค\nสัคคะ\nสั่ง\nสังกร\nสังกรณี\nสังกรประโยค\nสังกะตัง\nสังกะวัง\nสังกะวาด\nสังกะสี\nสังกัด\nสังกัปปะ\nสังกา\nสังการ\nสังกาศ\nสังกิเลส\nสังเกต\nสังข์\nสังขกร\nสังขตธรรม\nสังขตะ\nสังขยา\nสังขลิก\nสังขลิกา\nสังขาร\nสังขารา\nสังเขป\nสังค์\nสังคญาติ\nสังคม\nสังคหะ\nสังคัง\nสังคายนา\nสังคายนาย\nสังคีต\nสังคีติ\nสังเค็ด\nสังเคราะห์\nสังฆกรรม\nสังฆการี\nสังฆเถระ\nสังฆทาน\nสังฆนายก\nสังฆปาโมกข์\nสังฆภัต\nสังฆเภท\nสังฆมณฑล\nสังฆมนตรี\nสังฆราช\nสังฆสภา\nสังฆาณัติ\nสังฆาฏิ\nสังฆาทิเสส\nสังฆาธิการ\nสังฆานุสติ\nสังฆาวาส\nสังยุตนิกาย\nสังโยค\nสังโยชน์\nสังวร\nสังวัจฉระ\nสังวัธยาย\nสังวาล\nสังวาส\nสังเวคะ\nสังเวช\nสังเวชนียสถาน\nสังเวย\nสังเวียน\nสังสกฤต\nสังสการ\nสังสนทนา\nสั่งสนทนา\nสังสรรค์\nสังสารวัฏ\nสังสิทธิ\nสังสุทธ์\nสังสุทธิ\nสังหร\nสังหรณ์\nสังหาร\nสังหาริมทรัพย์\nสังหาริมะ\nสังหิต\nสัจ\nสัจกิริยา\nสัจจะ\nสัจญาณ\nสัจธรรม\nสัจนิยม\nสัจพจน์\nสัชฌะ\nสัชฌุ\nสัญจร\nสัญเจตนา\nสัญชาตญาณ\nสัญชาติ\nสัญฌา\nสัญญา\nสัญญาณ\nสัญญี\nสัญโญชน์\nสัญนิยม\nสัญประกาศ\nสัญลักษณ์\nสัฐิ\nสัณฐาน\nสัณฐิติ\nสัณฑ์\nสัณห์\nสัด\nสัดจอง\nสัต\nสัตตะ\nสัตตาหกรณียะ\nสัตตาหกาลิก\nสัตมวาร\nสัตสดก\nสัตตบงกช\nสัตตบรรณ\nสัตตบุษย์\nสัตตู\nสัตถันดร\nสัตถา\nสัตถิ\nสัตถุ\nสัตถุศาสนา\nสัตบรรณ\nสัตย์\nสัตยพรต\nสัตยวาที\nสัตยาเคราะห์\nสัตยาธิษฐาน\nสัตยาบัน\nสัตว์\nสัตวชาติ\nสัตวบาล\nสัตวแพทย์\nสัตววิทยา\nสัตวา\nสัทธรรม\nสัทธา\nสัทธาจริต\nสัทธาธิกะ\nสัทธินทรีย์\nสัทธิงวิหาริก\nสัทธิวิหาริก\nสัทวิทยา\nสัทศาสตร์\nสัทอักษร\nสัน\nสั่น\nสั้น\nสันดาน\nสันดาป\nสันโดษ\nสันต์\nสันตติ\nสันตะปาปา\nสันตะวา\nสันติ\nสันตุฏฐี\nสันถวไมตรี\nสันถวะ\nสันถัต\nสันถาร\nสันทนะ\nสันทะ\nสันทัด\nสันทัสนะ\nสันทาน\nสันทิฐิก\nสันทิส\nสันเทหะ\nสันธาน\nสันนิธิ\nสันนิบาต\nสันนิวาส\nสันนิษฐาน\nสันสกฤต\nสับ\nสับปลับ\nสับปลี้\nสับปะรด\nสัปคับ\nสัปดาห์\nสัปดาหะ\nสัปดน\nสัปตศก\nสัปทน\nสัปปะ\nสัปปิ\nสัปปุริส\nสัปปุรุษ\nสัประยุทธ์\nสัปหงก\nสัปเหร่อ\nสัพ\nสัพพะ\nสัพพัญญู\nสัพเพเหระ\nสัพยอก\nสัมบูรณ์\nสัมปชัญญะ\nสัมปทา\nสัมปทาน\nสัมปยุต\nสัมปโยค\nสัมประสิทธิ์\nสัมประหาร\nสัมปรายภพ\nสัมปรายิกภพ\nสัมปัตติ\nสัมผัปลาป\nสัมผัปลาปะ\nสัมผัส\nสัมพล\nสัมพหุลา\nสัมพัจฉรฉินท์\nสัมพัตสร\nสัมพัทธ์\nสัมพันธ์\nสัมพันธน์\nสัมพันธภาพ\nสัมพันธมิตร\nสัมพันธไมตรี\nสัมพาหะ\nสัมพุทธ\nสัมพุทธะ\nสัมโพธิ\nสัมภวะ\nสัมภเวสี\nสัมภัต\nสัมภัตตะ\nสัมภาระ\nสัมภาษณ์\nสัมโภคกาย\nสัมมนา\nสัมมัปธาน\nสัมมา\nสัมโมทนียกถา\nสัมฤทธิ\nสัมฤทธิ์\nสัมฤทธิศก\nสัยน์\nสัลเลข\nสัสดี\nสัสตทิฐิ\nสัสสะ\nสัสสุ\nสัสสู\nสา\nส่า\nสาก\nสากรรจ์\nสากล\nสากัจฉา\nสากัลย์\nสากิยะ\nสาเก\nสาขา\nสาคร\nสาคเรศ\nสาคู\nสาง\nส้าง\nสาชล\nสาฎก\nสาฏิก\nสาณี\nสาด\nสาไถย\nสาทร\nสาทิส\nสาทุ\nสาโท\nสาธก\nสาธยะ\nสาธยาย\nสาธารณะ\nสาธารณชน\nสาธารณประโยชน์\nสาธารณภัย\nสาธารณรัฐ\nสาธารณสถาน\nสาธารณสมบัติ\nสาธารณสุข\nสาธารณูปการ\nสาธารณูปโภค\nสาธารณ์\nสาธิต\nสาธุ\nสาน\nส่าน\nสานุ\nสานู\nสานุศิษย์\nสาบ\nสาบสูญ\nสาบาน\nสาป\nสาปไตย\nสาม\nสามชุก\nสามเณร\nสามเณรี\nสามนต์\nสามนตราช\nสามยทรัพย์\nสามล\nสามหาว\nสามะ\nสามัคคี\nสามัญ\nสามัตถิยะ\nสามานย์\nสามานยนาม\nสามารถ\nสามิต\nสามินี\nสามิภักดิ์\nสามี\nสามีจิกรรม\nสาย\nส่าย\nส้าย\nสายชู\nสายัณห์\nสายาห์\nสาร\nสารคดี\nสารธรรม\nสารนิเทศ\nสารบบ\nสารบรรณ\nสารบัญ\nสารบาญ\nสารบาญชี\nสารประโยชน์\nสารสนเทศ\nสารทุกข์\nสารถี\nสารท\nสารพัด\nสารพัน\nสารพางค์\nสารภาพ\nสารภี\nสารวัตร\nสาระ\nสาระแน\nสาระพา\nสาระยำ\nสาระวารี\nสาระสะมา\nสารัตถประโยชน์\nสารัตถศึกษา\nสารัตถะ\nสารัทธ์\nสารัมภ์\nสาราณียกร\nสาราณียธรรม\nสาราณียะ\nสารานุกรม\nสารีริกธาตุ\nสารูป\nสาโรช\nสาละ\nสาละวน\nสาลิ\nสาลิกา\nสาลินี\nสาลี\nสาลี่\nสาลู\nสาโลหิต\nสาว\nสาวก\nสาวิกา\nสาวิตร\nสาวิตรี\nสาสน\nสาสน์\nสาส์น\nสาสนา\nสาสม\nสาหร่าย\nสาหรี\nส่าหรี\nสาหัส\nสาเหตุ\nสาแหรก\nสำ\nสำส่อน\nส่ำ\nสำคัญ\nสำซ่าง\nสำแดง\nสำทับ\nสำนวน\nสำนอง\nสำนัก\nสำนาน\nสำนึก\nสำนึง\nสำเนา\nสำเนียง\nสำบัด\nสำปะลอ\nสำปะหลัง\nสำปั้น\nสำปันนี\nสำเภา\nสำมะงา\nสำมะโน\nสำมะลอ\nสำมะเลเทเมา\nสำมะหา\nสำรด\nสำรวจ\nสำรวม\nสำรวย\nสำรวล\nสำรอก\nสำรอง\nสำรับ\nสำราก\nสำราญ\nสำริด\nสำเร็จ\nสำเรา\nสำเริง\nสำโรง\nสำลัก\nสำลาน\nสำลี\nสำแลง\nสำหรวด\nสำหรับ\nสำหา\nสำเหนียก\nสำเหร่\nสำออย\nสำอาง\nสิ\nสิกข์\nสิข\nสิกขมานา\nสิกขา\nสิขร\nสิขรี\nสิขเรศ\nสิขา\nสิขานล\nสิขี\nสิคาล\nสิง\nสิ่ง\nสิงขร\nสิงค์\nสิงคลิ้ง\nสิงคลี\nสิงคาร\nสิงคาล\nสิงคี\nสิงโต\nสิงห์\nสิงหนาท\nสิงหบัญชร\nสิงหรา\nสิงหราช\nสิงหาคม\nสิงหาสน์\nสิงหล\nสิญจน์\nสิตะ\nสิตางศุ์\nสิถิล\nสิทธ์\nสิทธัตถะ\nสิทธา\nสิทธาจารย์\nสิทธารถ\nสิทธิ\nสิทธิ์\nสิทธิการิยะ\nสิธยะ\nสิน\nสิ้น\nสินเทา\nสินธพ\nสินธุ\nสินธุ์\nสินธุระ\nสินธู\nสินเธาว์\nสินาด\nสินิทธ์\nสินี\nสิเนรุ\nสิเนหก\nสิเนหะ\nสิเนหา\nสิเน่หา\nสิบ\nสิปปะ\nสิมพลี\nสิระ\nสิโรดม\nสิโรตม์\nสิริ\nสิรี\nสิลา\nสิว\nสิ่ว\nสิวะ\nสิวาลัย\nสิวิกา\nสี\nสี่\nสี้\nสีกา\nสีกุน\nสีข้าง\nสีด\nสีดอ\nสีดา\nสีตลรัศมี\nสีตโลทก\nสีโตทก\nสีทันดร\nสีมันต์\nสีมา\nสีละมัน\nสีวิกา\nสีสอ\nสีสะ\nสีสา\nสีสุก\nสีเสียด\nสีห์\nสีหนาท\nสีหบัญชร\nสีหราช\nสีหไสยา\nสีหไสยาสน์\nสีหะ\nสึก\nสึง\nสืบ\nสื่อ\nสุ\nสุก\nสุกข์\nสุกร\nสุกรม\nสุกำศพ\nสุกียากี้\nสุข\nสุขา\nสุขาภิบาล\nสุขารมณ์\nสุขาวดี\nสุขิน\nสุขี\nสุขุม\nสุขุมาล\nสุโข\nสุคต\nสุคติ\nสุคนธ\nสุคนธ์\nสุคนธชาติ\nสุคนธรส\nสุคันธ์\nสุคันธรส\nสุงกะ\nสุงกากร\nสุงสิง\nสุงสุมาร\nสุจริต\nสุจหนี่\nสุจิ\nสุจิต\nสุจิตร\nสุชน\nสุชัมบดี\nสุชา\nสุชาดา\nสุญ\nสุญญากาศ\nสุญตา\nสุญนิยม\nสุณ\nสุณิสา\nสุด\nสุดา\nสุต\nสุตตนิบาต\nสุตตะ\nสุตตันตปิฎก\nสุตตันตะ\nสุติ\nสุทธ\nสุทธ์\nสุทธาวาส\nสุทธิ\nสุทรรศน์\nสุทัศน์\nสุธา\nสุธาโภชน์\nสุธารส\nสุธาสินี\nสุธาสี\nสุธี\nสุนทร\nสุนทรี\nสุนทรียภาพ\nสุนทรียศาสตร์\nสุนทรียะ\nสุนัข\nสุนันท์\nสุโนก\nสุบดี\nสุบรรณ\nสุบิน\nสุปรีดิ์\nสุปรีย์\nสุปาณี\nสุพพัต\nสุพรรณ\nสุพรรณบัฏ\nสุพรรณภาชน์\nสุพรรณราช\nสุพรรณศรี\nสุพรรณถัน\nสุพรรณิการ์\nสุภร\nสุภัค\nสุภา\nสุภาพ\nสุภาษิต\nสุม\nสุ่ม\nสุมทุม\nสุมน\nสุมนะ\nสุมนัส\nสุมนา\nสุ้มเสียง\nสุมะ\nสุมาลี\nสุเมธ\nสุเมรุ\nสุรคต\nสุรเชษฐ์\nสุรบดี\nสุรภาพ\nสุรโลก\nสุรสีหนาท\nสุรเสียง\nสุรงค์\nสุรังค์\nสุรภี\nสุรัติ\nสุรัสวดี\nสุรา\nสุรางค์จำเรียง\nสุรางคนา\nสุรางคนางค์\nสุรารักษ์\nสุราลัย\nสุรินทร์\nสุรินทราหู\nสุริยะ\nสุริยกันต์\nสุริยกานต์\nสุริยการ\nสุริยกาล\nสุริยคติ\nสุริยคราส\nสุริยมณฑล\nสุริยวงศ์\nสุริยง\nสุริยา\nสุริเยนทร์\nสุริเยศ\nสุริโย\nสุริยน\nสุริยัน\nสุริยุปราคา\nสุรีย์\nสุรุ่ยสุร่าย\nสุลต่าน\nสุวคนธ์\nสุวภาพ\nสุวรรณ\nสุวรรณภูมิ\nสุวะ\nสุวาน\nสุวินัย\nสุวิมล\nสุษิระ\nสุสาน\nสุหนัต\nสุหร่ง\nสุหร่าย\nสุหฤท\nสุหัท\nสุเหร่า\nสู\nสู่\nสู้\nสูง\nสูจิ\nสูจิบัตร\nสูญ\nสูด\nสูต\nสูตร\nสูติ\nสูติกรรม\nสูตินรีเวช\nสูติบัตร\nสูติแพทย์\nสูติศาสตร์\nสูท\nสูทกรรม\nสูทศาสตร์\nสูบ\nสูปะ\nสูร\nสูรย์\nสูรยกานต์\nสูริ\nสูสี\nเส\nเสก\nเสกขบุคคล\nเสกขะ\nเสขบุคคล\nเสขะ\nเส็ง\nเส้ง\nเส็งเคร็ง\nเสงี่ยม\nเสฏฐี\nเสณี\nเสด\nเสด็จ\nเสตุ\nเสถียร\nเสทะ\nเสโท\nเสน\nเส้น\nเสนง\nเสน่ง\nเสน่ห์\nเสนหา\nเสน่หา\nเสนอ\nเสนะ\nเสนา\nเสนาธิการ\nเสนาบดี\nเสน่า\nเสนากุฎ\nเสนางค์\nเสนางคนิกร\nเสนานี\nเสนาสนะ\nเสนาะ\nเสนี\nเสนีย์\nเสนียะ\nเสนียด\nเสบย\nเสบียง\nเสพ\nเสพย์\nเสเพล\nเสภา\nเสม็ด\nเสมหะ\nเสมอ\nเสมา\nเสมียน\nเสมือน\nเสย\nเสร็จ\nเสริด\nเสริม\nเสรี\nเสลด\nเสลบรรพต\nเสลา\nเสลี่ยง\nเสลือกสลน\nเสโล\nเสวก\nเสวกามาตย์\nเสวนะ\nเสวนา\nเสวย\nเสวียน\nเสสรวง\nเสสรวล\nเสา\nเส้า\nเสาร์\nเสารภย์\nเสารี\nเสาวคนธ์\nเสาวธาร\nเสาวภา\nเสาวภาคย์\nเสาวภาพ\nเสาวรภย์\nเสาวรส\nเสาวลักษณ์\nเสาวณิต\nเสาวนะ\nเสาวนา\nเสาวนีย์\nเสาหฤท\nเสาะ\nเสาะแสะ\nเสิร์จ\nเสิร์ฟ\nเสีย\nเสียง\nเสี่ยง\nเสียด\nเสี้ยน\nเสียบ\nเสียม\nเสี่ยม\nเสี้ยม\nเสียว\nเสี่ยว\nเสี้ยว\nเสือ\nเสื่อ\nเสื้อ\nเสือก\nเสื่อม\nแส\nแส่\nแส้\nแสก\nแสง\nแสด\nแสดง\nแสตมป์\nแสน\nแสนย์\nแสนยากร\nแสนยานุภาพ\nแสบ\nแสม\nแสยก\nแสยง\nแสยะ\nแสรก\nแสร้ง\nแสลง\nแสล้ม\nแสวง\nแสะ\nโสก\nโสกโดก\nโสกันต์\nโสโครก\nโสณฑ์\nโสณิ\nโสณี\nโสด\nโสดก\nโสดม\nโสดา\nโสดาบัน\nโสดาปัตติผล\nโสดาปัตติมรรค\nโสต\nโสตทัศนวัสดุ\nโสตทัศนอุปกรณ์\nโสตทัศนูปกรณ์\nโสตินทรีย์\nโสตถิ\nโสทก\nโสทร\nโสธก\nโสธนะ\nโสน\nโสภณ\nโสภา\nโสภี\nโสภิณี\nโสเภณี\nโสม\nโสมนัส\nโสมม\nโสมย์\nโสร่ง\nโสรจ\nโสรวาร\nโสโร\nโสวรรณ\nโสหุ้ย\nโสฬส\nใส\nใส่\nไส\nไส้\nไสย\nไสยา\nไสยาสน์\nไสร้\nไสว\nหก\nหกคะเมน\nหง\nหงก\nหงส์\nหงสบาท\nหงสรถ\nหงอ\nหงอก\nหง่อง\nหงองแหงง\nหงอด\nหงอน\nหง่อม\nหงอย\nหง่อย\nหงัก\nหงับ\nหง่าง\nหงาย\nหง่าว\nหงำ\nหงิก\nหงิง\nหงิม\nหงึก\nหงุงหงิง\nหงุดหงิด\nหงุบ\nหงุ่ย\nหญ้า\nหญ้าฝรั่น\nหญ้ายายเภา\nหญิง\nหญิบ\nหด\nหตะ\nหทัย\nหน\nหนวก\nหน่วง\nหนวด\nหน่วย\nหน่วยกิต\nหนอ\nหน่อ\nหนอก\nหนอง\nหนอน\nหนอนตายหยาก\nหน่อย\nหน็อยแน่\nหนัก\nหนัง\nหนังสติ๊ก\nหนังสือ\nหนั่น\nหนับ\nหนา\nหน้า\nหน่าง\nหนาด\nหนาน\nหนาม\nหน่าย\nหนาว\nหนำ\nหนำเลี้ยบ\nหนี\nหนี้\nหนีบ\nหนึก\nหนึ่ง\nหนึบ\nหนืด\nหนุ\nหนุน\nหนุบ\nหนุ่ม\nหนุ่ย\nหนู\nห่ม\nหมก\nหมด\nหม่น\nหมวก\nหมวด\nหมวน\nหมอ\nหม่อ\nหม้อ\nหมอก\nหมอง\nหม่อง\nหมอน\nหม่อน\nหมอบ\nหม่อม\nหมอย\nหม้อห้อม\nหมัก\nหมักหมม\nหมัด\nหมัน\nหมั่น\nหมั้น\nหมับ\nหมา\nหม่า\nหมาก\nหมากฮอส\nหมาง\nหมาด\nหมามุ่ย\nหมามุ้ย\nหมาย\nหม้าย\nหมาร่า\nหม่ำ\nหม้ำ\nหมิ่น\nหมี\nหมี่\nหมึก\nหมืน\nหมื่น\nหมุด\nหมุน\nหมุบ\nหมุบหมับ\nหมุบหมิบ\nหมุ่ย\nหมุยขาว\nหมู\nหมู่\nหมูหริ่ง\nหยก\nหย่ง\nหยด\nหยวก\nหยวบ\nหยอก\nหยอกเอิน\nหย็อกหย็อย\nหยอง\nหย็อง\nหย่อง\nหย็องกรอด\nหย็องแหย็ง\nหยอด\nหยอน\nหย่อน\nหย่อม\nหย็อมแหย็ม\nหย็อย\nหย่อย\nหยัก\nหยักไย่\nหยักเหยา\nหยัง\nหยั่ง\nหยังหยัง\nหยัด\nหยัน\nหยับ\nหยั่วเมือง\nหย่า\nหยากเยื่อ\nหยากไย่\nหยาด\nหยาบ\nหยาม\nหยาว\nหย้าว\nหยำเป\nหยำเหยอะ\nหยำแหยะ\nหยิก\nหยิ่ง\nหยิบ\nหยิม\nหยี\nหยี่\nหยุกหยิก\nหยุด\nหยุ่น\nหยุบ\nหยุมหยิม\nหยูกยา\nหโยดม\nหรคุณ\nหรณะ\nหรดาล\nหรดี\nหรรษ์\nหรรษา\nหรอ\nหรอก\nหร็อมแหร็ม\nหรอย\nหระ\nหรับ\nหรา\nหริ\nหริ่ง\nหริณะ\nหริต\nหริตกี\nหรีตกี\nหรี่\nหรีด\nหรือ\nหรุบ\nหรุบรู่\nหรุบหรู่\nหรุ่ม\nหรู\nหรูหรา\nหฤทัย\nหฤทย์\nหฤษฎ์\nหฤษฎี\nหฤหรรษ์\nหฤโหด\nหลง\nหลงใหล\nหลงจู๊\nหลด\nหลน\nหล่น\nหลบ\nหล่ม\nหลวง\nหลวม\nหลอ\nหล่อ\nหลอก\nหลอด\nหลอน\nหล็อน\nหล่อน\nหลอม\nหละ\nหละหลวม\nหลัก\nหลัง\nหลั่ง\nหลัด\nหลั่น\nหลับ\nหลัว\nหลา\nหล้า\nหลาก\nหลาน\nหลาบ\nหลาม\nหลาย\nหลาว\nหลิ่ง\nหลิท\nหลิน\nหลิม\nหลิว\nหลิ่ว\nหลี\nหลีก\nหลีโก\nหลีบ\nหลีฮื้อ\nหลืบ\nหลุกหลิก\nหลุด\nหลุน\nหลุบ\nหลุม\nหลุมพอ\nหลุมพี\nหลู่\nหวง\nห่วง\nห้วง\nหวด\nหวน\nห้วน\nหวย\nห้วย\nหวอ\nหวอด\nหวะ\nหวัง\nหวัด\nหวั่น\nหวันยิหวา\nหวัว\nหวัวร่อ\nหวัวเราะ\nหวา\nหว่า\nหว้า\nหวาก\nหว่าง\nหวาด\nหวาน\nหว่าน\nหวาม\nหวาย\nหวำ\nหวิด\nหวิว\nหวี\nหวี่\nหวีด\nหวือ\nหวุดหวิด\nหวูด\nหอ\nห่อ\nห้อ\nหอก\nหอง\nห้อง\nหอน\nห่อน\nหอบ\nหอม\nห้อม\nหอย\nห้อย\nหะ\nหะยี\nหะหาย\nหัก\nหัจญ์\nหัจญี\nหัช\nหัฏฐะ\nหัด\nหัต\nหัตถ์\nหัตถกรรม\nหัตถการ\nหัตถกิจ\nหัตถบาส\nหัตถพันธ์\nหัตถาภรณ์\nหัตถศาสตร์\nหัตถศิลป์\nหัตถศึกษา\nหัตถาจารย์\nหัตถินี\nหัตถี\nหัน\nหั่น\nหั้น\nหันตรา\nหับ\nหัย\nหัว\nหัวร่อ\nหัวเราะ\nหัส\nหัสดิน\nหัสดี\nหัสต์\nหัสตะ\nหา\nห่า\nห้า\nหาก\nหาง\nห่าง\nห้าง\nหาญ\nหาด\nห่าน\nหาบ\nหาม\nห่าม\nห้าม\nหาย\nหายใจ\nหายนะ\nหาร\nหารือ\nหาว\nห้าว\nหาสะ\nหำ\nห้ำ\nหิ้ง\nหิงคุ\nหิงสา\nหิงห้อย\nหิ่งห้อย\nหิ่งหาย\nหิด\nหิต\nหิตานุหิตประโยชน์\nหิน\nหิมพาน\nหิมพานต์\nหิมวัต\nหิมวันต์\nหิมวา\nหิมะ\nหิมาลัย\nหิรัญ\nหิรัญญิการ์\nหิรัญบัฏ\nหิรัณย์\nหิรัณยรัศมี\nหิริ\nหิว\nหิ้ว\nหี\nหีด\nหีนยาน\nหีบ\nหึ\nหึง\nหึ่ง\nหึงสา\nหืด\nหืน\nหื่น\nหือ\nหื้อ\nหุง\nหุน\nหุ่น\nหุ้น\nหุนหัน\nหุบ\nหุ้ม\nหุยฮา\nหู\nหู่\nหูก\nหูด\nเห\nเห่\nเหง\nเหง่ง\nเหงา\nเหง้า\nเหงื่อ\nเหงือก\nเห็จ\nเห็ด\nเหติ\nเหตุ\nเห็น\nเหน่ง\nเหนงนายพราน\nเหน็ดเหนื่อย\nเหน็บ\nเหน่อ\nเห็นอ้ม\nเหนอะ\nเหนอะหนะ\nเหน้า\nเหนาะ\nเหนียง\nเหนี่ยง\nเหนี่ยน\nเหนียม\nเหนียว\nเหนี่ยว\nเหนือ\nเหนื่อย\nเห็บ\nเหม\nเหม่\nเหม็ง\nเหม่ง\nเหม็น\nเหมวดี\nเหม่อ\nเหมันต์\nเหมันตฤดู\nเหมา\nเหมายัน\nเหมาะ\nเหมียว\nเหมี่ยว\nเหมือง\nเหมือด\nเหมือน\nเหมื่อย\nเหย\nเหยง\nเหย่อย\nเหยา\nเหย่า\nเหย้า\nเหยาะ\nเหยาะแหยะ\nเหยิง\nเหยิบ\nเหยียด\nเหยียบ\nเหยี่ยว\nเหยื่อ\nเหยือก\nเหรอ\nเหรอะ\nเหรัญญิก\nเหรา\nเหราะ\nเหรียญ\nเหล่\nเหล็ก\nเหลน\nเหลว\nเหลอ\nเหลา\nเหล่า\nเหล้า\nเหลาะแหละ\nเหลิง\nเหลิงเจิ้ง\nเหลียน\nเหลี่ยม\nเหลียว\nเหลือ\nเหลือก\nเหลือง\nเหลือบ\nเหลือม\nเหลื่อม\nเหว\nเหว่\nเหวง\nเหวย\nเหวอะ\nเหวอะหวะ\nเหวี่ยง\nเห่อ\nเหอะ\nเหะ\nเหะหะ\nเหา\nเห่า\nเหาะ\nเหิน\nเหิม\nเหี้ย\nเหียง\nเหียน\nเหี้ยน\nเหี้ยม\nเหี่ยว\nเหื่อ\nเหือด\nแห\nแห่\nแห้\nแหก\nแหง\nแหง่\nแห่ง\nแห้ง\nแหงแก๋\nแหง่ง\nแหงน\nแหน\nแห้น\nแหนง\nแหนบ\nแหนม\nแหบ\nแหม\nแหม่\nแหม่ม\nแหมะ\nแหย\nแหย่\nแหยง\nแหย่ง\nแหยม\nแหย็ม\nแหยะ\nแหล่\nแหลก\nแหล่ง\nแหลน\nแหลม\nแหละ\nแหว\nแห้ว\nแหวก\nแหว่ง\nแหวด\nแหวน\nแหวะ\nแหะ\nโห่\nโหง\nโหด\nโหน\nโหนก\nโหน่ง\nโหม\nโหม่ง\nโหมด\nโหย\nโหยกเหยก\nโหยง\nโหย่ง\nโหร\nโหรง\nโหรงเหรง\nโหรดาจารย์\nโหระพา\nโหรา\nโหราจารย์\nโหราศาสตร์\nโหล\nโหล่\nโหลงโจ้ง\nโหว\nโหว่\nโหว้\nโหวกเหวก\nโหวง\nโหวด\nโหวต\nให้\nใหญ่\nใหม่\nไห\nไห่\nไห้\nไหน\nไหม\nไหม้\nไหรณย์\nไหล\nไหล่\nไหว\nไหว้\nไหหลำ\nอก\nอกตเวทิตา\nอกตเวที\nอกตัญญุตา\nอกตัญญู\nอกนิษฐ์\nอกรณีย์\nอกรรมกริยา\nอกัปปิยวัตถุ\nอกัปปิยะ\nอกุศล\nอคติ\nอคาธ\nอโฆษะ\nองก์\nองค์\nองคชาต\nองคมนตรี\nองครักษ์\nองคาพยพ\nองคุลี\nองศ์\nองศา\nองอาจ\nองุ่น\nอจลา\nอจินตา\nอจินไตย\nอจิระ\nอเจลก\nอเจละ\nอชะ\nอชิน\nอชินี\nอชิระ\nอฏวี\nอณิ\nอณู\nอโณทัย\nอด\nอดิถี\nอดิเทพ\nอดิเรก\nอดิศร\nอดิศวร\nอดิศัย\nอดีต\nอดุล\nอดุลย์\nอติ\nอติชาต\nอติมานะ\nอติราช\nอติเรก\nอติสาร\nอถรรพเวท\nอาถรรพเวท\nอทระ\nอทินนาทาน\nอธรรม\nอธิ\nอธิกมาส\nอธิกรณ์\nอธิกวาร\nอธิกสุรทิน\nอธิการ\nอธิคม\nอธิฏฐาน\nอธิบดี\nอธิบาย\nอธิป\nอธิปไตย\nอธิมาตร\nอธิมุตติ\nอธิโมกข์\nอธิราช\nอธิวาส\nอธิวาสนะ\nอธิศีล\nอธิษฐาน\nอธึก\nอ้น\nอนงค์\nอนงคณะ\nอนงคเลขา\nอนธการ\nอนนต์\nอนยะ\nอนรรฆ\nอนรรถ\nอนล\nอนวัช\nอนัญ\nอนัตตา\nอนันต์\nอนันตริยกรรม\nอนัม\nอนาคต\nอนาคามิผล\nอนาคามิมรรค\nอนาคามี\nอนาจาร\nอนาถ\nอนาถา\nอนาทร\nอนาธิปไตย\nอนามัย\nอนามิกา\nอนารยชน\nอนารยธรรม\nอนารยะ\nอนาลัย\nอนำ\nอนิจ\nอนิจจัง\nอนิจจา\nอนิฏฐารมณ์\nอนิยต\nอนิยม\nอนิล\nอนิวรรต\nอนิวรรตน์\nอนีกะ\nอนีจะ\nอนึก\nอนึ่ง\nอนุ\nอนุกร\nอนุกรม\nอนุกรรมการ\nอนุกระเบียด\nอนุกาชาด\nอนุการ\nอนุกูล\nอนุคามิก\nอนุเคราะห์\nอนุจร\nอนุช\nอนุชน\nอนุชา\nอนุชาต\nอนุชิต\nอนุญาต\nอนุญาโตตุลาการ\nอนุตร\nอนุเถระ\nอนุทิน\nอนุบท\nอนุบาล\nอนุประโยค\nอนุปริญญา\nอนุปสัมบัน\nอนุปัสนา\nอนุพงศ์\nอนุพัทธ์\nอนุพันธ์\nอนุโพธ\nอนุภรรยา\nอนุภริยา\nอนุภาค\nอนุภาษ\nอนุมัติ\nอนุมาตรา\nอนุมาน\nอนุมูล\nอนุโมทนา\nอนุโยค\nอนุรักษ์\nอนุรักษนิยม\nอนุราช\nอนุราธ\nอนุราธะ\nอนุราธา\nอนุรูป\nอนุโลม\nอนุวงศ์\nอนุวรรตน์\nอนุวัต\nอนุวัตน์\nอนุวัตร\nอนุวัติ\nอนุวาต\nอนุศาสก\nอนุศาสน์\nอนุศาสนาจารย์\nอนุศิษฏ์\nอนุสติ\nอนุสนธิ\nอนุสร\nอนุสรณ์\nอนุสัญญา\nอนุสัย\nอนุสาวรีย์\nอนุสาสนี\nอเนก\nอเนกคุณ\nอเนกรรถประโยค\nอเนจอนาถ\nอโนชา\nอโนดาต\nอบ\nอบเชย\nอบาย\nอปจายนธรรม\nอปจายนมัย\nอปมงคล\nอปยศ\nอประไมย\nอปรัณณชาติ\nอปรา\nอปราชัย\nอปราชิต\nอปริมาณ\nอปลักษณ์\nอปโลกน์\nอปวาท\nอเปหิ\nอพพะ\nอพยพ\nอภัพ\nอภัย\nอภิ\nอภิฆาต\nอภิชฌา\nอภิชน\nอภิชนาธิปไตย\nอภิชัย\nอภิชาต\nอภิชิต\nอภิญญา\nอภิญญาณ\nอภิธรรม\nอภิธาน\nอภิไธย\nอภินันท์\nอภินันทนาการ\nอภินัย\nอภินิหาร\nอภิเนษกรมณ์\nอภิบาล\nอภิปรัชญา\nอภิปราย\nอภิมหาอำนาจ\nอภิมานะ\nอภิมุข\nอภิรดี\nอภิรติ\nอภิรมย์\nอภิรักษ์\nอภิราม\nอภิรุต\nอภิรุม\nอภิรูป\nอภิลักขิต\nอภิลักขิตสมัย\nอภิเลปน์\nอภิวันท์\nอภิวาท\nอภิวาทน์\nอภิเษก\nอภิสมโพธิ\nอภิสมัย\nอภิสมาจาร\nอภิสัมโพธิ\nอภิสัมโพธิญาณ\nอภิสิต\nอภิสิทธิ์\nอภูตะ\nอม\nอมพะนำ\nอ้ม\nอมตธรรม\nอมตบท\nอมตะ\nอมนุษย์\nอมร\nอมรา\nอมราวดี\nอมรินทร์\nอมเรนทร์\nอมเรศ\nอมฤต\nอมัตร\nอมาตย์\nอมาวสี\nอมาวสุ\nอมาวาสี\nอมิตร\nอเมริกัน\nอย่า\nอยาก\nอย่าง\nอยุทธ์\nอยู่\nอร\nอรชร\nอรชุน\nอรดี\nอรติ\nอรทัย\nอรไท\nอรนุช\nอรพินท์\nอรพิม\nอรรค\nอรรฆ\nอรรฆย์\nอรรจน์\nอรรณพ\nอรรถ\nอรรถกถา\nอรรถกถาจารย์\nอรรถาธิบาย\nอรรธ\nอรสุม\nอรหะ\nอรหัง\nอรหัต\nอรหัตผล\nอรหัตมรรค\nอรหัน\nอรหันต์\nอรหันตฆาต\nอร่อย\nอรัญ\nอรัญญิก\nอรัญวาส\nอรัญวาสี\nอรัณย์\nอราดี\nอร่าม\nอริ\nอรินทร์\nอริน\nอริยกะ\nอริยทรัพย์\nอริยบุคคล\nอริยผล\nอริยมรรค\nอริยสั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nโฮก\nโฮ่ง\nโฮ้ง\nโฮเต็ล\nโฮลเมียม\nโฮะ\nไฮ้\nไฮโกรมิเตอร์\nไฮดรา\nไฮโดร\nไฮโดรคาร์บอน\nไฮโดรเจน\nไฮโดรมิเตอร์\nไฮไฟ\nไฮโล\nไฮฮี\n"
+      words = "ก.ก.\nก.ก.น.\nก.ข.ค.\nก.ค.\nก.จ.\nก.ช.น.\nก.ฌ.\nก.ต.\nก.ต.ง.\nก.ต.ช.\nก.ตร.\nก.ท.\nก.น.ช.\nก.บช.\nก.บถ.\nก.ป.ส.\nก.พ.\nก.ม.\nก.ย.\nก.ร.\nก.ล.ต.\nก.ว.\nก.ศ.ว.\nก.ส.ท.\nก.ส.ธ.\nก.ส.อ.\nก.อ.\nกก.ตชด.\nกก.ตร.น.\nกก.ภ.จว.\nกก.รสช.\nกกบ.ขส.ทบ.\nกกล.รพน.\nกง.กห.\nกง.ทบ.\nกง.ทร.\nกซข.ป.\nกซม.ป.\nกทม.กรุงเทพมหานคร\nกบ.ทบ.\nกป.สป.\nกพ.ทบ.\nกพ.ทร.\nกพ.ทหาร\nกร.ทบ.\nกรป.กลาง\nกรอ.พอ.\nกศ.ด.\nกศ.บ.\nกศ.บป.\nกศ.ม.\nกษ.ด.\nกษ.บ.\nกษ.ม.\nกส.ด.\nกส.ทบ.\nกส.บ.\nกส.ม.\nกอ.ปค.\nกอ.รพน.\nกอ.รมน.\nกอ.รสต.\nข.ต.ว.\nขว.ทบ.\nขว.ทร.\nขว.ทหาร\nขส.ทบ.\nขส.ทร.\nขส.ทอ.\nค.ด.\nค.บ.\nค.พ.ศ.\nค.ม.\nค.ร.น.\nค.ร.ฟ.\nค.ร.ม.\nค.ศ.\nค.อ.ด.\nค.อ.บ.\nค.อ.ม.\nคศ.ด.\nคศ.บ.\nคศ.ม.\nง.ด.\nจ.จ.\nจ.จ.จ.\nจ.ช.\nจ.ต.\nจ.ท.\nจ.ป.ร.\nจ.ม.\nจ.ศ.\nจ.ส.ต.\nจ.ส.ท.\nจ.ส.อ.\nจ.อ.\nจ.อ.ร.\nจ.๑๘\nจก.ธน.\nจก.สน.\nช.ค.\nช.ค.บ.\nช.พ.ค.\nช.ส.\nช.ส.ค.\nฌ.ป.ค.\nฌ.ศ.ร.\nฌ.ส.อ.\nฐท.สห.\nด.ช.\nด.ญ.\nด.ต.\nด.ศ.ค.\nด.ศ.ร.\nดย.ทร.\nต.ก.\nต.ค.\nต.จ.\nต.จ.ว.\nต.ช.\nต.ต.\nต.บ.\nต.ม.\nต.ร.\nต.ศ.ร.\nต.ห.\nต.อ.\nต.อ.จ.\nตร.กม.\nตร.ซม.\nตร.ต.\nตร.ทล.\nตร.น.\nตร.ปม.\nตร.ภ.\nตร.ม.\nตร.รฟ.\nตร.ว.\nตร.ส.\nตร.สข.\nท.จ.\nท.จ.ว.\nท.ช.\nท.ญ.\nท.ด.\nท.ท.ท.\nท.ทบ.\nท.บ.\nท.พ.\nท.ม.\nท.ศ.\nทก.ด.\nทก.บ.\nทก.ม.\nทส.ปช.\nทส.รมว.กห.\nทุ.ส.นิ.ม.\nธ.ก.ส.\nธ.ค.\nธ.ญ\nธ.บ.\nน.ช.\nน.ญ.\nน.ด.\nน.ต.\nน.ท.\nน.น.\nน.บ.\nน.บ.ท.\nน.ป.ท.\nน.พ.\nน.ม.\nน.ร.\nน.ว.\nน.ศ.\nน.ส.\nน.ส.พ.\nน.ส.๓\nน.สพ.\nน.อ.\nนปพ.ภ.\nนศ.ด.\nนศ.บ.\nนศ.ม.\nบ.ก.\nบ.ข.ส.\nบ.ช.\nบ.ด.ท.\nบ.ตร.\nบ.ภ.\nบ.ม.\nบก.จร.\nบก.ตชด.\nบก.ตม.\nบก.ทล.\nบก.น.\nบก.ป.\nบก.ปค.\nบก.ปม.\nบก.ภ.เขต\nบก.รน.\nบก.รฟ.\nบก.ร้อย.ตชด.\nบก.ส.\nบกข.ป.\nบจพ.ป.\nบช.ก.\nบช.ด.\nบช.ตชด.\nบช.น.\nบช.บ.\nบช.ปส.\nบช.ภ.\nบช.ม.\nบชท.ป.\nบชน.ป.\nบชส.ป.\nบธ.ด.\nบธ.บ.\nบธ.ม.\nบนท.ป.\nบนอ.ป.\nบปช.ป.\nป.กท.\nป.กศ.\nป.กศ.สูง\nป.จ.\nป.จ.ว.\nป.ช.\nป.ธ.\nป.ป.\nป.ป.ก.\nป.ป.ช.\nป.ป.ป.\nป.ป.ร.\nป.ป.ส.\nป.พ.\nป.พ.พ.\nป.พย.\nป.ม.\nป.ม.ก.\nป.ม.ช.\nป.ม.ธ.\nป.ม.ศ.\nป.ม.อ.\nป.ร.ร.๔\nป.ร.ร.๕\nป.ร.ร.๖\nป.ล.\nป.ว.พ.\nป.วิ.อ.\nป.ส.ส.\nป.อ.\nป.อ.ร.ส.\nป.๑\nปม.วส.\nปอ.พ.\nผกก.ภ.\nผช.ผอ.\nผต.มท.\nผบ.ตร.\nผบ.ทบ.\nผบ.ทร.\nผบ.ทสส.\nผบ.ทอ.\nผบก.น.\nผบก.ป.\nผบก.ปค.\nผบก.ปม.\nผบก.ภ.\nผบช.ก.\nผบช.ตชด.\nผบช.น.\nผบช.ภ.\nผว.กทม.\nผอ.ปจ.\nพ.ก.ง.\nพ.กศ.\nพ.ข.ต.\nพ.ค.\nพ.ค.ช.\nพ.ค.ว.\nพ.ค.ศ.\nพ.จ.ต.\nพ.จ.ท.\nพ.จ.อ.\nพ.ช.\nพ.ช.ค.\nพ.ด.\nพ.ต.\nพ.ต.ต.\nพ.ต.ท.\nพ.ต.อ.\nพ.ต.อ.พิเศษ\nพ.ท.\nพ.บ.\nพ.ป.\nพ.ภ.ม.\nพ.ม.\nพ.ม.ช.\nพ.ย.\nพ.ร.ก.\nพ.ร.ฎ.\nพ.ร.ต.\nพ.ร.ธ.\nพ.ร.บ.\nพ.ศ.\nพ.ศ.บ.\nพ.ส.ร.\nพ.ส.ล.\nพ.อ.\nพ.อ.ต.\nพ.อ.ท.\nพ.อ.พิเศษ\nพ.อ.อ.\nพณ.ด.\nพณ.บ.\nพณ.ม.\nพธ.ด.\nพธ.บ.\nพธ.ม.\nพบ.ด.\nพบ.บ.\nพบ.ม.\nพย.ด.\nพย.บ.\nพย.ม.\nพล.จ.\nพล.ต.\nพล.ต.จ.\nพล.ต.ต.\nพล.ต.ท.\nพล.ต.อ.\nพล.ท.\nพล.ปตอ.\nพล.ม.\nพล.ม.๒\nพล.ร.จ.\nพล.ร.ต.\nพล.ร.ท.\nพล.ร.อ.\nพล.อ.\nพล.อ.จ.\nพล.อ.ต.\nพล.อ.ท.\nพล.อ.อ.\nพลา.ทร.\nพศ.ด.\nพศ.บ.\nพศ.ม.\nพอ.สว.\nภ.ง.ด.\nภ.ง.ด.๙\nภ.ด.\nภ.บ.\nภ.บ.ท.๕\nภ.ป.ร.\nภ.พ.\nภ.ม.\nภ.สถ.บ.\nม.ค.\nม.จ.\nม.ป.ท.\nม.ป.ป.\nม.ป.พ.\nม.ร.ว.\nม.ศ.\nม.อ.\nม.อ.ปัตตานี\nมิ.ย.\nมี.ค.\nยศ.ทบ.\nยศ.ทร.\nยศ.ทอ.\nร.ง.\nร.ด.\nร.ต.\nร.ต.ต.\nร.ต.ท.\nร.ต.อ.\nร.ท.\nร.น.\nร.บ.\nร.พ.\nร.ฟ.ล.\nร.ย.ล.\nร.ย.ส.ท.\nร.ล.\nร.ศ.\nร.ส.พ.\nร.อ.\nรป.ม.\nรร.จปร.\nรร.จอ.\nรร.ชท.\nรร.ตท.\nรร.นร.\nรร.นรต.\nรร.นอ.\nล.ญ.\nล.ว.\nลส.ชบ.\nว.ค.\nว.ฉ.\nว.ช.\nว.ด.ป.\nว.ป.ถ.\nวท.บ.\nศ.บ.\nศ.ป.ก.\nศ.ศ.ป.\nศฝร.ภ.\nศศ.บ.\nศษ.บ.\nศส.บ.\nส.ก.\nส.ก.ศ.ท.\nส.ค.\nส.ค.1\nส.ค.ร.\nส.ค.ส.\nส.ต.\nส.ต.ต.\nส.ต.ท.\nส.ต.อ.\nส.ท.\nส.ทร.\nส.ป.ช.\nส.ป.ส.ท.\nส.ป.อ.\nส.ร.\nส.ล.น.\nส.ว.\nส.ว.ท.\nส.ว.ส.ท.\nส.ส.\nส.ส.ท.\nส.ส.ร.\nส.ห.\nส.อ.\nสถ.บ.\nสนง.สสอ.\nสพ.ญ.\nสพ.บ.\nสว.จร.\nสว.ธร.\nสว.ส.\nสว.สป.\nสว.สส.\nสว.อก.\nสส.บ.\nสุ.จิ.ปุ.ลิ.\nห.ร.ม.\nอ.ก.ค.\nอ.ก.จ.\nอ.จ.\nอ.ช.พ.\nอ.ตร.\nอ.บ.\nอ.ส.ท.\nอ.ส.ม.ท.\nอ.ส.ย.\nอ.อ.ป.\nอส.รด.\nอุ.อา.ก.ส.\nฮ.จ.\nฮ.ท.\nฮ.ฝ.\nฮ.ล.\nฮ.ศ.\nเม.ย.\n\nกรีนิช\nกลันตัน\nกัลกัตตา\nกัวลาลัมเปอร์\nกัศมีร์\nกาฐมาณฑุ\nโกลกาตา\nควิเบก\nคอนเนตทิคัต\nคาบูล\nคุชราต\nคุนหมิง\nเคนตักกี\nเคนทักกี\nเคมบริดจ์\nแคชเมียร์\nแคนซัส\nแคนเบอร์รา\nแคโรไลนา\nแคลิฟอร์เนีย\nโคเปนเฮเกน\nโคลัมโบ\nโคโลราโด\nไครสต์เชิร์ช\nไคโร\nจาการ์ตา\nจำปาศักดิ์\nเจนไน\nเจนีวา\nเจ้อเจียง\nฉงชิ่ง\nเฉิงตู\nชานตง\nชิคาโก\nเชนไน\nเชอร์โนบิล\nซัปโปโร\nซานมารีโน\nซาบาห์\nซาราเยโว\nซาราวัก\nซิดนีย์\nซีอาน\nซีแอตเทิล\nซูริก\nซูริค\nเซเชลส์\nเซนได\nเซี่ยงไฮ้\nโซเฟีย\nโซล\nโซโลมอน\nไซ่ง่อน\nไซบีเรีย\nดัลลัส\nดาโคตา\nดานัง\nดีทรอยต์\nดูไบ\nเดนเวอร์\nเดลาแวร์\nเดียนเบียนฟู\nโดเวอร์\nโดฮา\nไดฟุกุ\nไดฟูกุ\nตรังกานู\nตริโปลี\nตูวาลู\nเตหะราน\nโตเกียว\nโตรอนโต\nทมิฬนาฑู\nทริโปลี\nทิเบต\nเทกซัส\nเท็กซัส\nเทนเนสซี\nเทลอาวีฟ\nแทสเมเนีย\nโทรอนโต\nไทเป\nธากา\nนางาซากิ\nนาริตะ\nนิวเจอร์ซีย์\nนิวเดลี\nนิวยอร์ก\nนิวยอร์ค\nนิวแฮมป์เชียร์\nเนบราสกา\nเนแบรสกา\nเนวาดา\nบรัสเซลส์\nบราซิเลีย\nบอมเบย์\nบอสตัน\nบังกาลอร์\nบังคาลอร์\nบูคาเรสต์\nบูดาเปสต์\nเบงกาซี\nเบนกาซี\nเบรุต\nเบลเกรด\nเบอร์ลิน\nแบกแดด\nปอยเปต\nปะลิส\nปะหัง\nปักกิ่ง\nปัญจาบ\nปัฏนา\nปารีส\nปีนัง\nเประ\nเปียงยาง\nพนมเปญ\nพระตะบอง\nพะโค\nพะสิม\nพาราณสี\nพิหารี\nเพนซิลวาเนีย\nเพนซิลเวเนีย\nฟรานซ์\nฟลอริดา\nฟิลาเดลเฟีย\nฟุกุชิมะ\nฟุกุชิมา\nฟุกุโอกะ\nฟูกูโอกะ\nแฟรงก์เฟิร์ต\nมอสโก\nมะนิลา\nมะละแหม่ง\nมัณฑะเลย์\nมัทราส\nมาดริด\nมิชิแกน\nมินนิโซตา\nมิยางิ\nมิลาน\nมิวนิก\nมิสซูรี\nมุมไบ\nเมน\nเมลเบิร์น\nเมาะตะมะ\nเมาะลำเลิง\nแมนจูเรีย\nแมนเชสเตอร์\nแมนฮัตตัน\nแมริแลนด์\nแมรีแลนด์\nแมสซาชูเซตส์\nยะไข่\nย่างกุ้ง\nยูทาห์\nยูนนาน\nเยรูซาเล็ม\nโยโกฮามา\nริยาด\nรีโอเดจาเนโร\nโรดไอแลนด์\nลอนดอน\nลอสแองเจลิส\nลาปาซ\nลาสเวกัส\nลิสบอน\nลุยเซียนา\nโลซาน\nโลซานน์\nวอชิงตัน\nวอร์ซอ\nวิสคอนซิน\nเวนิส\nเวลส์\nเวอร์จิเนีย\nเวอร์มอนต์\nเวียงจันทน์\nเวียนนา\nแวนคูเวอร์\nไวโอมิง\nสกอตแลนด์\nสก็อตแลนด์\nสตอกโฮล์ม\nสลังงอร์\nเสฉวน\nเสียมราฐ\nเสียมเรียบ\nหงสา\nหงสาวดี\nหนานไห่\nหลวงพระบาง\nหูเป่ย\nหูเป่ย์\nหูหนาน\nเหอเป่ย\nเหอเป่ย์\nเหอหนาน\nอชันตา\nอลาสกา\nอวันตี\nออริกอน\nออสโล\nอะแลสกา\nอัตตะปือ\nอัมมาน\nอัมสเตอร์ดัม\nอัสสัม\nอาบูดาบี\nอาร์คันซอ\nอินเดียนา\nอิบารากิ\nอิลลินอยส์\nอิสตันบูล\nอิสลามาบัด\nอุรุมชี\nอูลานบาตอร์\nเอดินบะระ\nเอเธนส์\nแอตแลนตา\nแอริโซนา\nแอลเจียร์\nโอคลาโฮมา\nโอค็อตสค์\nโอกินาวา\nโอซากา\nโอริสสา\nโอเรกอน\nโอไฮโอ\nไอดาโฮ\nไอโอวา\nฮอนโนลูลู\nฮานอย\nฮาเนดะ\nฮาราเร\nฮาวาย\nฮิโรชิมา\nฮุสตัน\nเฮลซิงกิ\n\nมกรา\nกุมภา\nมีนา\nเมษา\nพฤษภา\nมิถุนา\nกรกฎา\nสิงหา\nกันยา\nตุลา\nพฤศจิกา\nธันวา\nเอ\nบี\nซี\nดี\nอี\nเอฟ\nจี\nเอช\nไอ\nเจ\nเค\nแอล\nเอ็ม\nเอ็น\nโอ\nพี\nคิว\nอาร์\nเอส\nที\nยู\nวี\nดับเบิล\nดับบลิว\nเอ็กซ์\nเอ๊กซ์\nวาย\nแซด\nแอลฟา\nแอลฟ่า\nเบตา\nเบต้า\nแกมมา\nแกมม่า\nเดลตา\nเดลต้า\nโอเมกา\nโอเมก้า\nเมกะ\nกิกะ\nนาโน\nไมโคร\n\nกรรมาชน\nกรอบรูป\nกระดี๊กระด๊า\nกระบับ\nกราวนด์\nกรีน\nกรุ๊ป\nกฤษณ์\nกลาส\nก๊วน\nกษัตริยา\nกษัตริยาธิราช\nก่อนหน้า\nกะบับ\nกับดัก\nกัมมันตะ\nก๊าก\nก๋ากั่น\nกาญจน์\nกาญจนาภิเษก\nกามิกาเซ่\nการันตี\nกาหลิบ\nกิฟท์\nกิมจิ\nกีวี\nกึ๊ก\nกึ๋ย\nกุนซือ\nกุมภาพันธ์\nกู๋\nเกจิ\nเกมส์\nเกย์\nเกรด\nเกรย์\nเกสต์เฮาส์\nเก๊ะ\nเก๋ากี้\nเกิร์ล\nแกงค์\nแกรนด์\nแกสโซฮอล์\nแก๊สโซฮอล์\nโกเต็กซ์\nโกลด์\nโกะ\nโก๊ะ\nไกด์\nขั้นตอน\nเขวี้ยง\nคณาญาติ\nครัวซอง\nครัวซองต์\nคร่ำครวญ\nครีเอทีฟ\nครูเสด\nคลับ\nคลาสสิก\nคลิตอริส\nคลิป\nความหมาย\nควิก\nควีน\nคองเกรส\nคอนซูเมอร์\nคอนเซปต์\nคอนเซ็ปต์\nคอนโด\nคอนโดมิเนียม\nคอนเทนเนอร์\nคอนแทค\nคอนแท็ค\nคอนโทรล\nคอนเฟิร์ม\nคอปเตอร์\nคอมพ์\nคอมเพล็กซ์\nคอมมอนส์\nคอมเมนท์\nคอมเมนต์\nคอร์ป\nคอร์ปอเรชั่น\nคอร์รัปชัน\nคอร์รัปชั่น\nคอรัปชัน\nคอรัปชั่น\nคอร์ส\nคอลเล็กชั่น\nคอลัมน์\nคอลัมนิสต์\nคัตเอาต์\nคันคาก\nคันถธุระ\nคันธาระ\nคันยิ\nคัสตาร์ด\nคาราโอเกะ\nคีตกวี\nคีตปฏิภาณ\nคีตราชัน\nคาปูชิโน\nคามิคาเซ่\nคาเฟ่\nคาร์\nคาร์โก้\nคาราเมล\nคาแรกเตอร์\nคาแร็กเตอร์\nคาแรคเตอร์\nคาแร็คเตอร์\nคาวบอย\nคาสิโน\nคิกขุ\nคิวบิก\nคูลเลอร์\nเคบับ\nเครป\nเคลม\nเคลียร์\nเคลื่อนย้าย\nเคส\nเคอร์ฟิว\nแคชเชียร์\nแคทวอล์ค\nแคนดิเดต\nแคนตาลูป\nแคนยอน\nแคนู\nแคป\nแคมป์\nแคมปัส\nแคมเปญ\nแคร์\nแครกเกอร์\nแคร็กเกอร์\nแครอท\nแคสต์\nแคสติง\nแคสติ้ง\nโค้ก\nโค้ช\nโคโยตี\nโคโยตี้\nโครนา\nโคอะล่า\nโคอาลา\nโคอาล่า\nไคลแมกซ์\nไคลแม็กซ์\nงั้น\nง่าว\nงี้\nเง็ง\nโง่เขลา\nไง\nจตุคาม\nจ๊อกกี้\nจอหงวน\nจังโก้\nจัมโบ้\nจ๊าบ\nจารกรรม\nจารชน\nจิ๊ก\nจิ๊กโก๋\nจิ๊กซอว์\nจิตพิสัย\nจิตเภท\nจีดีพี\nจึ๊ก\nจุ๊ย\nจูน\nจูเนียร์\nเจ๊\nเจได\nเจ็ต\nเจล\nเจ๊าะแจ๊ะ\nเจี๊ยว\nแจ็กเก็ต\nแจ๊กเก็ต\nแจ็กพอต\nแจ็กพ็อต\nแจ๊กพอต\nแจ๊กพ็อต\nแจม\nแจ๊ส\nโจ๋\nฉลุย\nเฉิ่ม\nชนะเลิศ\nช็อค\nช็อต\nช็อป\nช็อปปิ้ง\nช็อปเปอร์\nชะโนด\nชัตเตอร์\nชัวร์\nชาร์จ\nชาร์ต\nชาร์ป\nชินบัญชร\nชิฟฟอน\nชีส\nชีอะห์\nเช็ก\nเช็งเม้ง\nเชฟ\nเชลียร์\nเชอร์รี่\nแชเชือน\nแช่แข็ง\nแชมป์\nแชมปิยอง\nแชมเปญ\nแชมเปี้ยน\nแชมพู\nโชว์รูม\nโชห่วย\nใช้งาน\nไชน่า\nซ้อ\nซอมบี้\nซะ\nซังเต\nซันตาคลอส\nซัพพลาย\nซัพพลายเออร์\nซัมเมอร์\nซากุระ\nซาดิสต์\nซาดิสม์\nซาตาน\nซานตาคลอส\nซาฟารี\nซาบะ\nซามูไร\nซาร์\nซาร์ดีน\nซาเล้ง\nซิง\nซิ่ง\nซิงเกิล\nซิตี\nซิตี้\nซินโดรม\nซิม\nซิ้ม\nซิมโฟนี\nซิมโฟนี่\nซิลเวอร์\nซี้\nซี้ซั้ว\nซีดาน\nซีน\nซีนีเพล็กซ์\nซีเนียร์\nซีร็อกซ์\nซีรีส์\nซีเรียส\nซีอีโอ\nซื่อบื้อ\nซุนหนี่\nซุปเปอร์\nซูชิ\nซูเปอร์\nซูม\nซูโม่\nซูเอี๋ย\nซูฮก\nเซ็กซ์\nเซ็กซี่\nเซ็กส์\nเซนเซอร์\nเซ็นเซอร์\nเซนเตอร์\nเซ็นเตอร์\nเซ็นทรัล\nเซนส์\nเซ่นไหว้\nเซฟตี้\nเซรามิก\nเซลส์\nเซลส์แมน\nเซอร์\nเซอร์ไพรส์\nเซอร์วิส\nเซาท์\nเซี้ยว\nแซ็ก\nแซกโซโฟน\nแซ็กโซโฟน\nแซนด์วิช\nแซมบ้า\nแซลมอน\nแซว\nโซเชียล\nโซน\nโซนี่\nโซลาร์\nโซโล\nโซโล่\nญาณทัสสนะ\nดยุก\nดยุค\nดร็อป\nดรัมเมเยอร์\nดรามา\nดราม่า\nดอกเตอร์\nด็อกเตอร์\nดัมพ์\nดั๊มพ์\nดาวน์\nดิกชันนารี\nดิสเครดิต\nดีกรี\nดีเจ\nดีไซน์\nดีไซน์เนอร์\nดีไซเนอร์\nดีเบต\nดีพาร์ตเมนต์\nดีพาร์ตเมนท์\nดีพาร์ทเมนต์\nดีพาร์ทเมนท์\nดีมานด์\nดีล\nดีลเลอร์\nดีเลย์\nเดชานุภาพ\nเดบิต\nเดโม\nเดย์\nเด้อ\nเดอะ\nเด๊ะ\nเดี้ยง\nเดี๊ยะ\nแดนซ์\nแดนเซอร์\nแดรี่\nโดนัท\nโดมิโน\nโดรายากิ\nไดเอ็ต\nตถตา\nตนเอง\nตรวจทาน\nตรวจสอบ\nตอกย้ำ\nต๊อง\nต่อยอด\nต่อรอง\nตะหงิด\nตังค์\nตันเถียน\nตัวตน\nตัวเอง\nตาปรือ\nต้าอ่วย\nติงต๊อง\nติ๋ม\nติ่มซำ\nติว\nติวเตอร์\nตี๋\nตื้บ\nตุ๊ก\nตุ๊กตุ๊ก\nตุ๊ด\nตุ๋ย\nตู้เซฟ\nเต๊ะ\nเตี๊ยม\nแตงกวา\nแตงโม\nแต๋ว\nโต๋เต๋\nโต๊ะจีน\nไตรมาส\nถ่ายทำ\nถูกต้อง\nทงคัตสึ\nทริป\nทรู\nทอม\nท็อป\nทอร์นาโด\nทอล์ค\nทักซิโด\nทันตกรรม\nทันตแพทยศาสตร์\nทับซ้อน\nทัวร์\nทัวร์นาเมนต์\nทัวร์นาเมนท์\nทัวริสต์\nทาเลนต์\nทาวน์\nทาวน์เฮาส์\nทำงาน\nทิป\nทิพยสมบัติ\nทิวลิป\nทีรามิสุ\nทีวี\nทูน่า\nเท็กซ์\nเทค\nเทคโน\nเทคโนแครต\nเทควันโด\nเทป\nเทรด\nเทรนด์\nเทรนเนอร์\nเทรลเลอร์\nเทรลเล่อร์\nเทเลกราฟ\nเทวบัญชา\nเทวบุตร\nเทวา\nเทวาธิราช\nเทโวโรหนะ\nเทอร์โบ\nเที่ยงคืน\nเที่ยงวัน\nเทียมทาน\nแทกติค\nแทคติค\nแทงกั๊ก\nแทงโก้\nโทมาฮอก\nโทมาฮอว์ก\nโทมาฮอว์ค\nโทร\nโทรโข่ง\nไทม์\nไทยแลนด์\nไทเฮา\nธรรมา\nธรรมาภิบาล\nธัมโม\nธีม\nธุรกรรม\nธุหร่ำ\nเธค\nนพมาศ\nนรีแพทย์\nน็อก\nน็อค\nน้องใหม่\nนอมินี\nนอร์ท\nน่ะ\nนางแบบ\nนาฏยศาลา\nนายแบบ\nนายพราน\nนินจา\nนิรันดร์\nนิว\nนิวส์\nนู้ด\nเนอะ\nเนิร์สเซอรี\nเนิร์สเซอรี่\nเนี้ยบ\nโนติส\nไนท์\nไนน์\nบรรพชน\nบร็อกโคลี\nบร็อคโคลี\nบรา\nบริกร\nบริวเวอรี่ส์\nบลอนด์\nบลูเบอร์รี\nบลูเบอร์รี่\nบ๊วย\nบอกซ์\nบ็อกซ์\nบ๊อกซ์\nบอดี้\nบอนด์\nบ๊อบ\nบอมบ์\nบ๋อย\nบอยคอต\nบอยคอตต์\nบอร์ด\nบังเกอร์\nบัตเตอร์\nบัลลาสต์\nบัส\nบาบูน\nบาร์บีคิว\nบาร์บี้\nบาลานซ์\nบิ๊ก\nบิล\nบึม\nบึ้ม\nบุญคุณ\nบุ๋น\nบุปผา\nบู๊\nบูชิโด\nบูติก\nบูติค\nบูม\nเบเกอรี่\nเบญจมบพิตร\nเบตาดีน\nเบนโตะ\nเบนโล\nเบบี้\nเบลอ\nเบอร์เกอร์\nเบอร์รี\nเบิร์ด\nเบิร์น\nแบ็กโฮ\nแบคโฮ\nแบด\nแบต\nแบนเนอร์\nแบรนด์\nแบล็ก\nแบล็ค\nไบโอ\nโบกี้\nโบตั๋น\nโบ้ย\nโบรกเกอร์\nโบรชัวร์\nโบว์\nโบว์ลิ่ง\nไบเบิล\nปฏิสัมพันธ์\nป๊อก\nปอดแหก\nป๊อป\nป๋อหลอ\nปักขคณนา\nปัจเจกชน\nปัจฉิมนิเทศ\nป๊า\nป๋า\nป่าไม้\nปาร์ตี้\nปาสกาล\nปาสคาล\nปาสเตอร์\nปิกอัพ\nปิ๊ง\nปิโตรเคมี\nปิยมิตร\nปึ้ก\nปูอัด\nเปโซ\nเป็นไง\nเปปเปอร์มินต์\nเปเปอร์\nเปราะบาง\nเป๊ะ\nเป่ายิงฉุบ\nเป่ายิ้งฉุบ\nเปียโน\nแป้ก\nแป๋ว\nแป๊ะเจี๊ยะ\nโปร\nโปรเจกต์\nโปรเจ็กต์\nโปรเจกเตอร์\nโปรเจ็กเตอร์\nโปรเจคท์\nโปรเจ็คท์\nโปรดักชั่น\nโปรดิวเซอร์\nโปรโมชั่น\nโปรโมต\nโปรโมเตอร์\nโปรโมท\nโปลิศ\nโปสเตอร์\nผลไม้\nผลักดัน\nผ้าห่ม\nผิดพลาด\nผู้นำ\nแผดเผา\nเฝอ\nพงษ์\nพริตตี้\nพรีเซนต์\nพรีเซ็นเตอร์\nพรีเมียม\nพรีเมียร์\nพฤหัส\nพล็อต\nพลาซ่า\nพลานุภาพ\nพ่อค้า\nพอเพียง\nพะเรอ\nพันธกิจ\nพันธุวิศวกรรม\nพาร์\nพาร์ตเนอร์\nพาร์ทเนอร์\nพาวเวอร์\nพาสเจอร์ไรส์\nพาสตา\nพาสต้า\nพาสปอร์ต\nพาเหรด\nพิซซ่า\nพีเรียด\nพุดดิ้ง\nพุทธภูมิ\nพุทธศตวรรษ\nพุทโธ\nพูล\nเพทนาการ\nเพนกวิน\nเพนตากอน\nเพรส\nเพรียวบาง\nเพลซ\nเพลท\nเพลย์บอย\nเพียบแปร้\nเพียว\nเพาเวอร์\nแพกเกจ\nแพ็ค\nแพตเทิร์น\nแพทเทิร์น\nแพทยสภา\nแพนงเชิญ\nแพนดา\nแพนด้า\nแพลน\nโพลล์\nโพลารอยด์\nโพสต์\nไพลิน\nฟยอร์ด\nฟรังก์\nฟรุต\nฟลอร์\nฟลุก\nฟลุค\nฟลุต\nฟลุท\nฟอยล์\nฟอร์ม\nฟันด์\nฟาวล์\nฟาสต์ฟู้ด\nฟินิกซ์\nฟิวเจอร์\nฟีด\nฟีเวอร์\nฟุตบาท\nเฟรช\nเฟรชชี่\nเฟรม\nเฟมินิสต์\nเฟส\nเฟอร์นิเจอร์\nเฟอร์รี่\nเฟิร์ม\nเฟี้ยวฟ้าว\nแฟกซ์\nแฟ็กซ์\nแฟนซี\nแฟนตาซี\nแฟ้บ\nแฟร์\nแฟรนไชส์\nแฟรี\nแฟรี่\nแฟลช\nแฟล็ต\nโฟน\nโฟม\nโฟล์ค\nไฟต์\nไฟแนนซ์\nไฟลต์\nไฟลท์\nภควัทคีตา\nภควัมบดี\nภควัมปติ\nภคันทลาพาธ\nภววิสัย\nภารตะ\nภูมิทัศน์\nม้ง\nมวลชน\nมยุราภิรมย์\nมลภาวะ\nมหภาค\nมหาอุปราชา\nมอคคา\nมอคค่า\nมอนสเตอร์\nม็อบ\nมอบตัว\nมอยส์เจอไรเซอร์\nมอลล์\nมะกัน\nมั้ง\nมัฟฟิน\nมั้ย\nม้านั่ง\nมาเฟีย\nมาม่า\nมายองเนส\nมายาคติ\nมาร์ก\nมาร์เก็ต\nมาร์เก็ตติ้ง\nมาร์ค\nมาร์จิน\nมาร์ช\nมาร์ต\nมาร์ท\nมาราธอน\nม้าหินอ่อน\nมินต์\nมินท์\nมินิ\nมิลค์\nมิวสิค\nมิสซัง\nมิสไซล์\nมิสเตอร์\nมือถือ\nมุมมอง\nเมคอัพ\nเมจิก\nเมจิค\nเมทัล\nเมเปิล\nเมาท์\nเมี่ยงคำ\nแมกกาซีน\nแม็กกาซีน\nแมคเคอเรล\nแม่ค้า\nแมชชีน\nแมชีน\nแมนชั่น\nแมมบ้า\nแมมโบ้\nโมจิ\nโมเดล\nโมเดิร์น\nโมเต็ล\nโมโนเรล\nโมหจริต\nไมค์\nไมเกรน\nยนตรกรรม\nยอมรับ\nยะเยือก\nยังไง\nยากูซ่า\nยาวี\nยิม\nยิว\nยุวทูต\nยูโทเปีย\nยูโร\nยูวี\nเยน\nเยลลี่\nเย้ว\nเยอบีรา\nเยอบีร่า\nเยอร์บีรา\nเยอร์บีร่า\nแยมโรล\nโยเกิร์ต\nโยโย่\nรวมมิตร\nร็อค\nร็อคเก็ต\nรองรับ\nรอมฎอน\nรอยัลตี้\nระโงก\nรันเวย์\nรัม\nรากหญ้า\nราชบัณฑิตยสถาน\nราชานุญาต\nราชานุสาวรีย์\nรามเทพ\nรามาธิบดี\nรามายณะ\nราเม็ง\nราเมน\nรายชื่อ\nราสเบอร์รี\nริกเตอร์\nริคเตอร์\nรีไซเคิล\nรีดไถ\nรีทัช\nรีเทิร์น\nรีไทร์\nรีแบรนด์\nรีพอร์ท\nรีโมต\nรีโมท\nรีวิว\nรีสอร์ต\nรีสอร์ท\nรีเสิร์ช\nรุมบ้า\nรุสโซ\nรูบิก\nรูบิค\nเรซิน\nเรซิ่น\nเรดิโอ\nเรต\nเรตติ้ง\nแรงใจ\nแรงดูด\nแรงผลัก\nแรลลี\nแรลลี่\nโรดแมป\nโรเนียว\nโรแมนติก\nโรแมนติค\nโรล\nโรลออน\nไรเฟิล\nล็อกเกอร์\nลอจิสติกส์\nล็อต\nล็อบบี้\nลอร์ด\nล้มเหลว\nละติน\nละอ่อน\nลาซานญ่า\nลาติน\nลาเต้\nลานีญา\nลามะ\nลิมิต\nลิมูซีน\nลิสต์\nลีก\nลีด\nลีดเดอร์\nลีเมอร์\nลีลาวดี\nลุค\nลูกชาย\nลูกสาว\nเลกเชอร์\nเลคเชอร์\nเลดี้\nเลสเบี้ยน\nเลิฟ\nแลนด์\nแล็บ\nโลโก้\nโลชั่น\nไลท์\nไลน์\nไลฟ์\nวนาราม\nวราราม\nวโรกาส\nว้อดก้า\nวอเตอร์\nวอฟเฟิล\nว้อย\nวอร์ม\nวอร์มอัพ\nวอร์รูม\nวอล์ก\nวอล์ค\nวอลซ์\nวอลนัต\nวอลนัท\nวอลล์\nว่ะ\nวันเวย์\nวัสสา\nวาซาบิ\nวาทกรรม\nวาทะ\nวานิลลา\nวานิลา\nวาฟเฟิล\nวาริชศาสตร์\nว้าว\nวัคค์\nวัจนะ\nวาไรตี้\nวิก\nวิดีโอ\nวิทย์\nวิน\nวิป\nวิปปิ้ง\nวิภัชภาค\nวิว\nวิลล์\nวิลเลจ\nวีเจ\nวีซ่า\nวีดิทัศน์\nวีน\nวีไอพี\nวืด\nเวณิกา\nเวเฟอร์\nเวสต์\nเวอร์\nเวิร์ก\nเวิร์กช็อป\nเวิร์ค\nเวิร์ลด์\nเวิลด์\nแวมไพร์\nไวกิ้ง\nไวเบรเตอร์\nไวอะกร้า\nไวอากร้า\nศากยบุตร\nศิรินทร์\nศิลปวัฒนธรรม\nศิลปากร\nศิวิไลซ์\nศึกษาศาสตร์\nสกรัม\nสกาย\nสกู๊ป\nสเกตช์\nสเก็ตช์\nสคริปต์\nสแควร์\nสงบสุข\nสจ๊วต\nสตรอเบอร์รี\nสตรอเบอรี\nสตรอว์เบอร์รี\nสตริง\nสต็อก\nสต๊อก\nสต็อค\nสต๊อค\nสตอรี\nสตาร์\nสตาร์ท\nสติกเกอร์\nสติ๊กเกอร์\nสตีล\nสตูดิโอ\nสเตชัน\nสเตชั่น\nสเตเดียม\nสเตนเลส\nสเต็ป\nสเตย์\nสเตริโอ\nสเตอริโอ\nสแตนดาร์ด\nสแตนเลส\nสโตน\nสโตร์\nสไตรค์\nสไตล์\nสถาปัตย์\nสไนเปอร์\nสปอต\nสป็อต\nสปอนเซอร์\nสปอร์ต\nสปา\nสปาย\nสปิริต\nสเปก\nสเปค\nสไปเดอร์\nสมณพราหมณ์\nสมาพันธ์\nสมิติเวช\nสโรชา\nสลัม\nสแล็ก\nสโลแกน\nสโลว์\nสไลด์\nสวีท\nสหรัฐ\nสหัชญาณ\nสหัสวรรษ\nสะกอม\nสะเด่า\nสะบึม\nสะบึมส์\nสะออน\nสังโฆ\nสะโหลสะเหล\nสันทนาการ\nสัมนา\nสามช่า\nสามแยก\nสารขัณฑ์\nสี่แยก\nสึนามิ\nสุนทรีย์\nสุริยยาตร\nสุริยยาตร์\nสุหนี่\nเสกสรรค์\nเสพติด\nเสือโคร่ง\nหงวน\nหน่อมแน้ม\nหมวย\nหมั่นโถว\nหม่านโถว\nหมายปอง\nหมิง\nหยวน\nหลวงตา\nหลวงปู่\nหลวงพี่\nหล่อฮังก้วย\nหลินจือ\nห่วย\nเห็นด้วย\nเหมย\nเห่ย\nเหี่ยวย่น\nแหม็บ\nแหวว\nโหงว\nโหงวเฮ้ง\nโหลน\nโหลยโท่ย\nไหง\nไหร่\nอพาร์ตเมนต์\nอพาร์ตเมนท์\nอพาร์ทเมนต์\nอพาร์ทเมนท์\nอมาตยาธิปไตย\nอยุติธรรม\nอริยสงฆ์\nอ่วม\nอวอร์ด\nออกแบบ\nออดิชั่น\nออดิทอเรียม\nออเดอร์\nออโต้\nออทิสติก\nอ่อนด้อย\nออฟ\nออยล์\nออร์แกน\nออร์แกนิก\nออร์แกนิค\nออร์เดอร์\nออรัล\nออสซี่\nอะ\nอัตลักษณ์\nอัตวิสัย\nอันเดอร์\nอันตรกิริยา\nอัลตรา\nอัลไซเมอร์\nอัลบัม\nอัลบั้ม\nอัลมอนด์\nอาข่า\nอาโนเนะ\nอาฟเตอร์\nอาร์ติสต์\nอาร์พีจี\nอาว์\nอาสวะ\nอิกัวนา\nอินดอร์\nอินดัสตรีส์\nอินเตอร์\nอิ่มแปร้\nอิมพีเรียล\nอิเล็กทริก\nอิเล็กทริค\nอิเลียด\nอิสรชน\nอิเหนา\nอิออน\nอีแต๋น\nอีโรติก\nอีเวนท์\nอีสต์\nอีสเตอร์\nอึ๊บ\nอึ้ม\nอึ๋ม\nอึมครึม\nอุด้ง\nอุตสาหการ\nอุเทน\nอุปการคุณ\nอุปทาน\nอุปนายก\nอุปนายิกา\nอุปสงค์\nอุปัทวเหตุ\nอุรังคธาตุ\nอูคูเลเล่\nอู้ฟู่\nเอ๋\nเอ็กซ์โป\nเอ็กซ์เพรส\nเอ็กโซเซต์\nเอ็กโซเซ่ต์\nเอเซีย\nเอ็นจีโอ\nเอ็นเตอร์เทน\nเอนทรานซ์\nเอ็นทรานซ์\nเอฟเฟ็กต์\nเอเยนต์\nเอลนีโญ\nเอสเปรสโซ\nเอสเพรสโซ\nเอ๋อ\nเอาต์\nเอาท์\nเอาท์ดอร์\nเอ๊าะ\nแอ็กชั่น\nแอ็คชั่น\nแอคทีฟ\nแอดมิชชั่น\nแอดมิสชัน\nแอนด์\nแอ๊บแบ๊ว\nแอปเปิล\nแอปเปิ้ล\nแอปพริคอท\nแอพพริคอท\nแอพริคอต\nแอร์\nแอโรบิก\nแอโรบิค\nแอลมอนด์\nแอสเตอร์\nโอเค\nโอเปอเรเตอร์\nโอเปร่า\nโอเพ่น\nโอ้ย\nโอยัวะ\nโอรสาธิราช\nโอเลี้ยง\nโอวัลติน\nโอเวอร์\nไอซ์\nไอซียู\nไอดอล\nไอเดีย\nไอติม\nฮวงจุ้ย\nฮ่องเต้\nฮองเฮา\nฮอต\nฮ็อต\nฮอตดอก\nฮ็อตด็อก\nฮันนีมูน\nฮัม\nฮัลโลวีน\nฮัลโหล\nฮากกา\nฮาร์ด\nฮาราคีรี\nฮาลาล\nฮาโลวีน\nฮิ\nฮิต\nฮิบรู\nฮิปโป\nฮิปฮอป\nฮีโร่\nฮูลาฮูป\nฮูล่าฮูป\nเฮฟวี\nเฮฟวี่\nเฮอร์ริเคน\nเฮีย\nแฮนด์\nแฮปปี้\nแฮมเบอร์เกอร์\nโฮป\nโฮม\nโฮลดิงส์\nโฮลวีต\nโฮสเตส\nไฮกุ\nไฮแจ็ค\nไฮโซ\nไฮเทค\nไฮบริด\nไฮเปอร์\nไฮไลต์\nไฮไลท์\nไฮเวย์\nไฮสคูล\nไฮเอนด์\n\nกรีซ\nกัมพูชา\nกัวเตมาลา\nกาตาร์\nกานา\nกาบอง\nกายอานา\nกินี\nเกรนาดีนส์\nเกรเนดา\nเกาหลี\nแกมเบีย\nโกตดิวัวร์\nคองโก\nคอโมโรส\nคอสตาริกา\nคาซัคสถาน\nคิตส์\nคิริบาตี\nคิริบาส\nคิวบา\nคีร์กีซสถาน\nคูเวต\nเคนยา\nเคปเวิร์ด\nเคย์แมน\nแคนาดา\nแคเมอรูน\nโครเอเชีย\nโคลอมเบีย\nจอร์เจีย\nจอร์แดน\nจาเมกา\nจิบูตี\nจีน\nชาด\nชิลี\nเช็ก\nซามัว\nซาอุ\nซิมบับเว\nซีเรีย\nซูดาน\nซูรินาเม\nเซนต์\nเซเนกัล\nเซอร์เบีย\nเซาตูเม\nเซียร์รา\nแซมเบีย\nโซมาเลีย\nโซเวียต\nไซปรัส\nญี่ปุ่น\nดารุสซาลาม\nเดนมาร์ก\nโดมินิกัน\nโดมินิกา\nตรินิแดด\nตองกา\nติมอร์\nตุรกี\nตูนิเซีย\nเติร์กเมนิสถาน\nโตโก\nโตเบโก\nไต้หวัน\nทาจิกิสถาน\nแทนซาเนีย\nนอร์เวย์\nนามิเบีย\nนาอูรู\nนิการากัว\nนิวซีแลนด์\nเนเธอร์แลนด์\nเนปาล\nเนวิส\nไนจีเรีย\nไนเจอร์\nบราซิล\nบริติช\nบริเตน\nบรูไน\nบอตสวานา\nบอสเนีย\nบังกลาเทศ\nบังคลาเทศ\nบัลแกเรีย\nบาร์บูดา\nบาร์เบโดส\nบาห์เรน\nบาฮามาส\nบิสเซา\nบุรุนดี\nบูร์กินาฟาโซ\nเบนิน\nเบลเยียม\nเบลารุส\nเบลีซ\nเบอร์มิวดา\nโบลิเวีย\nปรินซิปี\nปากีสถาน\nปานามา\nปาปัวนิวกินี\nปารากวัย\nปาเลสไตน์\nปาเลา\nเปรู\nเปอร์เซีย\nเปอร์โตริโก\nโปรตุเกส\nโปแลนด์\nฝรั่งเศส\nพม่า\nฟิจิ\nฟินแลนด์\nฟิลิปปินส์\nเฟรนช์\nภูฏาน\nภูฐาน\nมองโกเลีย\nมอนเตเนโกร\nมอนแทนา\nมอริเชียส\nมอริเตเนีย\nมอลโดวา\nมอลตา\nมัลดีฟส์\nมาเก๊า\nมาซิโดเนีย\nมาดากัสการ์\nมาร์แชลล์\nมาลาวี\nมาลี\nมาเลเซีย\nเม็กซิโก\nเมียนมาร์\nโมซัมบิก\nโมนาโก\nโมนาโค\nโมร็อกโก\nไมโครนีเซีย\nยูกันดา\nยูโกสลาเวีย\nยูเครน\nเยเมน\nเยอรมนี\nรวันดา\nรัสเซีย\nโรมาเนีย\nลักเซมเบิร์ก\nลัตเวีย\nลาว\nลิกเตนสไตน์\nลิทัวเนีย\nลิเบีย\nลีโอน\nลูเซีย\nเลโซโท\nเลบานอน\nเลสเต\nไลบีเรีย\nวาติกัน\nวานูอาตู\nวินเซนต์\nเวเนซุเอลา\nเวียดนาม\nศรีลังกา\nสเปน\nสโลวะเกีย\nสโลวัก\nสโลวีเนีย\nสวาซิแลนด์\nสวิตเซอร์แลนด์\nสวีเดน\nสหรัฐ\nสหราชอาณาจักร\nสิกขิม\nสิงคโปร์\nอเมริกา\nออสเตรเลีย\nออสเตรีย\nอันดอร์รา\nอัฟกานิสถาน\nอาเซอร์ไบจาน\nอาร์เจนตินา\nอาร์เมเนีย\nอาระเบีย\nอิเควทอเรียล\nอิตาลี\nอินเดีย\nอินโดนีเซีย\nอิรัก\nอิสราเอล\nอิหร่าน\nอียิปต์\nอุซเบกิสถาน\nอุรุกวัย\nเอกวาดอร์\nเอธิโอเปีย\nเอมิเรตส์\nเอริเทรีย\nเอลซัลวาดอร์\nเอสโตเนีย\nแองโกลา\nแอนติกา\nแอลจีเรีย\nแอลเบเนีย\nโอมาน\nไอซ์แลนด์\nไอร์แลนด์\nฮ่องกง\nฮอนดูรัส\nฮังการี\nเฮติ\nเฮอร์เซโกวีนา\n\nกระบี่\nกรุงเทพ\nกาญจนบุรี\nกาฬสินธุ์\nกำแพงเพชร\nขอนแก่น\nจันทบุรี\nฉะเชิงเทรา\nชลบุรี\nชัยนาท\nชัยภูมิ\nชุมพร\nเชียงราย\nเชียงใหม่\nตรัง\nตราด\nตาก\nนครนายก\nนครปฐม\nนครพนม\nนครราชสีมา\nนครศรีธรรมราช\nนครสวรรค์\nนนทบุรี\nนราธิวาส\nน่าน\nบึงกาฬ\nบุรีรัมย์\nปทุมธานี\nประจวบคีรีขันธ์\nปราจีนบุรี\nปัตตานี\nพะเยา\nพังงา\nพัทลุง\nพิจิตร\nพิษณุโลก\nเพชรบุรี\nเพชรบูรณ์\nแพร่\nภูเก็ต\nมหาสารคาม\nมุกดาหาร\nแม่ฮ่องสอน\nยโสธร\nยะลา\nร้อยเอ็ด\nระนอง\nระยอง\nราชบุรี\nลพบุรี\nลำปาง\nลำพูน\nเลย\nศรีสะเกษ\nสกลนคร\nสงขลา\nสตูล\nสมุทรปราการ\nสมุทรสงคราม\nสมุทรสาคร\nสระแก้ว\nสระบุรี\nสิงห์บุรี\nสุโขทัย\nสุพรรณบุรี\nสุราษฎร์\nสุราษฎร์ธานี\nสุรินทร์\nหนองคาย\nหนองบัวลำภู\nอยุธยา\nอ่างทอง\nอำนาจเจริญ\nอุดรธานี\nอุตรดิตถ์\nอุทัยธานี\nอุบลราชธานี\nกันทรลักษ์\nจตุจักร\nไชยา\nซีคอน\nดอนเมือง\nถลาง\nไทรโยค\nธนบุรี\nธัญบุรี\nบางกอก\nบางปะกง\nบางระจัน\nปะทิว\nปาย\nพญาไท\nพัฒน์พงษ์\nพัทยา\nพารากอน\nภูมิซรอล\nรัตนาธิเบศร์\nรังสิต\nลันตา\nลาดพร้าว\nวโรรส\nวิภาวดี\nสตึก\nสมุย\nสัตหีบ\nสิมิลัน\nสุขุมวิท\nสุไหง\nเสลภูมิ\nอังรีดูนังต์\nอ่างขาง\nอินทนนท์\nเอ็มโพเรียม\n\nคิวชู\nแคริบเบียน\nแคสเปียน\nดานูบ\nตะนาวศรี\nนอร์วีเจียน\nนิโคบาร์\nเนรัญชรา\nไนล์\nบอร์เนียว\nบอลติก\nเบงกอล\nปิง\nแปซิฟิก\nมะละกา\nมินดาเนา\nมิสซิสซิปปี\nเมดิเตอร์เรเนียน\nเมโสโปเตเมีย\nยมุนา\nยุโรป\nยูเรเชีย\nยูเรเซีย\nแยงซี\nแยงซีเกียง\nสแกนดิเนเวีย\nสะโตง\nสาละวิน\nสุมาตรา\nสุเอซ\nอะเมซอน\nอันดามัน\nอัลไต\nอาร์กติก\nอาหรับ\nอินโดจีน\nอิรวดี\nอิระวดี\nอีเจียน\nอุษาคเณย์\nอูราล\nเอเชีย\nเอเดรียติก\nเอเวอเรสต์\nแอตแลนติก\nแอนตาร์กติก\nแอนตาร์กติกา\nแอฟริกา\nโอลิมปัส\nไอโอเนียน\nฮวงโห\nฮอกไกโด\nฮอนชู\n\nกบิลพัสดุ์\nกุสินารา\nโกลิยะ\nโกสัมพี\nโคตรบอง\nโคตรบูรณ์\nตองอู\nทรอย\nทวารวดี\nทวาราวดี\nเทวทหะ\nไทรบุรี\nนาลันทา\nไบแซนไทน์\nปรัสเซีย\nปัลลวะ\nปาฏลีบุตร\nพุทธคยา\nมถุรา\nมายัน\nมิถิลา\nราชคฤห์\nล้านช้าง\nล้านนา\nลุมพินี\nวัชชี\nเวสาลี\nสารนาถ\nสาวัตถี\nหริภุญชัย\nอโยธยา\nออตโตมัน\nอังวะ\nอินทปัตถ์\nอุชเชนี\n\nกราฟิก\nกราฟิกส์\nกราฟิค\nกริด\nกิกะไบต์\nกูเกิล\nกูเกิ้ล\nเกตเวย์\nโกลบอล\nคลัสเตอร์\nคลาส\nคลิก\nคลิปอาร์ต\nคอนโซล\nคอนเทนต์\nคอมพิวติ้ง\nคอมไพล์\nคอมไพเลอร์\nคอมมูนิเคชั่น\nคอร์\nคีย์\nคีย์บอร์ด\nเครือข่าย\nเคอร์เซอร์\nเคอร์เนล\nแคช\nแคมฟรอก\nแคมฟร็อก\nแคร็ก\nโค้ด\nจาวา\nจีพีเอส\nชิป\nชิพ\nเชลล์\nแช็ต\nแชนเนล\nแชนแนล\nซ็อกเก็ต\nซอฟต์แวร์\nซอฟท์แวร์\nซอร์ส\nซัพพอร์ต\nซัพพอร์ท\nซีดี\nซีดีรอม\nซีเนอร์\nเซิร์ฟเวอร์\nโซลูชัน\nโซลูชั่น\nไซต์\nไซเบอร์\nทรานแซกชัน\nทรานแซกชั่น\nทรานแซ็กชัน\nทรานแซ็กชั่น\nทรานแซคชัน\nทรานแซคชั่น\nทรานแซ็คชัน\nทรานแซ็คชั่น\nทวิตเตอร์\nทวีต\nทัชแพด\nเทมเพลต\nเทอร์มินัล\nแท็ก\nแท็บ\nแทบเล็ต\nโทรจัน\nเน็ต\nเน็ตบุ๊ก\nเน็ตบุค\nเน็ตบุ๊ค\nเน็ตเวิร์ก\nเน็ตเวิร์ค\nโน้ตบุ๊ก\nโน้ตบุค\nโน้ตบุ๊ค\nดอส\nดาวน์เกรด\nดาวน์โหลด\nดิจิตอล\nดิจิทัล\nดีบั๊ก\nดีวีดี\nดีไวซ์\nเดเบียน\nเดลไฟ\nเดสก์ท็อป\nโดเมน\nไดรว์\nไดรเวอร์\nไดเรกทอรี\nไดโอด\nเทเลคอม\nบล็อกเกอร์\nบรอดแบนด์\nบราวเซอร์\nบลูทูท\nบลูทูธ\nบลูเรย์\nบั๊ก\nบัฟเฟอร์\nบิต\nบิท\nบูต\nเบราว์เซอร์\nแบนด์วิดท์\nไบต์\nไบนารี\nโปรแกรมเมอร์\nโปรเซส\nโปรเซสเซอร์\nโปรโตคอล\nพร็อกซี\nพอร์ต\nพอร์ท\nพาเนล\nพาร์ทิชัน\nพารามิเตอร์\nพาสเวิร์ด\nพิกเซล\nเพจ\nเพจเจอร์\nแพกเก็ต\nแพตช์\nแพลตฟอร์ม\nโพรเซส\nโพรเซสเซอร์\nโพรโทคอล\nไพธอน\nฟล็อปส์\nฟอนต์\nฟอร์แมต\nฟอร์เวิร์ด\nฟอรัม\nฟีเจอร์\nเฟซบุ๊ก\nเฟิร์มแวร์\nแฟล็ก\nโฟลเดอร์\nไฟร์ฟอกซ์\nไฟร์วอลล์\nไฟล์\nมอดูล\nมอนิเตอร์\nมัลติ\nมัลติทัช\nมัลติเพล็กซ์\nมัลแวร์\nมาสเตอร์\nมีเดีย\nเมนู\nเมมโมรี\nเมล\nเมาส์\nแมค\nโมดูล\nโมเด็ม\nโมบาย\nโมบายล์\nโมไบล์\nไมโครซอฟท์\nยูนิกซ์\nยูนิโคด\nยูนิโค้ด\nริงโทน\nรีเฟรช\nรีเลย์\nเราเตอร์\nเรียลไทม์\nลิงก์\nลินุกซ์\nลีนุกซ์\nลูป\nเลเยอร์\nแล็ปท็อป\nไลเซนส์\nไลบรารี\nวิกิ\nวิกิพีเดีย\nวินโดวส์\nวินโดว์ส\nเว็บ\nเวอร์ชวล\nเวอร์ชัน\nเวอร์ชั่น\nเวิร์กสเตชัน\nเวิร์กสเตชั่น\nเวิร์คสเตชัน\nเวิร์คสเตชั่น\nเวิร์ด\nเวิร์ม\nไวแมกซ์\nสกรีน\nสแกน\nสแกนเนอร์\nสแต็ก\nสนิฟเฟอร์\nสปายแวร์\nสเปซ\nสแปม\nสมาร์ท\nสล็อต\nเสิร์ช\nโหลด\nออนไลน์\nออปติก\nออปติคอล\nออปติคัล\nออฟไลน์\nออราเคิล\nอัพเกรด\nอัพเดต\nอัพโหลด\nอัปเกรด\nอัปเดต\nอัปโหลด\nอัลกอริทึม\nอาร์กิวเมนต์\nอินเตอร์เน็ต\nอินทิเกรเตอร์\nอินเทอร์เน็ต\nอินเทอร์เฟซ\nอินเทล\nอินพุต\nอินพุท\nอีเมล\nอีเมล์\nอูบุนตู\nอูบันตู\nเอนจิน\nเอ็นจิน\nเอาต์พุต\nเอาต์พุท\nเอาท์พุต\nเอาท์พุท\nแอนะล็อก\nแอนิเมชัน\nแอนิเมชั่น\nแอปพลิเคชัน\nแอปพลิเคชั่น\nแอพพลิเคชัน\nแอพพลิเคชั่น\nแอสเซมบลี\nแอสเซมเบลอร์\nโอเพน\nไอคอน\nไอซี\nไอพอด\nไอพ็อด\nไอแพด\nไอโฟน\nฮับ\nฮาร์ดดิสก์\nฮาร์ดแวร์\nแฮกเกอร์\nแฮ็กเกอร์\nแฮนด์เฮลด์\nโฮสต์\n\nกรีก\nกัณณาฑ\nกัศมีรี\nคันจิ\nคาตาคานะ\nคุชราตี\nคุรุมุขี\nซีริลลิก\nซูลู\nเซลติก\nเซิร์บ\nตากาล็อก\nเตลุคู\nเติร์ก\nทราวิฑ\nเบงกาลี\nปัญจาบี\nพินอิน\nมลยาฬัม\nมองโกล\nมาราฐี\nมาเลย์\nเม็กซิกัน\nแมนจู\nแมนดาริน\nไมถิลี\nเยอรมัน\nรัสเซียน\nสวาฮิลี\nสวิส\nสินธี\nอูรดู\nอัสสมี\nอารบิก\nอิตาเลียน\nอุยกูร์\nแอฟริกัน\nโอริยา\nไอริช\nฮันกึล\nฮินดี\nฮิรางานะ\nฮีบรู\n\nกรีนพีซ\nกรือเซะ\nกวนอิม\nกวนอู\nกัดดาฟี\nกัลยาณวัตร\nกัสสปะ\nกาลิเลโอ\nกินเนส\nกุมภกรรณ\nกูเตนเบิร์ก\nเกตส์\nเกษมณี\nโกณฑัญญะ\nขงเบ้ง\nคริสโตเฟอร์\nคองคอร์ด\nคอลเกต\nคานธี\nคาเบรียล\nคาร์ฟูร์\nคาร์สัน\nคาราบาว\nคาสิโอ\nคิริน\nคุนลุ้น\nคูโบต้า\nเครมลิน\nแคทรีนา\nโคตมะ\nโคตมี\nโคลัมบัส\nไคฟง\nไครสเลอร์\nง้อไบ๊\nจตุพร\nจ็อบส์\nจอห์น\nจิ้น\nจิม\nจิ๋ม\nจิว\nจุฬาภรณ์\nจุฬาลงกรณ์\nเจมส์\nแจ็กสัน\nโจเซฟ\nโจว\nชมัยมรุเชฐ\nชมัยมรุเชษฐ์\nชเวดากอง\nชาร์ลส์\nชินราช\nชินวัตร\nชุนชิว\nเช็ง\nเชตวัน\nเชฟรอน\nเชฟโรเลต\nเชลซี\nไชยานุชิต\nซ่ง\nซังฮี้\nซัดดัม\nซันซิล\nซัมซุง\nซัวเจ๋ง\nซินหัว\nซีซาร์\nซีแพค\nซูซาน\nซูซูกิ\nซูบารุ\nเซ็นทารา\nเซเวน\nเซเว่น\nโซฟิเทล\nโซยุซ\nโซยูซ\nณัฐวุฒิ\nดาร์ลี่\nดาวโจนส์\nดิสนีย์\nดีแทค\nดูปองท์\nเดโมแครต\nเดลล์\nเดลินิวส์\nเดวิด\nแดวู\nโดนัลด์\nโดราเอมอน\nโดเรมอน\nต๋อง\nตั๊กม้อ\nตากสิน\nตาเมือน\nตาลีบัน\nตูน\nเตมีย์\nโต๋\nโตชิบา\nโตโยต้า\nถังซัมจั๋ง\nถังซำจั๋ง\nทรพา\nทราเวล\nทรูมูฟ\nทีปังกร\nเทปโก\nเทพรัตน\nเทวทัต\nเทสโก้\nโทมัส\nไททานิก\nไททานิค\nไทยรัฐ\nธีออส\nนครินทรา\nนโปเลียน\nนพดล\nนราดูร\nนเรนทร\nนอสตราดามุส\nนาซา\nนาซ่า\nนาซี\nนาโต\nนาโต้\nนาลแก\nนิคอน\nนิโคลัส\nนิด้า\nนินเทนโด\nนิปปอน\nนิวตัน\nนิสสัน\nเนคเทค\nเนชั่น\nเนชันแนล\nเนชั่นแนล\nเนวิน\nเนสเล่\nเนสาด\nแนท\nแนสแดค\nโนเกีย\nโนเบล\nโนเวลล์\nโนโวเทล\nไนเม็กซ์\nบรอดเวย์\nบรัดเลย์\nบรู๊ซ\nบัลเมอร์\nบารัก\nบารัค\nบู๊ตึ๊ง\nเบญกาย\nเบนซ์\nเบ็นซ์\nเบนจามิน\nโบตัน\nไบโอเทค\nประชาธิปัตย์\nปวีณา\nปอเต็กตึ๊ง\nปอเต๊กตึ๊ง\nป่อเต็กตึ๊ง\nปัตตะโชติ\nปารุสก์\nปีเตอร์\nปูติน\nเป๊ปซี่\nเป้ย\nเปอร์โยต์\nเปาบุ้นจิ้น\nโปเกมอน\nโป๊ยก่าย\nพรหมทัต\nพลาโต\nพอลล่า\nพานาโซนิค\nพานาโซนิก\nพิทยานุกูล\nพิมพิสาร\nเพนแทกซ์\nเพลโต\nไพโอเนียร์\nฟรอยด์\nฟรังซิส\nฟรานซิส\nฟลอเรนซ์\nฟอร์ด\nฟิลิปส์\nฟูจิ\nแฟซ่า\nโฟร์โมสต์\nภูมิพล\nภูริทัต\nมงฟอร์ต\nมณโฑ\nมติชน\nมหิตลาธิเบศร\nมโหสถ\nมัจฉานุ\nมาร์กาเร็ต\nมาร์ติน\nมาสด้า\nมิตซูบิชิ\nมิราเคิล\nมุสโสลินี\nเม้ง\nเมจิ\nเมอร์ซีเดส\nเมอร์เซเดส\nแมกซ์เวลล์\nแมกไซไซ\nแมคอินทอช\nแมชีนเนอรี่\nโมคคัลลานะ\nโมโตโรลา\nโมโตโรล่า\nโมเนีย\nไมเคิล\nไมยราพณ์\nยโสธรา\nยะโฮวา\nยะโฮวาห์\nยามาฮ่า\nยาเวห์\nยาฮู\nยูนิเซฟ\nยูเนสโก\nยูไล\nเยโฮวาห์\nรอยเตอร์\nรอยัล\nรัชดา\nรัสปูติน\nราฟาเอล\nรามาวตาร\nราเมศวร\nราหุล\nริชาร์ด\nรีพับลิกัน\nรูนีย์\nเรนโบว์\nแรมโบ้\nโรตารี\nโรนัลโด\nโรนัลโด้\nโรบินสัน\nโรเบิร์ต\nล็อกซเล่ย์\nลิงคอล์น\nลิจฉวี\nลิไท\nลิไทย\nลินคอล์น\nลิเวอร์พูล\nเลโนโว\nเลียดก๊ก\nโลตัส\nวชิราลงกรณ์\nวลาดิเมียร์\nวอลล์สตรีท\nวาเลนไทน์\nวิกตอเรีย\nวิทยานุสรณ์\nวิทยายน\nวิมเบิลดัน\nวิลเลียม\nวีระ\nวุฒิชัย\nเวียดกง\nไวตามิลค์\nศกุนตลา\nศรีวิชัย\nศิริพงษ์\nศิริราช\nศุภชลาศัย\nสดกก๊อกธม\nสดายุ\nสตาลิน\nสตีฟ\nสแตนฟอร์ด\nสวรินทิรา\nสังกัจจายน์\nสาทิตย์\nสารีบุตร\nสิริกิติ์\nสิรินธร\nสิหิงค์\nสีวลี\nสีหนุ\nสีหมุนี\nสีหโมนี\nสุครีพ\nสุทโธทนะ\nสุเทพ\nสุนทราภรณ์\nสุนีย์\nสุรนารี\nสุรยุทธ์\nสุริยาสน์\nเส้าหลิน\nโสกราตีส\nโสภิต\nหนุมาน\nหลินฮุ่ย\nหลุยส์\nเห้งเจีย\nไหหม่า\nองคต\nองคุลิมาล\nอชาตศัตรู\nอดุลยเดช\nอพอลโล\nอริสโตเติล\nอริสมันต์\nอลิซาเบธ\nอ๋อม\nออร์คิด\nออสการ์\nอะพอลโล\nอับราฮัม\nอั้ม\nอัลกออิดะห์\nอัลคาเทล\nอัลจาซีราห์\nอัลเฟรด\nอัลเลาะห์\nอัสซุส\nอัสสชิ\nอัสสัมชัญ\nอาเซม\nอาเซ็ม\nอาเซียน\nอาฟต้า\nอาร์เซนอล\nอินทรชิต\nอินทราทิตย์\nอีซูซุ\nอีเลฟเวน\nอีเลฟเว่น\nอุณรุท\nอุบลรัตน์\nอุบาลี\nอุ๋ย\nเอกทัศน์\nเอเซอร์\nเอ็ดเวิร์ด\nเอดิสัน\nเอแบค\nเอลิซาเบธ\nเอสพลานาด\nเอสพลานาร์ด\nแอคคอร์\nแอคคอร์ด\nแองเจลิน่า\nแอตแลนติส\nแอน\nแอ๋ม\nแอมบาสซาเดอร์\nแอมบาสเดอร์\nแอมเวย์\nแอ๋ว\nโอดีสซีย์\nโอเดียน\nโอบามา\nโอรสาราม\nโอลิมเปีย\nโออิชิ\nไอน์สไตน์\nฮอนด้า\nฮอปกินส์\nฮอลลีวูด\nฮอลลีวู้ด\nฮานามิ\nฮามาส\nฮิตเลอร์\nฮิตาชิ\nฮุนเซน\nฮุนเซ็น\nฮุนได\nฮุสเซ็น\nเฮนรี\nเฮนรี่\nเฮเลน\nโฮจิมินห์\nโฮปเวลล์\nโฮเมอร์\n\nกลีเซอรีน\nกำทอน\nแกนีมีด\nครอส\nคริสตัล\nคลอโรพลาสต์\nคลอไรด์\nควอนตัม\nคอนดักเตอร์\nคอปเปอร์\nคอลลาเจน\nคอเลสเตอรอล\nคอสมิก\nคูลอมบ์\nเคอราติน\nแคโรทีน\nแคสสินี\nโครมาโทกราฟี\nไคโตซาน\nจีโนม\nจุลชีววิทยา\nชิคุนกุนยา\nซัลฟิวริก\nซัลเฟต\nซัลไฟด์\nซิงค์\nซิริอุส\nซิลิกา\nซิลิเกต\nซิลิโคน\nซีเทน\nซีเวิร์ต\nเซ็กเตอร์\nเซ็กเมนต์\nเซมิ\nโซนาร์\nไซบอร์ก\nดอปเปลอร์\nดอปเพลอร์\nไดนามิก\nไดนามิกส์\nไดนามิค\nไดนามิคส์\nไดออกไซด์\nทรานส์\nทามิฟลู\nเทฟลอน\nเทสโทสเตอโรน\nเทอร์โม\nแทนนิน\nไททัน\nไทฟอยด์\nไทรอยด์\nธาลัสซีเมีย\nเนกาตีฟ\nโนวา\nบอแรกซ์\nโบทอกซ์\nโบท็อกซ์\nไบโอติน\nปฏิยานุพันธ์\nโปรเจสเตอโรน\nพอลิเมอร์\nพันธุศาสตร์\nพัลซาร์\nพาร์กินสัน\nพาราเซตามอล\nพาราโบลา\nเพอร์ออกไซด์\nโพรเจสเทอโรน\nโพลาไรซ์\nโพลิเมอร์\nโพลีเอทิลีน\nไพรเมต\nฟลาโวนอยด์\nฟลูออเรสเซนซ์\nฟลูออไรด์\nฟอสซิล\nฟิชชัน\nฟิวชัน\nฟีโรโมน\nไฟเบอร์\nมอนอกไซด์\nมิราจ\nเมตริกซ์\nเมทริกซ์\nเมลานิน\nเมลามีน\nโมเมนตัม\nไมโตคอนเดรีย\nไมโทคอนเดรีย\nยีสต์\nยูริก\nยูเรีย\nรูมาตอยด์\nวีก้า\nเวกเตอร์\nเวก้า\nเวสิเคิล\nโวลต์\nสเกล\nสเกลาร์\nสเต็ม\nสเตียรอยด์\nสปีชีส์\nสเปิร์ม\nสัมพัทธภาพ\nสุริยจักรวาล\nออกเทน\nออโรรา\nออโรร่า\nอะคริลิก\nอะครีลิก\nอะซีติก\nอะซีโตน\nอะมิโน\nอะลูมินา\nอันโดรเมดา\nอัลคาไลน์\nอัลตราซาวด์\nอัลตราซาวนด์\nอัลลอยด์\nอินทิกรัล\nอินทิเกรต\nอีโบลา\nอีโบล่า\nเอ็กซ์โพเนนเชียล\nเอทานอล\nเอทิลีน\nเอนโทรปี\nเอสเตอร์\nเอสโตรเจน\nเอสโทรเจน\nแอนดรอยด์\nแอนแทร็กซ์\nแอมพลิจูด\nแอมโมเนียม\nแอลกอฮอลิซึม\nแอสพาร์แตม\nโอเซลทามิเวียร์\nฮับเบิล\nฮิวมัส\nไฮดรอกไซด์\nไฮดรอลิก\nไฮโดรลิก\nไฮเพอร์โบลา\n\nกงเต็ก\nกฎุมพี\nกฏ\nกฏหมาย\nกบฎ\nกราไฟต์\nก๊อปปี้\nกะทะ\nกังวาล\nกุฎฐัง\nกุฏุมพี\nฃวด\nคฑา\nคลินิค\nคลีนิก\nคลีนิค\nคาทอลิค\nคึ่นฉ่าย\nแคตตาล็อก\nโควต้า\nฅน\nจุมพฎ\nช็อคโกแลต\nแซ่ด\nดัทช์\nทนง\nทรมาณ\nทราก\nทะแยง\nทิฏฐิ\nบล็อค\nบ๊องแบ๊ว\nบัลเล่ต์\nเบรค\nแบงค์\nปรากฎ\nปัคคหะ\nปาฏิโมกข์\nปิติ\nปิรามิด\nโปรเตสแตนท์\nพนิช\nพยักเพยิด\nพริ้ว\nพลูโตเนียม\nพากษ์\nเฟิร์น\nยากี้\nเยภุยยสิกา\nรุสเซีย\nฤาษี\nล็อค\nล็อคเกอร์\nวันทยาหัตถ์\nวานิช\nวิญญาน\nวิศิษฐ์\nศรีษะ\nสเปกโทรสโคป\nสฤษฎ์\nสลิ่ม\nสัตตสดก\nสาราณียากร\nสุกี้\nสุกี้ยากี้\nสูญญากาศ\nหยอมแหยม\nหยอย\nหล่ะ\nอะดรีนาลีน\nอะหลั่ย\nอัตคัต\nอัฟริกา\nอานิสงค์\nอาฟริกา\nอิริยาบท\nอิเลคโทรนิคส์\nอีรุงตุงนัง\nอุตรายัน\nอุลตรา\nอุลตร้า\nโอกาศ\n\nกกหู\nกงการ\nกงจักร\nกฎเกณฑ์\nกฎบัตร\nกฎหมาย\nกฎหมู่\nกดขี่\nกดดัน\nก้นกบ\nก้นบึ้ง\nก้นปล่อง\nกนิษฐภคินี\nกนิษฐภาดา\nกรงเล็บ\nกรมการ\nกรมท่า\nกรมธรรม์\nกรมนา\nกรมวัง\nกรรมกร\nกรรมการ\nกรรมฐาน\nกรรมบถ\nกรรมพันธุ์\nกรรมวิธี\nกรรมสิทธิ์\nกระจกเงา\nกระจกตา\nกระจกนูน\nกระจกเว้า\nกระจอกชวา\nกระจอกเทศ\nกระจ้อยร่อย\nกระจับบก\nกระจับปิ้ง\nกระจับปี่\nกระโชกโฮกฮาก\nกระดานดำ\nกระดานหก\nกระดาษแก้ว\nกระดาษไข\nกระดาษทราย\nกระดาษสา\nกระดูกงู\nกระทาชาย\nกระเท่เร่\nกระบวนการ\nกระบองเพชร\nกระผีกริ้น\nกระยาทิพย์\nกระยาสารท\nกระยาหาร\nกระสอบทราย\nกระสุนปืน\nกระแสจิต\nกระแสน้ำ\nกระแสลม\nกรับคู่\nกรับพวง\nกรับเสภา\nกราดเกรี้ยว\nกราวรูด\nกริ่งเกรง\nกรีฑาสถาน\nกรีดกราย\nกรี๊ดกร๊าด\nกลไก\nกลบท\nกลเม็ด\nกลยุทธ์\nกลวิธี\nกลศาสตร์\nกลอักษร\nกลบเกลื่อน\nกลมกล่อม\nกลมกลืน\nกลมเกลียว\nกล้วยแขก\nกล้วยไม้\nกล่องเสียง\nกล่อมเกลา\nกล่อมเกลี้ยง\nกลัดกลุ้ม\nกลัดมัน\nกลั่นกรอง\nกลั่นแกล้ง\nกลับกลอก\nกลางคน\nกลางคัน\nกลางค่ำ\nกลางคืน\nกลางแจ้ง\nกลางแปลง\nกลางวัน\nกลาดเกลื่อน\nกล่าวขวัญ\nกล่าวโทษ\nกล่าวหา\nกล้ำกราย\nกล้ำกลืน\nกลิ้งกลอก\nกลิ้งเกลือก\nกลิ่นอาย\nกลียุค\nกวดขัน\nกวัดแกว่ง\nกวัดไกว\nกวีนิพนธ์\nก่อกวน\nก่อการ\nก่อตั้ง\nก่อสร้าง\nก่อหวอด\nก้อร่อก้อติก\nกองกลาง\nกองเกิน\nกองโจร\nกองทัพ\nกองทุน\nกองพล\nกองพัน\nกองฟอน\nกองร้อย\nกองหนุน\nกอบโกย\nกะเกณฑ์\nกะบังลม\nกักกัน\nกักขัง\nกักตัว\nกักตุน\nกัณฑ์เทศน์\nกัดฟัน\nกันชน\nกันท่า\nกันสาด\nกันเอง\nกับแกล้ม\nกับข้าว\nกับระเบิด\nกากเพชร\nกากหมู\nกาชาด\nกาญจนาภิเษก\nก้านคอ\nกาฝาก\nก้ามกราม\nกามกิจ\nกามคุณ\nกามเทพ\nกามโรค\nก้ามปู\nกายกรรม\nกายบริหาร\nกายภาพ\nกายวิภาค\nกายสิทธิ์\nก่ายกอง\nการคลัง\nการเงิน\nการบ้าน\nการเปรียญ\nการเมือง\nการเรือน\nการละเล่น\nกาลกิริยา\nกาลเทศะ\nก้าวก่าย\nก้าวร้าว\nก้าวหน้า\nกาสาวพัสตร์\nกาฬพฤกษ์\nกาฬโรค\nกำปั้น\nกำมือ\nกำแพงขาว\nกำลังใจ\nกำลังม้า\nกี่งอำเภอ\nกิจกรรม\nกิจการ\nกิจวัตร\nกิจจะลักษณะ\nกิตติคุณ\nกิตติศัพท์\nกินขาด\nกินใจ\nกินดอง\nกินโต๊ะ\nกินแบ่ง\nกินเปล่า\nกินเลี้ยง\nกินเส้น\nกินแหนง\nกิโลกรัม\nกิโลเมตร\nกิโลลิตร\nกิโลเฮิรตซ์\nกีดกัน\nกีดกั้น\nกีดขวาง\nกึกก้อง\nกึกกัก\nกึกกือ\nกึ่งกลาง\nกุกกัก\nกุ๊กกิ๊ก\nกุ้งฝอย\nกุ้งมังกร\nกุ้งแห้ง\nกุ้งเต้น\nกุ้งยิง\nกุญแจผี\nกุญแจมือ\nกุญแจเลื่อน\nกุญแจเสียง\nกุลธิดา\nกุลบุตร\nกุลสตรี\nกู้ยืม\nเก้งก้าง\nเก็บกวาด\nเก็บเกี่ยว\nเก็บงำ\nเก็บตก\nเกรงกลัว\nเกรงใจ\nเกรงขาม\nเกรียงไกร\nเกรียมกรม\nเกรี้ยวกราด\nเกล็ดเลือด\nเกลี้ยกล่อม\nเกลี้ยงเกลา\nเกลือกกลั้ว\nเกลือกกลิ้ง\nเกลื่อนกล่น\nเกลื่อนกลาด\nเกศธาตุ\nเกษตรกร\nเกษตรกรรม\nเกษตรศาสตร์\nเกษมสันต์\nเกษียรสมุทร\nเก้อเขิน\nเกาะแกะ\nเกี่ยงงอน\nเกียจคร้าน\nเกียรติคุณ\nเกียรตินิยม\nเกียรติประวัติ\nเกียรติภูมิ\nเกียรติยศ\nเกียรติศักดิ์\nเกียรติมุข\nเกี่ยวข้อง\nเกี่ยวดอง\nเกี่ยวพัน\nเกี่ยวโยง\nเกี้ยวพาน\nเกี้ยวพาราสี\nแก่แดด\nแก้ขัด\nแก้ไข\nแก้ตัว\nแก้เผ็ด\nแก้ลำ\nแกงคั่ว\nแกงจืด\nแกงบวด\nแกงป่า\nแกงเผ็ด\nแกงส้ม\nแก่งแย่ง\nแก่นแก้ว\nแก่นสาร\nแก้วตา\nแก้วหู\nแกว่งกวัด\nแกว่งไกว\nแกะรอย\nโก้เก๋\nโกรธเกรี้ยว\nโกรธขึ้ง\nไก่เขี่ย\nไก่ชน\nไก่บ้าน\nไก่ป่า\nไก่ฟ้า\nไกล่เกลี่ย\nขจัดขจาย\nขนเพชร\nขนสัตว์\nขนหนู\nขนส่ง\nขนมจีน\nขบขัน\nขบวนการ\nข่มขี่\nข่มขู่\nข่มขืน\nข่มเหง\nขมหิน\nขมิ้นชัน\nขมุบขมิบ\nขยะแขยง\nขยักขย่อน\nขยักขย้อน\nขยับขยาย\nขยับเขยื้อน\nขวดโหล\nขวยเขิน\nขวัญใจ\nขวัญตา\nขวัญอ่อน\nขวากหนาม\nขวางโลก\nของ้าว\nขอสับ\nขอขมา\nขอทาน\nขอโทษ\nขอร้อง\nขออภัย\nข้อเขียน\nข้อความ\nข้อเท็จจริง\nของกลาง\nของขวัญ\nของแข็ง\nของชำ\nของลับ\nของเล่น\nของว่าง\nของเหลว\nของไหล\nของไหว้\nข้องใจ\nข้องแวะ\nขอบข่าย\nขอบเขต\nขอบคุณ\nขอบใจ\nขอบพระคุณ\nข้อมูล\nข้อแม้\nข้อหา\nข้อสังเกต\nขัดข้อง\nขัดขืน\nขัดเขิน\nขัดจังหวะ\nขัดดอก\nขัดแตะ\nขัดยอก\nขัดแย้ง\nขัดสมาธิ\nขันหมาก\nขันอาสา\nขับขี่\nขับเคี่ยว\nขั้วโลก\nขาจร\nขาประจำ\nข้าราชการ\nข้าศึก\nข้าหลวง\nขาดแคลน\nขาดใจ\nขาดตอน\nขาดตัว\nขาดทุน\nขาดเหลือ\nขายหน้า\nข่าวกรอง\nข่าวคราว\nข่าวล่า\nข่าวลือ\nข่าวสาร\nข้าวเกรียบ\nข้าวแกง\nข้าวของ\nข้าวจี่\nข้าวเจ้า\nข้าวซอย\nข้าวต้ม\nข้าวตอก\nข้าวตัง\nข้าวแตน\nข้าวทิพย์\nข้าวบิณฑ์\nข้าวเปลือก\nข้าวโพด\nข้าวฟ่าง\nข้าวเม่า\nข้าวสวย\nข้าวสาร\nข้าวเหนียว\nข้าวหมาก\nข้าวหลาม\nขี้เกียจ\nขี้ข้า\nขี้ครอก\nขี้คร้าน\nขี้คุก\nขี้ไคล\nขี้เซา\nขีดขั้น\nขีดคร่อม\nขีดคั่น\nขีดฆ่า\nขี้ตา\nขี้ตืด\nขี้เถ้า\nขี้ทูด\nขี้ปะติ๋ว\nขี้ผึ้ง\nขี้มูก\nขี้ยา\nขี้แย\nขี้ริ้ว\nขี้เรื้อน\nขี้เล็บ\nขี้หู\nขี้หน้า\nขี้เหนียว\nขี้เหล็ก\nขี้เหร่\nขึงขัง\nขึงพืด\nขึ้งโกรธ\nขึ้นใจ\nขึ้นชื่อ\nขึ้นมือ\nขืนใจ\nขื่นขม\nขุดคุ้ย\nขุนทอง\nขุนนาง\nขุนพล\nขุนศึก\nขุนหลวง\nขูดรีด\nเข็มกลัด\nเข็มขัด\nเข้มข้น\nเข้มแข็ง\nเข้มงวด\nเข็มทิศ\nเข็มหมุด\nเข้าขา\nเข้าเค้า\nเข้าใจ\nเข้าชื่อ\nเข้าตัว\nเข้าถึง\nเข้าทรง\nเข้าท่า\nเข้าที\nเข้าเนื้อ\nเข้าเล่ม\nเขียวเสวย\nเขียวหวาน\nแขกเต้า\nแข็งกร้าว\nแข็งกล้า\nแข็งแกร่ง\nแข็งข้อ\nแข็งขัน\nแข่งขัน\nแข็งใจ\nแข็งตัว\nแข็งเมือง\nแข็งแรง\nแขวนลอย\nโขกสับ\nโขยกเขยก\nไขข้อ\nไขควง\nไขมัน\nไข่มุก\nไขว่ห้าง\nไขสันหลัง\nไขสือ\nไข่เค็ม\nไข่เยี่ยวม้า\nไข่หงส์\nไข่เหี้ย\nคงกระพัน\nคงตัว\nคงทน\nคงที่\nคชลักษณ์\nคชสาร\nคชสีห์\nคชราช\nคณิตศาสตร์\nคดเคี้ยว\nคติธรรม\nคติพจน์\nคนกลาง\nคนไข้\nคนใช้\nคนทรง\nคบไฟ\nคบเพลิง\nคบค้า\nคบคิด\nคบหา\nคมคาย\nครบครัน\nครบถ้วน\nครอบครอง\nครอบคลุม\nครอบครัว\nครอบงำ\nครอบจักรวาล\nคริสตกาล\nคริสตจักร\nคริสต์มาส\nคริสต์ศตวรรษ\nคริสต์ศักราช\nคริสตัง\nคริสเตียน\nครุกรรม\nครุภัณฑ์\nครุศาสตร์\nครุฑพ่าห์\nครุ่นคิด\nคลอเคลีย\nคล่องแคล่ว\nคล่องตัว\nคล่องมือ\nคลั่งไคล้\nคลาคล่ำ\nคลาไคล\nคลาดเคลื่อน\nคลาดแคล้ว\nคลี่คลาย\nคลึงเคล้น\nคลึงเคล้า\nคลื่นไส้\nคลื่นเหียน\nคลุกคลี\nคลุกคลาน\nคลุมเครือ\nคลุมโปง\nคลุ้มคลั่ง\nควงสว่าน\nควบคุม\nควบคู่\nควบแน่น\nควันหลง\nความคิด\nความหลัง\nความเห็น\nคอหอย\nคอห่าน\nคอแห้ง\nค่อนขอด\nค่อนแคะ\nค้อนควัก\nคั่งค้าง\nคั่งแค้น\nคัดค้าน\nคัดง้าง\nคัดท้าย\nคัดเลือก\nคันจาม\nคันฉ่อง\nคันฉาย\nคันชัก\nคันชั่ง\nคันไถ\nคันนา\nคันเร่ง\nคับขัน\nคับคั่ง\nคับแค้น\nคับแคบ\nคางทูม\nคางหมู\nค้างคืน\nค้างปี\nคาดคั้น\nคาดเชือก\nคาดโทษ\nคาดหมาย\nคานหาม\nคาบเกี่ยว\nคาบศิลา\nคาบสมุทร\nคำขาด\nคำนำ\nคิดค้น\nคืนดี\nคืนตัว\nคุกเข่า\nคุณค่า\nคุณชาย\nคุณธรรม\nคุณนาย\nคุณภาพ\nคุณลักษณะ\nคุณวุฒิ\nคุณศัพท์\nคุณสมบัติ\nคุณหญิง\nคุณากร\nคุณูปการ\nคุโณปการ\nคุมเชิง\nคุ้มกัน\nคุยเขื่อง\nคุยโต\nคุ้ยเขี่ย\nคุ้มครอง\nคู่กรณี\nคู่กัด\nคู่ขา\nคู่แข่ง\nคู่ครอง\nคู่ควร\nคู่คิด\nคู่คี่\nคู่ใจ\nคู่ชีพ\nคู่ชีวิต\nคู่บารมี\nคู่บุญ\nคู่ปรปักษ์\nคู่ปรับ\nคู่ผสม\nคู่มือ\nคู่รัก\nคู่ลำดับ\nคู่สาย\nคู่หมั้น\nคู่หู\nคู่อริ\nคู่อาฆาต\nเคมีภัณฑ์\nเคยตัว\nเคร่งขรึม\nเคร่งครัด\nเคร่งเครียด\nเครดิตฟองซิเอร์\nเครื่องกล\nเครื่องกัณฑ์\nเครื่องแกง\nเครื่องเขิน\nเครื่องครัว\nเครื่องเคียง\nเครื่องเงิน\nเครื่องจักร\nเครื่องเซ่น\nเครื่องดนตรี\nเครื่องต้น\nเครื่องทุ่นแรง\nเครื่องเทศ\nเครื่องใน\nเครื่องบิน\nเครื่องบูชา\nเครื่องแบบ\nเครื่องประดับ\nเครื่องปรุง\nเครื่องปรุงรส\nเครื่องมือ\nเครื่องยนต์\nเครื่องร่อน\nเครื่องราง\nเครื่องเรือน\nเครื่องล่าง\nเครื่องเล่น\nเครื่องสาย\nเครื่องสำอาง\nเครื่องสุกำศพ\nเครื่องหมาย\nเครือรัฐ\nเคลียคลอ\nเคลื่อนที่\nเคลื่อนไหว\nเคลือบแคลง\nเคลือบแฝง\nเคลือบฟัน\nเคว้งคว้าง\nเคหสถาน\nเค้าโครง\nเคียดแค้น\nเคี่ยวเข็ญ\nเคี้ยวเอื้อง\nเคืองขุ่น\nโคนม\nโคบาล\nโคมูตร\nโคมลอย\nโครงการ\nโครงเรื่อง\nโครงงาน\nโครงสร้าง\nโครมคราม\nโคลงเคลง\nฆ้องกระแต\nฆ้องชัย\nฆ้องวง\nฆ้องหุ่ย\nฆ้องเหม่ง\nฆ้องโหม่ง\nฆาตกร\nฆาตกรรม\nฆานประสาท\nงงงวย\nงงงัน\nงดเว้น\nงบดุล\nงบประมาณ\nงมโข่ง\nงมงาย\nง่วงงุน\nง่วงเหงา\nงอหาย\nง้องอน\nงอนง้อ\nงอมแงม\nงาช้าง\nง่าเงย\nงานการ\nง่ายดาย\nงึมงำ\nเงินเดือน\nเงินตรา\nเงินยวง\nเงียบกริบ\nเงียบเชียบ\nเงียบเหงา\nเงื่องหงอย\nเงื่อนไข\nเงื่อนงำ\nเงื่อนเวลา\nเงื้อมมือ\nแง่งอน\nจงใจ\nจงรัก\nจดจ่อ\nจดจำ\nจดหมาย\nจดหมายเหตุ\nจรจัด\nจรรยาบรรณ\nจริงจัง\nจริงใจ\nจอมขวัญ\nจอมใจ\nจอมทัพ\nจอมปลวก\nจอมพล\nจ๊ะเอ๋\nจักสาน\nจักรพรรดิ\nจักรภพ\nจักรยาน\nจักรยานยนต์\nจักรราศี\nจักรวรรดิ\nจักรวรรดินิยม\nจักรวาล\nจังหนับ\nจัดการ\nจัดจ้าน\nจัดเจน\nจัดแจง\nจัดตั้ง\nจัดสรร\nจับกุม\nจับจด\nจับเจ่า\nจ่าหน้า\nจาตุทสี\nจาตุมหาราช\nจาตุมหาราชิก\nจาตุมหาราชิกา\nจาตุรงคสันนิบาต\nจาตุรราชการ\nจานเชิง\nจานบิน\nจานผี\nจานเสียง\nจาบจ้วง\nจำเป็น\nจำพรรษา\nจำวัด\nจ้ำจี้จ้ำไช\nจำเลาะตา\nจิงโจ้น้ำ\nจิตใจ\nจิตตภาวนา\nจิตตัง\nจิตตานุปัสสนา\nจิตนิยม\nจิตบำบัด\nจิตแพทย์\nจิตวิสัย\nจิตรกร\nจิตรกรรม\nจิตรลดา\nจิตวิทยา\nจิตเวช\nจิตเวชศาสตร์\nจินตกวี\nจินตนา\nจินตนาการ\nจินตภาพ\nจุฑามณี\nจุฑามาศ\nจุฑารัตน์\nจุนเจือ\nจุ้นจ้าน\nจุลชีพ\nจุลชีวัน\nจุลชีวิน\nจุลทรรศน์\nจุลภาค\nจุลวรรค\nจุลศักราช\nจุลสาร\nจุลินทรีย์\nจุฬามณี\nจุฬาลักษณ์\nเจตคติ\nเจตจำนง\nเจตนารมณ์\nเจตภูต\nเจริญพร\nเจ้ากรม\nเจ้ากรรม\nเจ้าของ\nเจ้าขา\nเจ้าข้า\nเจ้าคณะ\nเจ้าค่ะ\nเจ้าจอม\nเจ้าชู้\nเจ้าตัว\nเจ้าถิ่น\nเจ้าท่า\nเจ้าที่\nเจ้าทุกข์\nเจ้านาย\nเจ้าเนื้อ\nเจ้าบ้าน\nเจ้าบ่าว\nเจ้าประคุณ\nเจ้าประคู้น\nเจ้าพนักงาน\nเจ้าพระคุณ\nเจ้าพระยา\nเจ้าพ่อ\nเจ้าพายุ\nเจ้าฟ้า\nเจ้าภาพ\nเจ้ามือ\nเจ้าแม่\nเจ้าเรือน\nเจ้าสังกัด\nเจ้าสัว\nเจ้าสาว\nเจ้าหน้าที่\nเจ้าหนี้\nเจ้าอาวาส\nเจาะจง\nเจือจาง\nเจือจาน\nเจือปน\nเจื้อยแจ้ว\nแจกจ่าย\nแจ่มแจ้ง\nแจ่มใส\nโจงกระเบน\nโจมตี\nโจรกรรม\nโจรสลัด\nใจความ\nใจคอ\nฉกฉวย\nฉกชิง\nฉลองได\nฉ้อฉล\nฉัตรมงคล\nฉันทลักษณ์\nฉายาลักษณ์\nฉิบหาย\nฉุกเฉิน\nฉุกละหุก\nฉุนเฉียว\nฉุปศาสตร์\nเฉไฉ\nเฉยเมย\nเฉาโฉด\nเฉิดฉัน\nเฉิดฉาย\nเฉิดฉิน\nเฉียบขาด\nเฉียบพลัน\nเฉียบแหลม\nเฉื่อยชา\nแฉะแบะ\nโฉดเฉา\nโฉมงาม\nโฉมฉาย\nโฉมเฉลา\nโฉมตรู\nโฉมยง\nโฉมศรี\nโฉมหน้า\nชดช้อย\nชดเชย\nชดใช้\nชนบท\nชนินทร์\nชนกกรรม\nชนมพรรษา\nชนมายุ\nชมเชย\nชมพูทวีป\nชมพูนท\nชมพูนุท\nชราธรรม\nชราภาพ\nชลจร\nชลธาร\nชลธี\nชลนัยน์\nชลนา\nชลเนตร\nชลประทาน\nชลมารค\nชลาธาร\nชลาลัย\nชลาศัย\nชลาสินธุ์\nชโลทร\nช่วงชิง\nช่วงใช้\nชวนชม\nชวนหัว\nช่วยเหลือ\nช่อฟ้า\nช่อม่วง\nชอกช้ำ\nช่องเขา\nช่องแคบ\nช่องไฟ\nช่องว่าง\nช้องนาง\nชอบกล\nชอบใจ\nชอบธรรม\nชอบพอ\nชักโครก\nชักเงา\nชักจูง\nชักชวน\nชักนำ\nชักเนื้อ\nชักพระ\nชักเย่อ\nชักใย\nชั่งใจ\nชังฆวิหาร\nชัดเจน\nชั้นเชิง\nชั่วคน\nชั่วคราว\nชั่วช้า\nชั่วโมง\nชั่วแล่น\nชาเย็น\nช้านาน\nช่างเครื่อง\nช่างฝีมือ\nช่างฟิต\nช่างไฟ\nช้างน้ำ\nช้างเผือก\nช้างพลาย\nช้างพัง\nช้างสาร\nช้างสีดอ\nชาติธรรม\nชาตินิยม\nชาติพันธุ์\nชาติพันธุ์วิทยา\nชาติภูมิ\nชานชาลา\nชายชาตรี\nชายคา\nชายฝั่ง\nชายทะเล\nชาวเล\nชาววัง\nช้ำใจ\nช้ำชอก\nชิงชัง\nชิงพลบ\nชินชา\nชินบุตร\nชิ้นเอก\nชิมลาง\nชีเปลือย\nชี้ขาด\nชี้แจง\nชี้นำ\nชี้แนะ\nชี้ฟ้า\nชีพจร\nชีพิตักษัย\nชื่นชม\nชื่นบาน\nชื่นมื่น\nชื่อย่อ\nชื่อรอง\nชื่อเล่น\nชื่อเสียง\nชุกชุม\nชุติมา\nชุบตัว\nชุบเลี้ยง\nชุมชน\nชุมทาง\nชุมสาย\nชุ่มใจ\nชุ่มชื่น\nชุ่มชื้น\nชุมนุมชน\nชูชีพ\nชูโรง\nชู้สาว\nเชยชม\nเชลยศักดิ์\nเชลยศึก\nเช่าซื้อ\nเช้าตรู่\nเช้ามืด\nเชิงกราน\nเชิงกล\nเชิงชั้น\nเชิงชาย\nเชิงซ้อน\nเชิงเดียว\nเชิงเดี่ยว\nเชิงตะกอน\nเชิงเทิน\nเชิงมุม\nเชิดชู\nเชิงอรรถ\nเชี่ยนหมาก\nเชี่ยวชาญ\nเชื่องช้า\nเชื่อใจ\nเชื่อถือ\nเชื่อฟัง\nเชื่อมือ\nเชื้อชาติ\nเชื้อเพลิง\nเชื้อไฟ\nเชื้อโรค\nเชื้อสาย\nเชื้อเชิญ\nเชื่องช้า\nเชือนแช\nเชื่อวัน\nแช่เย็น\nแช่อิ่ม\nแช่มช้อย\nแช่มชื่น\nโชกโชน\nโชติช่วง\nโชติรส\nใช้สอย\nซบเซา\nซมซาน\nซวนเซ\nซอกซอน\nซอกแซก\nซ่องสุม\nซ่องเสพ\nซ่องแซ่ง\nซ่อนรูป\nซ่อนเร้น\nซ่อนหา\nซ่อนกลิ่น\nซ่อนทราย\nซ่อมแซม\nซักค้าน\nซักซ้อม\nซักไซ้\nซักฟอก\nซักแห้ง\nซังกะตาย\nซังตาย\nซัดเซ\nซัดทอด\nซับซ้อน\nซับใน\nซับพระพักตร์\nซากศพ\nซ่านเซ็น\nซ้ำซ้อน\nซ้ำซาก\nซ้ำเติม\nซ้ำร้าย\nซี่โครง\nซีดเซียว\nซึมกะทือ\nซึมซาบ\nซึมเซา\nซึมทราบ\nซึมเศร้า\nซื่อตรง\nซื่อสัตย์\nซื้อขาย\nซุกซน\nซุกซ่อน\nซุบซิบ\nซู่ซ่า\nเซซัง\nเซ่อซ่า\nแซ่ซ้อง\nโซดาไฟ\nญาณทัสนะ\nญาณวิทยา\nญาณศาสตร์\nญาติกา\nฐานราก\nดกดื่น\nดงดิบ\nดลใจ\nดลบันดาล\nดวงแก้ว\nดวงใจ\nดวงเดือน\nดวงตรา\nดวงตา\nดวงสมร\nดอกจัน\nดอกจิก\nดอกบัว\nดอกเบี้ย\nดอกฟ้า\nดอกไม้\nดอกยาง\nดอกเล็บ\nดอกทอง\nดอกสร้อย\nดองยา\nดักคอ\nดักฟัง\nดังนั้น\nดังนี้\nดังหนึ่ง\nดั้งเดิม\nดัดจริต\nดัดแปลง\nดันทุรัง\nดับขันธ์\nดับจิต\nดับชีพ\nด่าทอ\nด่างทับทิม\nด่างพร้อย\nดาดฟ้า\nดาราศาสตร์\nดาลเดือด\nดาวกระจาย\nดาวเคราะห์\nดาวตก\nดาวเทียม\nดาวรุ่ง\nดาวเรือง\nดาวฤกษ์\nดาวหาง\nดาวเหนือ\nดาษดื่น\nดินขาว\nดินดาน\nดินดำ\nดินประสิว\nดินปืน\nดินระเบิด\nดินสอ\nดินสอพอง\nดิ้นรน\nดิบดี\nดีเกลือ\nดีใจ\nดีซ่าน\nดีดัก\nดีเดือด\nดีฝ่อ\nดีดดิ้น\nดึกดำบรรพ์\nดึกดื่น\nดึงดัน\nดึงดูด\nดื่มด่ำ\nดื้อด้าน\nดื้อดึง\nดื้อแพ่ง\nดื้อยา\nดื้อรั้น\nดุดัน\nดุเดือด\nดุร้าย\nดุลการค้า\nดุลพินิจ\nดุลภาค\nดุลยพินิจ\nดุลยภาพ\nดุษฎีนิพนธ์\nดุษฎีบัณฑิต\nดุษณีภาพ\nดูแคลน\nดูถูก\nดูดาย\nดูเบา\nดูแล\nดูหมิ่น\nดูเหมือน\nดูดดื่ม\nเด็ดขาด\nเด็ดดวง\nเด็ดเดี่ยว\nเดนตาย\nเดาสวด\nเดาสุ่ม\nเดินทาง\nเดินสะพัด\nเดินสาย\nเดินเหิน\nเดิมพัน\nเดียงสา\nเดียดฉันท์\nเดียวกัน\nเดียวดาย\nเดี๋ยวเดียว\nเดี๋ยวนี้\nเดือดดาล\nเดือดร้อน\nเดือนมืด\nเดือนหงาย\nแดดาล\nแดดิ้น\nแดกดัน\nโด่เด่\nโด่งดัง\nโดดเดี่ยว\nโดยสาร\nได้การ\nได้แก่\nได้ใจ\nได้ที\nได้ยิน\nได้เสีย\nตกเขียว\nตกค้าง\nตกใจ\nตกต่ำ\nตกแต่ง\nตกทอด\nตกฟาก\nตกมัน\nตกยาก\nตกลง\nตกหล่น\nต้นขั้ว\nต้นคิด\nต้นฉบับ\nต้นตอ\nต้นตำรับ\nต้นทุน\nต้นแบบ\nต้นเพลิง\nต้นมือ\nต้นไม้\nต้นร่าง\nต้นเรื่อง\nต้นสังกัด\nต้นหน\nต้นเหตุ\nตบตา\nตบแต่ง\nตบแผละ\nตบมือ\nต้มข่า\nต้มโคล้ง\nต้มยำ\nต้มส้ม\nตรมตรอม\nตรรกวิทยา\nตรรกศาสตร์\nตรวจการ\nตรวจการณ์\nตรวจตรา\nตระบัดสัตย์\nตรัสรู้\nตราตั้ง\nตราบาป\nตรายาง\nตราสาร\nตริตรอง\nตรีกฏุก\nตรีกาย\nตรีโกณ\nตรีโกณมิติ\nตรีคูณ\nตรีทูต\nตรีปิฎก\nตรีภพ\nตรีมูรติ\nตรึกตรอง\nตรึงตรา\nตรุษจีน\nตฤณชาติ\nตฤณมัย\nตลกบาตร\nตลบตะแลง\nตลบหลัง\nตลาดนัด\nตลาดน้ำ\nตลาดมืด\nตลาดสด\nต่อตี\nต่อเติม\nต่อว่า\nต่อสู้\nต่อกร\nต่อต้าน\nต่อแย้ง\nต้องการ\nต้องโทษ\nต้องหา\nต้อนรับ\nตอบโต้\nตอบแทน\nต่อยหอย\nตะพาบน้ำ\nตักตวง\nตักบาตร\nตั้งเข็ม\nตั้งไข่\nตั้งเค้า\nตั้งแง่\nตั้งใจ\nตั้งต้น\nตั้งแต่\nตั้งท้อง\nตัดขาด\nตัดใจ\nตัดเชือก\nตัดตอน\nตัดทอน\nตัดบท\nตัดพ้อ\nตัดรอน\nตัดสิน\nตับเต่า\nตับแลบ\nตับอ่อน\nตัวกลาง\nตัวการ\nตัวเก็ง\nตัวดี\nตัวตั้ง\nตัวเต็ง\nตัวถัง\nตัวแทน\nตัวประกอบ\nตัวประกัน\nตัวแปร\nตัวผู้\nตัวพิมพ์\nตัวเมีย\nตัวยืน\nตัวเลข\nตัวอย่าง\nตั๋วเงิน\nตั๋วแลกเงิน\nตากล้อง\nตาไก่\nตาข่าย\nตาชั่ง\nตาตุ่ม\nตาทวด\nตาปลา\nตาราง\nต่างหาก\nต้านทาน\nตามใจ\nตายใจ\nตายซาก\nตายด้าน\nตายตัว\nตายทั้งกลม\nตายห่า\nตายโหง\nตาลปัตร\nต่ำช้า\nต่ำต้อย\nตำส้ม\nติเตียน\nติณชาติ\nติดขัด\nติดใจ\nติดต่อ\nติดตั้ง\nติดตาม\nติดตื้น\nติดพัน\nติดลม\nติดอ่าง\nตีเกลียว\nตีขลุม\nตีความ\nตีคู่\nตีจาก\nตีตื้น\nตีแผ่\nตีรวน\nตีลังกา\nตีวง\nตีเสมอ\nตีนกา\nตีนคู้\nตีนจก\nตีนตะขาบ\nตีนผี\nตีนเหยียด\nตึกแถว\nตึกระฟ้า\nตึงเครียด\nตึงตัง\nตื้นตัน\nตื่นตัว\nตื่นตูม\nตื่นเต้น\nตุ๊ต๊ะ\nตุ้บตั้บ\nตุ้มหู\nตุลาการ\nตุลาคม\nตู้นิรภัย\nตูมตาม\nตู้เสบียง\nเตโชธาตุ\nเตร็ดเตร่\nเต้นรำ\nเตาแก๊ส\nเตาผิง\nเตาฟู่\nเตาไฟ\nเตารีด\nเตาสูบ\nเต่าทอง\nเต้ารับ\nเต้าส่วน\nเต้าเสียบ\nเต้าหู้ยี้\nแต่ละ\nแตกคอ\nแตกคอก\nแตกฉาน\nแตกดับ\nแตกตื่น\nแตกพาน\nแตกแยก\nแตกร้าว\nแตกหัก\nแต่งงาน\nแต่งตั้ง\nแต้มคู\nแต้มต่อ\nแตรงอน\nแตรเดี่ยว\nแตรฝรั่ง\nแตรฟันฟาร์\nแตรวง\nโต้ตอบ\nโต้เถียง\nโต้แย้ง\nโต๊ะหมู่\nโต๊ะอิหม่าม\nใต้ถุน\nไต้ก๋ง\nไต่คู้\nไต่เต้า\nไต่ถาม\nไต้ฝุ่น\nไตรจักร\nไตรจีวร\nไตรตรึงษ์\nไตรทวาร\nไตรปิฎก\nไตรเพท\nไตรภพ\nไตรภูมิ\nไตรภาคี\nไตรยางศ์\nไตรรงค์\nไตรรัตน์\nไตรลักษณ์\nไตรโลก\nไตรสรณคมน์\nไตรสิกขา\nไต่สวน\nถกเถียง\nถดถอย\nถนัดถนี่\nถนิมสร้อย\nถมถืด\nถมเถ\nถมไป\nถลากไถล\nถ้วนถี่\nถ้วยฟู\nถ่องแท้\nถอดถอน\nถ้อยคำ\nถ้อยแถลง\nถากถาง\nถ่านไฟฉาย\nถ่านหิน\nถามไถ่\nถ่ายทอด\nถ่ายทุกข์\nถ่ายเท\nถาวรวัตถุ\nถ้ำมอง\nถี่ถ้วน\nถึงใจ\nถูกใจ\nถูกชะตา\nเถรวาท\nเถ้าแก่\nเถ้าแก่เนี้ย\nแถมพก\nแถลงการณ์\nไถ่ถอน\nไถ่ถาม\nทดแทน\nทดรอง\nทดลอง\nทดสอบ\nทนทาน\nทนายความ\nทบทวน\nทแยงมุม\nทรงกลด\nทรงเครื่อง\nทรงเจ้า\nทรัพย์สิน\nทรามชม\nทรามเชย\nทรามวัย\nทรามสงวน\nทรามสวาท\nทรุดโทรม\nทฤษฎีบท\nท้วงติง\nท่วมท้น\nทวาทศ\nทวาทศมาส\nทวาบรยุค\nทวารบาล\nทวิบถ\nทวิบท\nทวิบาท\nทวิภาค\nทวิภาคี\nทวีคูณ\nทศกัณฐ์\nทศชาติ\nทศทิศ\nทศนิยม\nทศพร\nทศพล\nทศพิธราชธรรม\nทศมาส\nทศวรรษ\nท่อไอเสีย\nท้อถอย\nท้อแท้\nทองขาว\nทองคำ\nทองคำขาว\nทองคำเปลว\nทองเค\nทองแดง\nทองบรอนซ์\nทองม้วน\nทองย้อย\nทองสัมฤทธิ์\nทองหยอด\nทองหยิบ\nทองเหลือง\nทองเอก\nท่องเที่ยว\nท้องตรา\nท้องถิ่น\nท้องที่\nท้องน้อย\nท้องร่อง\nท้องเรื่อง\nทอดมัน\nทอดทิ้ง\nทอดน่อง\nทอดยอด\nทอดหุ่ย\nทอยกอง\nทะเบียนบ้าน\nทะเลทราย\nทะเลสาบ\nทะเลหลวง\nทักขิณาวัฏ\nทักท้วง\nทักทาย\nทักษิณาวรรต\nทักษิณาทาน\nทักษิณานุประทาน\nทั้งกลม\nทั้งคน\nทั้งดุ้น\nทั้งที\nทั้งนั้น\nทั้งนี้\nทั้งปวง\nทั้งผอง\nทั้งเพ\nทั้งมวล\nทั้งสิ้น\nทั้งหมด\nทั้งหลาย\nทัณฑ์บน\nทัดทาน\nทัดเทียม\nทันควัน\nทันใจ\nทันใด\nทันตา\nทันสมัย\nทันที\nทับถม\nทับทรวง\nทับศัพท์\nทั่วถึง\nทั่วไป\nท่าทาง\nท่าที\nท้าทาย\nทางการ\nทางข้าม\nทางด่วน\nทางเท้า\nทางโท\nทางใน\nทางผ่าน\nทางม้าลาย\nทางหลวง\nทางออก\nทางเอก\nทานกัณฑ์\nทานตะวัน\nท่านชาย\nทานบารมี\nท่านผู้หญิง\nท่านหญิง\nทาบทาม\nท้ายทอย\nทารุณกรรม\nทำคลอด\nทำใจ\nทำซ้ำ\nทำท่า\nทำที\nทำแท้ง\nทำโทษ\nทำบาป\nทำบุญ\nทำพิษ\nทำฟัน\nทำร้าย\nทำวัตร\nทำสาว\nทำเสน่ห์\nทำหมัน\nทำให้\nทิ้งขว้าง\nทิ้งทวน\nทิ้งท้าย\nทินกร\nทิพจักขุ\nทิพโสต\nทิพยจักษุ\nทิพยญาณ\nทิพยเนตร\nทิพยรส\nทิพากร\nทิ่มตำ\nทิ่มแทง\nทิวากร\nทิวากาล\nทิศทาง\nทีเด็ด\nทีท่า\nทีนี้\nทีหลัง\nทีฆนิกาย\nทีฆสระ\nที่ดิน\nที่นอน\nที่นั่ง\nที่ปรึกษา\nที่พึ่ง\nที่มั่น\nที่ราบ\nที่ว่าการ\nที่สุด\nที่หมาย\nที่ไหน\nทุกที\nทุกเมื่อ\nทุกข์สุข\nทุนทรัพย์\nทุนนิยม\nทุนรอน\nทุนสำรอง\nทุ่มเถียง\nทุ่มเท\nทูนหัว\nทูลกระหม่อม\nเทกระจาด\nเทครัว\nเทพเจ้า\nเทพดา\nเทพธิดา\nเทพนม\nเทพนิยม\nเทพนิยาย\nเทพบุตร\nเทพสังหรณ์\nเทศกาล\nเทศนาโวหาร\nเทศบัญญัติ\nเทศบาล\nเทศมนตรี\nเทห์ฟากฟ้า\nเท่ากับ\nเท่าใด\nเท่าตัว\nเท่าทัน\nเท่าทุน\nเท่าเทียม\nเท่านั้น\nเท่าไร\nเท้าช้าง\nเทิดทูน\nเที่ยงตรง\nเที่ยงแท้\nเที่ยงธรรม\nเทียนชนวน\nเทียนพรรษา\nเทียบเคียง\nเทียบเท่า\nเทือกเขา\nเทือกเถา\nแท็งก์น้ำ\nแท่นพิมพ์\nแท่นมณฑล\nแท่นหมึก\nแทรกซอน\nแทรกซ้อน\nแทรกซึม\nแทรกแซง\nแทะโลม\nไทยดำ\nไทยทาน\nไทยธรรม\nไทยน้อย\nไทยใหญ่\nธงชัย\nธงชาติ\nธงทิว\nธรณีวิทยา\nธรณีสงฆ์\nธรรมกาย\nธรรมการ\nธรรมเกษตร\nธรรมขันธ์\nธรรมคุณ\nธรรมจรรยา\nธรรมจริยา\nธรรมจักร\nธรรมจักษุ\nธรรมจาคะ\nธรรมจารี\nธรรมชาติ\nธรรมดา\nธรรมเนียม\nธรรมราชา\nธรรมศาสตร์\nธรรมสภา\nธรรมสังเวช\nธัญพืช\nธารพระกร\nธีรภาพ\nธีรราช\nนกเขา\nนกต่อ\nนกยูง\nนกรู้\nนกหวีด\nนครบาล\nนครรัฐ\nนงคราญ\nนงนุช\nนงพะงา\nนงเยาว์\nนงราม\nนงลักษณ์\nนบนอบ\nนพเก้า\nนพคุณ\nนพเคราะห์\nนพปฎล\nนพพล\nนพรัตน์\nนพศก\nนพศูล\nนมข้น\nนมผง\nนมไม้\nนมนาน\nนมหนู\nนมแมว\nนรีเวช\nนรีเวชวิทยา\nนวดฟั้น\nนวยนาด\nนวลระหง\nนวลลออ\nนวลละออง\nนวลจันทร์\nนอกครู\nนอกคอก\nนอกจาก\nนอกใจ\nนอกชาน\nนอกรีต\nนอกเหนือ\nนองเนือง\nนองเลือด\nนอนก้น\nนอนใจ\nนอบนบ\nนอนเล่น\nนอบน้อม\nน้อมนำ\nน้อยใจ\nน้อยหน้า\nนักการ\nนักการเมือง\nนักกีฬา\nนักข่าว\nนักท่องเที่ยว\nนักเทศน์\nนักโทษ\nนักธรรม\nนักบวช\nนักบิน\nนักบุญ\nนักปราชญ์\nนักพรต\nนักรบ\nนักเรียน\nนักเลง\nนักวิชาการ\nนักศึกษา\nนักสิทธิ์\nนักสืบ\nนักหนา\nนั่งเทียน\nนั่งร้าน\nนัดแนะ\nนัดหมาย\nนั่นแหละ\nนั่นเอง\nนับถือ\nนับประสา\nนัยน์ตา\nนาดำ\nนาปรัง\nนาปี\nนาสวน\nนาหว่าน\nนาคบาศ\nนาคปรก\nนาคราช\nนางกวัก\nนางกำนัล\nนางงาม\nนางใน\nนางบำเรอ\nนางแบบ\nนางพญา\nนางฟ้า\nนางไม้\nนางโลม\nนางสาว\nนางห้าม\nนางเอก\nนาฏกรรม\nนาฏดนตรี\nนาฏศิลป์\nนานนม\nน่านน้ำ\nน่านฟ้า\nนามกร\nนามธรรม\nนามไธย\nนามบัตร\nนามปากกา\nนามแฝง\nนามสกุล\nนามสงเคราะห์\nนามสมญา\nนายทะเบียน\nนายท่า\nนายท้าย\nนายทุน\nนายประกัน\nนายหน้า\nนายอำเภอ\nนารายณ์หัตถ์\nนารีผล\nนาวิกโยธิน\nนำจับ\nนำพา\nนำทาง\nนำร่อง\nนำสืบ\nนำแสดง\nน้ำกรด\nน้ำกาม\nน้ำเกลือ\nน้ำข้าว\nน้ำแข็ง\nน้ำแข็งไส\nน้ำแข็งแห้ง\nน้ำครำ\nน้ำคร่ำ\nน้ำค้าง\nน้ำค้างแข็ง\nน้ำคาวปลา\nน้ำคำ\nน้ำเค็ม\nน้ำเคย\nน้ำเงิน\nน้ำเงี้ยว\nน้ำจัณฑ์\nน้ำจิ้ม\nน้ำใจ\nน้ำเชื้อ\nน้ำเชื่อม\nน้ำซาวข้าว\nน้ำดอกไม้\nน้ำดี\nน้ำตก\nน้ำตา\nน้ำตาล\nน้ำท่า\nน้ำนม\nน้ำนวล\nน้ำบาดาล\nน้ำประสานทอง\nน้ำประปา\nน้ำปลา\nน้ำป่า\nน้ำผึ้ง\nน้ำพริก\nน้ำพริกเผา\nน้ำพี้\nน้ำพุ\nน้ำมนต์\nน้ำมนตร์\nน้ำมัน\nน้ำมือ\nน้ำมูก\nน้ำเมา\nน้ำย่อย\nน้ำยา\nน้ำรัก\nน้ำแร่\nน้ำลาย\nน้ำเลี้ยง\nน้ำสต๊อก\nน้ำส้ม\nน้ำส้มสายชู\nน้ำสังข์\nน้ำสาบาน\nน้ำเสียง\nน้ำหนวก\nน้ำหนอง\nน้ำหนัก\nน้ำหน้า\nน้ำหนึ่ง\nน้ำหมึก\nน้ำหอม\nน้ำเหลือง\nน้ำอบ\nน้ำอ้อย\nน้ำอัดลม\nนิ่งเฉย\nนิจศีล\nนิดเดียว\nนิดหน่อย\nนิติกร\nนิติกรรม\nนิติธรรม\nนิตินัย\nนิติบัญญัติ\nนิติบุคคล\nนิติภาวะ\nนิติวิทยาศาสตร์\nนิติเวช\nนิติเวชศาสตร์\nนิติศาสตร์\nนิเทศศาสตร์\nนิ่มนวล\nนิรุกติศาสตร์\nนิเวศวิทยา\nนิศากร\nนิศากาล\nนิศาชล\nนิศารัตน์\nนี่แน่ะ\nนี่แหละ\nนี่เอง\nนึกคิด\nนุงถุง\nนุ่งห่ม\nนุ่มนวล\nนุ่มนิ่ม\nเนตรนารี\nเนติบัณฑิต\nเนยเทียม\nเนยใส\nเนิ่นนาน\nเนิบนาบ\nเนื้อความ\nเนื้อคู่\nเนื้อเค็ม\nเนื้องอก\nเนื้อตัว\nเนื้อตาย\nเนื้อที่\nเนื้อแท้\nเนื้อเปื่อย\nเนื้อผ้า\nเนื้อเพลง\nเนื้อไม้\nเนื้อเยื่อ\nเนื้อร้อง\nเนื้อร้าย\nเนื้อเรื่อง\nเนื้อหา\nเนืองนอง\nเนืองนิตย์\nเนืองแน่น\nแน่ใจ\nแน่ชัด\nแน่แท้\nแน่นอน\nแน่นิ่ง\nแน่แน่ว\nแน่นแฟ้น\nแน่นหนา\nแนบเนียน\nแนบแน่น\nแนวคิด\nแนวทาง\nแนวโน้ม\nแนวป่า\nแนวรบ\nแนวร่วม\nแนวหน้า\nแนวหลัง\nแน่วแน่\nแนะนำ\nแนะแนว\nโน้มน้าว\nในหลวง\nบกพร่อง\nบงกช\nบงการ\nบดบัง\nบทกลอน\nบทกวี\nบทความ\nบทคัดย่อ\nบทเฉพาะกาล\nบทนำ\nบทบัญญัติ\nบทบาท\nบทประพันธ์\nบทเพลง\nบทร้อง\nบทเรียน\nบทลงโทษ\nบทสนทนา\nบทอัศจรรย์\nบทจร\nบทบงสุ์\nบทมาลย์\nบทรัช\nบทเรศ\nบทวลัญช์\nบนบาน\nบรมครู\nบรมธาตุ\nบรมบพิตร\nบรมวงศานุวงศ์\nบรมอัฐิ\nบรรณพิภพ\nบรรณศาลา\nบรรณาการ\nบรรณาธิการ\nบรรณานุกรม\nบรรณารักษ์\nบรรณารักษศาสตร์\nบรรดามี\nบรรดาศักดิ์\nบรรทัดฐาน\nบรรพบุรุษ\nบรรลัยกัลป์\nบรรลัยจักร\nบริคณห์สนธิ\nบวงสรวง\nบ่วงบาศ\nบ้วนพระโอษฐ์\nบ่อเกิด\nบอกกล่าว\nบอกบท\nบอกบุญ\nบอกใบ้\nบอกปัด\nบ้องกัญชา\nบ้องตื้น\nบ้องไฟ\nบ้องหู\nบอดสี\nบ่อนทำลาย\nบอบช้ำ\nบอบบาง\nบอบแบบ\nบังโกลน\nบังโคลน\nบังใบ\nบั้งไฟ\nบังคับการ\nบังคับบัญชา\nบัญชาการ\nบัณฑุกัมพล\nบัดดล\nบัดเดี๋ยว\nบัดนั้น\nบัดนี้\nบัดสีบัดเถลิง\nบัตรเครดิต\nบัตรพลี\nบัตรสนเท่ห์\nบัตรสินเชื่อ\nบั่นทอน\nบั้นท้าย\nบั้นปลาย\nบั้นพระองค์\nบั้นเอว\nบันไดลิง\nบันไดเลื่อน\nบันเทิงคดี\nบัวลอย\nบัวบก\nบ้าจี้\nบ้าดีเดือด\nบ้าน้ำลาย\nบ้าบิ่น\nบ้าระห่ำ\nบ้าเลือด\nบ้าหอบฟาง\nบากบั่น\nบากหน้า\nบางตา\nบางเบา\nบางที\nบาดเจ็บ\nบาดแผล\nบาดหมาง\nบาตรใหญ่\nบาทบงกช\nบาทบงสุ์\nบาทบริจาริกา\nบาทวิถี\nบานเกล็ด\nบานตะเกียง\nบานตะไท\nบานเบอะ\nบานปลาย\nบานแผละ\nบานพับ\nบ้านจัดสรร\nบ้านช่อง\nบ้านนอก\nบ้านพัก\nบ้านเมือง\nบ้านรับรอง\nบ้านเรือน\nบาปกรรม\nบายศรี\nบ่ายเบี่ยง\nบ่ายหน้า\nบ่าวไพร่\nบิดเบี้ยว\nบิดเบือน\nบิดพลิ้ว\nบี้แบน\nบีบคั้น\nบีบรัด\nบึ้งตึง\nบึ้งบูด\nบุกบั่น\nบุกเบิก\nบุกรุก\nบุคลิกภาพ\nบุคลิกลักษณะ\nบุญธรรม\nบุญนิธิ\nบุญฤทธิ์\nบุบสลาย\nบุ้ยใบ้\nบุรุษเพศ\nบุหงารำไป\nบู้บี้\nบูชายัญ\nบูดบึ้ง\nบูดเบี้ยว\nเบาความ\nเบาใจ\nเบาบาง\nเบาปัญญา\nเบามือ\nเบาแรง\nเบาสมอง\nเบาหวาน\nเบาโหวง\nเบ้าตา\nเบาะแส\nเบิกความ\nเบิกบาน\nเบี้ยล่าง\nเบี้ยเลี้ยง\nเบี้ยหวัด\nเบี่ยงบ่าย\nเบียดบัง\nเบียดเบียน\nเบียดเสียด\nเบื้องต้น\nเบื้องบน\nเบื้องหน้า\nเบื้องหลัง\nแบกะดิน\nแบเบาะ\nแบ่งเบา\nแบ่งปัน\nแบ่งแยก\nแบบฉบับ\nแบบแปลน\nแบบแผน\nแบบฝึกหัด\nแบบพิมพ์\nแบบสอบถาม\nแบบอย่าง\nแบะแฉะ\nแบะท่า\nโบแดง\nโบราณคดี\nโบราณวัตถุ\nโบราณสถาน\nใบขับขี่\nใบจอง\nใบตอง\nใบแทรก\nใบบอก\nใบบุญ\nใบเบิกทาง\nใบปลิว\nใบพัด\nใบโพ\nใบไม้\nใบระกา\nใบรับรอง\nใบลา\nใบเลี้ยง\nใบสั่ง\nใบสำคัญ\nใบสุทธิ\nใบเสร็จ\nใบหน้า\nใบอนุญาต\nใบระกา\nปกครอง\nปกคลุม\nปกป้อง\nปกปิด\nปฏิบัติการ\nปฏิบัติบูชา\nปฐพีวิทยา\nปฐมฌาน\nปฐมทัศน์\nปฐมเทศนา\nปฐมนิเทศ\nปฐมพยาบาล\nปฐมยาม\nปฐมฤกษ์\nปฐมวัย\nปฐมสมโพธิ\nปนเป\nป่นปี้\nปมเขื่อง\nปมเด่น\nปมด้อย\nปรนเปรอ\nปรบไก่\nปรบมือ\nปรสิตวิทยา\nประโปรย\nประพรม\nประกันชีวิต\nประกันภัย\nประจักษ์พยาน\nประจัญบาน\nประจันหน้า\nประจำการ\nประจำเดือน\nประจำเมือง\nประจำยาม\nประชดประชัน\nประชากร\nประชากรศาสตร์\nประชาคม\nประชาชน\nประชาราษฎร์\nประชาชาติ\nประชาชี\nประชาทัณฑ์\nประชาบาล\nประชาพิจารณ์\nประชาภิบาล\nประชามติ\nประชาสงเคราะห์\nประชาสัมพันธ์\nประดับประดา\nประดามี\nประดาน้ำ\nประเดี๋ยวเดียว\nประเดี๋ยวนี้\nประทับใจ\nประทุษร้าย\nประเทศราช\nประพาสต้น\nประเพณีนิยม\nประลัยกัลป์\nประวัติการณ์\nประวัติศาสตร์\nประสบการณ์\nประสบการณ์นิยม\nประสาทการ\nประสูติการ\nประสูติกาล\nประเส\nปรับทุกข์\nปรับโทษ\nปรับปรุง\nปรากฏการณ์\nปราดเปรียว\nปราดเปรื่อง\nปราบปราม\nปริญญาบัตร\nปรัยัติธรรม\nปรุโปร่ง\nปลงใจ\nปลงตก\nปลดทุกข์\nปลดปลง\nปลดปล่อย\nปลดเปลื้อง\nปลดระวาง\nปลดแอก\nปล้นสะดม\nปลอกกระสุน\nปลอกคอ\nปลอดโปร่ง\nปลอดภัย\nปลอมปน\nปลอมแปลง\nปลอบโยน\nปล่อยใจ\nปล่อยตัว\nปล่อยปละ\nปลั๊กไฟ\nปลากริม\nปลาเค็ม\nปลาจ่อม\nปลาเจ่า\nปลาแดก\nปลาตู้\nปลาทอง\nปลาร้า\nปลาส้ม\nปลาดาว\nปลาบิน\nปลาฝา\nปลาวาฬ\nปลาหมึก\nปลาบปลื้ม\nปลายข้าว\nปลายแถว\nปลายทาง\nปลิ้นปลอก\nปลิ้นปล้อน\nปลีกตัว\nปลีกย่อย\nปลุกใจ\nปลุกปล้ำ\nปลุกปั่น\nปลุกระดม\nปลุกเสก\nปลูกฝัง\nปลูกสร้าง\nปวดถ่วง\nปวดมวน\nปวดร้าว\nป่วนปั่น\nป่วยการ\nปอกลอก\nป้องกัน\nปักใจ\nปักดำ\nปักหลัก\nปัจเจกบุคคล\nปัจเจกพุทธะ\nปัจเจกโพธิ\nปัจฉิมชน\nปัจฉิมทิศ\nปัจฉิมภาค\nปัจฉิมยาม\nปัจฉิมลิขิต\nปัจฉิมวัย\nปัจฉิมวาจา\nปัญญาชน\nปัญญาวิมุติ\nปัญญาอ่อน\nปัดเป่า\nปันส่วน\nปั่นป่วน\nปั่นแปะ\nปั่นหัว\nปั้นจิ้ม\nปั้นเจ๋อ\nปั้นปึ่ง\nปั้นสิบ\nปั๊มน้ำมัน\nป่าช้า\nป่าชายเลน\nป่าดง\nป่าดงดิบ\nป่าดิบ\nป่าเถื่อน\nป่าเบญจพรรณ\nป่าละเมาะ\nปากกา\nปากขอ\nปากแข็ง\nปากคอ\nปากคำ\nปากคีบ\nปากจัด\nปากน้ำ\nปากเปล่า\nปากเสียง\nปานกลาง\nป่านนี้\nป้านลม\nป้ายสี\nป่าวร้อง\nปิดฉาก\nปิดบัง\nปิตุฆาต\nปิตุภูมิ\nปีมะโว้\nปีแสง\nปี่กลาง\nปี่ไฉน\nปี่ชวา\nปี่นอก\nปี่ใน\nปี่พาทย์\nปี่อ้อ\nปีกกา\nปีนเกลียว\nปีนป่าย\nปึกแผ่น\nปึงปัง\nปืนกล\nปืนครก\nปืนพก\nปืนยา\nปืนยาว\nปืนลม\nปืนเล็ก\nปืนเล็กยาว\nปืนสั้น\nปืนใหญ่\nปุบปับ\nปุ๊บปั๊บ\nปุ่มเปือก\nปุยฝ้าย\nปุ๋ยคอก\nปุ๋ยเคมี\nปุ๋ยวิทยาศาสตร์\nปุ๋ยหมัก\nปุ๋ยอินทรีย์\nปูจ๋า\nปูเสฉวน\nปู่เจ้า\nปู่ทวด\nปูนขาว\nปูนซีเมนต์\nปูนดิบ\nปูนแดง\nปูนปลาสเตอร์\nปูนปั้น\nเป็ดเทศ\nเป็ดน้ำ\nเป็นกลาง\nเป็นใจ\nเป็นต้น\nเป็นต่อ\nเป็นรอง\nเป็นไร\nเป็นลม\nเป็นห่วง\nเป็นอยู่\nเปรมปรีดิ์\nเปรอะเปื้อน\nเปรียบเทียบ\nเปรียบเปรย\nเปรี้ยวปาก\nเปรี้ยวหวาน\nเปรื่องปราด\nเปลญวน\nเปล่งปลั่ง\nเปล่าดาย\nเปล่าเปลี่ยว\nเปลี่ยนใจ\nเปลี่ยนตัว\nเปลี่ยนแปลง\nเปลี่ยนมือ\nเปลี่ยนหน้า\nเป๋อเหลอ\nเปะปะ\nเป่ากบ\nเป้านิ่ง\nเป้าหมาย\nเปิดฉาก\nเปิดเปิง\nเปิดโปง\nเปิดผนึก\nเปิดเผย\nเปียกปูน\nแป้งสาลี\nแป้งนวล\nแป้งเปียก\nแป้งมัน\nแป้งฝุ่น\nแป้งร่ำ\nแป้งสิงคโปร์\nแป้งหมี่\nแปดปน\nแปดเปื้อน\nแปรปรวน\nแปรผัน\nแปรพักตร์\nแปรรูป\nแปรอักษร\nแปลกปลอม\nแปะโป้ง\nโป้ปด\nโปร่งแสง\nโปร่งใส\nโปรดปราน\nโปรยทาน\nโปรยปราย\nโปโลน้ำ\nผกผัน\nผกากรอง\nผงขาว\nผงชูรส\nผงซักฟอก\nผงฟู\nผดุงครรภ์\nผมไฟ\nผลพลอยได้\nผลลัพธ์\nผลัดเปลี่ยน\nผลิตผล\nผลิตภัณฑ์\nผลุบโผล่\nผสมเทียม\nผสมผสาน\nผสมผเส\nผสมพันธุ์\nผสมโรง\nผสมเสร็จ\nผ่องแผ้ว\nผ่องใส\nผ่อนคลาย\nผ่อนชำระ\nผ่อนปรน\nผ่อนผัน\nผ่อนส่ง\nผอมโซ\nผอมแห้ง\nผักชี\nผักตบชวา\nผักบุ้ง\nผังเมือง\nผัดผ่อน\nผันแปร\nผันผวน\nผ่าตัด\nผ่าเผย\nผ่าหมาก\nผ่าเหล่า\nผ้าขนหนู\nผ้าขาวม้า\nผ้าขี้ริ้ว\nผ้าเช็ดตัว\nผ้าเช็ดปาก\nผ้าเช็ดมือ\nผ้าเช็ดหน้า\nผ้าดิบ\nผ้าต่วน\nผ้าไตร\nผ้าถุง\nผ้าแถบ\nผ้านวม\nผ้านุ่ง\nผ้าใบ\nผ้าป่า\nผ้าป่าน\nผ้าผ่อน\nผ้าพันคอ\nผ้าพันแผล\nผ้าแพร\nผ้าโพกหัว\nผ้ามัดหมี่\nผ้ายาง\nผ้าลูกไม้\nผ้าเหลือง\nผ้าอนามัย\nผ้าอ้อม\nผาดโผน\nผาติกรรม\nผิดหวัง\nผิวเผิน\nผิวพรรณ\nผิวหนัง\nผีกระสือ\nผีกระหัง\nผีกองกอย\nผีโขมด\nผีดิบ\nผีตองเหลือง\nผีถ้วยแก้ว\nผีแถน\nผีทะเล\nผีบุญ\nผีปอบ\nผีพุ่งไต้\nผีฟ้า\nผีเรือน\nผีสาง\nผีเสื้อ\nผีห่า\nผึ่งผาย\nผุดผ่อง\nผุดผาด\nผู้คน\nผู้คุม\nผู้จัดการ\nผู้ชาย\nผู้เชี่ยวชาญ\nผู้ดี\nผู้โดยสาร\nผู้ต้องขัง\nผู้ต้องหา\nผู้แทน\nผู้น้อย\nผู้บริโภค\nผู้บังคับบัญชา\nผู้ปกครอง\nผู้ประกอบการ\nผู้ป่วย\nผู้พิพากษา\nผู้เยาว์\nผู้ร้าย\nผู้วิเศษ\nผู้สื่อข่าว\nผู้เสียหาย\nผู้หญิง\nผู้ใหญ่\nผู้ใหญ่บ้าน\nผูกขวัญ\nผูกขาด\nผูกพัน\nผูกมัด\nเผชิญหน้า\nเผด็จการ\nเผด็จศึก\nเผยแผ่\nเผยแพร่\nเผละผละ\nเผ่าพันธุ์\nเผื่อแผ่\nแผงลอย\nแผนการ\nแผนงาน\nแผนที่\nแผนผัง\nแผนภาพ\nแผนภูมิ\nแผ่นดิน\nแผ่นเสียง\nแผ้วพาน\nโผงผาง\nฝนทอง\nฝอยทอง\nฝักแค\nฝักบัว\nฝักฝ่าย\nฝักใฝ่\nฝังใจ\nฝังหัว\nฝาชี\nฝาแฝด\nฝาละมี\nฝ่าพระบาท\nฝ่าฝืน\nฝ่าฟัน\nฝ้าฟาง\nฝากตัว\nฝากฝัง\nฝีดาษ\nฝีมะม่วง\nฝีจักร\nฝีเท้า\nฝีปาก\nฝีพาย\nฝีมือ\nฝีเย็บ\nฝึกงาน\nฝึกปรือ\nฝึกฝน\nฝึกสอน\nฝึกหัด\nฝืดเคือง\nใฝ่ฝัน\nพงพี\nพงศ์พันธุ์\nพญาโศก\nพญาไฟ\nพบปะ\nพบพาน\nพรสวรรค์\nพรมคด\nพรมแดน\nพรมมิ\nพรรคพวก\nพรรณราย\nพรวดพราด\nพรหมชาติ\nพรหมลิขิต\nพรหมโลก\nพรหมวิหาร\nพร้อมใจ\nพร้อมพรั่ง\nพร้อมเพรียง\nพร้อมมูล\nพร้อมสรรพ\nพร้อมหน้า\nพระครู\nพระคุณ\nพระเคราะห์\nพระเครื่อง\nพระเจ้า\nพระเจ้าอยู่หัว\nพระชายา\nพระทัย\nพระนาง\nพระนางเจ้า\nพระเป็นเจ้า\nพระผู้เป็นเจ้า\nพระพิมพ์\nพระพุทธเจ้า\nพระพุทธองค์\nพระภูมิ\nพระยา\nพระรอง\nพระสนม\nพระสนมเอก\nพระองค์\nพระองค์เจ้า\nพระเอก\nพรั่งพร้อม\nพรั่งพรู\nพรั่นพรึง\nพร่างพราว\nพรายน้ำ\nพรายแพรว\nพราวแพรว\nพร่ำพลอด\nพร่ำเพรื่อ\nพร่ำเพ้อ\nพริกไทย\nพริ้งพราย\nพริ้งเพรา\nพริ้งเพริศ\nพริบตา\nพริ้มพราย\nพริ้มเพรา\nพรุ่งนี้\nพฤติกรรม\nพฤติการณ์\nพฤตินัย\nพลการ\nพลขับ\nพลความ\nพลเมือง\nพลรบ\nพลร่ม\nพลเรือน\nพลโลก\nพลศึกษา\nพลบค่ำ\nพลอดรัก\nพลังงาน\nพลังเงียบ\nพลังจิต\nพลั้งปาก\nพลั้งเผลอ\nพลั้งพลาด\nพลัดถิ่น\nพลัดพราก\nพลาดท่า\nพลาดพลั้ง\nพลิกแพลง\nพลีกรรม\nพลุ่งพล่าน\nพวกพ้อง\nพวงมาลัย\nพวงมาลา\nพวงหรีด\nพวงคราม\nพวงชมพู\nพวงแสด\nพ่วงพี\nพวยน้ำ\nพวยพุ่ง\nพสกนิกร\nพหุคูณ\nพหุภาคี\nพหูพจน์\nพหูสูต\nพอควร\nพอใจ\nพอใช้\nพอใช้ได้\nพอดี\nพอตัว\nพอทำเนา\nพอประมาณ\nพอเพียง\nพอแรง\nพอสมควร\nพอเหมาะ\nพ่อขุน\nพ่อครัว\nพ่อตา\nพ่อบ้าน\nพ่อพันธุ์\nพ่อม่าย\nพ่อเมือง\nพ่อเลี้ยง\nพ่อสื่อ\nพอกพูน\nพ้องพาน\nพักผ่อน\nพักพิง\nพักฟื้น\nพักร้อน\nพักแรม\nพัดยศ\nพัดลม\nพันพัว\nพับฐาน\nพับเพียบ\nพัวพัน\nพาซื่อ\nพาดพิง\nพิณพาทย์\nพิธีกร\nพิธีกรรม\nพิธีการ\nพิธีรีตอง\nพิธีสาร\nพินัยกรรม\nพิมพ์เขียว\nพิมพ์ใจ\nพิมพ์ดีด\nพิษสง\nพี่น้อง\nพี่เบิ้ม\nพี่เลี้ยง\nพึงใจ\nพึงพอใจ\nพึ่งพา\nพึ่งพิง\nพืชพันธุ์\nพืชมงคล\nพื้นฐาน\nพื้นที่\nพื้นบ้าน\nพื้นเพ\nพื้นเมือง\nพื้นเสีย\nพุพอง\nพุทธกาล\nพุทธคุณ\nพุทธจักร\nพุทธเจดีย์\nพุทธฎีกา\nพุทธปฏิมา\nพุทธปฏิมากร\nพุทธมามกะ\nพุทธศักราช\nพุทธศาสนิกชน\nพุทธองค์\nพุทธชาด\nพุทธรักษา\nพุ่มพวง\nพุ่มไม้\nพู่กัน\nพูดจา\nเพ่งเล็ง\nเพดานบิน\nเพดานปาก\nเพริศพราย\nเพริศพริ้ง\nเพริศแพร้ว\nเพรียกพร้อง\nเพรียวลม\nเพลงเชิด\nเพลงยาว\nเพลิงกัลป์\nเพลินใจ\nเพลินตา\nเพลี่ยงพล้ำ\nเพ้อฝัน\nเพาะกาย\nเพาะชำ\nเพาะปลูก\nเพิกถอน\nเพิกเฉย\nเพิ่มเติม\nเพิ่มพูน\nเพียงตา\nเพียงพอ\nเพียบแประ\nเพียบพร้อม\nเพื่อนเกลอ\nเพื่อนตาย\nเพื่อนบ้าน\nเพื่อนฝูง\nเพื่อนยาก\nแพ้ท้อง\nแพร่หลาย\nแพร่งพราย\nแพรวพราว\nโพธิญาณ\nโพธิบัลลังก์\nโพธิสมภาร\nโพธิสัตว์\nโพ้นทะเล\nโพยภัย\nไพ่ตาย\nไพ่ป๊อก\nไพรวัน\nไพรสณฑ์\nไพรสัณฑ์\nไพร่พล\nไพร่ฟ้า\nไพร่สม\nไพร่ส่วย\nไพร่หลวง\nฟกช้ำ\nฟองเต้าหู้\nฟองน้ำ\nฟองมัน\nฟ้องกลับ\nฟ้องร้อง\nฟอนเฟะ\nฟักทอง\nฟัดเฟียด\nฟันดาบ\nฟันฝ่า\nฟันแท้\nฟันน้ำนม\nฟันปลา\nฟันฟาง\nฟันเฟือง\nฟันม้า\nฟันเลื่อย\nฟันหนู\nฟั่นเฝือ\nฟั่นเฟือน\nฟื้นตัว\nฟื้นฝอย\nฟื้นฟู\nฟุ้งซ่าน\nฟุ้งเฟ้อ\nฟุ้งเฟื่อง\nฟุตบอล\nฟูฟ่อง\nฟูเฟื่อง\nฟูมฟัก\nฟูมฟาย\nเฟะฟะ\nเฟื่องฟ้า\nเฟื่องฟุ้ง\nเฟื่องฟู\nไฟฉาย\nไฟแช็ก\nไฟธาตุ\nไฟฟ้า\nภัตกิจ\nภาคทัณฑ์\nภาคพื้น\nภาคเรียน\nภาคภูมิ\nภาพถ่าย\nภาพนิ่ง\nภาพประกอบ\nภาพพจน์\nภาพยนตร์\nภาพลวงตา\nภาพลักษณ์\nภายนอก\nภายใน\nภายหน้า\nภายหลัง\nภารกิจ\nภารธุระ\nภารโรง\nภารตวิทยา\nภาษาศาสตร์\nภาสกร\nภิญโญภาพ\nภินชาติ\nภูธร\nภูธเรศ\nภูบาล\nภูเบศ\nภูเบศวร์\nภูเขา\nภูเขาไฟ\nภูผา\nภูตคาม\nภูตบดี\nภูตรูป\nภูเตศวร\nภูมินทร์\nภูมิบาล\nภูมิประเทศ\nภูมิภาค\nภูมิรัฐศาสตร์\nภูมิลำเนา\nภูมิศาสตร์\nภูมิอากาศ\nภูมิธรรม\nภูมิปัญญา\nภูมิรู้\nภูมิใจ\nภูมิฐาน\nภูมิคุ้มกัน\nภูมิแพ้\nภูษาโยง\nเภทภัย\nเภสัชกร\nเภสัชกรรม\nเภสัชวิทยา\nเภสัชศาสตร์\nโภคทรัพย์\nโภคภัณฑ์\nโภชนากร\nโภชนาการ\nมกุฎราชกุมาร\nมงคลแฝด\nมงคลสูตร\nมงคลหัตถี\nมณเฑียรบาล\nมดดำ\nมดแดง\nมดเท็จ\nมดยอบ\nมดลูก\nมธุปายาส\nมธุรส\nมนเทียรบาล\nมนุษย์กบ\nมโนกรรม\nมโนคติ\nมโนทุจริต\nมโนธรรม\nมโนภาพ\nมโนมัย\nมโนรถ\nมโนรมย์\nมโนสุจริต\nมรรคนายก\nมรรคผล\nมฤคชาติ\nมฤคทายวัน\nมฤคราช\nมลทิน\nมลพิษ\nมลสาร\nมวกเหล็ก\nม้วนหน้า\nมวยไทย\nมวยปล้ำ\nมวยล้ม\nมวยวัด\nมวยสากล\nมวยหมู่\nมวลสาร\nมอคราม\nมอซอ\nมอหมึก\nมองเมียง\nมอบตัว\nมอบหมาย\nมอมเมา\nมะขามเทศ\nมะขามป้อม\nมะขามเปียก\nมะเขือเทศ\nมะเขือพวง\nมะพร้าวแก้ว\nมักคุ้น\nมักจี่\nมักง่าย\nมักน้อย\nมักมาก\nมักใหญ่\nมั่งคั่ง\nมั่งมี\nมัจจุราช\nมัชฌิมนิกาย\nมัชฌิมประเทศ\nมัชฌิมยาม\nมัชฌิมวัย\nมัดจำ\nมัดหมี่\nมัธยมกาล\nมัธยมศึกษา\nมันแกว\nมันเทศ\nมันฝรั่ง\nมันเปลว\nมันสมอง\nมั่นคง\nมั่นใจ\nมั่นหมาย\nมั่นเหมาะ\nมัวเมา\nมัวหมอง\nมั่วสุม\nม้าเทศ\nม้าน้ำ\nม้ามืด\nม้าเร็ว\nม้าล่อ\nม้าลาย\nมากมาย\nมาตรการ\nมาตรฐาน\nมาตราส่วน\nมาตุคาม\nมาตุฆาต\nมาตุภูมิ\nม่านตา\nม่านบังตา\nมายากร\nมายากล\nมายาการ\nมายาวี\nมารผจญ\nมารวิชัย\nมารสังคม\nมารหัวขน\nมาลาการ\nมิ่งขวัญ\nมิ่งมิตร\nมิจฉาจาร\nมิจฉาชีพ\nมิดชิด\nมิดเมี้ยน\nมิดหมี\nมิตรจิต\nมิตรภาพ\nมิตรสหาย\nมิน่า\nมีหน้า\nมีดโกน\nมีดดาบ\nมีดโต้\nมีดพก\nมีดพับ\nมีดสั้น\nมึนงง\nมึนชา\nมึนตึง\nมึนเมา\nมืดครึ้ม\nมืดมน\nมืดมัว\nมือจับ\nมือดี\nมือเติบ\nมือปืน\nมือเปล่า\nมือมืด\nมือสอง\nมือเสือ\nมือหนึ่ง\nมือใหม่\nมุกตลก\nมุขปาฐะ\nมุขมนตรี\nมุ่งมั่น\nมุ่งมาด\nมุ่งหน้า\nมุ่งหมาย\nมุ่งหวัง\nมุ้งลวด\nมุ้งสายบัว\nมุมก้ม\nมุมกลับ\nมุมเงย\nมุมฉาก\nมุมตรง\nมุมป้าน\nมุมมืด\nมุมแย้ง\nมุมสะท้อน\nมุมหักเห\nมุมแหลม\nมุสาวาท\nมูกเลือด\nมูกมัน\nมูกหลวง\nมูนดิน\nมูลฐาน\nมูลนาย\nมูลนิธิ\nมูลเหตุ\nมูลค่า\nมูลฝอย\nเม็ดเงิน\nเม็ดเลือด\nเม็ดโลหิต\nเม่นทะเล\nเมรุมาศ\nเมรุราช\nเมล์อากาศ\nเมาดิบ\nเมามัน\nเมามัว\nเมามาย\nเมินเฉย\nเมียน้อย\nเมียหลวง\nเมียงมอง\nเมี่ยงลาว\nเมี่ยงส้ม\nเมื่อกี้\nเมื่อตะกี้\nเมื่อใด\nเมื่อไร\nเมื่อไหร่\nเมื่อนั้น\nเมืองขึ้น\nเมืองท่า\nเมืองนอก\nเมืองหลวง\nเมื่อยขบ\nเมื่อยล้า\nแม่กอง\nแม่กุญแจ\nแม่คุณ\nแม่งาน\nแม่เจ้า\nแม่ชี\nแม่ทัพ\nแม่นม\nแม่น้ำ\nแม่บท\nแม่บ้าน\nแม่เบี้ย\nแม่พระ\nแม่พิมพ์\nแม่เพลง\nแม่มด\nแม่ม่าย\nแม่ไม้\nแม่ยก\nแม่ยาย\nแม่ร้าง\nแม่เรือน\nแม่แรง\nแม่เล้า\nแม่เลี้ยง\nแม่สี\nแม่สื่อ\nแม่เหล็ก\nแมงมุม\nแม่นยำ\nแมลงช้าง\nแมลงวัน\nแมลงปอ\nแมลงภู่\nแมลงเม่า\nแมวเซา\nแมวน้ำ\nแมวป่า\nแมวมอง\nไม้กลัด\nไม้กวาด\nไม้กางเขน\nไม้เกาหลัง\nไม้ขีดไฟ\nไม้จิ้มฟัน\nไม้เด็ด\nไม้ตาย\nไม้ตีพริก\nไม้ที\nไม้เท้า\nไม้บรรทัด\nไม้เมตร\nไม้ระแนง\nไม้เรียว\nไม้หมอน\nไม้อัด\nไม้จัตวา\nไม้ตรี\nไม้ไต่คู้\nไม้โท\nไม้ผัด\nไม้มลาย\nไม้ม้วน\nไม้ยมก\nไม้หน้า\nไม้หันอากาศ\nไม้เอก\nยกกลีบ\nยกครู\nยกเครื่อง\nยกเค้า\nยกทรง\nยกฟ้อง\nยกเมฆ\nยกยอ\nยกย่อง\nยกเลิก\nยกเว้น\nย่นย่อ\nยมทูต\nยมบาล\nยมราช\nยมโลก\nยวดยิ่ง\nยวดยาน\nยวนยี\nยวบยาบ\nย่อท้อ\nย่อส่วน\nย่อหน้า\nย่อหย่อน\nยอกย้อน\nยองใย\nย่องเบา\nย่องแย่ง\nยอดเยี่ยม\nยอดอก\nย้อนยอก\nย้อนรอย\nย้อนศร\nย้อนแสง\nย้อนหลัง\nยอบแยบ\nยอมความ\nย่อมเยา\nย่อยยับ\nยักยอก\nยักย้าย\nยักเยื้อง\nยัญกรรม\nยัญพิธี\nยัดเยียด\nยับเยิน\nยับยั้ง\nยั่วยวน\nยั่วยุ\nยั่วเย้า\nยากวาด\nยากันยุง\nยาเขียว\nยาใจ\nยาฉุน\nยาชา\nยาซัด\nยาดอง\nยาแดง\nยาถ่าย\nยาธาตุ\nยานัตถุ์\nยาเบื่อ\nยาโป๊\nยาแฝด\nยาพิษ\nยาระบาย\nยาสลบ\nยาสั่ง\nยาสีฟัน\nยาสูบ\nยาเส้น\nยาเสพติด\nยาหม่อง\nยาเหลือง\nย่าทวด\nย่านาง\nยากแค้น\nยากจน\nยากเย็น\nยากไร้\nยางนอก\nยางใน\nยางมะตอย\nยางมะตูม\nยางลบ\nยางสน\nยางอาย\nย่างกราย\nย่างเยื้อง\nย่างสด\nย่างสามขุม\nย่างเหยียบ\nยานเกราะ\nยานพาหนะ\nยานอวกาศ\nยานคาง\nยายทวด\nยาวเฟื้อย\nยาวยืด\nยาวเหยียด\nยำทวาย\nยำใหญ่\nยำเกรง\nยำเยง\nย่ำต๊อก\nย่ำยี\nย่ำแย่\nยิงเป้า\nยิ่งนัก\nยิ่งยวด\nยิ่งใหญ่\nยินดี\nยินยอม\nยินร้าย\nยิ้มกริ่ม\nยิ้มแฉ่ง\nยิ้มแต้\nยิ้มแป้น\nยิ้มเผล่\nยิ้มเยาะ\nยิ้มแย้ม\nยียวน\nยึดครอง\nยึดถือ\nยึดมั่น\nยึดเหนี่ยว\nยืดยาด\nยืดยาว\nยืดเยื้อ\nยืดหยุ่น\nยืดอก\nยืนกราน\nยืนต้น\nยืนพื้น\nยืนยง\nยืนยัน\nยืนหยัด\nยื้อยุด\nยุยง\nยุแยง\nยุแหย่\nยุคลบาท\nยุคเข็ญ\nยุคทอง\nยุคมืด\nยุ่งขิง\nยุ่งยาก\nยุ่งเหยิง\nยุติธรรม\nยุทธการ\nยุทธนาวี\nยุทธปัจจัย\nยุทธภัณฑ์\nยุทธภูมิ\nยุทธวิธี\nยุทธศาสตร์\nยุทธหัตถี\nยุทธนาการ\nยุทธนาธิการ\nยุบยับ\nยุบยิบ\nยุพราช\nยู่ยี่\nเย็นเจี๊ยบ\nเย็นใจ\nเย็นฉ่ำ\nเย็นเฉียบ\nเย็นชา\nเย็นชืด\nเย็นตา\nเย็นเยียบ\nเย็นเยือก\nเย็นวาบ\nเย็นวูบ\nเย็บกี่\nเย็บจักร\nเย็บด้าย\nเย้ยหยัน\nเย้าหยอก\nเยาะเย้ย\nเยี่ยมกราย\nเยี่ยมเยียน\nเยี่ยมเยือน\nเยี่ยมยอด\nเยื่อเคย\nเยื่อใย\nเยือกเย็น\nเยื้องกราย\nเยื้องยัก\nเยื้องย่าง\nแยกย้าย\nแยกแยะ\nแย่งชิง\nแยบคาย\nแยบยล\nแย้มพราย\nแย้มยิ้ม\nแย้มสรวล\nโยเย\nโย้เย้\nโยกโคลง\nโยกย้าย\nโยกโย้\nโยนกลอง\nใยหิน\nรกชัฏ\nรกร้าง\nรกเรี้ยว\nรกเรื้อ\nรกราก\nรงควัตถุ\nรชนีกร\nรถกระบะ\nรถเก๋ง\nรถเข็น\nรถแข่ง\nรถจักร\nรถจี๊ป\nรถตู้\nรถทัวร์\nรถบรรทุก\nรถพ่วง\nรถพยาบาล\nรถไฟ\nรถไฟฟ้า\nรถม้า\nรถเมล์\nรถยนต์\nรถราง\nรถลาก\nรถสปอร์ต\nรถสิบล้อ\nรบกวน\nรบรา\nรบเร้า\nรมดำ\nร่มเกล้า\nร่มชูชีพ\nร่มเย็น\nร่มรื่น\nร่วงโรย\nรวงผึ้ง\nรวงรัง\nรวดเร็ว\nรวนเร\nรวบยอด\nรวบรวม\nรวบรัด\nรวมพล\nรวมหัว\nร่วมใจ\nร่วมเพศ\nร่วมมือ\nร่วมรัก\nร่วมสมัย\nรวยริน\nรวยรื่น\nรสชาติ\nรสนิยม\nรองท้อง\nรองเท้า\nรองพื้น\nร่องน้ำ\nร่องรอย\nร้องขอ\nร้องทุกข์\nร้องเรียน\nร้องห่ม\nร้องไห้\nรองช้ำ\nรองทรง\nรอดชีวิต\nรอดตัว\nรอดตาย\nรอนแรม\nร่อนเร่\nร้อนใจ\nร้อนตัว\nร้อนรน\nร้อนรุ่ม\nร้อนวิชา\nร้อนอาสน์\nรอบจัด\nรอบเดือน\nรอบรู้\nรอยร้าว\nร่อยหรอ\nร้อยละ\nร้อยกรอง\nร้อยแก้ว\nร้อยหวาย\nระนาดทุ้ม\nระนาดเอก\nระเบิดขวด\nระเบิดมือ\nระเบียบการ\nรักใคร่\nรักษาการ\nรักษาการณ์\nรังไข่\nรังแตน\nรังนก\nรังผึ้ง\nรังเพลิง\nรังมด\nรังสรรค์\nรังสฤษฏ์\nรั้งรอ\nรังสีแพทย์\nรังสีวิทยา\nรัชกาล\nรัชทายาท\nรัชนีกร\nรัฐธรรมนูญ\nรัฐบาล\nรัฐบุรุษ\nรัฐประศาสน์\nรัฐประหาร\nรัฐพิธี\nรัฐมนตรี\nรัฐวิสาหกิจ\nรัฐศาสตร์\nรัฐสภา\nรัดกุม\nรัดเกล้า\nรัดตัว\nรัดประคด\nรัดรึง\nรัดรูป\nรัตติกาล\nรับขวัญ\nรับจ้าง\nรับช่วง\nรับใช้\nรับซื้อ\nรับทราบ\nรับประกัน\nรับประทาน\nรับปาก\nรับผิด\nรับผิดชอบ\nรับฟ้อง\nรับฟัง\nรับมือ\nรับรอง\nรับรู้\nรับสมัคร\nรับสั่ง\nรับหน้า\nรับเหมา\nรั่วไหล\nรามือ\nร่าเริง\nรากแก้ว\nรากขวัญ\nรากฐาน\nรากฟัน\nรากศัพท์\nรากเหง้า\nร่างกาย\nร่างแห\nร้างรา\nราชกรณียกิจ\nราชการ\nราชกิจ\nราชครู\nราชฐาน\nราชทัณฑ์\nราชทินนาม\nราชทูต\nราชธานี\nราชนาวี\nราชบัณฑิต\nราชบัลลังก์\nราชบาตร\nราชบุตร\nราชปะแตน\nราชภัฏ\nราชมัล\nราชยาน\nราชรถ\nราชลัญจกร\nราชเลขาธิการ\nราชเลขานุการ\nราชวงศ์\nราชวัติ\nราชสกุล\nราชสมบัติ\nราชสาส์น\nราชหัตถเลขา\nราชองครักษ์\nราชโองการ\nราชาคณะ\nราชาศัพท์\nราชินีนาถ\nร้านชำ\nร้านรวง\nราบคาบ\nราบรื่น\nราบเรียบ\nรายการ\nรายงาน\nรายจ่าย\nรายได้\nรายทาง\nรายรับ\nรายล้อม\nรายละเอียด\nรายวิชา\nร่ายยาว\nร่ายรำ\nร้ายกาจ\nร้ายแรง\nราวนม\nราวป่า\nร้าวฉาน\nร้าวราน\nรำพัด\nรำแพน\nรำวง\nร่ำไป\nร่ำร้อง\nร่ำเรียน\nร่ำไร\nร่ำลา\nร่ำไห้\nริเริ่ม\nริอ่าน\nริมฝีปาก\nริ้วรอย\nรีบร้อน\nรีบรุด\nรีบเร่ง\nรื่นรมย์\nรื่นเริง\nรื้อถอน\nรื้อฟื้น\nรุกฆาต\nรุกราน\nรุกล้ำ\nรุกไล่\nรุ่งขึ้น\nรุ่งแจ้ง\nรุ่งเช้า\nรุ่งเรือง\nรุ่งโรจน์\nรุ่งสว่าง\nรุ่งสาง\nรุ่งอรุณ\nรุจิเรข\nรุดหน้า\nรุนแรง\nรุมเร้า\nรุมล้อม\nรุ่มรวย\nรุ่มร้อน\nรุ่ยร่าย\nรู้แกว\nรู้ความ\nรู้คุณ\nรู้งาน\nรู้จัก\nรู้แจ้ง\nรู้ใจ\nรู้เชิง\nรู้ตัว\nรู้ทัน\nรู้เท่า\nรู้เรื่อง\nรู้สำนึก\nรู้สึก\nรู้เห็น\nรูปการณ์\nรูปโฉม\nรูปฌาน\nรูปถ่าย\nรูปทรง\nรูปธรรม\nรูปแบบ\nรูปพรรณ\nรูปพรหม\nรูปภพ\nรูปภาพ\nรูปร่าง\nรูปสมบัติ\nเร่ร่อน\nเร่งด่วน\nเร่งมือ\nเร่งรัด\nเร่งรีบ\nเร่งเร้า\nเร้นลับ\nเร่อร่า\nเร่าร้อน\nเราะราย\nเราะร้าย\nเริงใจ\nเริงรมย์\nเริดร้าง\nเริ่มต้น\nเริ่มแรก\nเรี่ยราด\nเรี่ยไร\nเรียกคืน\nเรียกตัว\nเรียกร้อง\nเรียกหา\nเรียบร้อย\nเรียงความ\nเรียงตัว\nเรียงเบอร์\nเรียงพิมพ์\nเรียงเม็ด\nเรียงราย\nเรียนรู้\nเรียบร้อย\nเรียบเรียง\nเรียบวุธ\nเรี่ยมเร้\nเรี่ยวแรง\nเรือกลไฟ\nเรือกอและ\nเรือกำปั่น\nเรือจ้าง\nเรือดำน้ำ\nเรือโดยสาร\nเรือตรวจการณ์\nเรือตังเก\nเรือธง\nเรือนำร่อง\nเรือบด\nเรือบิน\nเรือใบ\nเรือประมง\nเรือพ่วง\nเรือพิฆาต\nเรือยนต์\nเรือยาว\nเรือโยง\nเรือรบ\nเรือลากจูง\nเรือสำปั้น\nเรือสำเภา\nเรือหลวง\nเรือหางยาว\nเรืออีโปง\nเรือเอี้ยมจุ๊น\nเรื้อรัง\nเรือกสวน\nเรืองนาม\nเรืองรอง\nเรืองแสง\nเรื่องราว\nเรื่องสั้น\nเรือนแก้ว\nเรือนจำ\nเรือนเบี้ย\nเรือนแพ\nเรือนหอ\nเรื่อยเจื้อย\nเรื่อยเฉื่อย\nเรื่อยเปื่อย\nแรเงา\nแรกนา\nแรกนาขวัญ\nแรงงาน\nแรงดึงดูด\nแรงเทียน\nแรงม้า\nแรงเหวี่ยง\nแรมรอน\nแรมรา\nแรมโรย\nโรคจิต\nโรงครัว\nโรงงาน\nโรงเจ\nโรงเตี๊ยม\nโรงทาน\nโรงนา\nโรงพยาบาล\nโรงพัก\nโรงพิมพ์\nโรงเรียน\nโรงเรือน\nโรงแรม\nโรงเลี้ยง\nโรงเลื่อย\nโรงสี\nโรงสีข้าว\nโรงอาหาร\nโรมรัน\nโรยรา\nฤชากร\nฤดูกาล\nลงขัน\nลงแขก\nลงคอ\nลงตัว\nลงท้าย\nลงทุน\nลงโทษ\nลงพุง\nลงมือ\nลงรัก\nลงรอย\nลงแรง\nลงโรง\nลงเอย\nลดตัว\nลดละ\nลดเลี้ยว\nลดหย่อน\nลดหลั่น\nลนลาน\nล้นพ้น\nล้นหลาม\nล้นเหลือ\nลบล้าง\nลบเลือน\nลบหลู่\nลมกรด\nลมค้า\nลมงวง\nลมแดด\nลมทะเล\nลมบก\nลมบน\nลมบ้าหมู\nลมปราณ\nลมปาก\nลมพิษ\nลมว่าว\nลมเสีย\nลมหนาว\nลมหายใจ\nล่มจม\nล่มสลาย\nล้มละลาย\nล้มลุก\nล้มเลิก\nล่วงเกิน\nล่วงรู้\nล่วงละเมิด\nล่วงลับ\nล่วงล้ำ\nล่วงเลย\nล่วงหน้า\nลวดลาย\nลวดสปริง\nลวดหนาม\nล้วนแล้ว\nลหุโทษ\nล่อลวง\nล่อหลอก\nล่อแหลม\nล้อต๊อก\nล้อเลื่อน\nล้อเล่น\nล้อเลียน\nล้อหลอก\nลองเชิง\nลองดี\nลองภูมิ\nล่องหน\nลอดช่อง\nล่อนจ้อน\nลอบกัด\nล้อมวง\nลอยแก้ว\nลอยชาย\nลอยตัว\nลอยนวล\nลอยแพ\nลอยลำ\nละทิ้ง\nละเลย\nละเว้น\nละครนอก\nละครใน\nละครเพลง\nละครร้อง\nละครรำ\nละครลิง\nละครสัตว์\nละเอียดอ่อน\nลักไก่\nลักพา\nลักเพศ\nลักยิ้ม\nลักลอบ\nลักลั่น\nลักหลับ\nลัดเลาะ\nลับตา\nลับแล\nลับหลัง\nลาออก\nล่าช้า\nล่าทัพ\nล้าสมัย\nล้าหลัง\nลากข้าง\nล้างบาง\nล้างผลาญ\nลาดเขา\nลาดตระเวน\nลาดเท\nลาดยาง\nลานบิน\nลาภปาก\nลาภลอย\nลามปาม\nลามเลีย\nลายคราม\nลายเซ็น\nลายแทง\nลายน้ำ\nลายพร้อย\nลายมือ\nลายลักษณ์\nลายเส้น\nลำกล้อง\nลำแข้ง\nลำธาร\nลำแสง\nลำไส้\nลำตัด\nลำนำ\nล่ำสัน\nล้ำยุค\nล้ำสมัย\nล้ำลึก\nล้ำเลิศ\nล้ำเส้น\nล้ำหน้า\nลิงจุ่น\nลิงลม\nลิงโลด\nลิดรอน\nลิ้นไก่\nลิ้นชัก\nลิ้นปี่\nลิ้นควาย\nลิ้นงูเห่า\nลิ้นหมา\nลิบลับ\nลิบลิ่ว\nลิ่มเลือด\nลี้ภัย\nลี้ลับ\nลึกซึ้ง\nลึกลับ\nลึกล้ำ\nลืมตน\nลืมต้น\nลืมตัว\nลืมตา\nลืมเลือน\nลือชา\nลือชื่อ\nลือลั่น\nลุล่วง\nลุกลน\nลุกลาม\nลุกลี้ลุกลน\nลุกฮือ\nลุ่มน้ำ\nลุ่มลึก\nลุ่มหลง\nลุ่ทาง\nลูกกรง\nลูกกรอก\nลูกกรุง\nลูกกลอน\nลูกกลิ้ง\nลูกกวาด\nลูกกะจ๊อก\nลูกกุญแจ\nลูกเกด\nลูกแก้ว\nลูกขนไก่\nลูกข่าง\nลูกขุน\nลูกเขย\nลูกครึ่ง\nลูกคลื่น\nลูกความ\nลูกคอ\nลูกค้า\nลูกคิด\nลูกคู่\nลูกจ้าง\nลูกช้าง\nลูกชิด\nลูกชิ้น\nลูกชุบ\nลูกซอง\nลูกโซ่\nลูกดอก\nลูกดิ่ง\nลูกตะกั่ว\nลูกตุ้ม\nลูกเต้า\nลูกเต๋า\nลูกถ้วย\nลูกทุ่ง\nลูกเธอ\nลูกน้อง\nลูกน้ำ\nลูกนิมิต\nลูกบอล\nลูกบ้าน\nลูกบาศก์\nลูกบิด\nลูกเบี้ยว\nลูกประคบ\nลูกประคำ\nลูกปัด\nลูกปืน\nลูกโป่ง\nลูกผสม\nลูกผู้ชาย\nลูกผู้หญิง\nลูกพรรค\nลูกพี่\nลูกฟูก\nลูกไฟ\nลูกมือ\nลูกโม่\nลูกไม้\nลูกยาเธอ\nลูกรอก\nลูกรัง\nลูกเรือ\nลูกล้อ\nลูกลอย\nลูกเล่น\nลูกเลี้ยง\nลูกโลก\nลูกวัด\nลูกศร\nลูกศิษย์\nลูกสมุน\nลูกสะใภ้\nลูกสูบ\nลูกเสือ\nลูกหนัง\nลูกหนี้\nลูกหนู\nลูกหมาก\nลูกหลง\nลูกหลาน\nลูกหาบ\nลูกหิน\nลูกเห็บ\nลูกเหม็น\nลูกแหง่\nลูกอม\nลูกหม้อ\nลูบคม\nลูบคลำ\nลูบไล้\nเล็กน้อย\nเลขคณิต\nเลขผา\nเลขหมาย\nเล็ดลอด\nเล่นงาน\nเล่นแง่\nเล่นชู้\nเล่นตัว\nเล่นลิ้น\nเล่นหัว\nเลนส์นูน\nเลนส์เว้า\nเล็บครุฑ\nเลยเถิด\nเลศนัย\nเล่ห์กล\nเล่ห์เหลี่ยม\nเลอโฉม\nเลอมาน\nเลอเลิศ\nเลอสรวง\nเล่อล่า\nเลอะเลือน\nเล่าเรียน\nเล่าลือ\nเลาะลัด\nเลิกรา\nเลิกร้าง\nเลิกล้ม\nเลิศเลอ\nเลี้ยงชีพ\nเลี้ยงดู\nเลี้ยงต้อย\nเลียบเคียง\nเลี้ยวลด\nเลือกตั้ง\nเลือกเฟ้น\nเลือกสรร\nเลื่องลือ\nเลือดกำเดา\nเลือดเนื้อ\nเลือดฝาด\nเลือดเย็น\nเลือดร้อน\nเลือดหมู\nเลือดอุ่น\nเลือนราง\nเลื่อนเปื้อน\nเลื่อนลอย\nเลื่อมพราย\nเลื่อมใส\nเลื่อยฉลุ\nเลื่อยลันดา\nเลื่อยวงเดือน\nเลื้อยคลาน\nแลเหลียว\nแลกเปลี่ยน\nแล้วกัน\nและเล็ม\nโล่งใจ\nโล่งโถง\nโล่งอก\nโลดเต้น\nโลดโผน\nโลดลิ่ว\nโลดแล่น\nไล่ที่\nไล่เบี้ย\nไล่เลี่ย\nไล่เลียง\nไล่หลัง\nไล่ออก\nวกวน\nวงกบ\nวงกลม\nวงการ\nวงแขน\nวงเงิน\nวงจร\nวงนอก\nวงใน\nวงรี\nวงเล็บ\nวงเวียน\nวงแหวน\nวงศ์วาน\nวจีกรรม\nวจีเภท\nวจีภาค\nวนเวียน\nวอดวาย\nว็อบแว็บ\nวังวน\nวังหน้า\nวังหลวง\nวังหลัง\nวัดราษฎร์\nวัดวา\nวัดหลวง\nวัดผล\nวัดพื้น\nวัตถุนิยม\nวัตถุประสงค์\nวัตรปฏิบัติ\nวันโกน\nวันพระ\nวันเพ็ญ\nวัยรุ่น\nวัยวุฒิ\nว่ากล่าว\nว่าจ้าง\nว่าด้วย\nว่าที่\nวางก้าม\nวางใจ\nวางตัว\nวางตา\nวางโต\nวางท่า\nวางมวย\nวางมาด\nวางมือ\nวางวาย\nว่างเปล่า\nว่างเว้น\nวาดเขียน\nว่านเครือ\nวาบหวาม\nวายชนม์\nวายปราณ\nวายวาง\nวายวอด\nวายร้าย\nวายุภักษ์\nวาววับ\nวาววาม\nวาวแวว\nวาวแสง\nวิกฤตการณ์\nวิกฤติการณ์\nวิกฤตกาล\nวิกฤติกาล\nวิกลจริต\nวิงเวียน\nวิ่งเต้น\nวิ่งผลัด\nวิ่งรอก\nวิ่งราว\nวิจิตรศิลป์\nวิชาการ\nวิชาชีพ\nวิชาธร\nวิญญูชน\nวิดพื้น\nวิตกจริต\nวิถีทาง\nวิทยากร\nวิทยากล\nวิทยาการ\nวิทยาเขต\nวิทยาทาน\nวิทยาธร\nวิทยานิพนธ์\nวิทยาศาสตร์\nวิเทศสัมพันธ์\nวิธีการ\nวินัยธร\nวินัยปิฎก\nวินาศกรรม\nวินาศภัย\nวินาศสันตะโร\nวิภัชพยากรณ์\nวิภัชวาที\nวิไลวรรณ\nวิสัญญีแพทย์\nวิสัญญีภาพ\nวิสัญญีวิทยา\nวุฒิบัตร\nวุฒิสภา\nวุฒิสมาชิก\nวุ่นวาย\nวุ้นเส้น\nวูบวาบ\nเวจกุฎี\nเวจมรรค\nเวชกรรม\nเวชภัณฑ์\nเวชศาสตร์\nเวทมนตร์\nเวนคืน\nเวรกรรม\nเวฬุการ\nเวฬุวัน\nเว้าวอน\nเวิ้งว้าง\nเวียงวัง\nเวียนเทียน\nแว้งกัด\nแวดล้อม\nแวดวง\nแว่นขยาย\nแว่นแคว้น\nแว่นตา\nแวบวับ\nแววตา\nแวววาม\nแวววาว\nแวะเวียน\nโวยวาย\nไวไฟ\nไว้ใจ\nไว้ชื่อ\nไว้ตัว\nไว้ทุกข์\nไว้ลาย\nไว้หน้า\nไว้อาลัย\nศนิวาร\nศอกกลับ\nศอกกำ\nศอกกำมา\nศักดิ์ศรี\nศักดิ์สิทธิ์\nศารทวิษุวัติ\nศาลแขวง\nศาลจังหวัด\nศาลชั้นต้น\nศาลฎีกา\nศาลเตี้ย\nศาลทหาร\nศาลปกครอง\nศาลพระภูมิ\nศาลเพียงตา\nศาลแพ่ง\nศาลรัฐธรรมนูญ\nศาลแรงงาน\nศาลล้มละลาย\nศาลโลก\nศาลสูง\nศาลสูงสุด\nศาลอาญา\nศาลอุทธรณ์\nศาลากลาง\nศาลาดิน\nศาลาราย\nศาลาวัด\nศิลาฤกษ์\nศิลาแลง\nศิษย์เก่า\nศิษย์เอก\nศีลจุ่ม\nศีลธรรม\nศีลวัต\nศีลอด\nศูนย์กลาง\nศูนย์การค้า\nศูนย์ถ่วง\nศูนย์สูตร\nศูนย์หน้า\nเศร้าใจ\nเศร้าโศก\nเศร้าสร้อย\nเศร้าสลด\nเศร้าหมอง\nเศวตฉัตร\nเศษเกิน\nเศษซ้อน\nเศษวรรค\nเศษส่วน\nเศษเหล็ก\nโศกนาฏกรรม\nโศกศัลย์\nโศกเศร้า\nโศกสลด\nสกลโลก\nส่งเดช\nส่งท้าย\nส่งเสริม\nส่งเสีย\nส่งเสียง\nสงบเงียบ\nสงบเสงี่ยม\nสง่างาม\nสง่าราศี\nสดชื่น\nสดใส\nสตรีเพศ\nสติปัญญา\nสถลมารค\nสถานกงสุล\nสถานที่\nสถานทูต\nสถานการณ์\nสถานภาพ\nสถิติศาสตร์\nสนตะพาย\nสนใจ\nส้นตีน\nสนธิสัญญา\nสนนราคา\nสนับแข้ง\nสนับเพลา\nสนับมือ\nสนามบิน\nสนามเพลาะ\nสนิทสนม\nสนิมขุม\nสนิมสร้อย\nสนุกสนาน\nสบประมาท\nสบายใจ\nสภาพธรรม\nสมควร\nสมจริง\nสมใจ\nสมนัย\nสมน้ำหน้า\nสมประกอบ\nสมส่วน\nสมหวัง\nสมคบ\nสมทบ\nสมยอม\nสมรัก\nสมรู้\nสมสู่\nส้มฉุน\nส้มตำ\nส้มลิ้ม\nส้มกุ้ง\nส้มเช้า\nสมญานาม\nสมมติฐาน\nสมมุติฐาน\nสมมติเทพ\nสมรภูมิ\nสมัครใจ\nสมัยนิยม\nสมุทรศาสตร์\nสมุทรเสนา\nสยดสยอง\nสยองขวัญ\nสยามรัฐ\nสรรหา\nสรวมชีพ\nสรวลเส\nสร้อยเศร้า\nสร้างสรรค์\nสร้างเสริม\nสลดใจ\nสลบไสล\nสละสลวย\nสลาเหิน\nสลากภัต\nสวนครัว\nสวนป่า\nสวนสนุก\nสวนหย่อม\nส่วนกลาง\nส่วนเกิน\nส่วนตัว\nส่วนบุญ\nส่วนแบ่ง\nส่วนประกอบ\nส่วนพระองค์\nส่วนผสม\nส่วนรวม\nส่วนร่วม\nส่วนลด\nส่วนสัด\nสวมกอด\nสวมเขา\nสวมรอย\nสวยมภู\nสว่างไสว\nสวามิภักดิ์\nสวิงสวาย\nสสารนิยม\nส่อเสียด\nสอดคล้อง\nสอดแทรก\nสอดแนม\nสอบถาม\nสอบทาน\nสอบไล่\nสอบสวน\nส้อมเสียง\nสะสวย\nสะแกวัลย์\nสะแกแสง\nสะใจ\nสะเด็ดยาด\nสะเทือนใจ\nสะบัดช่อ\nสั่งสม\nสั่งสอน\nสั่งเสีย\nสังเกตการณ์\nสังคมนิยม\nสังคมวิทยา\nสังคมศาสตร์\nสังคมศึกษา\nสังคมสงเคราะห์\nสัญญาบัตร\nสัดส่วน\nสัตการ\nสัตบุรุษ\nสัตบริภัณฑ์\nสัตภัณฑ์\nสัตมหาสถาน\nสัตโลหะ\nสันเขา\nสันดอน\nสันหลัง\nสั่นเทา\nสั่นเทิ้ม\nสันติบาล\nสันติภาพ\nสันติวิธี\nสันติสุข\nสับเปลี่ยน\nสับสน\nสับหลีก\nสับหว่าง\nสัมมาคารวะ\nสัมมาชีพ\nส่าเหล้า\nสากกะเบือ\nสาทิสลักษณ์\nสาธุการ\nสาธุชน\nสาบเสือ\nสาปสรร\nสาปแช่ง\nสาปส่ง\nสามง่าม\nสามล้อ\nสามเหลี่ยม\nสามเวท\nสามัญชน\nสามัญสำนึก\nสายดิ่ง\nสายดิน\nสายตรวจ\nสายน้ำ\nสายบัว\nสายพาน\nสายฟ้า\nสายยาง\nสายยู\nสายใย\nสายรก\nสายรุ้ง\nสายล่อฟ้า\nสายลับ\nสายเลือด\nสายโลหิต\nสายวัด\nสายส่ง\nสายสวาท\nสายสะดือ\nสายสะพาย\nสายสัมพันธ์\nสายสิญจน์\nสายสืบ\nสายไหม\nสายอากาศ\nสายตา\nสายหยุด\nสารตรา\nสารประกอบ\nสารละลาย\nสารส้ม\nสารหนู\nสารทฤดู\nสาวใช้\nสาวน้อย\nสาวใหญ่\nสำนักงาน\nสำนักพิมพ์\nสำนักสงฆ์\nสำมะโนครัว\nสำเร็จรูป\nสิกขาบท\nสิงสถิต\nสิงสู่\nสิ่งก่อสร้าง\nสิ่งของ\nสิ่งปฏิกูล\nสิ่งพิมพ์\nสิ่งแวดล้อม\nสิ่งศักดิ์สิทธิ์\nสิทธิกร\nสิทธิ์ขาด\nสิทธิชัย\nสิทธิโชค\nสิทธิบัตร\nสินค้า\nสินจ้าง\nสินเชื่อ\nสินไถ่\nสินทรัพย์\nสินน้ำใจ\nสินบน\nสินแร่\nสินสมรส\nสินสอด\nสินไหม\nสิ้นเชิง\nสิ้นสุด\nสีผึ้ง\nสีลม\nสีชอล์ก\nสีถ่าน\nสีเทียน\nสีน้ำ\nสีน้ำมัน\nสีโปสเตอร์\nสีฝุ่น\nสี่เหลี่ยม\nสีหน้า\nสึกหรอ\nสืบทอด\nสืบค้น\nสืบสวน\nสืบสาว\nสืบเสาะ\nสื่อผสม\nสื่อมวลชน\nสื่อสาร\nสุกงอม\nสุกดิบ\nสุกปลั่ง\nสุกใส\nสุขนาฏกรรม\nสุขภัณฑ์\nสุขภาพ\nสุขลักษณะ\nสุขวิทยา\nสุขศาลา\nสุขศึกษา\nสุดท้าย\nสุตกวี\nสุนทรพจน์\nสุภาพชน\nสู่ขอ\nสู่รู้\nสู่สม\nสูงส่ง\nสูญเปล่า\nสูญสิ้น\nสูญเสีย\nสูญหาย\nเสสรวล\nเสแสร้ง\nเสกสรร\nเสถียรภาพ\nเส้นชัย\nเส้นตรง\nเส้นตาย\nเส้นทาง\nเส้นใย\nเส้นรุ้ง\nเส้นเลือด\nเส้นแวง\nเส้นสาย\nเส้นเสียง\nเส้นหมี่\nเส้นเอ็น\nเสบียงกรัง\nเสมอภาค\nเสมอหน้า\nเสมอเหมือน\nเสมียนตรา\nเสร็จสรรพ\nเสร็จสิ้น\nเสริมส่ง\nเสริมสร้าง\nเสริมสวย\nเสรีไทย\nเสรีธรรม\nเสรีนิยม\nเสรีภาพ\nเสาเข็ม\nเสาธง\nเสียใจ\nเสียเชิง\nเสียดาย\nเสียที\nเสียเที่ยว\nเสียเปรียบ\nเสียเปล่า\nเสียรู้\nเสียแรง\nเสียสละ\nเสียหลัก\nเสียหาย\nเสี่ยงทาย\nเสียดแทง\nเสียดแทรก\nเสียดสี\nเสี้ยนศึก\nเสี้ยนหนาม\nเสี้ยมสอน\nเสียวซ่าน\nเสียวไส้\nเสือดาว\nเสือดำ\nเสือปลา\nเสือป่า\nเสือไฟ\nเสื่อกก\nเสื่อกระจูด\nเสื่อน้ำมัน\nเสื่อลำแพน\nเสื้อกล้าม\nเสื้อกั๊ก\nเสื้อเกราะ\nเสื้อครุย\nเสื้อแสง\nเสื้อเมือง\nเสือกคลาน\nเสือกสน\nเสือกไส\nเสื่อมคลาย\nเสื่อมถอย\nเสื่อมทราม\nเสื่อมโทรม\nเสื่อมสลาย\nเสื่อมสูญ\nเสื่อมเสีย\nเสือหมอบ\nแสกหน้า\nแสดงออก\nแสเถา\nแสนกล\nแสนรู้\nแสร้งว่า\nใส่ความ\nใส่ไคล้\nใส่ใจ\nใส่ไฟ\nไส้กรอก\nไส้ไก่\nไส้ติ่ง\nไส้ศึก\nไส้อั่ว\nไส้เดือน\nไส้ตัน\nไสยเวท\nไสยศาสตร์\nหกล้ม\nหงส์หยก\nหงอนไก่\nหงอยก๋อย\nหงอยเหงา\nหงายท้อง\nหงายหลัง\nหงำเหงอะ\nหงำเหงือก\nหดหาย\nหดหู่\nหนทาง\nหนวกหู\nหน่วงเหนี่ยว\nหน่วยก้าน\nหน่อไม้\nหนองใน\nหนองแซง\nหนักข้อ\nหนักใจ\nหนักแน่น\nหนักหน่วง\nหนักหนา\nหนังกลับ\nหนังตะลุง\nหนังเรียด\nหนังสด\nหนังใหญ่\nหนังสือพิมพ์\nหนาแน่น\nหน้ากระดาน\nหน้ากาก\nหน้ากาฬ\nหน้าแข้ง\nหน้าจั่ว\nหน้าฉาน\nหน้าตัก\nหน้าตา\nหน้าต่าง\nหน้าท้อง\nหน้าทับ\nหน้าที่\nหน้าที่นั่ง\nหน้าบัน\nหน้าปัด\nหน้าผา\nหน้าผาก\nหน้าม้า\nหน้ามุข\nหน้าไม้\nหน้าเลือด\nหน้าอก\nหนามเตย\nหน่ายหนี\nหน่ายแหนง\nหนาวเหน็บ\nหนำใจ\nหนี้สิน\nหนี้ส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น\nกณิกนันต์\nกณิการ์\nกด\nกตเวทิตา\nกตเวที\nกตัญชลี\nกตัญญุตา\nกตัญญู\nกตาธิการ\nกตาภินิหาร\nกติกา\nกถา\nกถิกาจารย์\nกทลี\nกน\nก่น\nก้น\nกนก\nกนิษฐ์\nกนิษฐา\nกบ\nกบฏ\nกบดาน\nกบทู\nกบาล\nกบินทร์\nกบิล\nกบี่\nกบูร\nกเบนทร์\nกม\nก้ม\nกมณฑลาภิเษก\nกมณฑโลทก\nกมล\nกมลา\nกมลาศ\nกมลาสน์\nกมเลศ\nกมัณฑลุ\nกมุท\nกร\nกรกฎ\nกรกฎาคม\nกรกฏ\nกรง\nกรชกาย\nกรณฑ์\nกรณิการ์\nกรณี\nกรณีย์\nกรณียกิจ\nกรณียะ\nกรด\nกรน\nกรบ\nกรบูร\nกรพินธุ์\nกรม\nกรรกฎ\nกรรกศ\nกรรเกด\nกรรไกร\nกรรเจียก\nกรรชิง\nกรรเชียง\nกรรโชก\nกรรฐ์\nกรรฐา\nกรรณ\nกรรณา\nกรรณิกา\nกรรณิการ์\nกรรดิ\nกรรดิก\nกรรดึก\nกรรตุ\nกรรไตร\nกรรทบ\nกรรแทก\nกรรบิด\nกรรบูร\nกรรภิรมย์\nกรรม\nกรรม์\nกรรม์ภิรมย์\nกรรมชวาต\nกรรมัชวาต\nกรรมาชีพ\nกรรมาธิการ\nกรรมาร\nกรรษก\nกรรสะ\nกรรแสง\nกรวด\nกรวบ\nกรวม\nกร้วม\nกรวย\nกรวิก\nกรสาปน์\nกรสุทธิ์\nกรอ\nกร้อ\nกรอก\nกร็อกกร๋อย\nกรอกแกรก\nกรอง\nกรองกรอย\nกรอด\nกร่อน\nกรอบ\nกรอม\nกร่อย\nกระ\nกระกร\nกระกรุ่น\nกระกลับกลอก\nกระกี้\nกระเกรอก\nกระเกริก\nกระเกริ่น\nกระคน\nกระคาย\nกระงกกระเงิ่น\nกระง่องกระแง่ง\nกระง่อนกระแง่น\nกระเง้ากระงอด\nกระโงก\nกระจก\nกระจง\nกระจร\nกระจอก\nกระจองหง่อง\nกระจ๋องหง่อง\nกระจองอแง\nกระจ้อน\nกระจอนหู\nกระจ้อย\nกระจ๋อหวอ\nกระจะ\nกระจัก\nกระจัง\nกระจัด\nกระจับ\nกระจ่า\nกระจ่าง\nกระจาด\nกระจาน\nกระจาบ\nกระจาม\nกระจาย\nกระจาว\nกระจิก\nกระจิ๋ง\nกระจิด\nกระจิบ\nกระจิ๋ม\nกระจิริด\nกระจิ๋ว\nกระจี้\nกระจี๋\nกระจุก\nกระจุ๋งกระจิ๋ง\nกระจุบ\nกระจุ๊บ\nกระจุ๋มกระจิ๋ม\nกระจุย\nกระจู้\nกระจู๋กระจี๋\nกระจูด\nกระเจอะกระเจิง\nกระเจา\nกระเจ่า\nกระเจ้า\nกระเจาะ\nกระเจิง\nกระเจิดกระเจิง\nกระเจี้ยง\nกระเจี๊ยบ\nกระเจียว\nกระเจี๊ยว\nกระแจะ\nกระโจน\nกระโจม\nกระฉอก\nกระฉ่อน\nกระฉับกระเฉง\nกระฉิ่ง\nกระฉีก\nกระฉูด\nกระเฉก\nกระเฉด\nกระแฉก\nกระโฉกกระเฉก\nกระโฉม\nกระชดกระช้อย\nกระชอน\nกระชอมดอก\nกระช้อย\nกระชัง\nกระชั้น\nกระชับ\nกระชาก\nกระชาย\nกระชิง\nกระชิด\nกระชุ\nกระชุก\nกระชุ่มกระชวย\nกระเชอ\nกระเชา\nกระเช้า\nกระเชียง\nกระแชง\nกระแชะ\nกระโชก\nกระซ่องกระแซ่ง\nกระซับ\nกระซาบ\nกระซิก\nกระซิบ\nกระซี้\nกระซุง\nกระซุบกระซิบ\nกระซุ้ม\nกระซู่\nกระเซ็น\nกระเซอ\nกระเซอะกระเซอ\nกระเซอะกระเซิง\nกระเซ้า\nกระเซิง\nกระแซ\nกระแซะ\nกระโซกระเซ\nกระฎี\nกระฎุมพี\nกระดก\nกระด้ง\nกระดนโด่\nกระดวง\nกระดวน\nกระด้วมกระเดี้ยม\nกระดอ\nกระดอง\nกระดองหาย\nกระดอน\nกระดอม\nกระดักกระเดี้ย\nกระดังงัว\nกระดังงา\nกระดาก\nกระด้าง\nกระดางลาง\nกระดาด\nกระดาดขาว\nกระดาน\nกระดานพน\nกระดาษ\nกระดำกระด่าง\nกระดิก\nกระดิ่ง\nกระดิ้ง\nกระดิบ\nกระดี่\nกระดี้กระเดียม\nกระดึง\nกระดืบ\nกระดุ\nกระดุกกระดิก\nกระดุ้งกระดิ้ง\nกระดุบ\nกระดุบกระดิบ\nกระดุม\nกระดูก\nกระเดก\nกระเด้ง\nกระเด็น\nกระเด้า\nกระเดาะ\nกระเดิด\nกระเดี้ย\nกระเดียด\nกระเดือก\nกระเดื่อง\nกระแด็ก\nกระแด้ง\nกระแด้แร่\nกระแด่ว\nกระแดะ\nกระโดก\nกระโดง\nกระโดด\nกระโดน\nกระได\nกระตรับ\nกระตราก\nกระตรุด\nกระตรุม\nกระต้วมกระเตี้ยม\nกระต้อ\nกระต่องกระแต่ง\nกระต๊อบ\nกระต้อยตีวิด\nกระตัก\nกระตั้ว\nกระต่าย\nกระติก\nกระติ๊ด\nกระติบ\nกระตือรือร้น\nกระตุก\nกระตุ้งกระติ้ง\nกระตุ่น\nกระตุ้น\nกระตูบ\nกระเตง\nกระเต็น\nกระเตอะ\nกระเตาะ\nกระเตาะกระแตะ\nกระเตื้อง\nกระแต\nกระแตแต้แว้ด\nกระโตกกระตาก\nกระโตน\nกระถด\nกระถอบ\nกระถั่ว\nกระถาง\nกระถิก\nกระถิน\nกระเถิบ\nกระโถน\nกระทก\nกระทง\nกระทบ\nกระทรวง\nกระทอก\nกระท้อน\nกระท่อนกระแท่น\nกระท่อม\nกระท้อมกระแท้ม\nกระทะ\nกระทั่ง\nกระทั้น\nกระทา\nกระทาย\nกระทาสี\nกระทาหอง\nกระทำ\nกระทิกกระทวย\nกระทิง\nกระทึง\nกระทืบ\nกระทุ\nกระทุง\nกระทุ้ง\nกระทุ่ม\nกระทู้\nกระเท่\nกระเทียบ\nกระเทียม\nกระเทือน\nกระเทื้อม\nกระแทก\nกระแท่น\nกระแทะ\nกระไทชาย\nกระน่อง\nกระนั้น\nกระนี้\nกระแนะกระแหน\nกระโน้น\nกระไน\nกระบก\nกระบม\nกระบวน\nกระบวย\nกระบวร\nกระบอก\nกระบอง\nกระบะ\nกระบัด\nกระบั้วกระเบี้ย\nกระบ่า\nกระบ้า\nกระบาก\nกระบาย\nกระบาล\nกระบิ\nกระบิ้ง\nกระบิด\nกระบิล\nกระบี่\nกระบือ\nกระบุง\nกระบุ่มกระบ่าม\nกระบู้กระบี้\nกระบูน\nกระบูร\nกระเบง\nกระเบญ\nกระเบ็ดกระบวน\nกระเบน\nกระเบา\nกระเบิก\nกระเบียด\nกระเบียน\nกระเบื้อง\nกระแบก\nกระแบะ\nกระโบม\nกระปมกระปำ\nกระปมกระเปา\nกระปรอก\nกระปรอกว่าว\nกระปรี้กระเปร่า\nกระปอก\nกระป้อกระแป้\nกระป่อง\nกระป๋อง\nกระปอดกระแปด\nกระป๋อหลอ\nกระปั้วกระเปี้ย\nกระป่ำ\nกระปุก\nกระปุ๊กลุก\nกระปุ่ม\nกระปุ่มกระป่ำ\nกระปุ่มกระปิ่ม\nกระเป๋า\nกระเปาะ\nกระโปก\nกระโปรง\nกระผม\nกระผลีกระผลาม\nกระผาน\nกระผีก\nกระพรวน\nกระพริ้ม\nกระพอก\nกระพอง\nกระพ้อม\nกระพัก\nกระพัง\nกระพังเหิร\nกระพังโหม\nกระพัด\nกระพัตร\nกระพัน\nกระพั่น\nกระพา\nกระพาก\nกระพี้\nกระพือ\nกระพุ้ง\nกระพุ่ม\nกระเพาะ\nกระเพิง\nกระเพื่อม\nกระแพ้ง\nกระฟัดกระเฟียด\nกระฟูมกระฟาย\nกระมล\nกระมอบ\nกระมอมกระแมม\nกระมัง\nกระมัน\nกระมิดกระเมี้ยน\nกระมุท\nกระเมาะ\nกระย่อง\nกระย่องกระแย่ง\nกระย่อน\nกระย่อม\nกระยา\nกระยาง\nกระยาจก\nกระยาหงัน\nกระยิก\nกระยิ้มกระย่อง\nกระยึกกระยือ\nกระยืดกระยาด\nกระเย้อกระแหย่ง\nกระรอก\nกระเรียน\nกระโรกน้ำข้าว\nกระโรกใหญ่\nกระไร\nกระลด\nกระลบ\nกระลอก\nกระลอม\nกระละหล่ำ\nกระลัด\nกระลับ\nกระลัมพร\nกระลา\nกระลำ\nกระลำพัก\nกระลำพุก\nกระลิง\nกระลี\nกระลุมพาง\nกระลุมพุก\nกระลุมพู\nกระลูน\nกระลู่น์\nกระเล็น\nกระเลียด\nกระเลือก\nกระโลง\nกระวน\nกระวัด\nกระวาด\nกระวาน\nกระวาย\nกระวิน\nกระวี\nกระวีกระวาด\nกระวูดกระวาด\nกระเวน\nกระเวยกระวาย\nกระแวน\nกระโวยกระวาย\nกระษัย\nกระษาปณ์\nกระสง\nกระสบ\nกระสม\nกระสรวล\nกระสร้อย\nกระสวน\nกระสวย\nกระสอบ\nกระสะ\nกระสัง\nกระสัน\nกระสับกระส่าย\nกระสา\nกระสานติ์\nกระสาบ\nกระสาย\nกระสือ\nกระสุงกระสิง\nกระส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ทิง\nกากะเยีย\nกากี\nกาง\nก้าง\nกางเกง\nกางเขน\nก๊าซ\nกาซะลอง\nกาญจนา\nกาฐ\nกาด\nก๊าด\nกาน\nก่าน\nก้าน\nก๊าน\nกานดา\nกานต์\nกานน\nก้านพร้าว\nกานพลู\nกาน้า\nกาบ\nก้าบ\nกาบู\nกาพย์\nกาเฟอีน\nกาแฟ\nกาม\nก้าม\nกามารมณ์\nกามินี\nกาเมสุมิจฉาจาร\nกาย\nก่าย\nกาเยน\nการ\nการณ์\nการ์ด\nการ์ตูน\nการบูร\nการย์\nการวิก\nการเวก\nการะเกด\nการะบุหนิง\nการัณย์\nการันต์\nการางหัวขวาน\nการิตการก\nการิตวาจก\nการุญ\nการุณย์\nกาเรการ่อน\nกาล\nกาลกรรณี\nกาลกิณี\nกาลจักร\nกาลัญญุตา\nกาลัญญู\nกาลัด\nกาลานุกาล\nกาลิก\nกาลี\nกาแล\nกาแล็กซี\nกาแล็กโทส\nกาว\nก้าว\nกาววาว\nกาวาง\nกาแวน\nกาศิก\nกาษฐะ\nกาษา\nกาสร\nกาสะ\nกาสา\nกาสาร\nกาสาวะ\nกาสิโน\nกาหล\nกาหลง\nกาหลา\nกาเหว่า\nกาไหล่\nกาฬ\nกาฬาวก\nกาฮัง\nกำ\nก่ำ\nกำกวม\nกำกัด\nกำกับ\nก้ำกึ่ง\nกำกูน\nก้ำเกิน\nกำเกียง\nกำคูน\nกำจร\nกำจัด\nกำจาย\nกำชับ\nกำชำ\nกำซาบ\nกำซำ\nกำด้น\nกำดัด\nกำดาล\nกำเดา\nกำธร\nกำนล\nกำนัน\nกำนัล\nกำเนิด\nกำบัง\nก่ำบึ้ง\nกำเบ้อ\nกำปอ\nกำปั่น\nกำผลา\nกำพง\nกำพด\nกำพต\nกำพร้า\nกำพราก\nกำพวด\nกำพอง\nกำพืด\nกำพุด\nกำพู\nกำเพลิง\nกำแพง\nกำภู\nกำมลาศน์\nกำมเลศ\nกำมะถัน\nกำมะลอ\nกำมะหยี่\nกำมะหริด\nกำมังละการ\nกำมังวิลิต\nกำมัชพล\nกำยาน\nกำยำ\nกำรอ\nกำราบ\nกำราล\nกำเริบ\nกำไร\nกำลัง\nกำลุง\nกำเลา\nกำไล\nกำสรด\nกำสรวล\nกำหนด\nกำหนัด\nกำเหน็จ\nกำแหง\nกิก\nกิ๊ก\nกิ่ง\nกิ้งก่า\nกิ้งกือ\nกิ้งโครง\nกิจ\nกิจจะ\nกิจจา\nกิดาการ\nกิดาหยัน\nกิตติ\nกิตติมศักดิ์\nกิน\nกินนร\nกินปลี\nกินเปี้ยว\nกินริน\nกินรี\nกิ๊บ\nกิมตึ๋ง\nกิมิชาติ\nกิมิวิทยา\nกิโมโน\nกิโยตีน\nกิระ\nกิริณี\nกิรินท\nกิริเนศวร\nกิริยา\nกิเลน\nกิเลส\nกิโล\nกิโลมกะ\nกิ่ว\nกิ๋ว\nกี\nกี่\nกี้\nกี๊\nกี๋\nกีฏวิทยา\nกีด\nกีตาร์\nกีบ\nกีรติ\nกีฬา\nกึก\nกึง\nกึ่ง\nกึ๋น\nกุ\nกุก\nกุ๊ก\nกุกกุฏ\nกุกกุร\nกุกรรม\nกุ้ง\nกุงอน\nกุงาน\nกุโงก\nกุจี\nกุญแจ\nกุญชร\nกุฎ\nกุฎา\nกุฎี\nกุฎุมพี\nกุฏฐัง\nกุฏิ\nกุณฑ์\nกุณฑล\nกุณฑี\nกุณโฑ\nกุณาล\nกุณี\nกุด\nกุดัง\nกุดั่น\nกุดา\nกุทัณฑ์\nกุน\nกุ๊น\nกุนเชียง\nกุนที\nกุโนกามอ\nกุบ\nกุบกับ\nกุม\nกุ่ม\nกุมฝอย\nกุมภ์\nกุมภนิยา\nกุมภัณฑ์\nกุมภา\nกุมภิล\nกุมภีล์\nกุมเหง\nกุมาร\nกุมารา\nกุมารี\nกุมุท\nกุย\nกุ๊ย\nกุ๋ย\nกุยช่าย\nกุยเฮง\nกุรระ\nกุรุพินท์\nกุเรา\nกุล\nกุลา\nกุลาหล\nกุลี\nกุลีกุจอ\nกุเลา\nกุแล\nกุเวร\nกุศราช\nกุศล\nกุศโลบาย\nกุสุม\nกุสุมภ์\nกุสุมา\nกุสุมาลย์\nกุสุมิตลดาเวลลิตา\nกุหนี\nกุหนุง\nกุหร่า\nกุหล่า\nกุหลาบ\nกุแหละ\nกู\nกู่\nกู้\nกู๊ก\nกูฏ\nกูฏา\nกูณฑ์\nกูด\nกูบ\nกูปรี\nกูรมะ\nกูรมาวตาร\nเก\nเก้\nเก๊\nเก๋\nเกก\nเก๊ก\nเกกมะเหรก\nเก๊กฮวย\nเก้กัง\nเก็ง\nเก่ง\nเก้ง\nเก๋ง\nเกงกอย\nเก่งกาจ\nเกงเขง\nเก๋งเคง\nเก็จ\nเกจิอาจารย์\nเกชา\nเกณฑ์\nเกด\nเก็ด\nเกตุ\nเกน\nเก็บ\nเกม\nเกย\nเกยูร\nเกรง\nเกร็ง\nเกร็ด\nเกรน\nเกร่อ\nเกรอะ\nเกราะ\nเกริก\nเกริน\nเกริ่น\nเกรียก\nเกรียง\nเกรียด\nเกรียน\nเกรียบ\nเกรียม\nเกรียว\nเกรี้ยว\nเกเร\nเกล็ด\nเกลศ\nเกลอ\nเกลา\nเกล้า\nเกลาะ\nเกลี่ย\nเกลี้ย\nเกลียง\nเกลี้ยง\nเกลียด\nเกลียว\nเกลือ\nเกลื้อ\nเกลือก\nเกลื่อน\nเกลื้อน\nเกวัฏ\nเกวียน\nเกศ\nเกศว\nเกศวะ\nเกศา\nเกศินี\nเกศี\nเกษตร\nเกษม\nเกษียณ\nเกษียน\nเกษียร\nเกส\nเกสร\nเกสรี\nเกสา\nเกสี\nเก้อ\nเกอิชา\nเกะ\nเกะกะ\nเกา\nเก่า\nเก้า\nเก๋า\nเกาต์\nเกาทัณฑ์\nเกาบิล\nเกาลัด\nเกาลิน\nเกาไศย\nเกาหลี\nเกาเหลา\nเกาเหลียง\nเก้าอี้\nเกาะ\nเกิ้ง\nเกิด\nเกิน\nเกิบ\nเกีย\nเกียกกาย\nเกียง\nเกี่ยง\nเกี๋ยง\nเกียจ\nเกียด\nเกียน\nเกี้ยมไฉ่\nเกี้ยมอี๋\nเกียร์\nเกียรติ\nเกียรติ์\nเกี่ยว\nเกี้ยว\nเกี๊ยว\nเกี๊ยะ\nเกื้อ\nเกือก\nเกื้อกูล\nเกือบ\nแก\nแก่\nแก้\nแกง\nแก่ง\nแก้ง\nแก๊ง\nแกงได\nแกงแนง\nแกโดลิเนียม\nแกน\nแก่น\nแก๊ป\nแกม\nแก้ม\nแกมมา\nแกรก\nแกร่ง\nแกร็น\nแกรนิต\nแกรไฟต์\nแกร่ว\nแกระ\nแกล\nแกล้ง\nแกลน\nแกลบ\nแกล้ม\nแกลลอน\nแกลเลียม\nแกล้ว\nแกละ\nแกแล\nแกว\nแก้ว\nแกว่ง\nแก๊ส\nแกะ\nโก\nโก่\nโก้\nโก๋\nโกก\nโกกนุท\nโกกิลา\nโกโก้\nโกง\nโก่ง\nโกงกาง\nโก้งเก้ง\nโกงโก้\nโก้งโค้ง\nโกเชาว์\nโกญจนาท\nโกญจา\nโกฏิ\nโกฐ\nโกฐาส\nโกณะ\nโกดัง\nโกทัณฑ์\nโกน\nโก่น\nโก๋น\nโกมล\nโกมุท\nโกเมน\nโกเมศ\nโกย\nโกรก\nโกรกกราก\nโกรง\nโกร่ง\nโกร่งกร่าง\nโกรงเกรง\nโกร๋งเกร๋ง\nโกรญจ\nโกรต๋น\nโกรธ\nโกรธา\nโกร๋น\nโกรม\nโกรย\nโกรศ\nโกโรโกเต\nโกโรโกโรก\nโกโรโกโส\nโกลน\nโกลาหล\nโกไล\nโกวิท\nโกศ\nโกศล\nโกษม\nโกสน\nโกสัช\nโกสินทร์\nโกสีย์\nโกสุม\nโกไสย\nโกหก\nใกล้\nไก\nไก่\nไก๊\nไก๋\nไก่กอม\nไกพัล\nไกร\nไกรพ\nไกรลาส\nไกรศร\nไกรศรี\nไกรสร\nไกรสรี\nไกรสิทธิ\nไกล\nไกล่\nไกลาส\nไกว\nไกวัล\nขงจื๊อ\nขจร\nขจรจบ\nขจัด\nขจ่าง\nขจาย\nขจาว\nขจิต\nขจี\nขจุย\nขเจา\nขณะ\nขด\nขน\nข้น\nขนง\nขนด\nขนบ\nขนม\nขนอง\nขนอน\nขนอบ\nขนัด\nขนัน\nขนาก\nขนาง\nขนาด\nขนาน\nขนาบ\nขนาย\nขนำ\nขนิษฐ\nขนิษฐา\nขนุน\nขนุนนก\nขบ\nขบถ\nขบวน\nขบวร\nขม\nข่ม\nขมงโกรย\nขมวด\nขมวน\nขมอง\nขม่อม\nขมัง\nขมับ\nขมา\nขม้ำ\nขมิ้น\nขมิบ\nขมีขมัน\nขมึง\nขมึงทึง\nขมุ\nขมุกขมัว\nขมุบ\nขโมย\nขยด\nขยม\nขย่ม\nขยอก\nขยอง\nขย่อน\nขย้อน\nขยะ\nขยัก\nขยัน\nขยั้น\nขยับ\nขยาด\nขยาย\nขยำ\nขย้ำ\nขยิก\nขยิบ\nขยิ่ม\nขยี้\nขยุกขยิก\nขยุกขยุย\nขยุบ\nขยุบขยิบ\nขยุม\nขยุ้ม\nขยุย\nขรม\nขรรค์\nขรัว\nขริบ\nขรี\nขรึม\nขรุขระ\nขลบ\nขล้อ\nขลัง\nขลับ\nขลาด\nขลาย\nขลิบ\nขลุก\nขลุกขลัก\nขลุกขลิก\nขลุบ\nขลุม\nขลุ่ย\nขลู\nขลู่\nขวง\nข่วง\nขวด\nข่วน\nขวนขวาย\nขวบ\nขวย\nขวักไขว่\nขวัญ\nขวั้น\nขวับ\nขวับเขวียว\nขวา\nขวาก\nขวาง\nขว้าง\nขวาด\nขวาน\nขวายขวน\nขวาว\nขว้าว\nขวิด\nขอ\nข่อ\nข้อ\nของ\nข้อง\nขอด\nขอน\nข้อน\nขอบ\nขอม\nข่อย\nข้อย\nข่อยหยอง\nขะแจะ\nขะเน็ด\nขะมอมขะแมม\nขะมักเขม้น\nขะมุกขะมอม\nขะยิก\nขะยุก\nขะเย้อแขย่ง\nขัค\nขัง\nขังขอก\nขัช\nขัณฑสกร\nขัณฑสีมา\nขัด\nขัดมอน\nขัตติยมานะ\nขัน\nขั้น\nขันติ\nขันตี\nขันโตก\nขันที\nขันธ์\nขันธาวาร\nขับ\nขัว\nขั้ว\nขา\nข่า\nข้า\nขาก\nขาก๊วย\nขาง\nข่าง\nข้าง\nขาณุ\nขาด\nขาทนียะ\nขาน\nขาบ\nข้าพเจ้า\nขาม\nข่าม\nข้าม\nขาย\nข่าย\nขาล\nขาว\nข่าว\nข้าว\nข้าวอังกุลี\nขำ\nขิก\nขิง\nขิงแกลง\nขิงแครง\nขิด\nขิปสัทโท\nขิม\nขี่\nขี้\nขี้เข็บ\nขีณาสพ\nขีด\nขี้ตังนี\nขีปนาวุธ\nขี้ยอก\nขีระ\nขึง\nขึ้ง\nขึ้น\nขึ้นฉ่าย\nขืน\nขื่น\nขื่อ\nขุก\nขุด\nขุน\nขุ่น\nขุนเพ็ด\nขุม\nขุย\nขู่\nขูด\nเข\nเข้\nเขก\nเข็ง\nเข่ง\nเขจร\nเข็ญ\nเข็ด\nเขดา\nเขต\nเขน\nเข็น\nเข่น\nเขนง\nเขน็ด\nเขนย\nเขบ็จขบวน\nเขบ็ต\nเขม\nเข็ม\nเข้ม\nเข้มขาบ\nเขม็ง\nเขม็ดแขม่\nเขม่น\nเขม้น\nเขม้นขะมัก\nเขมร\nเขมา\nเขม่า\nเขมือบ\nเขย\nเขยก\nเขย่ง\nเขย้อแขย่ง\nเขย่า\nเขยิน\nเขยิบ\nเขยื้อน\nเขรอะ\nเขลง\nเขลอะ\nเขละ\nเขลา\nเขลาะ\nเขว\nเขษม\nเขฬะ\nเขะขะ\nเขา\nเข่า\nเข้า\nเขิง\nเขิน\nเขิบ\nเขี่ย\nเขียง\nเขียด\nเขียดตะปาด\nเขียน\nเขี่ยน\nเขียม\nเขียว\nเขี้ยว\nเขียะ\nเขือ\nเขือง\nเขื่อง\nเขื่อน\nเขือม\nแข\nแข้\nแขก\nแข็ง\nแข่ง\nแข้ง\nแขน\nแข่น\nแข้น\nแขนง\nแขม\nแขม็บ\nแขม่ว\nแขยง\nแขย่ง\nแขวก\nแขวง\nแขวน\nแขวะ\nโข\nโขก\nโขง\nโข่ง\nโขด\nโขดง\nโขน\nโขนง\nโขม\nโขมง\nโขมด\nโขยก\nโขยง\nโขย่ง\nโขยด\nโขลก\nโขลง\nโขลน\nโขษม\nไข\nไข่\nไข้\nไขว่\nไขว้\nคคนะ\nคคนัมพร\nคคนางค์\nคคนานต์\nคง\nคงคา\nคงไคย\nคช\nคชาชาติ\nคชาชีพ\nคชาธาร\nคชาภรณ์\nคณนา\nคณบดี\nคณะ\nคณาจารย์\nคณาธิการ\nคณาธิปไตย\nคณานับ\nคณิกา\nคณิต\nคเณศ\nคด\nคดี\nคติ\nคทา\nคน\nค้น\nคนทา\nคนทิสอ\nคนที\nคนโท\nคนธ์\nคันธ์\nคนธรรพ์\nคเนจร\nคบ\nคม\nคมน์\nคมนาการ\nคมนาคม\nคมิกภัต\nครก\nครบ\nครรชิต\nครรภ\nครรภ์\nครรลอง\nครรโลง\nครรไล\nครวญ\nครวี\nครหา\nครอก\nครอง\nครองแครง\nคร่อเงาะ\nคร่อเทียน\nครอบ\nคร่อม\nคระเมิม\nคระแลง\nคระไล\nคระแวง\nคระหน\nคระหวน\nคระหาย\nคระโหย\nครั่ง\nครั้ง\nครัดเคร่ง\nครัน\nครั่น\nครั้น\nครับ\nครัว\nครา\nคร่า\nคราก\nคราง\nคราญ\nคราด\nคร้าน\nคราบ\nคราม\nคร้าม\nครามครัน\nคราว\nคร่าว\nคราส\nครำ\nคร่ำ\nคร่ำเคร่ง\nคริปทอน\nคริสต์\nครีบ\nครีม\nครีษมายัน\nครึ\nครึกครื้น\nครึกโครม\nครึ่ง\nครึ่ด\nครึน\nครึ้ม\nครืด\nครืน\nครื้น\nครืนครั่น\nครื้นครั่น\nครื้นครึก\nครื้นเครง\nครือ\nครุ\nครุคระ\nครุฑ\nครุ่น\nครุมเครือ\nครุย\nครุวนา\nครู\nครู่\nครูด\nคฤโฆษ\nคฤนถ์\nคฤหบดี\nคฤหัสถ์\nคฤหาสน์\nคลวง\nคลอ\nคล้อ\nคลอก\nคลอง\nคล่อง\nคล้อง\nคลอด\nคลอน\nคล้อย\nคลอรีน\nคลอโรฟอร์ม\nคลอโรฟีลล์\nคละ\nคละคลุ้ง\nคลัก\nคลั่ก\nคลัง\nคลั่ง\nคลัตช์\nคลับคล้าย\nคลับคลา\nคลา\nคล้า\nคลางแคลง\nคลาด\nคลาน\nคลาย\nคล้าย\nคล้ายคลึง\nคล่าว\nคลำ\nคล่ำ\nคล้ำ\nคลิ้งโคลง\nคลิด\nคลินิก\nคลี\nคลี่\nคลึง\nคลื่น\nคลุก\nคลุ้ง\nคลุบ\nคลุม\nคลุ่ม\nคลุ้ม\nควง\nควณ\nควน\nควบ\nควย\nควร\nควัก\nควั่ก\nควั่งคว้าง\nควัน\nควั่น\nคว้า\nควาก\nคว้าง\nควาญ\nควาน\nคว้าน\nความ\nควาย\nคว่าว\nคว่ำ\nควินิน\nควิวคว่าง\nคหกรรม\nคหกรรมศาสตร์\nคหบดี\nคหัฐ\nคอ\nค้อ\nคอก\nค็อกคัส\nค็อกเทล\nคอเคซอยด์\nค่องอ้อย\nคอด\nคอแดง\nคอน\nค่อน\nค้อน\nคอนกรีต\nคอนเดนเซอร์\nคอนแวนต์\nคอนเสิร์ต\nคอม\nค่อม\nค้อม\nคอมพิวเตอร์\nคอมมานโด\nคอมมิวนิสต์\nคอย\nค่อย\nค้อย\nคอยล์\nคอร์ด\nคอแลน\nคอสติกโซดา\nคะ\nค่ะ\nคะไขว่\nคะค้อย\nคะคาน\nคะนน\nคะนอง\nคะน้า\nคะนึง\nคะเน\nคะเนงร้าย\nคะเน็ด\nคะแนน\nคะมำ\nคะยั้นคะยอ\nคะเยอ\nคัก\nคั่ก\nคัคนะ\nคัคนัมพร\nคัคนางค์\nคัคนานต์\nคั่ง\nคังไคย\nคัจฉ\nคัณฑมาลา\nคัณฑสูตร\nคัด\nคัดเค้า\nคัดมอน\nคัดเม็ง\nคัทลียา\nคัน\nคั่น\nคั้น\nคันถรจนาจารย์\nคันธกุฎี\nคันธมาทน์\nคันธารราษฎร์\nคับ\nคับค้อน\nคับคา\nคับแค\nคัพภ์\nคัมภีร์\nคัมภีรภาพ\nคัล\nคั่ว\nคา\nค่า\nค้า\nค่าคบ\nคาง\nค่าง\nค้าง\nคางคก\nค้างคาว\nคาด\nคาถา\nคาทอลิก\nคาน\nค้าน\nคาบ\nคาพยุต\nคาม\nคามวาสี\nคามณีย์\nคามภีร์\nคาย\nค่าย\nคาร์บอน\nคาร์บอเนต\nคาร์บอลิก\nคาร์บูเรเตอร์\nคาร์โบรันดัม\nคาร์โบไฮเดรต\nคารม\nคารวะ\nคาราเต้\nคาราวาน\nคาว\nค่าว\nค้าว\nคาวตอง\nคาวี\nคาวุต\nคาส\nคำ\nค่ำ\nค้ำ\nคำนวณ\nคำนวร\nคำนับ\nคำนัล\nคำนึง\nคำนูณ\nคำฝอย\nคำเพลิง\nคำรน\nคำรบ\nคำราม\nคำแสด\nคำแหง\nคำโอง\nคิก\nคิง\nคิด\nคิมหันต์\nคิรี\nคิลาน\nคิลานะ\nคิว\nคิ้ว\nคี่\nคีต\nคีบ\nคีม\nคีรี\nคีรีบูน\nคึก\nคึ่ก\nคึกคัก\nคืน\nคืบ\nคือ\nคุ\nคุก\nคุกกี้\nคุกคาม\nคุกพาทย์\nคุ้ง\nคุณ\nคุด\nคุดทะราด\nคุต\nคุตติ\nคุ่น\nคุ้น\nคุป\nคุปต์\nคุปติ\nคุม\nคุ่ม\nคุ้ม\nคุย\nคุ้ย\nคุยหฐาน\nคุยหประเทศ\nคุรุ\nคุลา\nคุลิก่า\nคุลีการ\nคุหา\nคู\nคู่\nคู้\nคูณ\nคูถ\nคูน\nคูปอง\nคูเรียม\nคูหา\nเค้ก\nเค้เก้\nเค้ง\nเคณฑะ\nเคด\nเค็ด\nเคน\nเค้น\nเคเบิล\nเค็ม\nเคมี\nเคย\nเครง\nเคร่ง\nเครงครา\nเครงครำ\nเครดิต\nเครน\nเครา\nเคร่า\nเคราหณี\nเคราะห์\nเครียด\nเครียว\nเครือ\nเครื่อง\nเคล้ง\nเคล็ด\nเคล้น\nเคล้า\nเคล่าคล่อง\nเคลิบเคลิ้ม\nเคลิ้ม\nเคลีย\nเคลื่อน\nเคลือบ\nเคว้ง\nเคหะ\nเคหา\nเคอะ\nเค้า\nเคาน์เตอร์\nเคารพ\nเคาะ\nเคาะแคะ\nเคียง\nเคียด\nเคียน\nเคียม\nเคี่ยม\nเคียร\nเคียว\nเคี่ยว\nเคี้ยว\nเคือง\nแค\nแค่\nแค้\nแคแล\nแคดเมียม\nแค็ตตาล็อก\nแคแตร\nแคโทด\nแคน\nแค่น\nแค้น\nแคบ\nแคบหมู\nแคปซูล\nแคม\nแคร่\nแครก\nแครง\nแคระ\nแคลคูลัส\nแคลง\nแคลเซียม\nแคลน\nแคล้ว\nแคล่วคล่อง\nแคลอรี\nแคลิฟอร์เนียม\nแคว\nแควก\nแคว้น\nแคแสด\nแคะ\nโค\nโคก\nโคเคน\nโค่ง\nโค้ง\nโคจร\nโคเซแคนต์\nโคไซน์\nโคตร\nโคแทนเจนต์\nโคธา\nโคน\nโค่น\nโคบอลต์\nโคปผกะ\nโคม\nโคม่า\nโครก\nโครกคราก\nโครง\nโคร่ง\nโคร่งคร่าง\nโครม\nโครเมียม\nโครโมโซม\nโคราช\nโครำ\nโคล\nโคลง\nโคลน\nโควตา\nโคออร์ดิเนต\nใคร\nใคร่\nใคร่ครวญ\nไค\nไค้\nไคร้\nไคร้เครือ\nไคล\nไคล้\nฆนะ\nฆราวาส\nฆ้อง\nฆ่า\nฆาต\nฆาน\nฆานินทรีย์\nเฆี่ยน\nโฆรวิส\nโฆษก\nโฆษณา\nโฆษะ\nโฆษ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nเฉ่ง\nเฉด\nเฉท\nเฉนียน\nเฉพาะ\nเฉย\nเฉลย\nเฉลว\nเฉลา\nเฉลิม\nเฉลี่ย\nเฉลียง\nเฉลี่ยง\nเฉลียบ\nเฉลียว\nเฉวียง\nเฉวียน\nเฉอะแฉะ\nเฉา\nเฉาก๊วย\nเฉาฮื้อ\nเฉาะ\nเฉิด\nเฉิบ\nเฉียง\nเฉียงพร้านางแอ\nเฉียด\nเฉียบ\nเฉียว\nเฉี่ยว\nเฉือน\nเฉื่อย\nแฉ\nแฉ่\nแฉก\nแฉง\nแฉ่ง\nแฉลบ\nแฉล้ม\nแฉะ\nโฉ\nโฉ่\nโฉเก\nโฉ่งฉ่าง\nโฉงเฉง\nโฉด\nโฉนด\nโฉบ\nโฉเบ๊\nโฉม\nโฉลก\nไฉน\nไฉไล\nชก\nชคัตตรัย\nชง\nชงโค\nชงฆ์\nชงฆา\nชงโลง\nชฎา\nชฎามังษี\nชฎามังสี\nชฎิล\nชด\nชน\nชนก\nชนนี\nชนม์\nชนวน\nชนะ\nชนัก\nชนา\nชนาง\nชนิด\nชเนตตี\nชบา\nชม\nชมดชม้อย\nชมนาด\nชมพู\nชมพู่\nชมรม\nชม้อย\nชม้าย\nชไม\nชยา\nชโย\nชรทึง\nชรริน\nชรอุ่ม\nชระล้ำ\nชระลุ\nชระอาบ\nชระเอม\nชรัด\nชรา\nชล\nชโลง\nชโลม\nช่วง\nชวด\nชวน\nชวย\nช่วย\nชวร\nชวลิต\nชวา\nชวาล\nชวาลา\nช่อ\nชอก\nช็อก\nช็อกโกเลต\nช็อกโกแลต\nชอง\nช่อง\nช้อง\nชองระอา\nชอน\nช่อน\nช้อน\nชอบ\nชอม\nช้อย\nชอล์ก\nชอ่ำ\nชอุ่ม\nชะ\nชะคราม\nชะงอก\nชะง่อน\nชะงัก\nชะงัด\nชะง้ำ\nชะงุ้ม\nชะเง้อ\nชะเงื้อม\nชะแง้\nชะโงก\nชะฉ่า\nชะช่อง\nชะชะ\nชะช้า\nชะโด\nชะตา\nชะต้า\nชะนี\nชะเนาะ\nชะเนียง\nชะพลู\nชะเพลิง\nชะมด\nชะมบ\nชะมวง\nชะมัง\nชะมัด\nชะแม่\nชะรอย\nชะลอ\nชะลอม\nชะล่า\nชะลาน\nชะลิน\nชะลูด\nชะเลง\nชะเลย\nชะแล็ก\nชะแลง\nชะวาก\nชะวาด\nชะเวิกชะวาก\nชะแวง\nชะอม\nชะอ้อน\nชะเอม\nชะโอน\nชัก\nชักคราม\nชักช้า\nชัง\nชั่ง\nชังคา\nชังฆ\nชัชวาล\nชัฏ\nชัด\nชัดช้า\nชัน\nชั้น\nชันกาด\nชันชี\nชันตุ\nชันนะตุ\nชันนุ\nชันโรง\nชันษา\nชันสูตร\nชัปนะ\nชัพ\nชัมพูนท\nชัย\nชัยพฤกษ์\nชัยภูมิ\nชัลลุกา\nชั่ว\nชั้ว\nชัวชม\nชา\nช้า\nชาคระ\nชาคริต\nชาคริยานุโยค\nช่าง\nช้าง\nช้าช่อน\nชาญ\nชาด\nชาดก\nชาต\nชาตบุษย์\nชาตรี\nชาตะ\nชาตา\nชาติ\nชาน\nชานุ\nช้าปี่\nชาปีไหน\nช้าแป้น\nช้าพลู\nชาม\nชามพูนท\nชามาดร\nชามาดา\nชามาตุ\nชาย\nชายา\nชาล\nชาลา\nชาลินี\nช้าเลือด\nชาว\nชาวี\nชำ\nช่ำ\nช้ำ\nชำงัด\nชำงาย\nช่ำชอง\nชำนะ\nชำนัญ\nชำนัน\nชำนาญ\nชำนิ\nชำเนียร\nชำมะนาด\nชำมะเลียง\nชำร่วย\nชำระ\nช้ำรั่ว\nชำรุด\nชำเรา\nชำเราะ\nชำแรก\nชำแระ\nชำเลือง\nชำแหละ\nชิ\nชิง\nชิ่ง\nชิงชัน\nชิงช้า\nชิงช้าชาลี\nชิงชี่\nชิงฮื้อ\nชิชะ\nชิชิ\nชิณณะ\nชิด\nชิเดนทรีย์\nชิต\nชิตินทรีย์\nชิน\nชิ้น\nชินโต\nชิโนรส\nชิม\nชิมแปนซี\nชิยา\nชิรณะ\nชิระ\nชิวหา\nชิสา\nชี\nชี่\nชี้\nชีปะขาว\nชีผะขาว\nชีผ้าขาว\nชีพ\nชีฟอง\nชีรณ\nชีระ\nชีวเคมี\nชีวประวัติ\nชีวภาพ\nชีววิทยา\nชีวะ\nชีวัน\nชีวา\nชีวาตม์\nชีวาลัย\nชีวิต\nชีวิตักษัย\nชีวิน\nชีวี\nชืด\nชื่น\nชื้น\nชื่อ\nชุก\nชุกชี\nชุ้ง\nชุณห\nชุด\nชุติ\nชุน\nชุบ\nชุม\nชุ่ม\nชุมนุม\nชุมพร\nชุมพา\nชุมแพรก\nชุมรุม\nชุมแสง\nชุมเห็ด\nชุ่ย\nชุลมุน\nชุลี\nชุษณะ\nชู\nชู้\nเช็ค\nเช้ง\nเช้งวับ\nเชงเลง\nเช็ด\nเชน\nเช่น\nเชย\nเชลง\nเชลย\nเชลแล็ก\nเชลียง\nเชวง\nเชษฐะ\nเชษฐา\nเชอ\nเช่า\nเช้า\nเชาว์\nเชาวน์\nเชิง\nเชิญ\nเชิด\nเชิ้ต\nเชียง\nเชี่ยน\nเชียบ\nเชี่ยม\nเชียร\nเชียร์\nเชียว\nเชี่ยว\nเชื่อ\nเชื้อ\nเชือก\nเชื่อง\nเชือด\nเชือน\nเชื่อม\nแช\nแช่\nแช่ง\nแชงมา\nแชบ๊วย\nแช่ม\nแชร์\nแชล่ม\nแชสซี\nแชะ\nโชก\nโชค\nโชงโลง\nโชดก\nโชดึก\nโชต\nโชตก\nโชติ\nโชติก\nโชน\nโชมโรม\nโชย\nโชยงการ\nโชยชาย\nโชยติส\nโชว์\nใช่\nใช้\nไช\nไชนะ\nไชย\nไชโย\nซก\nซ่ก\nซงดำ\nซ่งฮื้อ\nซด\nซน\nซ้น\nซบ\nซม\nซวดเซ\nซวน\nซวย\nซอ\nซอก\nซอง\nซ่อง\nซ้อง\nซองแมว\nซ้องแมว\nซอน\nซ่อน\nซ้อน\nซอม\nซ่อม\nซ้อม\nซอมซ่อ\nซอย\nซอส\nซัก\nซักส้าว\nซัง\nซั้ง\nซัด\nซับ\nซัลฟา\nซั้ว\nซา\nซ่า\nซาก\nซาง\nซ่าง\nซาด\nซาน\nซ่าน\nซาบซ่าน\nซาบซึ้ง\nซ่าโบะ\nซาแมเรียม\nซ้าย\nซาลาเปา\nซาว\nซ่าหริ่ม\nซำ\nซ้ำ\nซิ\nซี\nซิก\nซิกข์\nซิกซี้\nซิกแซ็ก\nซิการ์\nซิงโคนา\nซิ่น\nซินนามิก\nซินแส\nซิบ\nซิป\nซิฟิลิส\nซิลิคอน\nซิว\nซี่\nซีก\nซีเซียม\nซีด\nซี้ด\nซีนอน\nซีป่าย\nซีเมนต์\nซีเรียม\nซีลีเนียม\nซีอิ๊ว\nซึก\nซึง\nซึ่ง\nซึ้ง\nซึม\nซื่อ\nซื้อ\nซุก\nซุง\nซุน\nซุบ\nซุป\nซุ่ม\nซุ้ม\nซุ่มซ่าม\nซุย\nซู่\nซูโครส\nซูด\nซู้ด\nซูดซาด\nซูบ\nเซ\nเซ็ก\nเซแคนต์\nเซ็ง\nเซ่ง\nเซ้ง\nเซ็งแซ่\nเซต\nเซน\nเซ็น\nเซ่น\nเซนติกรัม\nเซนติเกรด\nเซนติเมตร\nเซนติลิตร\nเซปักตะกร้อ\nเซราะ\nเซรุ่ม\nเซลเซียส\nเซลล์\nเซลลูลอยด์\nเซลลูโลส\nเซ่อ\nเซอร์โคเนียม\nเซอะ\nเซา\nเซ้าซี้\nเซาะ\nเซิง\nเซิ้ง\nเซียน\nเซียบ\nเซียมซี\nเซียว\nเซี่ยว\nเซี่ยวกาง\nเซื่อง\nแซ\nแซ่\nแซ็กคาริน\nแซง\nแซงแซว\nแซด\nแซบ\nแซม\nแซยิด\nแซ่ว\nแซะ\nโซ\nโซ่\nโซก\nโซ่ง\nโซงโขดง\nโซเซ\nโซดา\nโซเดียม\nโซม\nโซรม\nโซลา\nไซ\nไซ้\nไซเกิล\nไซโคลน\nไซน์\nไซยาไนด์\nไซร้\nไซเรน\nไซโล\nฌาน\nฌาปน\nฌาปนกิจ\nฌาปนสถาน\nเฌอ\nเฌอเอม\nญวน\nญัตติ\nญาณ\nญาติ\nญานาซะฮ์\nญิบ\nญี่ปุ่น\nเญยธรรม\nไญยธรรม\nฎีกา\nฐกัด\nฐากูร\nฐาน\nฐานะ\nฐานันดร\nฐานานุกรม\nฐานานุรูป\nฐานานุศักดิ์\nฐานียะ\nฐาปน\nฐาปนา\nฐายี\nฐิต\nฐิติ\nฑาหก\nฑาหะ\nเฒ่า\nณรงค์\nเณร\nดก\nดง\nด้ง\nด้น\nดนโด่\nดนตรี\nดนัย\nดนุ\nดนู\nดบัสวิน\nดบัสวี\nดม\nดรงค์\nดรณี\nดรรชนี\nดราฟต์\nดรุณ\nดรุณี\nดล\nดวง\nด้วง\nดวด\nด่วน\nด้วน\nด้วย\nดอก\nดอง\nด่อง\nด้อง\nดองฉาย\nดองดึง\nดอด\nดอน\nด่อน\nดอม\nด้อม\nดอย\nด้อย\nดอลลาร์\nดะ\nดะโต๊ะ\nดะหมัง\nดัก\nดักดาน\nดักแด้\nดัง\nดั่ง\nดั้ง\nดัชนี\nดัด\nดัตช์\nดัน\nดั้น\nดับ\nดัมพ์\nดั้วเดี้ย\nดัสกร\nดา\nด่า\nดาก\nด่าง\nด้าง\nดาด\nดาน\nด่าน\nด้าน\nดาบ\nดาบส\nดาม\nด้าม\nด้ามจิ้ว\nดามพ์\nดาย\nด้าย\nดารกะ\nดารณี\nดารดาษ\nดาระ\nดารา\nดาล\nดาลัด\nดาว\nด่าว\nด้าว\nดาวดึงส์\nดาวบส\nดาษ\nดาษดา\nดำ\nด่ำ\nด้ำ\nดำกล\nดำเกิง\nดำแคง\nดำดง\nดำนาณ\nดำเนิน\nดำบล\nดำรง\nดำรวจ\nดำรัส\nดำริ\nดำรี\nดำรู\nดำฤษณา\nดำเลิง\nดิก\nดิ่ง\nดิฉัน\nดิฐ\nดิตถ์\nดิถี\nดิน\nดิ้น\nดิบ\nดิรัจฉาน\nดิลก\nดิ่ว\nดิ้ว\nดิ้วเดี้ยว\nดิษฐ์\nดิสโพรเซียม\nดี\nดีเซล\nดีด\nดีดีที\nดีบุก\nดีปลี\nดีเปรสชัน\nดีหมี\nดีหลี\nดึก\nดึง\nดึ่ง\nดึ่ม\nดื่น\nดื่ม\nดือ\nดื้อ\nดุ\nดุก\nดุกดิก\nดุกทะเล\nดุ้ง\nดุ้งดิ้ง\nดุจ\nดุด\nดุน\nดุ้น\nดุบ\nดุม\nดุ่ม\nดุ่ย\nดุรงค์\nดุริยะ\nดุริยางค์\nดุริยางคศาสตร์\nดุริยางคศิลป์\nดุล\nดุษฎี\nดุษณี\nดุษณีภาพ\nดุษิต\nดุสิต\nดุเหว่า\nดู\nดูกค่าง\nดูกร\nดูด\nดูรา\nดูแล\nเด\nเด่\nเดก\nเด็ก\nเดกซ์โทรส\nเดคากรัม\nเดคาเมตร\nเดคาลิตร\nเด้ง\nเด็จ\nเดช\nเดชน์\nเดชนะ\nเดชะ\nเดโช\nเดซิกรัม\nเดซิเมตร\nเดซิลิตร\nเด็ด\nเดน\nเด่น\nเดนมาร์ก\nเดรัจฉาน\nเด๋อ\nเด๋อด๋า\nเดา\nเด้า\nเดาะ\nเดิน\nเดิ่น\nเดิม\nเดียง\nเดียด\nเดียรดาษ\nเดียรถีย์\nเดียรัจฉาน\nเดียว\nเดี่ยว\nเดี๋ยว\nเดียะ\nเดื่อ\nเดือก\nเดื่อง\nเดือด\nเดือน\nเดือย\nแด\nแด่\nแดก\nแด็ก\nแดกงา\nแดกแด้\nแดง\nแดด\nแดน\nแด่น\nแด่ว\nแดะ\nแดะแด๋\nโด\nโด่\nโดกเดก\nโด่ง\nโดด\nโดน\nโดม\nโดมร\nโดย\nโดรณ\nใด\nได\nได้\nไดแซ็กคาไรด์\nไดนาโม\nไดนาไมต์\nไดโนเสาร์\nไดเรกตริกซ์\nตก\nต๊กโต\nตง\nต๋ง\nตงฉิน\nตงิด\nตงุ่น\nตด\nตติย\nตถาคต\nตน\nต้น\nตนัย\nตนุ\nตบ\nตบะ\nตปนียะ\nตม\nต้ม\nตมูก\nตยาคี\nตรง\nตรณี\nตรม\nตรรก\nตรรกะ\nตรลบ\nตรลอด\nตรลาด\nตรวจ\nตรวน\nตรอก\nตรอง\nตรอมใจ\nตรอมตรม\nตระ\nตระกล\nตระกวน\nตระกอง\nตระการ\nตระกูล\nตระคัร\nตระเตรียม\nตระนาว\nตระบก\nตระบอก\nตระบอง\nตระบัด\nตระบัน\nตระเบ็ง\nตระแบก\nตระแบง\nตระโบม\nตระพอง\nตระพัง\nตระลาการ\nตระวัน\nตระเว็ด\nตระเวน\nตระสัก\nตระหง่าน\nตระหนก\nตระหนัก\nตระหน่ำ\nตระหนี่\nตรัง\nตรังค์\nตรับ\nตรับฟัง\nตรัย\nตรัยตรึงศ์\nตรัส\nตรัสสา\nตรา\nตรากตรำ\nตราชู\nตราบ\nตราสัง\nตรำ\nตริ\nตริว\nตรี\nตรีปวาย\nตรีพิธพรรณ\nตรียัมปวาย\nตรึก\nตรึง\nตรุ\nตรุณ\nตรุณะ\nตรุษ\nตรู\nตรู่\nตฤณ\nตฤตีย\nตฤษณา\nตลก\nตลบ\nตลอด\nตลับ\nตลาด\nตลิ่ง\nตลึง\nตวง\nต่วน\nต้วมเตี้ยม\nตวัก\nตวัด\nตวาด\nตอ\nตอม่อ\nต่อ\nต้อ\nตอก\nต๊อก\nต๊อกต๋อย\nตอง\nต้อง\nตองกราย\nต้องเต\nตองแตก\nต่องแต่ง\nตองเหลือง\nตอด\nตอน\nต้อน\nตอบ\nตอเบา\nตอม\nต่อม\nต๋อม\nต่อย\nต้อย\nต้อยตริ่ง\nต้อยติ่ง\nต้อยตีวิด\nตอแย\nตอร์ปิโด\nต่อไส้\nตอแหล\nตะ\nตะกรน\nตะกร้อ\nตะกรัน\nตะกรับ\nตะกร้า\nตะกราม\nตะกรุด\nตะกรุม\nตะกรุมตะกราม\nตะกละ\nตะกลาม\nตะกวด\nตะกอ\nตะกอน\nตะกัง\nตะกั่ว\nตะกาง\nตะกาด\nตะกาย\nตะกาว\nตะกุกตะกัก\nตะกุย\nตะกู\nตะกูด\nตะเกียกตะกาย\nตะเกียง\nตะเกียบ\nตะแก\nตะแก่\nตะแกรง\nตะโก\nตะโก้\nตะโกก\nตะโกน\nตะโกรง\nตะโกรม\nตะไกร\nตะขบ\nตะขอ\nตะขาบ\nตะขิดตะขวง\nตะเข้\nตะเข็บ\nตะโขง\nตะคร้อ\nตะครอง\nตะครั่นตะครอ\nตะคร้ำ\nตะคริว\nตะคิว\nตะครุบ\nตะคอก\nตะคัน\nตะค้า\nตะคาก\nตะค้าน\nตะคุ่ม\nตะเครียว\nตะเคียว\nตะเคียน\nตะแคง\nตะไคร่\nตะไคร้\nตะเฆ่\nตะนอย\nตะนาว\nตะบม\nตะบอง\nตะบอย\nตะบัน\nตะบิ้ง\nตะบิด\nตะบิดตะบอย\nตะบี้ตะบัน\nตะบึง\nตะบูน\nตะเบ็ง\nตะเบ็งมาน\nตะเบ๊ะ\nตะแบก\nตะแบง\nตะโบม\nตะไบ\nตะปบ\nตะปลิง\nตะปิ้ง\nตะปุ่มตะป่ำ\nตะปู\nตะพง\nตะพด\nตะพอง\nตะพัก\nตะพัง\nตะพัด\nตะพั้น\nตะพาก\nตะพาน\nตะพาบ\nตะพาย\nตะพึด\nตะพึดตะพือ\nตะพุ่น\nตะเพรา\nตะเพิง\nตะเพิด\nตะเพียน\nตะโพก\nตะโพง\nตะโพน\nตะเภา\nตะใภ้\nตะม่อ\nตะมอย\nตะรังกะนู\nตะรังตังกวาง\nตะรังตังช้าง\nตะราง\nตะลอง\nตะลอน\nตะล่อม\nตะละ\nตะลาน\nตะลิงปลิง\nตะลิบ\nตะลีตะลาน\nตะลึง\nตะลึงพรึงเพริด\nตะลุง\nตะลุ่ม\nตะลุ่มนก\nตะลุมบอน\nตะลุ่มโปง\nตะลุมพอ\nตะลุมพุก\nตะลุย\nตะเลง\nตะแลงแกง\nตะไล\nตะวัน\nตะเวน\nตะหลิว\nตะหลุก\nตะหลุง\nตะแหง่ว\nตะแหมะแขะ\nตะโหงก\nตัก\nตักกะ\nตักเตือน\nตั๊กแตน\nตักษัย\nตัง\nตั่ง\nตั้ง\nตังเก\nตังฉ่าย\nตังเม\nตังวาย\nตังโอ๋\nตัจฉก\nตัจฉนี\nตัณฑุล\nตัณหา\nตัด\nตัน\nตันตระ\nตันติ\nตันหยง\nตับ\nตับปิ้ง\nตัว\nตั๋ว\nตัวจี๊ด\nตัวตืด\nตั้วโผ\nตั้วเหี่ย\nตา\nตาก\nตากวาง\nต่าง\nตาด\nตาน\nต่าน\nต้าน\nตานนกกด\nตานี\nตาบ\nตาม\nตามะแน\nตามิน\nตาย\nตาราไต\nตาล\nตาลุ\nต๋าว\nตาเสือ\nตาหนู\nตาฬ\nตำ\nต่ำ\nตำนาน\nตำบล\nตำแบ\nตำแย\nตำรวจ\nตำรับ\nตำรา\nตำรุ\nตำลึง\nตำเสา\nตำหนัก\nตำหนิ\nตำแหน่ง\nติ\nติก\nติ๊ก\nติกะ\nติกาหรัง\nติง\nติ่ง\nติ๋ง\nติ่งตั่ง\nติ๋งต่าง\nติงส\nติงสติ\nติณ\nติด\nติตติกะ\nติตติร\nติตถ\nติตถะ\nติถี\nติมิงคละ\nติรัจฉาน\nติลก\nติละ\nติ้ว\nตี\nตี่\nตีน\nตีบ\nตีรถะ\nตีระ\nตึ\nตึก\nตึ้ก\nตึ้กตั้ก\nตึง\nตึดตื๋อ\nตึ๊ดตื๋อ\nตืด\nตื่น\nตื้น\nตื้อ\nตื๊อ\nตื๋อ\nตุ\nตุ๊\nตุ๊กแก\nตุ๊กตา\nตุ๊กต่ำ\nตุกติก\nตุ๊กติ๊ก\nตุ๊กตุ่น\nตุ๊กตุ๋ย\nตุง\nตุ้งก่า\nตุ้งติ้ง\nตุ๊ดตู่\nตุน\nตุ่น\nตุ๋น\nตุนาหงัน\nตุบ\nตุ้บ\nตุ๊บป่อง\nตุปัดตุป่อง\nตุปัดตุเป๋\nตุ่ม\nตุ้ม\nตุ๋ม\nตุ้มกว้าว\nตุมกา\nตุ้มแซะ\nตุมตัง\nตุ้มเต๋น\nตุ้มปี่\nตุมพะ\nตุ่ย\nตุ้ย\nตุ๊ย\nตุ๋ยตุ่ย\nตุรคะ\nตุรงค์\nตุล\nตุลา\nตุหรัดตุเหร่\nตู\nตู่\nตู้\nตูก\nตูด\nตูบ\nตูม\nเต๊ก\nเต็ง\nเต่ง\nเตช\nเตโช\nเต้น\nเต็นท์\nเต็ม\nเตย\nเตร่\nเตร็ด\nเตรตา\nเตรียม\nเตรียมตรม\nเตละ\nเตลิด\nเตว็ด\nเต่อ\nเตอะ\nเตะ\nเตา\nเต่า\nเต้า\nเต๋า\nเต่าเกียด\nเต้าเจี้ยว\nเต้าทึง\nเต้าหู้\nเต้าฮวย\nเต๊าะ\nเตาะแตะ\nเติ่ง\nเติบ\nเติม\nเตี้ย\nเตียง\nเตียน\nเตียบ\nเตี๋ยม\nเตียรถ์\nเตียว\nเตี่ยว\nเตือน\nแต่\nแต้\nแตก\nแตง\nแต่ง\nแตงเม\nแต้จิ๋ว\nแตด\nแต๊ดแต๋\nแตน\nแต้ม\nแตร\nแตระ\nแต้ว\nแต้วแร้ว\nแต้วแล้ว\nแต่ว่า\nแต้แว้ด\nแตะ\nโต\nโต้\nโตก\nโต่ง\nโต้ง\nโตงเตง\nโตฎก\nโต๊ด\nโตน\nโตนด\nโต้โผ\nโตมร\nโตย\nโตรก\nโต๊ะ\nใต้\nไต\nไต่\nไต้\nไต๋\nไตร\nไตรกิศยา\nไตรดายุค\nไตร่ตรอง\nไตรย\nไต้หวัน\nถก\nถกล\nถงาด\nถด\nถนน\nถนอม\nถนัด\nถนัน\nถนำ\nถนิม\nถม\nถ่ม\nถมอ\nถมึงทึง\nถลก\nถลกบาตร\nถลน\nถล่ม\nถลอก\nถลัน\nถลา\nถลาก\nถลาย\nถลำ\nถลึงตา\nถลุง\nถ่วง\nถ้วน\nถ้วย\nถวัล\nถวัลย์\nถวาย\nถวิน\nถวิล\nถ่อ\nถ้อ\nถอก\nถอง\nถ่อง\nถ้อง\nถอด\nถอน\nถอบ\nถอบแถบ\nถ่อม\nถอย\nถ่อย\nถ้อย\nถะ\nถะถั่น\nถะมัดถะแมง\nถัก\nถัง\nถั่ง\nถัด\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นยา\nบิลเลียด\nบิวเรตต์\nบิสมัท\nบี้\nบีฑา\nบีตา\nบีบ\nบีเยศ\nบึก\nบึกบึน\nบึง\nบึ่ง\nบึ้ง\nบุ\nบุก\nบุคคล\nบุคลากร\nบุคลาธิษฐาน\nบุคลิก\nบุง\nบุ่ง\nบุ้ง\nบุ้งกี๋\nบุญ\nบุญญาธิการ\nบุญญานุภาพ\nบุญญาภินิหาร\nบุญญาภิสังขาร\nบุณฑริก\nบุณมี\nบุณย์\nบุตร\nบุตรี\nบุถุชน\nบุทคล\nบุนนาค\nบุบ\nบุบบิบ\nบุปผชาติ\nบุพกรรม\nบุพการี\nบุพกิจ\nบุพชาติ\nบุพทักษิณ\nบุพนิมิต\nบุพบท\nบุพพาจารย์\nบุพเพสันนิวาส\nบุพโพ\nบุ๋ม\nบุ่มบ่าม\nบุ้ย\nบุรณะ\nบุรพทิศ\nบุรพบท\nบุรพาจารย์\nบูรพาจารย์\nบุระ\nบุราณ\nบุรินทร์\nบุริมทิศ\nบุริมพรรษา\nบุริมสิทธิ\nบุรี\nบุรุษ\nบุโรทั่ง\nบุษกร\nบุษบก\nบุษบง\nบุษบัน\nบุษบา\nบุษบามินตรา\nบุษปราค\nบุษปะ\nบุษย์\nบุษย์น้ำทอง\nบุษยมาส\nบุษยะ\nบุษราคัม\nบุหงัน\nบุหงา\nบุหรง\nบุหรี่\nบุหลัน\nบู่\nบู้\nบูชนียสถาน\nบูชา\nบูชิต\nบูด\nบูดู\nบูร\nบูรณ์\nบูรณภาพ\nบูรณมี\nบูรณะ\nบูรณาการ\nบูรพ์\nบูรพะ\nบูรพา\nเบ้\nเบ่ง\nเบ๊จี๋\nเบญกานี\nเบญจกัลยาณี\nเบญจกามคุณ\nเบญจขันธ์\nเบญจดุริยางค์\nเบญจธรรม\nเบญจบรรพต\nเบญจพรรณ\nเบญจเพส\nเบญจมาศ\nเบญจรงค์\nเบญจศก\nเบญจศีล\nเบญจะ\nเบญจา\nเบญจางค์\nเบญจางคประดิษฐ์\nเบญญา\nเบญพาด\nเบ็ด\nเบ็ดเตล็ด\nเบ็ดเสร็จ\nเบน\nเบนซิน\nเบรก\nเบริลเลียม\nเบส\nเบ้อ\nเบอร์\nเบอร์คีเลียม\nเบ้อเร่อ\nเบ้อเร่อเท่อ\nเบ้อเริ่ม\nเบ้อเริ่มเทิ่ม\nเบอะ\nเบอะบะ\nเบะ\nเบา\nเบ้า\nเบาราณ\nเบาะ\nเบิก\nเบิ่ง\nเบี้ย\nเบี่ยง\nเบียด\nเบียน\nเบียร์\nเบี้ยว\nเบือ\nเบื่อ\nเบื้อ\nเบื้อง\nเบือน\nแบ\nแบ้\nแบก\nแบคทีเรีย\nแบ่ง\nแบงก์\nแบดมินตัน\nแบตเตอรี่\nแบน\nแบนโจ\nแบบ\nแบ็บ\nแบเรียม\nแบหลา\nแบะ\nโบ\nโบ้\nโบ๋\nโบก\nโบกขรณี\nโบกขรพรรษ\nโบชุก\nโบต\nโบนัส\nโบ๊เบ๊\nโบย\nโบรมีน\nโบรอน\nโบราณ\nโบสถ์\nใบ\nใบ้\nไบ่\nปก\nปกติ\nปกรณ์\nปกรณัม\nปกิณกะ\nปกีรณัม\nปโกฏิ\nปง\nป่ง\nปงช้าง\nปฎล\nปฏัก\nปฏิกรณ์\nปฏิกรรม\nปฏิการะ\nปฏิกิริยา\nปฏิกูล\nปฏิคม\nปฏิคหิต\nปฏิคาหก\nปฏิฆะ\nปฏิชีวนะ\nปฏิญญา\nปฏิญาณ\nปฏิทิน\nปฏิบถ\nปฏิบัติ\nปฏิปทา\nปฏิปักษ์\nปฏิปัน\nปฏิปุจฉาพยากรณ์\nปฏิปุจฉาวาที\nปฏิพัทธ์\nปฏิพากย์\nปฏิภาค\nปฏิภาณ\nปฏิมา\nปฏิมากร\nปฏิยุทธ์\nปฏิรพ\nปฏิรูป\nปฏิโลม\nปฏิวัติ\nปฏิวาต\nปฏิวาท\nปฏิเวธ\nปฏิสนธิ\nปฏิสวะ\nปฏิสังขรณ์\nปฏิสันถาร\nปฏิสัมภิทา\nปฏิเสธ\nปฐพี\nปฐม\nปฐวี\nปณต\nปณาม\nปณิธาน\nปณิธิ\nปณีต\nปด\nปดิวรัดา\nปติ\nปถพี\nปถมัง\nปถวี\nปทัฏฐาน\nปทัสถาน\nปทานุกรม\nปทีป\nปทุม\nปน\nป่น\nปนัดดา\nปปัญจะ\nปม\nปรนัย\nปรปักษ์\nปรโลก\nปรวาที\nปรก\nปรกติ\nปรง\nปรตยักษ์\nปรน\nปรนนิบัติ\nปรนิมมิตวสวัตดี\nปรบ\nปรปักษ์\nปรมัตถ์\nปรมาจารย์\nปรมาณู\nปรมาภิไธย\nปรมาภิเษก\nปรมินทร์\nบรเมนทร์\nปรเมศวร์\nปรเมษฐ์\nปรวด\nปรวนแปร\nปรศุ\nปรสิต\nปร๋อ\nปรองดอง\nปรอด\nปรอท\nปรอย\nประ\nประกบ\nประกฤต\nประกฤติ\nประกล\nประกวด\nประกวดประขัน\nประกอบ\nประกัน\nประกับ\nประกาย\nประกายพรึก\nประการ\nประกาศ\nประกาศนียบัตร\nประกาศิต\nประกำ\nประกิด\nประกิต\nประคด\nประคนธรรพ\nประคนธรรพ์\nประคบ\nประคบประหงม\nประคอง\nประคับประคอง\nประคัลภ์\nประคำ\nประคิ่น\nประคุณ\nประเคน\nประเคราะห์\nประโคน\nประโคนธรรพ\nประโคนธรรพ์\nประโคม\nประจง\nประจญ\nประจบ\nประจบประแจง\nประจวบ\nประจ๋อประแจ๋\nประจักษ์\nประจักษนิยม\nประจัญ\nประจัน\nประจาก\nประจาค\nประจาน\nประจำ\nประจิม\nประจิ้มประเจ๋อ\nประจุ\nประจุคมน์\nประจุบัน\nประเจก\nประเจิด\nประเจิดประเจ้อ\nประเจียด\nประแจ\nประชด\nประชน\nประชวม\nประชวร\nประชัน\nประชา\nประชาธิปไตย\nประชิด\nประชี\nประชุม\nประเชิญ\nประณต\nประณม\nประณาม\nประณิธาน\nประณิธิ\nประณีต\nประณุท\nประดง\nประดน\nประดวน\nประดอน\nประดอย\nประดัก\nประดักประเดิด\nประดัง\nประดับ\nประดา\nประดาก\nประดาป\nประดาษ\nประดิชญา\nประดิดประดอย\nประดิทิน\nประดิษฐ์\nประดิษฐกรรม\nประดิษฐาน\nประดุง\nประดุจ\nประดู่\nประเด\nประเด็น\nประเดิม\nประเดียง\nประเดี๋ยว\nประเดี๋ยวประด๋าว\nประแดง\nประแดะ\nประโดง\nประโดย\nประตง\nประตัก\nประตาป\nประติชญา\nประติญาณ\nประติทิน\nประติมากร\nประติมากรรม\nประติรพ\nประตู\nประถม\nประถมจินดา\nประทม\nประท้วง\nประทวน\nประทักษ์\nประทักษิณ\nประทัง\nประทัด\nประทับ\nประทาน\nประทาย\nประทาศี\nประทิน\nประทิ่น\nประทีป\nประทุฐ\nประทุน\nประทุษ\nประทุษฐ์\nประเทศ\nประเทา\nประเทียด\nประเทียบ\nประเทือง\nประธาน\nประธานาธิบดี\nประนม\nประนอ\nประนอม\nประนัง\nประนัปดา\nประนีประนอม\nประปราน\nประปราย\nประปา\nประเปรี้ยง\nประเปรียว\nประพจน์\nประพนธ์\nประพรม\nประพฤติ\nประพฤทธิ์\nประพัด\nประพัทธ์\nประพันธ์\nประพาต\nประพาส\nประพาสมหรณพ\nประพาฬ\nประพิณ\nประพิมพ์ประพาย\nประพุทธ์\nประเพณี\nประโพธ\nประไพ\nประไพร\nประภพ\nประภัสสร\nประภา\nประภาคาร\nประภาพ\nประภาษ\nประภาส\nประเภท\nประมง\nประมวล\nประมาณ\nประมาท\nประมุข\nประมุท\nประมูล\nประเมิน\nประโมง\nประโมทย์\nประยงค์\nประยุกต์\nประยุทธ์\nประยุร\nประยูร\nประโยค\nประโยชน์\nประโรหิต\nประลมพ์\nประลอง\nประลัย\nประลาต\nประลาย\nประลุ\nประเล่ห์\nประเล้าประโลม\nประโลม\nประวรรต\nประวรรตน์\nประวัติ\nประวาล\nประวาลปัทม์\nประวาส\nประวิง\nประวิช\nประวิตร\nประวิน\nประวีณ\nประเวณี\nประเวศ\nประเวศน์\nประศม\nประศาสน์\nประศุ\nประสก\nประสงค์\nประสบ\nประสพ\nประสม\nประสะ\nประสัก\nประสันนาการ\nประสัยห์\nประสา\nประสาท\nประสาธน์\nประสาน\nประสาร\nประสิทธิ์\nประสิทธิผล\nประสิทธิภาพ\nประสีประสา\nประสูต\nประสูติ\nประเสบัน\nประเสบันอากง\nประเสริฐ\nประหนึ่ง\nประหม่า\nประหยัด\nประหลาด\nประหล่ำ\nประหวัด\nประหวั่น\nประหัตประหาร\nประหาณ\nประหาร\nประเหล\nประเหส\nประไหมสุหรี\nประอบ\nประอร\nปรัก\nปรักปรำ\nปรักมะ\nปรัง\nปรัชญา\nปรัตถจริยา\nปรัตยุบัน\nปรัน\nปรับ\nปรัมปรา\nปรัศจิม\nปรัศนา\nปรัศนี\nปรัศว์\nปรัสสบท\nปร่า\nปรากฏ\nปรากรม\nปรากฤต\nปราการ\nปราง\nปรางค์\nปราจีน\nปราชญ์\nปราชญา\nปราชัย\nปราณ\nปราณี\nปราด\nปราน\nปรานี\nปราบ\nปราบดาภิเษก\nปราปต์\nปราม\nปรามาส\nปราโมช\nปราโมทย์\nปราย\nปรารถนา\nปรารภ\nปรารมภ์\nปราศ\nปราศจาก\nปราศรัย\nปราษณี\nปราษาณ\nปราสัย\nปราสาท\nปรำ\nปริ\nปริก\nปริกขาร\nปริกรรม\nปริกัป\nปริคณห์\nปริจาค\nปริจาริกา\nปริเฉท\nปริชน\nปริซึม\nปริญญา\nปริณาม\nปริณายก\nปริต\nปริตตะ\nปริตโตทก\nปริตร\nปริทรรศน์\nปริทัยหัคคี\nปริทัศน์\nปริเทพ\nปริเทวะ\nปรินิพพาน\nปริบ\nปริบท\nปริปันถ์\nปริพนธ์\nปริพัตร\nปริพันธ์\nปริพาชก\nปริภัณฑ์\nปริภาษ\nปริภูมิ\nปริโภค\nปริ่ม\nปริมณฑล\nปริมาณ\nปริมาตร\nปริยัติ\nปริยานุช\nปริยาย\nปริเยศ\nปริโยสาน\nปริวรรต\nปริวัตร\nปริวาร\nปริวาส\nปริวิตก\nปริเวณ\nปริศนา\nปริษัท\nปริสัญญู\nปริสุทธิ\nปริหาน\nปริหาร\nปรี่\nปรีชญา\nปรีชา\nปรี๊ด\nปรีดา\nปรีดิ\nปรีดิ์\nปรีดี\nปรีติ\nปรียะ\nปรียา\nปรึก\nปรึกษา\nปรึง\nปรือ\nปรื๋อ\nปรุ\nปรุง\nปรู\nปรู๋\nปรูด\nปรู๊ด\nปรู๊ดปร๊าด\nปรู๊ฟ\nปฤงคพ\nปฤจฉา\nปฤษฎางค์\nปฤษฐ\nปลก\nปลกเปลี้ย\nปลง\nปล่ง\nปลด\nปล้น\nปลวก\nปลอก\nปล่อง\nปล้อง\nปลอด\nปล้อน\nปลอบ\nปลอม\nปล่อย\nปละ\nปลัก\nปลั๊ก\nปลัง\nปลั่ง\nปลัด\nปลัดขิก\nปลา\nปลาต\nปลาบ\nปลาย\nปลาสเตอร์\nปลาสนาการ\nปล้ำ\nปลิง\nปลิด\nปลิ้น\nปลิโพธ\nปลิม\nปลิ่ม\nปลิว\nปลี\nปลีก\nปลื้ม\nปลุก\nปลูก\nปวกเปียก\nปวง\nป่วง\nปวด\nป่วน\nป้วน\nป้วนเปี้ยน\nป่วย\nปวัตน์\nปวารณา\nปวาล\nปวาส\nปวาฬ\nปวิช\nปวิตร\nปวิธ\nปวิเวก\nปวีณ\nปวุติ\nปเวณี\nปเวส\nปเวสน์\nปศุ\nปสันนะ\nปสันนาการ\nปสาท\nปสาน\nปสาสน์\nปสุ\nปสุต\nปสูติ\nปหังสนะ\nปหาน\nปหาร\nปหาส\nปอ\nป้อ\nป๋อ\nปอก\nปอง\nป่อง\nป้อง\nปอด\nปอน\nป้อน\nปอนด์\nปอเนาะ\nปอบ\nป้อแป้\nปอม\nป้อม\nป๋อม\nปอย\nป้อย\nป้อยอ\nปะ\nปะกน\nปะกัง\nปะการัง\nปะกำ\nปะขาว\nปะงาบ\nปะตาปา\nปะตาระกาหลา\nปะติดปะต่อ\nปะติยาน\nปะทะ\nปะทะปะทัง\nปะทุ\nปะทุน\nปะปน\nปะมง\nปะราลี\nปะรำ\nปะไร\nปะลอม\nปะเลง\nปะแล่ม\nปะโลง\nปะวะหล่ำ\nปะหงับ\nปะหนัน\nปะหัง\nปะเหลาะ\nปัก\nปักข์\nปักเป้า\nปักษ์\nปักษา\nปักษิน\nปักษี\nปัง\nปั๋ง\nปังสุ์\nปังสุกุล\nปัจจัตตะ\nปัจจันต์\nปัจจันตคาม\nปัจจันตชนบท\nปัจจันตประเทศ\nปัจจัย\nปัจจามิตร\nปัจจุคมน์\nปัจจุทธรณ์\nปัจจุบัน\nปัจจุสมัย\nปัจเจก\nปัจโจปการกิจ\nปัจฉา\nปัจฉิม\nปัจถรณ์\nปัจนึก\nปัจยาการ\nปัจเวกขณ์\nปัชชุน\nปัญจนที\nปัญจวัคคีย์\nปัญจก\nปัญจกะ\nปัญจมี\nปัญจวีสติ\nปัญญัติ\nปัญญา\nปัญหา\nปัฏ\nปัฏนะ\nปัฐยาวัต\nปัณฑรหัตถี\nปัณณะ\nปัณณาส\nปัณณาสก์\nปัณรสี\nปัณหิ\nปัด\nปัตคาด\nปัตตะ\nปัตตานึก\nปัตตานุโมทนา\nปัตตาเวีย\nปัตติ\nปัตติก\nปัตถร\nปัตถะ\nปัตนิ\nปัตนี\nปัตหล่า\nปัถพี\nปัถวี\nปัทม์\nปัทมะ\nปัทมาสน์\nปัน\nปั่น\nปั้น\nปั้นจั่น\nปันจุเหร็จ\nปั้นลม\nปั้นหยา\nปั้นเหน่ง\nปับ\nปั๊บ\nปัปผาสะ\nปัพพาชนียกรรม\nปัพภาระ\nปั๊ม\nปัยกา\nปัยยิกา\nปัวเปีย\nปัศจิม\nปัศตัน\nปัสสาวะ\nปัสสาสะ\nปา\nป่า\nป้า\nปาก\nปาง\nป้าง\nปาจรีย์\nปาจิตตีย์\nปาจีน\nปาฏลิ\nปาฏิบท\nปาฏิบุคลิก\nปาฏิโภค\nปาฏิหาริย์\nปาฐก\nปาฐกถา\nปาฐะ\nปาณกชาติ\nปาณะ\nปาณาติบาต\nปาณิ\nปาณี\nปาด\nปาติโมกข์\nปาตี\nปาเต๊ะ\nปาท่องโก๋\nปาทังกา\nปาทาน\nปาทุกา\nปาน\nป่าน\nป้าน\nปานะ\nปานียะ\nป้าบ\nป๊าบ\nปาพจน์\nปาม\nปาโมกข์\nป่าย\nป้าย\nปายาส\nปาร์เกต์\nปารมี\nปารเมศ\nปาราชิก\nปาริฉัตร\nปาริชาต\nปารุสกวัน\nปาล\nปาล์ม\nปาลิไลยก์\nปาลี\nปาว\nป่าว\nป๊าว\nปาวาร\nปาษาณ\nปาส\nปาสาณ\nปาสาทิกะ\nปาหนัน\nปาหี่\nปำ\nป้ำ\nป้ำเป๋อ\nปิกนิก\nปิ้ง\nปิงคละ\nปิงปอง\nปิฎก\nปิฏฐะ\nปิฐิ\nปิณฑะ\nปิด\nปิตตะ\nปิตา\nปิตามหัยกา\nปิตามหัยยิกา\nปิตุ\nปิตุจฉา\nปิตุภูมิ\nปิตุลา\nปิโตรเลียม\nปิ่น\nปิ่นแก้ว\nปิ่นโต\nปิปผลี\nปิ่ม\nปิ้ม\nปิยะ\nปิยังคุ\nปิโยรส\nปิลันธน์\nปิ๋ว\nปิศาจ\nปิหกะ\nปี\nปี่\nปี้\nปี๋\nปีก\nปีฐะ\nปี๊ด\nปีติ\nปีน\nปีบ\nปี๊บ\nปีศาจ\nปีฬกะ\nปึก\nปึง\nปึ่ง\nปึ๋ง\nปึ้ด\nปึมปื้อ\nปืน\nปื้น\nปือ\nปื้อ\nปื๋อ\nปุ\nปุ๊\nปุก\nปุกปุย\nปุคละ\nปุ้งกี๋\nปุงควะ\nปุงลิงค์\nปุงลึงค์\nปุจฉา\nปุฏะ\nปุณฑริก\nปุด\nปุตตะ\nปุถุชน\nปุนนาค\nปุนภพ\nปุนัพสุ\nปุบ\nปุ๊บ\nปุปผะ\nปุปะ\nปุพพะ\nปุ่ม\nปุ่มป่ำ\nปุ้ม\nปุ๋ม\nปุย\nปุ้ย\nปุ๋ย\nปุรณะ\nปุระ\nปุราณ\nปุราณะ\nปุริมพรรษา\nปุเรจาริก\nปุโรหิต\nปุลลิงค์\nปุลลึงค์\nปุลินท์\nปุโลปุเล\nปุษยะ\nปุสสะ\nปู\nปู่\nปูชกะ\nปูชนียบุคคล\nปูชนียวัตถุ\nปูชนียะ\nปูชา\nปูชิต\nปูด\nปูน\nปูม\nปู้ยี่ปู้ยำ\nปูระ\nปูลู\nเป้\nเป๋\nเปก\nเป๊ก\nเป่ง\nเป้ง\nเป๋ง\nเป็ด\nเปตพลี\nเปตอง\nเป็น\nเปยยาล\nเปร็ง\nเปรต\nเปรม\nเปรย\nเปรอ\nเปรอะ\nเปราะ\nเปรียง\nเปรี้ยง\nเปรี้ยงปร้าง\nเปรียญ\nเปรียบ\nเปรี่ยม\nเปรียว\nเปรี้ยว\nเปรียะ\nเปรี๊ยะ\nเปรื่อง\nเปรื้อย\nเปล\nเปล่ง\nเปลว\nเปลา\nเปล่า\nเปล้า\nเปลาะ\nเปลี้ย\nเปลี่ยน\nเปลี่ยม\nเปลี่ยว\nเปลือก\nเปลือง\nเปลื้อง\nเปลือย\nเปศะ\nเปศัส\nเปสการ\nเปสละ\nเปสุญวาท\nเป๋อ\nเป้อเย้อ\nเปอร์\nเปอร์เซ็นต์\nเปะ\nเปา\nเป่า\nเป้า\nเป๊า\nเป๋า\nเป๋าฮื้อ\nเปาะ\nเป๊าะ\nเปาะเปี๊ยะ\nเปาะแปะ\nเปิก\nเปิง\nเปิงมาง\nเปิด\nเปิ่น\nเปิบ\nเปิ๊บ\nเปีย\nเปียก\nเปี๊ยก\nเปี๊ยบ\nเปี่ยม\nเปี้ยว\nเปี๊ยว\nเปียะ\nเปี๊ยะ\nเปือก\nเปื้อน\nเปื่อย\nแป\nแป้ง\nแป๋ง\nแปด\nแป๊ด\nแปทู\nแป้น\nแป๊น\nแปบ\nแป๊บ\nแปม\nแปร\nแปร๋\nแปรก\nแปรง\nแปร่ง\nแปร๊ด\nแปร้น\nแปร๋น\nแปรปรวน\nแประ\nแปล\nแปล้\nแปลก\nแปลง\nแปลน\nแปลบ\nแปล๊บ\nแป้ว\nแป๊ว\nแปะ\nแป๊ะ\nแป๊ะซะ\nโป\nโป่\nโป้\nโป๊\nโปก\nโป๊ก\nโป๊กเกอร์\nโปกขรณี\nโปกขรพรรษ\nโปเก\nโปง\nโป่ง\nโป้ง\nโป่งข่าม\nโปงลาง\nโป่งวิด\nโปฐบท\nโปฐปทมาส\nโปดก\nโปตถกะ\nโปน\nโป๊ป\nโป๊ยเซียน\nโปรแกรม\nโปรง\nโปร่ง\nโปรด\nโปรตอน\nโปรตีน\nโปรเตสแตนต์\nโปรแทรกเตอร์\nโปรย\nโปล่ง\nโปลิโอ\nโปโล\nโปสก\nโปสต์การ์ด\nโปะ\nโป๊ะ\nโป๊ะจ้าย\nไป\nไป่\nไป๋\nไปยาล\nไปรษณีย์\nไปรษณียบัตร\nไปรษณียภัณฑ์\nไปรษณียากร\nไปล่\nไปศาจ\nผก\nผกา\nผกาย\nผคม\nผง\nผงก\nผงม\nผงะ\nผงาด\nผง่าน\nผจง\nผจญ\nผจัญ\nผจาน\nผชุม\nผณิน\nผณินทร\nผณิศวร\nผด\nผดุง\nผเดิน\nผทม\nผนวก\nผนวช\nผนัง\nผนิด\nผนึก\nผม\nผยอง\nผรณาปีติ\nผรสุ\nผริต\nผรุสวาท\nผล\nผลคุน\nผลคุนี\nผล็อย\nผละ\nผลัก\nผลัด\nผลับ\nผลัวะ\nผลา\nผลาญ\nผลานิสงส์\nผลาผล\nผลาหาร\nผลิ\nผลิกะ\nผลิต\nผลิน\nผลี\nผลีผลาม\nผลึก\nผลึ่ง\nผลือ\nผลุ\nผลุง\nผลุด\nผลุน\nผลุนผลัน\nผลุบ\nผลุบผลับ\nผลุย\nผลู\nผวน\nผวย\nผวา\nผสม\nผสาน\nผอง\nผ่อง\nผ่อน\nผอบ\nผอม\nผ็อย\nผอูน\nผะ\nผะดา\nผะสา\nผัก\nผัคคุณ\nผัคคุณี\nผัง\nผัด\nผัน\nผับ\nผัว\nผัวะ\nผัสสะ\nผัสสาหาร\nผา\nผ่า\nผ้า\nผาก\nผาง\nผ่าง\nผาณิต\nผาด\nผาติ\nผ่าน\nผาม\nผาย\nผ่ายผอม\nผาล\nผาลคุน\nผาลา\nผ่าว\nผาสุก\nผ้าฮาด\nผำ\nผ้ำ\nผิ\nผิง\nผิด\nผิตะ\nผิน\nผิว\nผี\nผี้ว์\nผึง\nผึ่ง\nผึ้ง\nผึ้งรวง\nผืน\nผื่น\nผุ\nผุด\nผุยผง\nผุสราคา\nผู้\nผูก\nเผ\nเผง\nเผชิญ\nเผ็ด\nเผด็จ\nเผดิม\nเผดียง\nเผ่น\nเผนิก\nเผย\nเผยอ\nเผยิบ\nเผยิบผยาบ\nเผล\nเผล่\nเผล็ด\nเผลอ\nเผลอไผล\nเผละ\nเผลาะ\nเผลาะแผละ\nเผลียง\nเผอเรอ\nเผอิญ\nเผอิล\nเผะ\nเผา\nเผ่า\nเผ้า\nเผาะ\nเผิ้ง\nเผิน\nเผียน\nเผือ\nเผื่อ\nเผือก\nเผือด\nเผือน\nเผื่อน\nแผ่\nแผก\nแผง\nแผด\nแผน\nแผ่น\nแผนก\nแผล\nแผลง\nแผล็บ\nแผล็ว\nแผละ\nแผ่ว\nแผ้ว\nโผ\nโผง\nโผฏฐัพพะ\nโผน\nโผเผ\nโผย\nโผล่\nโผลกเผลก\nโผละ\nโผอน\nโผะ\nไผ\nไผ่\nไผท\nฝน\nฝรั่ง\nฝรั่งเศส\nฝ่อ\nฝอย\nฝัก\nฝัง\nฝั่ง\nฝัด\nฝัน\nฝา\nฝ่า\nฝ้า\nฝาก\nฝาง\nฝาด\nฝาน\nฝาย\nฝ่าย\nฝ้าย\nฝิ่น\nฝี\nฝี่\nฝีก\nฝึก\nฝืด\nฝืน\nฝุ่น\nฝูง\nเฝ้า\nเฝือ\nเฝือก\nเฝือง\nเฝื่อน\nแฝก\nแฝง\nแฝด\nใฝ่\nไฝ\nพก\nพกุล\nพง\nพงศ์\nพงศกร\nพงศธร\nพงศา\nพงศาวดาร\nพจน์\nพจนา\nพจนานุกรม\nพจนารถ\nพจนีย์\nพจมาน\nพจี\nพชระ\nพญา\nพญาลอ\nพณิช\nพณิชย์\nพดด้วง\nพธู\nพนันดร\nพนาดร\nพนาดอน\nพนาราม\nพนาลัย\nพนาลี\nพนาวาส\nพนาเวศ\nพนาศรม\nพนาสณฑ์\nพนาสัณฑ์\nพเนจร\nพ่น\nพ้น\nพนม\nพนอง\nพนอม\nพนัก\nพนักงาน\nพนัง\nพนัน\nพนัส\nพนา\nพนาย\nพนิดา\nพนิต\nพบ\nพม่า\nพยนต์\nพยศ\nพยัก\nพยักพเยิด\nพยัคฆ์\nพยัคฆา\nพยัคฆิน\nพยัคฆี\nพยัชน์\nพยัญชนะ\nพยัต\nพยับ\nพยากรณ์\nพยาฆร์\nพยางค์\nพยาธิ\nพยาน\nพยาบาท\nพยาบาล\nพยาม\nพยามะ\nพยายาม\nพยุ\nพยุง\nพยุหยาตรา\nพยุหโยธา\nพยุหเสนา\nพยุหะ\nพยู่ห์\nพเยีย\nพร\nพรต\nพรม\nพรรค\nพรรค์\nพรรคานต์\nพรรณ\nพรรณนา\nพรรดึก\nพรรลาย\nพรรษ\nพรรษา\nพรรเอิญ\nพรวด\nพรวน\nพรหม\nพรหมจรรย์\nพรหมจาริณี\nพรหมจารี\nพรหมา\nพรหมาสตร์\nพรหมินทร์\nพรอก\nพร่อง\nพร้อง\nพรอด\nพร้อม\nพร้อมพรัก\nพร่อย\nพร้อย\nพระ\nพระนอม\nพระนาด\nพระฮาม\nพรักพร้อม\nพรั่ง\nพรัด\nพรั่น\nพรับ\nพร่า\nพร้า\nพราก\nพราง\nพร่าง\nพราด\nพราน\nพราย\nพราว\nพราหมณ์\nพราหมณะ\nพราหมณี\nพราหมี\nพรำ\nพร่ำ\nพริก\nพริ้ง\nพริบ\nพริ้ม\nพรึง\nพรึน\nพรึบ\nพรึ่บ\nพรืด\nพรุ\nพรุ่ง\nพรุน\nพรู\nพรูด\nพฤกษ์\nพฤกษชาติ\nพฤกษเทวดา\nพฤกษราช\nพฤกษศาสตร์\nพฤกษา\nพฤฒ\nพฤฒา\nพฤฒาจารย์\nพฤฒิ\nพฤต\nพฤติ\nพฤทธ์\nพฤทธิ์\nพฤนต์\nพฤนท์\nพฤศจิก\nพฤศจิกายน\nพฤษภ\nพฤษภาคม\nพฤหัสบดี\nพล\nพละ\nพลากร\nพลาดิศัย\nพลาธิการ\nพลานามัย\nพลบ\nพลวก\nพลวง\nพลวัต\nพลศาสตร์\nพลอ\nพล้อ\nพลอง\nพลอด\nพลอน\nพลอมแพลม\nพลอย\nพล่อย\nพลั่ก\nพลัง\nพลั่ง\nพลั้ง\nพลัด\nพลัน\nพลับ\nพลับพลา\nพลับพลึง\nพลั่ว\nพล่า\nพลาง\nพลาญ\nพลาด\nพล่าน\nพลาม\nพล่าม\nพลาย\nพลาสติก\nพลาสมา\nพลาหก\nพลำ\nพล้ำ\nพลำภัง\nพลิก\nพลิพัท\nพลิ้ว\nพลี\nพลุ\nพลุก\nพลุ่ก\nพลุกพล่าน\nพลุ่ง\nพลุ้ย\nพลู\nพลูโต\nพลูโทเนียม\nพวก\nพวง\nพ่วง\nพวน\nพวย\nพสก\nพสุ\nพสุธา\nพสุสงกรานต์\nพหล\nพหุ\nพหุล\nพหู\nพอ\nพ่อ\nพ้อ\nพอก\nพอง\nพ้อง\nพอน\nพ้อม\nพอโลเนียม\nพะ\nพะงา\nพะงาบ\nพะจง\nพะทำมะรง\nพะนอ\nพะเน้าพะนอ\nพะเนิน\nพะเนียง\nพะแนง\nพะพาน\nพะพิง\nพะเพิง\nพะยอม\nพ่ะย่ะค่ะ\nพะยูง\nพะยูน\nพะเยิบ\nพะเยิบพะยาบ\nพะรุงพะรัง\nพะโล้\nพะไล\nพะวง\nพะวักพะวน\nพะวา\nพะว้าพะวัง\nพะอง\nพะอากพะอำ\nพะอืดพะอม\nพัก\nพักตร์\nพักตรา\nพักร\nพัง\nพังกา\nพังงา\nพังผืด\nพังพวย\nพังพอน\nพังพาน\nพังพาบ\nพังเพย\nพัช\nพัชนี\nพัชระ\nพัญจน์\nพัฒนะ\nพัฒนา\nพัฒนากร\nพัฒนาการ\nพัด\nพัดชา\nพัดดึงส์\nพัตร\nพัทธ์\nพัทธยา\nพัทธสีมา\nพัทร\nพัน\nพันตู\nพันทาง\nพันธ์\nพันธะ\nพันธกรณี\nพันธบัตร\nพันธมิตร\nพันธนะ\nพันธนาคาร\nพันธนาการ\nพันธุ์\nพันธุกรรม\nพันลึก\nพันลือ\nพันเลิศ\nพันเอิญ\nพับ\nพัลลภ\nพัลวัน\nพัว\nพัวะ\nพัศดี\nพัสดุ\nพัสตร์\nพัสถาน\nพา\nพาก\nพากเพียร\nพากย์\nพาง\nพ่าง\nพาชี\nพาณิช\nพาณิชย์\nพาณิชยกรรม\nพาณิชยการ\nพาณิชยศาสตร์\nพาณิชยศิลป์\nพาณินี\nพาณี\nพาณีนี\nพาด\nพาท\nพาทย์\nพาธ\nพาธา\nพาน\nพ่าน\nพานร\nพานรินทร์\nพาม\nพาย\nพ่าย\nพายม้า\nพายัพ\nพายุ\nพาร์เซก\nพารณ\nพารา\nพาราฟิน\nพาล\nพาลา\nพาลี\nพาลุก\nพาโล\nพาไล\nพาส\nพาสน์\nพาสนา\nพาสุกรี\nพ่าห์\nพาหนะ\nพาหะ\nพาหา\nพาหิรกะ\nพาหิระ\nพาหุ\nพาหุรัด\nพาหุสัจจะ\nพาเหียร\nพาฬ\nพำ\nพำนัก\nพำพึม\nพำลา\nพิกล\nพิกสิต\nพิกัด\nพิกัติ\nพิกัน\nพิการ\nพิกุล\nพิเคราะห์\nพิฆน์\nพิฆเนศ\nพิฆเนศวร\nพิฆาต\nพิง\nพิจัย\nพิจาร\nพิจารณ์\nพิจารณา\nพิจิก\nพิจิต\nพิจิตร\nพิชญ์\nพิชัย\nพิชาน\nพิชิต\nพิเชฐ\nพิเชียร\nพิฑูรย์\nพิณ\nพิดทูล\nพิดาน\nพิโดร\nพิตร\nพิถี\nพิถีพิถัน\nพิทย\nพิทย์\nพิทยา\nพิทยาคม\nพิทยาคาร\nพิทยาธร\nพิทยาลัย\nพิทักษ์\nพิทูร\nพิเทศ\nพิธาน\nพิธี\nพิธุ\nพินทุ\nพินอบพิเทา\nพินัย\nพินาศ\nพินิจ\nพินิต\nพินิศ\nพินิศจัย\nพิเนต\nพิบัติ\nพิบุล\nพิบูล\nพิปริต\nพิปลาส\nพิพรรธ\nพิพรรธน์\nพิพักพิพ่วน\nพิพัฒ\nพิพัฒน์\nพิพากษ์\nพิพากษา\nพิพาท\nพิพิธ\nพิพิธภัณฑ์\nพิพิธภัณฑสถาน\nพิภพ\nพิภัช\nพิภาค\nพิภูษณะ\nพิเภก\nพิมปะการัง\nพิมพ์\nพิมพการัง\nพิมพา\nพิมพาภรณ์\nพิมล\nพิมเสน\nพิมาน\nพิมุข\nพิโมกข์\nพิโมกษ์\nพิโยกพิเกน\nพิโยค\nพิรอด\nพิระ\nพิรากล\nพิราบ\nพิราม\nพิราลัย\nพิริยะ\nพิรี้พิไร\nพิรุณ\nพิรุธ\nพิรุฬห์\nพิเรนทร์\nพิเราะ\nพิโรธ\nพิไร\nพิลังกาสา\nพิลาป\nพิลาส\nพิลิปดา\nพิลึก\nพิลึกกึกกือ\nพิลึกพิลั่น\nพิโลน\nพิไล\nพิศ\nพิศวง\nพิศวาส\nพิศาล\nพิศุทธ์\nพิศุทธิ์\nพิเศษ\nพิษ\nพิษฐาน\nพิษนาศน์\nพิสดาร\nพิสมร\nพิสมัย\nพิสัง\nพิสัช\nพิสัย\nพิสิฐ\nพิสุทธิ์\nพิสูจน์\nพิหค\nพิหาร\nพิฬาร\nพี\nพี่\nพี้\nพีชคณิต\nพีระมิด\nพึง\nพึ่ง\nพึ่บ\nพึ่บพั่บ\nพึม\nพึมพำ\nพืช\nพืด\nพื้น\nพุ\nพุก\nพุกาม\nพุง\nพุ่ง\nพุงดอ\nพุฒ\nพุฒิ\nพุด\nพุดตาน\nพุทธ\nพุทธะ\nพุทธังกูร\nพุทธางกูร\nพุทธันดร\nพุทธาภิเษก\nพุทธาวาส\nพุทธิ\nพุทโธ่\nพุทรา\nพุธ\nพุ่ม\nพุมเรียง\nพุ้ย\nพู\nพูพอน\nพู่\nพูด\nพูน\nพู้น\nพู่ระหง\nเพ\nเพ็ก\nเพกา\nเพคะ\nเพ็ง\nเพ่ง\nเพ็จ\nเพชฉลูกรรม\nเพชฌฆาต\nเพชร\nเพชรดา\nเพชรปาณี\nเพชรฤกษ์\nเพชรายุธ\nเพชรกลับ\nเพชรสังฆาต\nเพชรหลีก\nเพชรหึง\nเพ็ญ\nเพฑูริย์\nเพณี\nเพ็ดทูล\nเพดาน\nเพท\nเพทนา\nเพทาย\nเพทุบาย\nเพโทบาย\nเพ่นพ่าน\nเพนียด\nเพไนย\nเพ้ย\nเพรง\nเพรซีโอดิเมียม\nเพรา\nเพราะ\nเพริด\nเพริศ\nเพรียก\nเพรียง\nเพรียบ\nเพรี้ยม\nเพรียว\nเพรื่อ\nเพรือง\nเพล\nเพลง\nเพล็ด\nเพล้โพล้\nเพลา\nเพลาะ\nเพลิง\nเพลิดเพลิน\nเพลิน\nเพลีย\nเพลี้ย\nเพลี่ยง\nเพศ\nเพส\nเพสลาด\nเพ่อ\nเพ้อ\nเพ้อเจ้อ\nเพอิญ\nเพะ\nเพา\nเพาะ\nเพิก\nเพิง\nเพิ่ง\nเพิดเพ้ย\nเพิ่ม\nเพี้ย\nเพียง\nเพี้ยง\nเพียงออ\nเพี้ยน\nเพียบ\nเพียร\nเพื่อ\nเพื่อน\nแพ\nแพ้\nแพง\nแพ่ง\nแพงพวย\nแพทย์\nแพทยศาสตร์\nแพน\nแพ่น\nแพนก\nแพนงเชิง\nแพร\nแพร่\nแพรก\nแพร่ง\nแพรว\nแพร้ว\nแพลง\nแพลทินัม\nแพล็บ\nแพลม\nแพลเลเดียม\nแพละ\nแพละโลม\nแพว\nแพ้ว\nแพศย์\nแพศยา\nแพะ\nโพ\nโพก\nโพกพาย\nโพง\nโพงพาง\nโพชฌงค์\nโพซิตรอน\nโพด\nโพทะเล\nโพแทสเซียม\nโพธ\nโพธิ\nโพธิ์\nโพน\nโพ้น\nโพนทะนา\nโพบาย\nโพย\nโพยก๊วน\nโพยม\nโพรก\nโพรง\nโพรโทแอกทิเนียม\nโพรมีเทียม\nโพระดก\nโพล่\nโพลง\nโพล่ง\nโพล้ง\nโพลน\nโพล้เพล้\nโพละ\nโพสพ\nไพ\nไพ่\nไพจิตร\nไพชน\nไพชยนต์\nไพฑูรย์\nไพที\nไพบูลย์\nไพพรรณ\nไพร\nไพร่\nไพรจิตร\nไพรชน\nไพรชยนต์\nไพรฑูรย์\nไพรที\nไพรบูลย์\nไพรเราะ\nไพรัช\nไพรำ\nไพริน\nไพรินทร์\nไพรี\nไพเราะ\nไพโรจน์\nไพล\nไพล่\nไพศาขะ\nไพศาล\nไพเศษ\nไพสพ\nไพสิฐ\nไพหาร\nฟก\nฟ้ง\nฟรักโทส\nฟรี\nฟลูออรีน\nฟ่อ\nฟ้อ\nฟอก\nฟอง\nฟ่อง\nฟ้อง\nฟอด\nฟอน\nฟ่อน\nฟ้อน\nฟ้อแฟ้\nฟอร์มาลดีไฮด์\nฟอร์มาลิน\nฟอสฟอรัส\nฟอสเฟต\nฟัก\nฟักฟุ้น\nฟัง\nฟังก์ชัน\nฟัด\nฟัน\nฟั่น\nฟั้น\nฟ้า\nฟาก\nฟาง\nฟ่าง\nฟาด\nฟาทอม\nฟาน\nฟ่าม\nฟาย\nฟาร์ม\nฟาสซิสต์\nฟิด\nฟิต\nฟิบ\nฟิล์ม\nฟิวส์\nฟิสิกส์\nฟี่\nฟี้\nฟืดฟาด\nฟืน\nฟื้น\nฟืม\nฟุ\nฟุ้ง\nฟุต\nฟุน\nฟุบ\nฟุ่บ\nฟุ่มเฟือย\nฟุลสแก๊ป\nฟู\nฟู่\nฟูก\nฟูด\nฟูม\nเฟ็ด\nเฟ้น\nเฟลด์สปาร์\nเฟ้อ\nเฟอร์เมียม\nเฟอะ\nเฟอะฟะ\nเฟะ\nเฟะฟะ\nเฟิน\nเฟี้ยม\nเฟี้ยว\nเฟือ\nเฟื้อ\nเฟือง\nเฟื่อง\nเฟื้อง\nเฟือน\nเฟือย\nเฟื้อย\nแฟ่\nแฟง\nแฟชั่น\nแฟน\nแฟบ\nแฟ้ม\nแฟรนเซียม\nแฟลกซ์\nแฟลต\nแฟะ\nโฟกัส\nไฟ\nภควดี\nภควัต\nภควันต์\nภควัม\nภควา\nภควาน\nภคะ\nภคันทลา\nภคินี\nภณะ\nภณิดา\nภพ\nภมร\nภมริน\nภมรี\nภมุกา\nภยันตราย\nภยาคติ\nภระ\nภรณี\nภรต\nภรรดร\nภรรดา\nภรรยา\nภระมร\nภระมรี\nภราดร\nภราดรภาพ\nภราดา\nภริยา\nภฤศ\nภวะ\nภวตัณหา\nภวนะ\nภวังค์\nภวังคจิต\nภักดี\nภักตะ\nภักติ\nภักษ์\nภักษา\nภักษาหาร\nภัค\nภัคน์\nภังคะ\nภังคี\nภัจ\nภัณฑ์\nภัณฑาคาร\nภัณฑาคาริก\nภัณฑารักษ์\nภัณฑนะ\nภัณฑู\nภัต\nภัตตาคาร\nภัตตาหาร\nภัตร\nภัทระ\nภัทรกัป\nภัพ\nภัย\nภัสดา\nภัสตรา\nภัสมะ\nภัสสร\nภา\nภาค\nภาคย์\nภาคยานุวัติ\nภาคินี\nภาคิไนย\nภาคี\nภาคียะ\nภาชนะ\nภาชี\nภาณ\nภาณวาร\nภาณกะ\nภาณี\nภาณุ\nภาดร\nภาดา\nภาตระ\nภาตา\nภาตุ\nภาติกะ\nภาติยะ\nภาพ\nภาพย์\nภาม\nภาย\nภาร\nภาระ\nภารดี\nภารต\nภารตี\nภารยทรัพย์\nภารยา\nภารา\nภาวนา\nภาวะ\nภาษ\nภาษณ์\nภาษา\nภาษิต\nภาษี\nภาส\nภาสน์\nภาสวร\nภาสา\nภาสุระ\nภิกขา\nภิกขาจาร\nภิกขุ\nภิกขุนี\nภิกษา\nภิกษาจาร\nภิกษาหาร\nภิกษุ\nภิกษุณี\nภิงคาร\nภิญโญ\nภิตติ\nภินท์\nภินทนาการ\nภิยโย\nภิรมย์\nภิรมย์สุรางค์\nภิษัช\nภิสัก\nภีตะ\nภีมะ\nภีรุ\nภุกต์\nภุขัน\nภุช\nภุชงค์\nภุต\nภุมมะ\nภุมรัตน์\nภุมวาร\nภุมรา\nภุมริน\nภุมรี\nภุมเรศ\nภู\nภู่\nภูต\nภูติ\nภูม\nภูมิ\nภูมี\nภูริ\nภูรี\nภูวดล\nภูวนาถ\nภูวเนตร\nภูวไนย\nภูษา\nภูษิต\nเภกะ\nเภตรา\nเภท\nเภทุบาย\nเภรี\nเภสัช\nโภค\nโภคะ\nโภคิน\nโภคี\nโภไคย\nโภไคศวรรย์\nโภช\nโภชย์\nโภชก\nโภชนะ\nโภชนา\nโภชนาหาร\nโภชนียะ\nไภริน\nไภรี\nไภษัชคุรุ\nไภษัชย์\nมกร\nมกราคม\nมกุฎ\nมคธ\nมฆวัน\nมฆะ\nมฆา\nม่ง\nมงกุฎ\nมงโกรย\nมงคล\nมงคลวาร\nมณฑ์\nมณฑก\nมณฑนะ\nมณฑป\nมณฑล\nมณฑา\nมณฑารพ\nมณฑิระ\nมณเฑียร\nมณี\nมด\nมตะ\nมตกภัต\nมติ\nมทนะ\nมทะ\nมธุ\nมธุกร\nมธุการี\nมธุลีห์\nมธุระ\nมธุรพจน์\nมน\nมนินทรีย์\nม่น\nมนต์\nมนตร์\nมนตรี\nมนท์\nมนทิราลัย\nมนเทียร\nมนสิการ\nมนัส\nมนัสวี\nมนินทรีย์\nมนิมนา\nมนิลา\nมนุญ\nมนุษย์\nมนุษยชาติ\nมนุษยธรรม\nมนุษย์มนา\nมนุษยโลก\nมนุษยศาสตร์\nมนุษยสัมพันธ์\nมนุสาร\nมนู\nมนูสาร\nมโน\nมโนช\nมโนชญ์\nมโนราห์\nมโนสาเร่\nมโนห์รา\nมมังการ\nมยุรฉัตร\nมยุระ\nมยุรา\nมยุรี\nมยุเรศ\nมยูร\nมรกต\nมรคา\nมรฑป\nมรณ์\nมรณะ\nมรณกรรม\nมรณบัตร\nมรณภัย\nมรณภาพ\nมรดก\nมรรค\nมรรคา\nมรรตัย\nมรรยาท\nมรรษ\nมรสุม\nมริจ\nมริยาท\nมรีจิ\nมรุต\nมฤค\nมฤคย์\nมฤคศิระ\nมฤคศิรมาส\nมฤคเศียร\nมฤคินทร์\nมฤเคนทร์\nมฤดก\nมฤต\nมฤตยู\nมฤทุ\nมล\nมละ\nมลัก\nมลังเมลือง\nมล้าง\nมลาย\nมลายู\nมวก\nม่วง\nมวน\nม่วน\nม้วน\nม้วนต้วน\nมวย\nม้วย\nมวล\nมหกรรม\nมหรณพ\nมหรรณพ\nมหรสพ\nมหัจฉริยะ\nมหัต\nมหัทธนะ\nมหันต์\nมหันตโทษ\nมหัพภาค\nมหัศจรรย์\nมหา\nมหากฐิน\nมหากาฬ\nมหาขันธกะ\nมหาจักร\nมหาชน\nมหาชัย\nมหาชาติ\nมหาโชตรัต\nมหาดไทย\nมหาดเล็ก\nมหาตมะ\nมหาไถ่\nมหาเทพ\nมหาเทพี\nมหาเทวี\nมหาธาตุ\nมหานิกาย\nมหานิล\nมหาบพิตร\nมหาบัณฑิต\nมหาพน\nมหาพรหม\nมหาภารตะ\nมหาภิเนษกรมณ์\nมหาภูต\nมหาเมฆ\nมหายาน\nมหายุค\nมหาราช\nมหาฤกษ์\nมหาละลวย\nมหาละลาย\nมหาวงศ์\nมหาวรรค\nมหาวิทยาลัย\nมหาศักราช\nมหาศาล\nมหาสงกรานต์\nมหาสดมภ์\nมหาสดำ\nมหาสมุทร\nมหาสาวก\nมหาหงส์\nมหาหิงคุ์\nมหาอำนาจ\nมหาอุจ\nมหาอุด\nมหาอุปรากร\nมหาอุปราช\nมหิ\nมหิดล\nมหิธร\nมหิป\nมหิงส์\nมหิทธิ\nมหินท์\nมหิมา\nมหิศร\nมหิศวร\nมหิษ\nมหิษี\nมหึมา\nมเหยงค์\nมเหศ\nมเหศวร\nมเหศักดิ์\nมเหสักข์\nมเหสิ\nมเหสี\nมเหาฬาร\nมโหรสพ\nมโหระทึก\nมโหรี\nมโหฬาร\nมไหศวรรย์\nมอ\nมอง\nมองโกลอยด์\nมองโกเลีย\nมองคร่อ\nมอญ\nมอด\nม่อต้อ\nมอเตอร์\nมอเตอร์ไซค์\nมอน\nม่อน\nมอบ\nมอม\nมอมแมม\nม่อย\nมอร์ฟีน\nมอระกู่\nมอลโทส\nม่อลอกม่อแลก\nม่อห้อม\nม่อฮ่อม\nมะ\nมะกรูด\nมะกล่ำ\nมะกอก\nมะก่อง\nมะกะโรนี\nมะกา\nมะเกลือ\nมะเกี๋ยง\nมะข่วง\nมะขวิด\nมะขาม\nมะเขือ\nมะแข่น\nมะคังแดง\nมะค่า\nมะคำไก่\nมะคำดีควาย\nมะงั่ว\nมะงุมมะงาหรา\nมะซัก\nมะซาง\nมะดัน\nมะดีหวี\nมะดูก\nมะเดหวี\nมะเดื่อ\nมะต้อง\nมะตะบะ\nมะตาด\nมะตาหะรี\nมะตึ่ง\nมะตื๋น\nมะตูม\nมะแตก\nมะโต\nมะนาว\nมะปราง\nมะปริง\nมะฝ่อ\nมะพร้าว\nมะพลับ\nมะพูด\nมะแพน\nมะแพร้ว\nมะเฟือง\nมะแฟน\nมะไฟ\nมะม่วง\nมะม่าว\nมะมี่\nมะมื่น\nมะมุด\nมะเมอ\nมะเมีย\nมะเมื่อย\nมะแม\nมะยง\nมะยม\nมะระ\nมะริด\nมะรืน\nมะรุม\nมะรุมมะตุ้ม\nมะเร็ง\nมะเรื่อง\nมะโรง\nมะลอกมะแลก\nมะละกอ\nมะลิ\nมะลื่น\nมะลืมดำ\nมะลุลี\nมะแว้ง\nมะสัง\nมะเส็ง\nมะหวด\nมะหะหมัด\nมะหาด\nมะหิ่ง\nมะเหงก\nมะอึก\nมะฮอกกานี\nมัก\nมักกะโรนี\nมักกะลีผล\nมักกะสัน\nมักขะ\nมั่กขั้ก\nมักขิกา\nมัค\nมัคคะ\nมัคคุเทศก์\nมัคนายก\nมัฆวาน\nมั่ง\nมังกง\nมังกร\nมังกุ\nมังคละ\nมังค่า\nมังคุด\nมังตาน\nมังสวิรัติ\nมังสะ\nมังสี\nมัจจะ\nมัจจุ\nมัจฉริยะ\nมัจฉรี\nมัจฉะ\nมัจฉา\nมัชชะ\nมัชวิรัติ\nมัชชาระ\nมัชฌันติกสมัย\nมัชฌิม\nมัชฌิมา\nมัญจา\nมัญชิษฐา\nมัญชุ\nมัญชุสา\nมัญชูสา\nมัญเชฏฐะ\nมัฏฐะ\nมัณฑนศิลป์\nมัณฑนา\nมัด\nมัตตะ\nมัตตัญญู\nมัตตา\nมัตติกา\nมัตถกะ\nมัตถลุงค์\nมัตสยะ\nมัตสยา\nมัตสระ\nมัตสริน\nมัททวะ\nมัทนะ\nมัทยะ\nมัธยฐาน\nมัธยม\nมัธยันห์\nมัธยัสถ์\nมัน\nมั่น\nมันตา\nมันถะ\nมันทิระ\nมันทิราลัย\nมับ\nมั้ม\nมัมมี่\nมัย\nมัลละ\nมัลลิกา\nมัว\nมัวซัว\nมั่ว\nมัศยา\nมัสตุ\nมัสตาร์ด\nมัสมั่น\nมัสยิด\nมัสรู่\nมัสลิน\nมัสสุ\nมา\nม้า\nมาก\nมาคสิระ\nมาฆบูชา\nมาฆะ\nม้าง\nมางสะ\nมาณพ\nมาณวิกา\nมาด\nมาดา\nมาตงค์\nมาตร\nมาตรา\nมาตฤ\nมาตังคะ\nมาตา\nมาตามหัยกะ\nมาตามหัยกา\nมาตามหัยยิกา\nมาติกะ\nมาติกา\nมาตุ\nมาตุจฉา\nมาตุรงค์\nมาตุเรศ\nมาตุละ\nมาตุลา\nมาตุลานี\nมาทะ\nมาธยมิก\nมาธยมิกะ\nมาธุระ\nมาธุสร\nมาธูระ\nมาน\nม่าน\nม้าน\nมานพ\nมานะ\nมานัต\nมานัส\nมานิต\nมานี\nมานุษ\nมานุษยวิทยา\nมาโนชญ์\nมาบ\nมาภา\nม้าม\nม่าย\nมายา\nมาร\nมาราธิราช\nมารค\nมารดร\nมารดา\nมารยา\nมารยาท\nมารศรี\nมารษา\nมาริต\nมารุต\nมาลย์\nมาลัย\nมาลา\nมาลาตี\nมาลาเรีย\nมาลินี\nมาลี\nมาลุต\nมาศ\nมาส\nมาสก\nมาห์\nม่าห์\nมาหิส\nม่าเหมี่ยว\nมาฬก\nมิ\nมิค\nมิคสัญญี\nมิ่ง\nมิจฉา\nมิด\nมิตร\nมิติ\nมิเตอร์\nมิถยา\nมิถุน\nมิถุนายน\nมิทธะ\nมินตรา\nมินตา\nมินหม้อ\nมิ่ม\nมิ้ม\nมิไย\nมิรันตี\nมิลลิกรัม\nมิลลิบาร์\nมิลลิเมตร\nมิลลิลิตร\nมิลักขะ\nมิลักขู\nมิส\nมิสกรี\nมิสกวัน\nมิสซา\nมี\nมี่\nมีด\nมีเทน\nมีน\nมีนาคม\nมี่สั้ว\nมึง\nมึน\nมืด\nมืน\nมื่น\nมือ\nมื้อ\nมุ\nมุก\nมุกดา\nมุกดาหาร\nมุกุระ\nมุข\nมุขเด็จ\nมุขยประโยค\nมุโขโลกนะ\nมุคคะ\nมุง\nมุ่ง\nมุ้ง\nมุจฉา\nมุจนะ\nมุจลินท์\nมุญจนะ\nมุญชะ\nมุฐิ\nมุณฑกะ\nมุณฑะ\nมุด\nมุตกิด\nมุตฆาต\nมุตตะ\nมุตตา\nมุตติ\nมุตะ\nมุติ\nมุททา\nมุทธชะ\nมุทธา\nมุทธาภิเษก\nมุทรา\nมุทริกา\nมุทะลุ\nมุทา\nมุทิกา\nมุทิงค์\nมุทิตา\nมุทุ\nมุทุตา\nมุ่น\nมุนิ\nมุนินทร์\nมุนี\nมุบ\nมุบมิบ\nมุม\nมุ้ม\nมุ่ย\nมุรธา\nมุรธาภิเษก\nมุสละ\nมุสลิม\nมุสา\nมุสิก\nมุหงิด\nมุหน่าย\nมุหุต\nมุฮัมมัด\nมูก\nมูเซอ\nมูตร\nมู่ทู่\nมูน\nมูมมาม\nมูรติ\nมูรธา\nมูรธาภิเษก\nมูล\nมูละ\nมูลา\nมูลิกากร\nมู่ลี่\nมู่เล่\nมูสัง\nมูสิก\nมูสิกะ\nมูสิกทันต์\nเม\nเม็ก\nเมกะเฮิรตซ์\nเมขลา\nเมฆ\nเมฆา\nเมฆินทร์\nเมฆี\nเม็ง\nเม็ด\nเมตตา\nเมตไตรย\nเมตร\nเมตริก\nเมตริกตัน\nเมถุน\nเมท\nเมโท\nเมทนี\nเมทินี\nเมทนีดล\nเมทานอล\nเมทิลแอลกอฮอล์\nเมธ\nเมธา\nเมธาวี\nเมธี\nเมน\nเม่น\nเม้น\nเมนเดลีเวียม\nเมนทอล\nเม้ม\nเมรัย\nเมริเดียน\nเมรุ\nเมล์\nเมล็ด\nเมลือง\nเมษ\nเมษายน\nเมห์\nเมหนะ\nเมหะ\nเมะ\nเมา\nเม่า\nเม้า\nเมารี\nเมาลี\nเมาฬี\nเมาะ\nเมิง\nเมิน\nเมิล\nเมีย\nเมียง\nเมี่ยง\nเมี้ยน\nเมือ\nเมื้อ\nเมื่อ\nเมือก\nเมือง\nเมือบ\nเมื่อย\nแม่\nแม้\nแมก\nแมกนีเซียม\nแมง\nแมงกะพรุน\nแมงกานิน\nแมงกานีส\nแมงคา\nแมงคาเรือง\nแมงช้าง\nแมงดา\nแมงลัก\nแม่ตะงาว\nแมน\nแม่น\nแม้น\nแมลง\nแมลบ\nแมว\nแม้ว\nแมะ\nโม\nโม่\nโม้\nโมก\nโมกข์\nโมกษะ\nโมฆกรรม\nโมฆสัญญา\nโมฆะ\nโมฆียกรรม\nโมฆียะ\nโมง\nโม่ง\nโมงครุ่ม\nโมทนา\nโมโนแซ็กคาไรด์\nโมไนย\nโมเม\nโมเมนต์\nโมเย\nโมรา\nโมรี\nโมเรส\nโมลิบดีนัม\nโมลี\nโมเลกุล\nโมเสก\nโมเสส\nโม่ห์\nโมหะ\nโมหันธ์\nโมหาคติ\nโมโห\nไม่\nไม้\nไมกา\nไมครอน\nไมโครกรัม\nไมโครฟิล์ม\nไมโครโฟน\nไมโครมิเตอร์\nไมโครเมตร\nไมโครลิตร\nไมโครเวฟ\nไมตรี\nไมยราบ\nไมล์\nยก\nยกกระบัตร\nยกนะ\nยง\nยงโย่\nยชุรเวท\nยติ\nยติภังค์\nยถากรรม\nยถาภูตญาณ\nย่น\nยนต์\nยนตร์\nยม\nยมก\nยมโดย\nยมนา\nยมล\nยมะ\nยรรยง\nยล\nยวง\nยวด\nยวน\nยวบ\nย้วย\nยวรยาตร\nยศ\nยโส\nยอ\nย่อ\nยอก\nย็อกแย็ก\nยอง\nย่อง\nย้อง\nยอด\nยอน\nย้อน\nยอบ\nยอม\nย่อม\nย้อม\nย่อย\nย้อย\nย้อแย้\nยะ\nย่ะ\nยะยอบ\nยะยับ\nยัก\nยักข์\nยักขินี\nยักษ์\nยักษา\nยักษิณี\nยักษี\nยัง\nยั้ง\nยั่งยืน\nยัชโญปวีต\nยัญ\nยัญญะ\nยัด\nยัติภังค์\nยัน\nยั่น\nยันต์\nยันตร\nยันตร์\nยันตรกรรม\nยั่นตะนี\nยับ\nยั่ว\nยั้ว\nยั้วเยี้ย\nยัวรยาตร\nยัวะ\nยัษฏิ\nยา\nย่า\nยาก\nยาคุ\nยาคู\nยาง\nย่าง\nยางพารา\nยาจก\nยาจนา\nยาไฉน\nยาด\nยาดา\nยาตร\nยาตรา\nยาน\nย่าน\nย่านพาโหม\nยานมาศ\nยานุมาศ\nยานี\nยาม\nย่าม\nยามะ\nยามักการ\nยามา\nยาย\nย้าย\nยายี\nยาว\nย้าว\nยาวกาลิก\nยาวชีวิก\nยาสูบ\nย่าหยา\nยาหยี\nยำ\nย่ำ\nย้ำ\nยำเยีย\nยิก\nยิง\nยิ่ง\nยิฏฐะ\nยิน\nยิบ\nยิบหยี\nยิปซัม\nยิปซี\nยิ้ม\nยิมนาสติก\nยิหวา\nยี\nยี่\nยี้\nยี่ก่า\nยี่เก\nยี่เข่ง\nยี่โถ\nยีน\nยี่โป้\nยี่ภู่\nยีราฟ\nยี่สก\nยี่สง\nยี่สน\nยี่สาน\nยี่สุ่น\nยี่หระ\nยี่หร่า\nยี่ห้อ\nยี่หุบ\nยึกยัก\nยึกยือ\nยึด\nยืด\nยืน\nยื่น\nยืม\nยื้อ\nยุ\nยุกกระบัตร\nยุกดิ\nยุกติ\nยุกติธรรม\nยุกต์\nยุค\nยุคนธร\nยุคล\nยุคันต์\nยุคันธร\nยุคุนธร\nยุง\nยุ่ง\nยุ้ง\nยุด\nยุต\nยุติ\nยุทธ\nยุทธ์\nยุทธนา\nยุทโธปกรณ์\nยุบ\nยุ่บ\nยุ่บยั่บ\nยุบล\nยุพดี\nยุพเรศ\nยุพา\nยุพาน\nยุพาพาล\nยุพาพิน\nยุ่มย่าม\nยุ่ย\nยุ้ย\nยุรยาตร\nยูรยาตร\nยุวชน\nยุวดี\nยุวราช\nยุวา\nยุวาน\nยู\nยู่\nยูง\nยูโด\nยูถะ\nยูถิกา\nยูริก\nยูเรนัส\nยูเรเนียม\nยูโรเพียม\nเย\nเย้\nเยง\nเยซู\nเย็ด\nเย็น\nเย็นตาโฟ\nเย็นเตาโฟ\nเย็บ\nเย้ย\nเยอ\nเย่อ\nเยอรมัน\nเยอว\nเย่อหยิ่ง\nเยอะ\nเยอะแยะ\nเยา\nเย้า\nเยาว์\nเยาวชน\nเยาวมาลย์\nเยาวยอด\nเยาวราช\nเยาวเรศ\nเยาวลักษณ์\nเยาวพา\nเยาวพาณี\nเยาวพาน\nเยาะ\nเยิง\nเยิน\nเยิ่น\nเยิ่นเย้อ\nเยินยอ\nเยิบ\nเยิบยาบ\nเยิ้ม\nเยีย\nเยี่ยง\nเยี่ยงอย่าง\nเยียงผา\nเยียดยัด\nเยียน\nเยียบ\nเยี่ยม\nเยียรบับ\nเยียรยง\nเยียว\nเยี่ยว\nเยียวยา\nเยือ\nเยื่อ\nเยื้อ\nเยือก\nเยือง\nเยื่อง\nเยื้อง\nเยือน\nเยื้อน\nแย่\nแย้\nแยก\nแยง\nแย่ง\nแย้ง\nแยงแย่\nแยงแย้\nแยบ\nแย็บ\nแยม\nแย้ม\nแยแส\nแยะ\nโย\nโย้\nโยก\nโยกเยก\nโยคาพจร\nโยคาวจร\nโยคเกณฑ์\nโยคยะ\nโยคะ\nโยคิน\nโยคี\nโยง\nโย่ง\nโย่งเย่ง\nโยงโย่\nโยชน์\nโยชนา\nโยถิกะ\nโยทะกา\nโยธวาทิต\nโยธา\nโยธิน\nโยน\nโยนก\nโยนิโส\nโยนี\nโยม\nโยโส\nใย\nไย\nไย่\nไยดี\nไยไพ\nรก\nรง\nรงค์\nรงควัตถุ\nรงรอง\nรจนา\nรจเรข\nรจเลข\nรจิต\nรชตะ\nรชนิ\nรชนี\nรชะ\nรณรงค์\nรด\nรดี\nรตนะ\nรตะ\nรติ\nรถ\nรน\nร่น\nรบ\nรบาญ\nรพี\nรม\nร่ม\nรมณี\nรมณีย์\nรมณียสถาน\nรมย์\nรมเยศ\nรยางค์\nรวก\nรวง\nร่วง\nรวด\nรวน\nรวนเร\nร่วน\nรวบ\nรวม\nร่วม\nรวย\nรวิ\nรวิวาร\nรวี\nรศนา\nรส\nรสนา\nรสสุคนธ์\nรสายนเวท\nรสิก\nรหัท\nรหัส\nรโห\nรโหฐาน\nรอ\nร่อ\nรอก\nรอง\nร่อง\nร้อง\nรองเง็ง\nร่องแร่ง\nรอด\nรอน\nร่อน\nร้อน\nรอบ\nรอบคอบ\nรอม\nรอมชอม\nรอมร่อ\nรอย\nร่อย\nร้อย\nร่อแร่\nระ\nระกะ\nระกา\nระกำ\nระเกะระกะ\nระคน\nระคาง\nระคาย\nระแคะ\nระฆัง\nระงม\nระงับ\nระแง้\nระโงกหิน\nระชวย\nระดม\nระดะ\nระดับ\nระดา\nระด่าว\nระดู\nระเด่น\nระเดียง\nระแด\nระตู\nระทก\nระทด\nระทม\nระทวย\nระทา\nระทึก\nระแทะ\nระนาด\nระนาบ\nระนาม\nระนาว\nระเนน\nระเนระนาด\nระเนียด\nระแนง\nระแนะ\nระบบ\nระบม\nระบอบ\nระบัด\nระบับ\nระบาด\nระบาย\nระบำ\nระบิล\nระบือ\nระบุ\nระเบง\nระเบ็ง\nระเบิด\nระเบียง\nระเบียน\nระเบียบ\nระแบบ\nระมัดระวัง\nระมาด\nระเมียร\nระย่อ\nระย่อม\nระยะ\nระยั้ง\nระยับ\nระย้า\nระยาบ\nระยำ\nระยิบระยับ\nระโยง\nระโยงระยาง\nระรวย\nระรอง\nระร่อน\nระรัว\nระราน\nระร่าย\nระริก\nระรี่\nระรึง\nระรื่น\nระรื้น\nระเร้ง\nระเริง\nระเรียง\nระเรื่อย\nระแรง\nระลวง\nระลอก\nระลึก\nระวัง\nระวาง\nระวาย\nระวิง\nระแวง\nระแวดระวัง\nระไว\nระส่ำระสาย\nระหกระเหิน\nระหง\nระหวย\nระหว่าง\nระหองระแหง\nระหัด\nระหาย\nระเห็จ\nระเหย\nระเหระหน\nระเหหน\nระเหิด\nระเหินระหก\nระแหง\nระโหย\nระอา\nระอิดระอา\nระอุ\nรัก\nรักข์\nรักขิต\nรักตะ\nรักบี้\nรักเร่\nรักแร้\nรักษ์\nรักษา\nรัง\nรั้ง\nรังเกียจ\nรังแก\nรังค์\nรังควาน\nรังแค\nรังรอง\nรังวัด\nรังสิ\nรังสี\nรังสิมันตุ์\nรังสิมา\nรัจฉา\nรัช\nรัชชูปการ\nรัชมังคลาภิเษก\nรัชชุ\nรัชฎาภิเษก\nรัชดาภิเษก\nรัชนะ\nรัชนี\nรัญจวน\nรัฏฐาภิปาลโนบาย\nรัฐ\nรัฐประศาสโนบาย\nรัฐประศาสนศาสตร์\nรัด\nรัต\nรัตกัมพล\nรัตมณี\nรัตคน\nรัตจันทน์\nรัตตัญญู\nรัตติ\nรัตน์\nรัตนะ\nรัตนโกสินทร์\nรัตนโกสินทรศก\nรัตนชาติ\nรัตนตรัย\nรัตนบัลลังก์\nรัตนวราภรณ์\nรัตนสิงหาสน์\nรัตนา\nรัตนากร\nรัตนาภรณ์\nรัตนาวลี\nรัตมา\nรัถ\nรัถยา\nรัทเทอร์ฟอร์เดียม\nรัน\nรั้น\nรันทด\nรันทวย\nรับ\nรัมณียสถาน\nรัมภา\nรัมมี่\nรัมย์\nรัย\nรัว\nรั่ว\nรั้ว\nรัศมิมัต\nรัศมิมาน\nรัศมี\nรัษฎากร\nรัสเซีย\nรัสสะ\nรัสสระ\nรา\nร่า\nร้า\nราก\nรากษส\nรากสาด\nราคะ\nราคจริต\nราคา\nราคิน\nราคี\nราง\nร่าง\nร้าง\nรางจืด\nรางชาง\nรางวัล\nราช\nราชกิจจานุเบกษา\nราชนิกุล\nราชวโรงการ\nราชญี\nราชดัด\nราชพฤกษ์\nราชมาณพ\nราชมาษ\nราชมาส\nราชย์\nราชสีห์\nราชะ\nราชัน\nราชันย์\nราชัย\nราชา\nราชาธิปไตย\nราชาธิราช\nราชาภิเษก\nราชายตนะ\nราชาวดี\nราชี\nราชินิกุล\nราชินีกุล\nราชินี\nราชินูปถัมภ์\nราชูปถัมภ์\nราชูปโภค\nราเชน\nราเชนทร์\nราเชนทรยาน\nราโชวาท\nราไชศวรรย์\nราญ\nราญรอน\nราด\nราต\nราตร\nราตรี\nราน\nร่าน\nร้าน\nราบ\nราพณ์\nราพณาสูร\nราม\nรามเกียรติ์\nรามสูร\nรามัญ\nรามา\nราย\nร่าย\nร้าย\nราว\nร้าว\nราวี\nราศี\nราษฎร\nราษฎร์\nราษตรี\nราษราตรี\nราหุ\nราหู\nรำ\nร่ำ\nรำคาญ\nรำงับ\nรำจวน\nรำบาญ\nรำพัน\nรำพาย\nรำพึง\nรำเพย\nรำไพ\nรำมะนา\nรำมะนาด\nรำมะร่อ\nร่ำรวย\nร่ำร่ำ\nรำไร\nรำลึก\nรำหัด\nรำหัส\nริ\nริก\nริดสีดวง\nริน\nริ้น\nริบ\nริบบิ้น\nริบรี่\nริบหรี่\nริปุ\nริปู\nริม\nริ้ว\nริษยา\nรี\nรี่\nรี้พล\nรีด\nรีดักชัน\nรีต\nรีเนียม\nรีบ\nรีม\nรีรอ\nรี้ริก\nรึง\nรึ้ง\nรื่น\nรื้น\nรื้อ\nรุ\nรุก\nรุกข์\nรุกขชาติ\nรุกขเทวดา\nรุกขมูล\nรุกขา\nรุกรุย\nรุ่ง\nรุ้ง\nรุงรัง\nรุ่งริ่ง\nรุจ\nรุจา\nรุจนะ\nรุจิ\nรุจี\nรุจิระ\nรุจิรา\nรุด\nรุต\nรุทธ์\nรุทระ\nรุธิร\nรุธิระ\nรุเธียร\nรุน\nรุ่น\nรุบรู่\nรุม\nรุ่ม\nรุ่มร่าม\nรุย\nรุ่ย\nรุรุ\nรุหะ\nรู\nรู่\nรู้\nรูจี\nรูด\nรูทีเนียม\nรูบิเดียม\nรูป\nรูปิยะ\nรูปี\nรูเล็ตต์\nเร่\nเรข\nเรขา\nเรขาคณิต\nเร็ง\nเร่ง\nเร้ง\nเรณุ\nเรณู\nเรดอน\nเรดาร์\nเรเดียม\nเร้น\nเรรวน\nเรไร\nเร็ว\nเร่ว\nเรวดี\nเรอ\nเร่อ\nเรา\nเร่า\nเร้า\nเราะ\nเริง\nเริด\nเริม\nเริ่ม\nเริ้ม\nเริศร้าง\nเรี่ย\nเรี้ย\nเรียก\nเรียง\nเรียด\nเรียน\nเรียบ\nเรียม\nเรี่ยม\nเรียว\nเรี่ยว\nเรี้ยวรก\nเรือ\nเรื่อ\nเรื้อ\nเรือก\nเรือง\nเรื่อง\nเรื้อง\nเรืองรอง\nเรือด\nเรือน\nเรื้อน\nเรื่อย\nแร\nแร่\nแรก\nแร็กเกต\nแรง\nแร่ง\nแร้ง\nแรด\nแร้นแค้น\nแรม\nแร้ว\nแระ\nโร\nโร่\nโรค\nโรคา\nโรคาพาธ\nโรง\nโรจ\nโรจน์\nโรเดียม\nโรตี\nโรท\nโรธ\nโรม\nโรมัน\nโรเมอร์\nโรย\nโรเร\nโรหิณี\nโรหิต\nไร\nไร่\nไร้\nไรย์\nฤกษ์\nฤกษณะ\nฤคเวท\nฤชา\nฤชุ\nฤณ\nฤดี\nฤดียา\nฤดู\nฤต\nฤติยา\nฤตุ\nฤทธา\nฤทธิ์\nฤทัย\nฤษภ\nฤษยา\nฤษี\nฤๅ\nฤๅดี\nฤๅทัย\nฤๅษี\nฤๅสาย\nลก\nล่ก\nลฆุ\nลง\nล่ง\nลงกา\nล้งเล้ง\nลด\nลดา\nลดาวัลย์\nลน\nล้น\nลบ\nลบอง\nลพ\nลพุช\nลม\nล่ม\nล้ม\nลมาด\nลรรลุง\nลลนา\nลลิต\nลวก\nลวง\nล่วง\nล้วง\nลวณะ\nลวด\nล้วน\nลวนลาม\nลวนะ\nล่วม\nลวะ\nลวิตร\nลหุ\nลหุกาบัติ\nล่อ\nล้อ\nลอก\nล็อก\nล็อกเกต\nลอกแลก\nลอการิทึม\nลอง\nล่อง\nลองกอง\nลองจิจูด\nลองไน\nลอด\nลอตเตอรี่\nลอน\nล่อน\nลอบ\nลอม\nล้อม\nลอมชอม\nลอมพอก\nลอย\nล่อย\nล่อแล่\nลอว์เรนเซียม\nลออ\nละ\nล่ะ\nละคร\nละติจูด\nละบม\nละบอง\nละบือ\nละเบ็ง\nละโบม\nละม่อม\nละมั่ง\nละมาน\nละม้าย\nละมุ\nละมุด\nละมุน\nละเมอ\nละเมาะ\nละเมิด\nละเมียด\nละแมะ\nละโมก\nละโมบ\nละไม\nละลวย\nละลอก\nละล้า\nละล้าละลัง\nละลาน\nละลาบละล้วง\nละลาย\nละล้าว\nละล่ำละลัก\nละลิบ\nละลุม\nละเลง\nละเล้า\nละเลาะ\nละเลิง\nละเลียด\nละเลียบ\nละไล้\nละว้า\nละวาด\nละเวง\nละแวก\nละโว้\nละหมาด\nละห้อย\nละหาน\nละหาร\nละหุ่ง\nละเหย\nละเหี่ย\nละอง\nละออง\nละอาย\nละเอียด\nละแอน\nลัก\nลักขณะ\nลักขณา\nลักขะ\nลักขี\nลักจั่น\nลักปิดลักเปิด\nลักษณ์\nลักษณนาม\nลักษณะ\nลักษณาการ\nลักษมณ์\nลักษมาณา\nลักษมี\nลักษะ\nลัคคะ\nลัคน์\nลัคนา\nลัง\nลั่ง\nลังกา\nลังคี\nลังถึง\nลังลอง\nลังเล\nลังสาด\nลัชชา\nลัชชี\nลัญจ์\nลัญจกร\nลัญฉกร\nลัญฉน์\nลัฐิ\nลัฐิกา\nลัด\nลัดา\nลัทธ์\nลัทธิ\nลัน\nลั่น\nลันเต\nลันเตา\nลันไต\nลั่นทม\nลันโทม\nลับ\nลัพธ์\nลัพธิ\nลัภ\nลัภนะ\nลัภย์\nลัมพ์\nลัย\nลา\nล่า\nล้า\nลาก\nลาง\nล่าง\nล้าง\nลางลิง\nลางสาด\nลาช\nลาชะ\nลาชา\nลาญ\nลาด\nลาดเลา\nล้าต้า\nล่าเตียง\nลาน\nล่าน\nล้าน\nลาบ\nลาพอน\nลาภ\nลาม\nล่าม\nลามก\nลาย\nล้าย\nลายสือ\nลาลา\nลาว\nลาวัณย์\nลาวา\nลำ\nล่ำ\nล้ำ\nลำเข็ญ\nลำแข\nลำเค็ญ\nลำเจียก\nลำดวน\nลำดับ\nลำเนา\nลำบอง\nลำบาก\nลำปำ\nลำพวน\nลำพอง\nลำพัง\nลำพู\nลำเพ็ญ\nลำเพา\nลำแพน\nลำโพง\nลำไพ่\nลำภุขัน\nลำมะลอก\nลำยอง\nลำไย\nลำลอง\nล่ำลา\nลำลาบ\nลำลึก\nลำเลาะ\nลำเลิก\nลำเลียง\nลำเวียง\nลำเอียก\nลำเอียง\nลิ\nลิกขา\nลิกไนต์\nลิกู\nลิเก\nลิขนะ\nลิขสิทธิ์\nลิขิต\nลิง\nลิงค์\nลิด\nลิต\nลิตมัส\nลิตร\nลิเทียม\nลิ่น\nลิ้น\nลินจง\nลิ้นจี่\nลินลา\nลินสีด\nลิ่นฮื้อ\nลินิน\nลิบ\nลิปดา\nลิปสติก\nลิปิ\nลิฟต์\nลิเภา\nลิ่ม\nลิ้ม\nลิมป์\nลิมปนะ\nลิลิต\nลิว\nลิ่ว\nลิสง\nลี\nลี่\nลี้\nลีซอ\nลีบ\nลีลา\nลีลาศ\nลีฬหา\nลึก\nลึงค์\nลืด\nลื่น\nลื้น\nลืบ\nลืม\nลือ\nลื่อ\nลื้อ\nลุ\nลุก\nลุง\nลุ้ง\nลุ่น\nลุ้น\nลุพธ์\nลุ่ม\nลุมพี\nลุมพู\nลุย\nลุ่ย\nลุ้ย\nลู่\nลูก\nลูกระมาศ\nลูกเอ็น\nลูขะ\nลูทีเชียม\nลูบ\nเลก\nเล็ก\nเลข\nเลขา\nเลขาธิการ\nเลขานุการ\nเล็ง\nเล้ง\nเล่งฮื้อ\nเลเซอร์\nเลฑฑุ\nเลณฑุ\nเลณะ\nเล็ด\nเลน\nเล็น\nเล่น\nเลนส์\nเล็บ\nเลบง\nเลปกร\nเลปน์\nเลเป\nเลเพ\nเล็ม\nเล่ม\nเลย\nเลว\nเลวง\nเลวูโลส\nเลศ\nเลษฏุ\nเล่ห์\nเล่ห์กระเท่ห์\nเลหลัง\nเลหะ\nเลอ\nเล่อ\nเลอะ\nเลอะเทอะ\nเละ\nเละเทะ\nเลา\nเล่า\nเล้า\nเลากัย\nเล้าโลม\nเลาะ\nเลิก\nเลิ่กลั่ก\nเลิง\nเลิ้ง\nเลินเล่อ\nเลิศ\nเลีย\nเลียง\nเลี่ยง\nเลี้ยง\nเลียงผา\nเลียงฝ้าย\nเลียงมัน\nเลียน\nเลี่ยน\nเลียนไฟ\nเลียบ\nเลี่ยม\nเลียว\nเลี้ยว\nเลือก\nเลือง\nเลื่อง\nเลือด\nเลือน\nเลื่อน\nเลื่อม\nเลื่อย\nเลื้อย\nเลื่อยล้า\nแล\nแล่\nแล้\nแลก\nแล็กเกอร์\nแล็กโทส\nแลง\nแล่ง\nแล้ง\nแลน\nแล่น\nแลนทานัม\nแลบ\nแล้ว\nและ\nโล่\nโล้\nโลก\nโลกเชษฐ์\nโลกธรรม\nโลกธาตุ\nโลกนาถ\nโลกบาล\nโลกย์\nโลกัย\nโลกวัชชะ\nโลกวิทู\nโลกัตถจริยา\nโลกันตร์\nโลกา\nโลกาธิบดี\nโลกาธิปไตย\nโลกานุวัตร\nโลกาภิวัตน์\nโลกามิส\nโลกายัต\nโลกาวินาศ\nโลกิยะ\nโลกีย์\nโลกียวัตร\nโลกียวิสัย\nโลกียสุข\nโลกุตระ\nโลกุตรธรรม\nโลกุตรภูมิ\nโลง\nโล่ง\nโล้ง\nโล่งโจ้ง\nโล่งโต้ง\nโล้งโต้ง\nโลจนะ\nโลณะ\nโลด\nโล่ติ๊น\nโลโต\nโลท\nโลน\nโล้น\nโลภ\nโลม\nโลมเล้า\nโลมะ\nโลมา\nโลลุป\nโลเล\nโลโล\nโลโล้\nโลหะ\nโลหกุมภี\nโลหัช\nโลหิต\nไล่\nไล้\nไลย\nไลลา\nไล่เลี่ย\nฦๅ\nฦๅชา\nฦๅสาย\nวก\nวง\nวงก์\nวงกต\nวงศ์\nวงศกร\nวงศา\nวงษ์\nวจนะ\nวจี\nวชิระ\nวชิรปาณี\nวชิรหัตถ์\nวชิราวุธ\nวฏะ\nวฏาการ\nวณิช\nวณิชชา\nวณิชย์\nวณิชยา\nวณิพก\nวดี\nวทนะ\nวทัญญุตา\nวทัญญู\nวธุกา\nวธู\nวน\nวนศาสตร์\nวนสณฑ์\nวนสัณฑ์\nวนอุทยาน\nวนัส\nวนัสบดี\nวนา\nวนาดร\nวนาดอน\nวนานต์\nวนาลัย\nวนาลี\nวนาวาส\nวนาศรม\nวนาสณฑ์\nวนาสัณฑ์\nวนิดา\nวนิพก\nวเนจร\nวโนทยาน\nวยัคฆ์\nวยากรณ์\nวรดนู\nวรทาน\nวรมหาวิหาร\nวรงค์\nวรณะ\nวรรค\nวรรคย์\nวรรช\nวรรชย์\nวรรณะ\nวรรณกรรม\nวรรณคดี\nวรรณยุกต์\nวรรณยุต\nวรรณศิลป์\nวรรณนา\nวรรณพฤติ\nวรรณึก\nวรรธกะ\nวรรธนะ\nวรรษ\nวรรษา\nวรวิหาร\nวรัญญู\nวรางคณา\nวรางคนา\nวราห์\nวราหะ\nวรุณ\nวโรดม\nวฤก\nวลัช\nวลัญช์\nวลัญชน์\nวลัย\nวลาหก\nวลี\nวศค\nวศะ\nวศิน\nวสนะ\nวสภะ\nวสละ\nวสวัดดี\nวสวัตตี\nวสะ\nวสันต์\nวสันตดิลก\nวสันตฤดู\nวสันตวิษุวัต\nวสา\nวสี\nวสุ\nวสุธา\nวสุนธรา\nวสุมดี\nวหะ\nวอ\nวอก\nวอกแวก\nว่องไว\nวอด\nวอน\nว่อน\nว็อบ\nวอมแวม\nวอลเลย์บอล\nวอแว\nวะ\nวัก\nวักกะ\nวัคคีย์\nวัคคุ\nวัคซีน\nวัง\nวังก์\nวังชา\nวังเวง\nวังศะ\nวังสะ\nวัจจะ\nวัจกุฎี\nวัจฉละ\nวัจน์\nวัช\nวัชชะ\nวัชพืช\nวัชฌ์\nวัชระ\nวัชรปาณี\nวัชรยาน\nวัชรอาสน์\nวัชราสน์\nวัชรินทร์\nวัชรี\nวัชเรนทร์\nวัฏ\nวัฏฏะ\nวัฏจักร\nวัฏทุกข์\nวัฏสงสาร\nวัฏกะ\nวัฏฏิ\nวัฒกะ\nวัฒกี\nวัฒนธรรม\nวัฒนะ\nวัฒนา\nวัณ\nวัณโรค\nวัณฏ์\nวัณณะ\nวัณนา\nวัด\nวัต\nวัตต์\nวัตตา\nวัตถ์\nวัตถาภรณ์\nวัตถาลังการ\nวัตถุ\nวัตนะ\nวัตร\nวัตสดร\nวัตสะ\nวัติ\nวัทน์\nวัน\nวันต์\nวันทนา\nวันทนาการ\nวันทนีย์\nวันทยหัตถ์\nวันทยาวุธ\nวันทา\nวันทิ\nวับ\nวับวาบ\nวับวาม\nวับแวบ\nวับแวม\nวัปปะ\nวัมมิกะ\nวัย\nวัลก์\nวัลคุ\nวัลย์\nวัลลภ\nวัลลี\nวัว\nวัสสะ\nวัสโสทก\nวัสดุ\nวัสตร์\nวัสน์\nวัสนะ\nวัสสานะ\nวัสสานฤดู\nวา\nว่า\nว้า\nว้าเหว่\nวาก\nว้าก\nวากยสัมพันธ์\nวากยะ\nวาง\nว่าง\nว้าง\nวาจก\nวาจา\nวาจาไปยะ\nวาจาล\nวาชเปยะ\nวาณิช\nวาณิชกะ\nวาณิชย์\nวาณี\nวาด\nวาต\nวาตะ\nวาตภัย\nวาท\nวาทศาสตร์\nวาทศิลป์\nวาทกะ\nวาทนะ\nวาทย์\nวาทยกร\nวาทิต\nวาทิน\nวาที\nวาน\nวานซืน\nว่าน\nวานร\nวานรินทร์\nวาเนเดียม\nวาบ\nวาปี\nวาม\nวามน\nวามนาวตาร\nวามะ\nวาย\nว่าย\nว้าย\nวายะ\nวาโย\nวายามะ\nวายุ\nวายุกูล\nวาร\nวาระ\nวารสาร\nวารสารศาสตร์\nวาริ\nวารี\nวาริช\nวารีช\nวาริท\nวาริธร\nวารุณ\nวารุณี\nวาล\nวาลวีชนี\nวาล์ว\nวาลิกา\nวาลุกา\nวาว\nว่าว\nว้าว่อน\nว้าวุ่น\nวาสนะ\nวาสนา\nวาสพ\nวาสะ\nวาสิน\nวาสี\nวาสุกรี\nวาสุกี\nวาสุเทพ\nวาหนะ\nวาหะ\nวาหินี\nวาฬ\nวิกขัมภ์\nวิกขัมภนะ\nวิกเขป\nวิกรม\nวิกรัย\nวิกรานต์\nวิกฤต\nวิกฤติ\nวิกล\nวิกสิต\nวิกัต\nวิกัติ\nวิกัติการก\nวิกัป\nวิกัย\nวิการ\nวิกาล\nวิกาลโภชน์\nวิคหะ\nวิเคราะห์\nวิฆเนศ\nวิฆเนศวร\nวิฆาต\nวิง\nวิ่ง\nวิ่งเปี้ยว\nวิงวอน\nวิจฉิกะ\nวิจล\nวิจักขณ์\nวิจักษ์\nวิจักษณ์\nวิจัย\nวิจาร\nวิจารณ์\nวิจารณญาณ\nวิจิ\nวิจิกิจฉา\nวิจิต\nวิจิตร\nวิจิน\nวิจุณ\nวิจุรณ\nวิชชา\nวิชชุ\nวิชชุดา\nวิชชุตา\nวิชชุลดา\nวิชญะ\nวิชน\nวิชนี\nวิชย\nวิชัย\nวิชา\nวิชานนะ\nวิชิต\nวิเชียร\nวิญญัตติ\nวิญญาณ\nวิญญาณกทรัพย์\nวิญญู\nวิฑูรย์\nวิด\nวิตก\nวิตถาร\nวิตามิน\nวิถี\nวิทธะ\nวิทยฐานะ\nวิทยา\nวิทยาคม\nวิทยาคาร\nวิทยาลัย\nวิทยุ\nวิทยุต\nวิทวัส\nวิทัตถิ\nวิทัศน์\nวิทารณ์\nวิทิต\nวิทู\nวิทูร\nวิเทศ\nวิเทโศบาย\nวิธ\nวิธวา\nวิธาน\nวิธี\nวิธุระ\nวิธู\nวิธูปนะ\nวิ่น\nวินตกะ\nวินัย\nวินาที\nวินายก\nวินาศ\nวินิจ\nวินิจฉัย\nวินิต\nวินิบาต\nวินิปาติก\nวิเนต\nวิบัติ\nวิบาก\nวิบุล\nวิบุลย์\nวิบูล\nวิบูลย์\nวิปการ\nวิปฏิสาร\nวิปโยค\nวิประโยค\nวิปริต\nวิปลาส\nวิปวาส\nวิปักษ์\nวิปัสสก\nวิปัสสนา\nวิปัสสนายานิก\nวิพากษ์\nวิพิธทัศนา\nวิพุธ\nวิภว\nวิภวตัณหา\nวิภังค์\nวิภัช\nวิภัตติ\nวิภา\nวิภาค\nวิภาช\nวิภาดา\nวิภาวี\nวิภาษ\nวิภาส\nวิภู\nวิภูษณะ\nวิภูษา\nวิภูษิต\nวิมน\nวิมล\nวิมลัก\nวิมังสา\nวิมัติ\nวิมาน\nวิมุข\nวิมุต\nวิมุตติ\nวิเมลือง\nวิโมกข์\nวิโยค\nวิระ\nวิรงรอง\nวิรังรอง\nวิรัช\nวิรัต\nวิรัติ\nวิราคะ\nวิราม\nวิริยภาพ\nวิริยะ\nวิรุธ\nวิรุฬห์\nวิรุฬหก\nวิรูป\nวิรูปักษ์\nวิเรนทร์\nวิโรจ\nวิโรจน์\nวิโรฒ\nวิโรธ\nวิลันดา\nวิลัย\nวิลาด\nวิลาศ\nวิลาป\nวิลาวัณย์\nวิลาส\nวิลาสินี\nวิลิปดา\nวิลิศมาหรา\nวิเลป\nวิเลปนะ\nวิโลกนะ\nวิโลม\nวิไล\nวิไลวรรณ\nวิวรณ์\nวิวรรธน์\nวิวัฏ\nวิวัฒน์\nวิวัฒนาการ\nวิวัน\nวิวาท\nวิวาห์\nวิวาหมงคล\nวิวาหะ\nวิวิต\nวิวิธ\nวิเวก\nวิศรุต\nวิศว\nวิศวกร\nวิศวกรรม\nวิศวกรรมศาสตร์\nวิศัลย์\nวิศาขบูชา\nวิศาขา\nวิศาล\nวิศิษฏ์\nวิศุทธ์\nวิศุทธิ์\nวิเศษ\nวิเศษณ์\nวิษณุ\nวิษณุกรรม\nวิษธร\nวิษัย\nวิษาณ\nวิษุวัต\nวิสกี้\nวิสรรชนีย์\nวิสฤต\nวิสสุกรรม\nวิสัชนา\nวิสัญญี\nวิสัย\nวิสัยทัศน์\nวิสาขบูชา\nวิสาขะ\nวิสาขา\nวิสามัญ\nวิสามานยนาม\nวิสาร\nวิสารทะ\nวิสาล\nวิสาสะ\nวิสาหกิจ\nวิสิฐ\nวิสุงคามสีมา\nวิสุทธ์\nวิสุทธิ์\nวิสูตร\nวิเสท\nวิหค\nวิหลั่น\nวิหาร\nวิหิงสา\nวิเหสา\nวิฬาร\nวิฬาร์\nวี\nวีจิ\nวีชนี\nวีณา\nวี้ด\nวีรกรรม\nวีรชน\nวีรบุรุษ\nวีรสตรี\nวี่วัน\nวี่แวว\nวีสะ\nวุ้ง\nวุฐิ\nวุฒ\nวุฒิ\nวุด\nวุ่น\nวุ้น\nวุบ\nวุ้ย\nวุลแฟรม\nวู้\nวูดวาด\nวูบ\nวู่วาม\nเว้\nเวค\nเวคิน\nเวคี\nเวจ\nเวช\nเวชยันต์\nเวฐน์\nเวณิ\nเวณิก\nเวณุ\nเวตน์\nเวตร\nเวตาล\nเวท\nเวทคู\nเวทนา\nเวทย์\nเวทัลละ\nเวทางค์\nเวทางคศาสตร์\nเวทานต์\nเวทานตะ\nเวทิ\nเวที\nเวธะ\nเวน\nเว้น\nเวนไตย\nเวไนย\nเวมัต\nเว้ย\nเวยยากรณะ\nเวร\nเวรมณี\nเวรี\nเวโรจน์\nเวลา\nเวเลนซี\nเวศม์\nเวศย์\nเวศยา\nเวสน์\nเวสภู\nเวสม์\nเวสวัณ\nเวสสะ\nเวสสันดร\nเวสสุกรรม\nเวสสุวัณ\nเวสารัช\nเวสิยา\nเวหน\nเวหะ\nเวหา\nเวหาส\nเวฬุ\nเวฬุริยะ\nเว่อ\nเว้า\nเวิก\nเวิ้ง\nเวี่ย\nเวียง\nเวียด\nเวียดนาม\nเวียน\nเวียร\nเวี่ยว\nแว้\nแวง\nแว้ง\nแวด\nแว้ด\nแวน\nแว่น\nแวนดา\nแวบ\nแว็บ\nแวม\nแว็ม\nแวว\nแว่ว\nแวะ\nโว\nโว่\nโวการ\nโว่ง\nโวทาน\nโวย\nโว้ย\nโว้เว้\nโวหาร\nไว\nไว้\nไวกูณฐ์\nไวฑูรย์\nไวทย์\nไวน์\nไวพจน์\nไวยากรณ์\nไวยาวัจกร\nไวยาวัจมัย\nไวรัส\nไววรรณ\nไวษณพ\nไวโอลิน\nศก\nศกุน\nศกุนต์\nศกุนิ\nศกุนี\nศจี\nศตะ\nศตภิษัช\nศตวรรษ\nศตพรรษ\nศตกะ\nศนิ\nศพ\nศมนะ\nศมะ\nศยาม\nศยามล\nศร\nศรายุธ\nศราวรณ์\nศรรกรา\nศรวณะ\nศรวณีย์\nศรวิษฐา\nศรัณย์\nศรัณยู\nศรัท\nศรัทธา\nศรัย\nศราทธ์\nศราทธพรต\nศราพก\nศราวก\nศราวณะ\nศรี\nศรีตรัง\nศรุติ\nศฤคาล\nศฤงค์\nศฤงคาร\nศฤงคาริน\nศฤงคารี\nศลิษฏ์\nศลิษา\nศวะ\nศวัส\nศวา\nศวาน\nศศะ\nศศธร\nศศพินทุ์\nศศลักษณ์\nศศิ\nศศิน\nศศี\nศศิขัณฑ์\nศศิธร\nศศิมณฑล\nศศิวิมล\nศอ\nศอก\nศักดา\nศักดิ\nศักดิ์\nศักดินา\nศักติ\nศักย\nศักยภาพ\nศักย์\nศักยะ\nศักร\nศักรินทร์\nศักเรนทร์\nศักราช\nศังกร\nศัตรู\nศันสนะ\nศันสนีย์\nศัพท์\nศัยยา\nศัล\nศัลย์\nศัลยกรรม\nศัลยแพทย์\nศัลยศาสตร์\nศัสดร\nศัสตร\nศัสตรศาสตร์\nศัสตรา\nศัสตราวุธ\nศากตะ\nศากย\nศากยะ\nศากยพุทธ\nศากยมุนี\nศาฎก\nศาณ\nศานต์\nศานติ\nศาป\nศารท\nศารทูล\nศาริกา\nศาล\nศาลา\nศาศวัต\nศาสดา\nศาสตร์\nศาสตรา\nศาสตราจารย์\nศาสนา\nศาสนกิจ\nศาสนจักร\nศาสนธรรม\nศาสนบุคคล\nศาสนพิธี\nศาสนวัตถุ\nศาสนศาสตร์\nศาสนสถาน\nศาสนสมบัติ\nศาสนิกชน\nศาสนีย์\nศาสนูปถัมภก\nศาสน์\nศิกษก\nศิการ\nศิขร\nศิขริน\nศิขรี\nศิขัณฑ์\nศิคาล\nศิงขร\nศิงขริน\nศิตะ\nศิถี\nศิพิระ\nศิระ\nศิรประภา\nศิราภรณ์\nศิโรรัตน์\nศิโรเวฐน์\nศิรา\nศิรามพุช\nศิโรราบ\nศิลป\nศิลป์\nศิลปะ\nศิลปกร\nศิลปกรรม\nศิลปกิจ\nศิลปวัตถุ\nศิลปวิทยา\nศิลปศาสตร์\nศิลปศึกษา\nศิลปหัตถกรรม\nศิลปิน\nศิลปี\nศิลา\nศิวะ\nศิวโมกข์\nศิวลึงค์\nศิวเวท\nศิวาลัย\nศิศีระ\nศิษฎิ\nศิษฏ์\nศิษย์\nศิษยานุศิษย์\nศีขร\nศีต\nศีตกาล\nศีรษะ\nศีล\nศึก\nศึกษา\nศึกษาธิการ\nศึกษานิเทศก์\nศุกร์\nศุกรวรรณ\nศุกรวาร\nศุกระ\nศุกล\nศุกลปักษ์\nศุจิ\nศุทธะ\nศุทธิ\nศุนะ\nศุนิ\nศุภกร\nศุภเคราะห์\nศุภนิมิต\nศุภมัสดุ\nศุภมาตรา\nศุภมาส\nศุภอักษร\nศุภางค์\nศูกร\nศุลกากร\nศุลการักษ์\nศุลี\nศุษิร\nศูทร\nศูนย์\nศูนยวาท\nศูละ\nศูลิน\nเศรณี\nเศรษฐ\nเศรษฐ์\nเศรษฐกิจ\nเศรษฐศาสตร์\nเศรษฐี\nเศร้า\nเศลษ\nเศวต\nเศวตร\nเศวตัมพร\nเศษ\nเศาจ\nเศาร์\nเศารยะ\nเศิก\nเศียร\nโศก\nโศกา\nโศกาดูร\nโศกาลัย\nโศกี\nโศจิ\nโศธนะ\nโศภน\nโศภะ\nโศภา\nโศภิต\nโศภิน\nโศภิษฐ์\nโศภี\nโศรดา\nโศรตร\nโศลก\nไศล\nไศวะ\nษมา\nษัฏ\nษัฑ\nษัณ\nษัษ\nษัษฐะ\nษัษฐี\nโษฑศัน\nสก\nสกวาที\nสกฏะ\nสกทาคามิผล\nสกิทาคามิผล\nสกทาคามิมรรค\nสกิทาคามิมรรค\nสกทาคามี\nสกิทาคามี\nสกนธ์\nสกปรก\nสกรณีย์\nสกรรจ์\nสกรรมกริยา\nสกล\nสกลมหาสังฆปริณายก\nสกัด\nสกา\nสกาว\nสกี\nสกุณ\nสกุณา\nสกุณี\nสกุน\nสกุนต์\nสกุล\nสเกต\nสแกนเดียม\nสขะ\nสง\nส่ง\nสงกร\nสงกรานต์\nสงกา\nสงค์\nสงคร\nสงคราม\nสงเคราะห์\nสงฆ์\nสงบ\nสงวน\nส่งสการ\nสงสัย\nสงสาร\nสงสารวัฏ\nสงัด\nสง่า\nสฐะ\nสณฑ์\nสด\nสดมภ์\nสดับ\nสดับปกรณ์\nสดำ\nสดุดี\nสตะ\nสตน\nสตภิสชะ\nสตรอนเชียม\nสตริกนิน\nสตรี\nสตัฟฟ์\nสตัมภ์\nสตางค์\nสติ\nสติปัฏฐาน\nสตี\nสตู\nสตูป\nสเต๊ก\nสถบดี\nสถล\nสถวีระ\nสถาน\nสถานะ\nสถานี\nสถาบัน\nสถาปนา\nสถาปนิก\nสถาปัตยกรรม\nสถาปัตยกรรมศาสตร์\nสถาปัตยเรขา\nสถาปัตยเวท\nสถาพร\nสถาวร\nสถิต\nสถิตยศาสตร์\nสถิติ\nสถิร\nสถีรวาท\nสถุล\nสถูป\nสทิง\nสทึง\nสทุม\nสธนะ\nสาธุสะ\nสน\nส้น\nสนทนา\nสนทรรศ\nสนทรรศน์\nสนเทศ\nสนเท่ห์\nสนธยา\nสนธิ\nสนน\nสนม\nสนวน\nสนอง\nสนอบ\nสนอม\nสนะ\nสนัด\nสนั่น\nสนับ\nสนับทึบ\nสนับสนุน\nสนาน\nสนาม\nสนายุ\nสนิกะ\nสนิท\nสนิธ\nสนิม\nสนุก\nสนุกเกอร์\nสนุข\nสนุต\nสนุ่น\nสบ\nสบง\nสบถ\nสบัน\nสบาย\nสบู่\nสไบ\nสปริง\nสปอร์\nสปาเกตตี\nสเปกตรัม\nสเปกโทรสโกป\nสไปริลลัม\nสพาบ\nสภา\nสภาพ\nสภาวการณ์\nสภาวะ\nสม\nสมการ\nสมจารี\nสมดุล\nสมมูล\nส้ม\nสมญา\nสมณะ\nสมณบริขาร\nสมณศักดิ์\nสมณสารูป\nสมเด็จ\nสมถะ\nสมถยานิก\nสมถวิปัสสนา\nสมนาคุณ\nสมบัติ\nสมบุกสมบัน\nสมบูรณ์\nสมบูรณาญาสิทธิราชย์\nสมประดี\nสมปฤดี\nสมปฤๅดี\nส้มป่อย\nสมปัก\nสมผุส\nสมพง\nสมพงศ์\nสมพล\nสมพัตสร\nสมพาส\nสมเพช\nสมโพธน์\nสมโพธิ\nสมภพ\nสมภาร\nสมโภค\nสมโภช\nสมมต\nสมมติ\nสมมุติ\nสมมาตร\nส้มมือ\nสมโมท\nสมโยค\nสมร\nสมรด\nสมรรถ\nสมรรถนะ\nสมรรถภาพ\nสมรส\nสมฤดี\nสมฤติ\nสมวายะ\nสมเสร็จ\nสมอ\nสมอง\nสมะ\nสมัคร\nสมังคี\nสมัช\nสมัชชา\nสมัญญา\nสมัต\nสมัน\nสมันต์\nสมัย\nสมา\nสมาคม\nสมาจาร\nสมาชิก\nสมาทาน\nสมาธิ\nสมาน\nสมานฉันท์\nสมาบัติ\nสมาพันธรัฐ\nสมาส\nสม่ำเสมอ\nสมิง\nสมิต\nสมิติ\nสมิทธ์\nสมิทธิ\nสมี\nสมุก\nสมุจจัย\nสมุจเฉท\nสมุฏฐาน\nสมุด\nสมุทร\nสมุทรโคดม\nสมุทัย\nสมุน\nสมุนไพร\nสมุลแว้ง\nสมุห\nสมุห์\nสมุหกลาโหม\nสมุหเทศาภิบาล\nสมุหนาม\nสมุหนายก\nสโมธาน\nสโมสร\nสยด\nสยนะ\nสยบ\nสยมพร\nสยมภู\nสยอง\nสยอน\nสยัมวรา\nสยาม\nสยามานุสติ\nสยามินทร์\nสยาย\nสยิว\nสยิ้ว\nสยุมพร\nสยุมภู\nสร\nสรง\nสร่ง\nสรณะ\nสรณคมน์\nสรณาคมน์\nสรณตรัย\nสรตะ\nสรทะ\nสรนุก\nสรเนาะ\nสรไน\nสรเพชญ\nสรภะ\nสรภัญญะ\nสรภู\nสรม\nสรร\nสรรค์\nสรรพ\nสรรพคุณ\nสรรพนาม\nสรรพสามิต\nสรรพัชญ\nสรรพากร\nสรรพางค์\nสรรเพชญ\nสรรเพชุดา\nสรรเสริญ\nสรลอน\nสรเลข\nสรวง\nสรวม\nสรวล\nสรเสริญ\nสร้อย\nสระ\nสระกอ\nสระท้อน\nสระพรั่ง\nสระอาด\nสรั่ง\nสรัสวดี\nสร่าง\nสร้าง\nสราญ\nสรี้\nสรีระ\nสรีรกิจ\nสรีรธาตุ\nสรีรวิทยา\nสรีรศาสตร์\nสรีรังคาร\nสรีรางคาร\nสรุป\nสโรช\nสโรชะ\nสฤก\nสฤต\nสฤษฎิ\nสฤษฎี\nสฤษฏ์\nสฤษดิ์\nสลด\nสลบ\nสลวน\nสลวย\nสลอด\nสลอน\nสลอย\nสละ\nสลัก\nสลัด\nสลัดได\nสลับ\nสลัว\nสลา\nสลาก\nสลาง\nสล้าง\nสลาด\nสลาตัน\nสลาบ\nสลาย\nสลิด\nสลิล\nสลึก\nสลึง\nสลุต\nสลุบ\nสลุมพร\nสแลง\nสวการย์\nสวภาพ\nสวราชย์\nสวก\nส้วง\nสวด\nสวน\nสวนะ\nสวนาการ\nส่วน\nสวนิต\nสวบ\nสวม\nส้วม\nสวย\nส่วย\nส้วย\nสวยม\nสวรรค\nสวรรค์\nสวรรคต\nสวรรคาลัย\nสวรรยา\nสวระ\nสวะ\nสวัสดิ\nสวัสดิ์\nสวัสดิการ\nสวัสดิภาพ\nสวัสดิมงคล\nสวัสดี\nสวัสติ\nสวาตี\nสวัสติกะ\nสวา\nสวาปาม\nสวาคตะ\nสวาง\nสว่าง\nสวาด\nสวาดิ\nสวาท\nสว่าน\nสว้าน\nสวาบ\nสวามิ\nสวามี\nสวามินี\nสวาย\nสวาสดิ์\nสวาหะ\nสวิง\nสวิญญาณกทรัพย์\nสวิตช์\nสสาร\nสสุระ\nสสุรี\nสหกรณ์\nสหการ\nสหจร\nสหชาต\nสหชาติ\nสหธรรม\nสหธรรมิก\nสหประชาชาติ\nสหพันธ์\nสหพันธรัฐ\nสหภาพ\nสหศึกษา\nสหัช\nสหัมบดี\nสหัส\nสหัสสะ\nสหัสธารา\nสหัสนัยน์\nสหัสเนตร\nสหัสรังสี\nสหัสา\nสหาย\nสอ\nส่อ\nสอง\nส่อง\nส้อง\nสอด\nสอน\nส่อน\nสอบ\nสอพลอ\nส้อม\nสอย\nสะ\nสะกด\nสะกอ\nสะกาง\nสะการะ\nสะกิด\nสะกิดสะเกา\nสะเก็ด\nสะแก\nสะคร้อ\nสะคราญ\nสะค้าน\nสะเงาะสะแงะ\nสะดม\nสะดวก\nสะดิ้ง\nสะดึง\nสะดือ\nสะดุ้ง\nสะดุด\nสะเด็ด\nสะเดา\nสะเดาะ\nสะตอ\nสะตาหมัน\nสะตึ\nสะตือ\nสะตุ\nสะเต๊ะ\nสะโตก\nสะทก\nสะท้อน\nสะท้าน\nสะทึก\nสะเทิน\nสะเทิ้น\nสะเทือน\nสะเทื้อน\nสะบะ\nสะบัก\nสะบักสะบอม\nสะบัด\nสะบัดสะบิ้ง\nสะบั้น\nสะบันงา\nสะบ้า\nสะบู\nสะแบง\nสะเปะสะปะ\nสะพรั่ง\nสะพรึงกลัว\nสะพรึบ\nสะพรึ่บ\nสะพัก\nสะพัง\nสะพัด\nสะพั้น\nสะพาน\nสะพาย\nสะเพร่า\nสะโพก\nสะเภา\nสะใภ้\nสะโมง\nสะระตะ\nสะระแหน่\nสะลาง\nสะลาบ\nสะลึมสะลือ\nสะวี้ดสะว้าด\nสะสม\nสะสวย\nสะสาง\nสะเหล่อ\nสะอาง\nสะอาด\nสะอ้าน\nสะอิ้ง\nสะอิดสะเอียน\nสะอึก\nสะอื้น\nสะเอ้ง\nสะเอว\nสะเออะ\nสะโอดสะอง\nสะไอ\nสัก\nสักกะ\nสักยะ\nสักกัจจะ\nสักกายทิฐิ\nสักการ\nสักการะ\nสักขี\nสักวา\nสักหลาด\nสัค\nสัคคะ\nสั่ง\nสังกร\nสังกรณี\nสังกรประโยค\nสังกะตัง\nสังกะวัง\nสังกะวาด\nสังกะสี\nสังกัด\nสังกัปปะ\nสังกา\nสังการ\nสังกาศ\nสังกิเลส\nสังเกต\nสังข์\nสังขกร\nสังขตธรรม\nสังขตะ\nสังขยา\nสังขลิก\nสังขลิกา\nสังขาร\nสังขารา\nสังเขป\nสังค์\nสังคญาติ\nสังคม\nสังคหะ\nสังคัง\nสังคายนา\nสังคายนาย\nสังคีต\nสังคีติ\nสังเค็ด\nสังเคราะห์\nสังฆกรรม\nสังฆการี\nสังฆเถระ\nสังฆทาน\nสังฆนายก\nสังฆปาโมกข์\nสังฆภัต\nสังฆเภท\nสังฆมณฑล\nสังฆมนตรี\nสังฆราช\nสังฆสภา\nสังฆาณัติ\nสังฆาฏิ\nสังฆาทิเสส\nสังฆาธิการ\nสังฆานุสติ\nสังฆาวาส\nสังยุตนิกาย\nสังโยค\nสังโยชน์\nสังวร\nสังวัจฉระ\nสังวัธยาย\nสังวาล\nสังวาส\nสังเวคะ\nสังเวช\nสังเวชนียสถาน\nสังเวย\nสังเวียน\nสังสกฤต\nสังสการ\nสังสนทนา\nสั่งสนทนา\nสังสรรค์\nสังสารวัฏ\nสังสิทธิ\nสังสุทธ์\nสังสุทธิ\nสังหร\nสังหรณ์\nสังหาร\nสังหาริมทรัพย์\nสังหาริมะ\nสังหิต\nสัจ\nสัจกิริยา\nสัจจะ\nสัจญาณ\nสัจธรรม\nสัจนิยม\nสัจพจน์\nสัชฌะ\nสัชฌุ\nสัญจร\nสัญเจตนา\nสัญชาตญาณ\nสัญชาติ\nสัญฌา\nสัญญา\nสัญญาณ\nสัญญี\nสัญโญชน์\nสัญนิยม\nสัญประกาศ\nสัญลักษณ์\nสัฐิ\nสัณฐาน\nสัณฐิติ\nสัณฑ์\nสัณห์\nสัด\nสัดจอง\nสัต\nสัตตะ\nสัตตาหกรณียะ\nสัตตาหกาลิก\nสัตมวาร\nสัตสดก\nสัตตบงกช\nสัตตบรรณ\nสัตตบุษย์\nสัตตู\nสัตถันดร\nสัตถา\nสัตถิ\nสัตถุ\nสัตถุศาสนา\nสัตบรรณ\nสัตย์\nสัตยพรต\nสัตยวาที\nสัตยาเคราะห์\nสัตยาธิษฐาน\nสัตยาบัน\nสัตว์\nสัตวชาติ\nสัตวบาล\nสัตวแพทย์\nสัตววิทยา\nสัตวา\nสัทธรรม\nสัทธา\nสัทธาจริต\nสัทธาธิกะ\nสัทธินทรีย์\nสัทธิงวิหาริก\nสัทธิวิหาริก\nสัทวิทยา\nสัทศาสตร์\nสัทอักษร\nสัน\nสั่น\nสั้น\nสันดาน\nสันดาป\nสันโดษ\nสันต์\nสันตติ\nสันตะปาปา\nสันตะวา\nสันติ\nสันตุฏฐี\nสันถวไมตรี\nสันถวะ\nสันถัต\nสันถาร\nสันทนะ\nสันทะ\nสันทัด\nสันทัสนะ\nสันทาน\nสันทิฐิก\nสันทิส\nสันเทหะ\nสันธาน\nสันนิธิ\nสันนิบาต\nสันนิวาส\nสันนิษฐาน\nสันสกฤต\nสับ\nสับปลับ\nสับปลี้\nสับปะรด\nสัปคับ\nสัปดาห์\nสัปดาหะ\nสัปดน\nสัปตศก\nสัปทน\nสัปปะ\nสัปปิ\nสัปปุริส\nสัปปุรุษ\nสัประยุทธ์\nสัปหงก\nสัปเหร่อ\nสัพ\nสัพพะ\nสัพพัญญู\nสัพเพเหระ\nสัพยอก\nสัมบูรณ์\nสัมปชัญญะ\nสัมปทา\nสัมปทาน\nสัมปยุต\nสัมปโยค\nสัมประสิทธิ์\nสัมประหาร\nสัมปรายภพ\nสัมปรายิกภพ\nสัมปัตติ\nสัมผัปลาป\nสัมผัปลาปะ\nสัมผัส\nสัมพล\nสัมพหุลา\nสัมพัจฉรฉินท์\nสัมพัตสร\nสัมพัทธ์\nสัมพันธ์\nสัมพันธน์\nสัมพันธภาพ\nสัมพันธมิตร\nสัมพันธไมตรี\nสัมพาหะ\nสัมพุทธ\nสัมพุทธะ\nสัมโพธิ\nสัมภวะ\nสัมภเวสี\nสัมภัต\nสัมภัตตะ\nสัมภาระ\nสัมภาษณ์\nสัมโภคกาย\nสัมมนา\nสัมมัปธาน\nสัมมา\nสัมโมทนียกถา\nสัมฤทธิ\nสัมฤทธิ์\nสัมฤทธิศก\nสัยน์\nสัลเลข\nสัสดี\nสัสตทิฐิ\nสัสสะ\nสัสสุ\nสัสสู\nสา\nส่า\nสาก\nสากรรจ์\nสากล\nสากัจฉา\nสากัลย์\nสากิยะ\nสาเก\nสาขา\nสาคร\nสาคเรศ\nสาคู\nสาง\nส้าง\nสาชล\nสาฎก\nสาฏิก\nสาณี\nสาด\nสาไถย\nสาทร\nสาทิส\nสาทุ\nสาโท\nสาธก\nสาธยะ\nสาธยาย\nสาธารณะ\nสาธารณชน\nสาธารณประโยชน์\nสาธารณภัย\nสาธารณรัฐ\nสาธารณสถาน\nสาธารณสมบัติ\nสาธารณสุข\nสาธารณูปการ\nสาธารณูปโภค\nสาธารณ์\nสาธิต\nสาธุ\nสาน\nส่าน\nสานุ\nสานู\nสานุศิษย์\nสาบ\nสาบสูญ\nสาบาน\nสาป\nสาปไตย\nสาม\nสามชุก\nสามเณร\nสามเณรี\nสามนต์\nสามนตราช\nสามยทรัพย์\nสามล\nสามหาว\nสามะ\nสามัคคี\nสามัญ\nสามัตถิยะ\nสามานย์\nสามานยนาม\nสามารถ\nสามิต\nสามินี\nสามิภักดิ์\nสามี\nสามีจิกรรม\nสาย\nส่าย\nส้าย\nสายชู\nสายัณห์\nสายาห์\nสาร\nสารคดี\nสารธรรม\nสารนิเทศ\nสารบบ\nสารบรรณ\nสารบัญ\nสารบาญ\nสารบาญชี\nสารประโยชน์\nสารสนเทศ\nสารทุกข์\nสารถี\nสารท\nสารพัด\nสารพัน\nสารพางค์\nสารภาพ\nสารภี\nสารวัตร\nสาระ\nสาระแน\nสาระพา\nสาระยำ\nสาระวารี\nสาระสะมา\nสารัตถประโยชน์\nสารัตถศึกษา\nสารัตถะ\nสารัทธ์\nสารัมภ์\nสาราณียกร\nสาราณียธรรม\nสาราณียะ\nสารานุกรม\nสารีริกธาตุ\nสารูป\nสาโรช\nสาละ\nสาละวน\nสาลิ\nสาลิกา\nสาลินี\nสาลี\nสาลี่\nสาลู\nสาโลหิต\nสาว\nสาวก\nสาวิกา\nสาวิตร\nสาวิตรี\nสาสน\nสาสน์\nสาส์น\nสาสนา\nสาสม\nสาหร่าย\nสาหรี\nส่าหรี\nสาหัส\nสาเหตุ\nสาแหรก\nสำ\nสำส่อน\nส่ำ\nสำคัญ\nสำซ่าง\nสำแดง\nสำทับ\nสำนวน\nสำนอง\nสำนัก\nสำนาน\nสำนึก\nสำนึง\nสำเนา\nสำเนียง\nสำบัด\nสำปะลอ\nสำปะหลัง\nสำปั้น\nสำปันนี\nสำเภา\nสำมะงา\nสำมะโน\nสำมะลอ\nสำมะเลเทเมา\nสำมะหา\nสำรด\nสำรวจ\nสำรวม\nสำรวย\nสำรวล\nสำรอก\nสำรอง\nสำรับ\nสำราก\nสำราญ\nสำริด\nสำเร็จ\nสำเรา\nสำเริง\nสำโรง\nสำลัก\nสำลาน\nสำลี\nสำแลง\nสำหรวด\nสำหรับ\nสำหา\nสำเหนียก\nสำเหร่\nสำออย\nสำอาง\nสิ\nสิกข์\nสิข\nสิกขมานา\nสิกขา\nสิขร\nสิขรี\nสิขเรศ\nสิขา\nสิขานล\nสิขี\nสิคาล\nสิง\nสิ่ง\nสิงขร\nสิงค์\nสิงคลิ้ง\nสิงคลี\nสิงคาร\nสิงคาล\nสิงคี\nสิงโต\nสิงห์\nสิงหนาท\nสิงหบัญชร\nสิงหรา\nสิงหราช\nสิงหาคม\nสิงหาสน์\nสิงหล\nสิญจน์\nสิตะ\nสิตางศุ์\nสิถิล\nสิทธ์\nสิทธัตถะ\nสิทธา\nสิทธาจารย์\nสิทธารถ\nสิทธิ\nสิทธิ์\nสิทธิการิยะ\nสิธยะ\nสิน\nสิ้น\nสินเทา\nสินธพ\nสินธุ\nสินธุ์\nสินธุระ\nสินธู\nสินเธาว์\nสินาด\nสินิทธ์\nสินี\nสิเนรุ\nสิเนหก\nสิเนหะ\nสิเนหา\nสิเน่หา\nสิบ\nสิปปะ\nสิมพลี\nสิระ\nสิโรดม\nสิโรตม์\nสิริ\nสิรี\nสิลา\nสิว\nสิ่ว\nสิวะ\nสิวาลัย\nสิวิกา\nสี\nสี่\nสี้\nสีกา\nสีกุน\nสีข้าง\nสีด\nสีดอ\nสีดา\nสีตลรัศมี\nสีตโลทก\nสีโตทก\nสีทันดร\nสีมันต์\nสีมา\nสีละมัน\nสีวิกา\nสีสอ\nสีสะ\nสีสา\nสีสุก\nสีเสียด\nสีห์\nสีหนาท\nสีหบัญชร\nสีหราช\nสีหไสยา\nสีหไสยาสน์\nสีหะ\nสึก\nสึง\nสืบ\nสื่อ\nสุ\nสุก\nสุกข์\nสุกร\nสุกรม\nสุกำศพ\nสุกียากี้\nสุข\nสุขา\nสุขาภิบาล\nสุขารมณ์\nสุขาวดี\nสุขิน\nสุขี\nสุขุม\nสุขุมาล\nสุโข\nสุคต\nสุคติ\nสุคนธ\nสุคนธ์\nสุคนธชาติ\nสุคนธรส\nสุคันธ์\nสุคันธรส\nสุงกะ\nสุงกากร\nสุงสิง\nสุงสุมาร\nสุจริต\nสุจหนี่\nสุจิ\nสุจิต\nสุจิตร\nสุชน\nสุชัมบดี\nสุชา\nสุชาดา\nสุญ\nสุญญากาศ\nสุญตา\nสุญนิยม\nสุณ\nสุณิสา\nสุด\nสุดา\nสุต\nสุตตนิบาต\nสุตตะ\nสุตตันตปิฎก\nสุตตันตะ\nสุติ\nสุทธ\nสุทธ์\nสุทธาวาส\nสุทธิ\nสุทรรศน์\nสุทัศน์\nสุธา\nสุธาโภชน์\nสุธารส\nสุธาสินี\nสุธาสี\nสุธี\nสุนทร\nสุนทรี\nสุนทรียภาพ\nสุนทรียศาสตร์\nสุนทรียะ\nสุนัข\nสุนันท์\nสุโนก\nสุบดี\nสุบรรณ\nสุบิน\nสุปรีดิ์\nสุปรีย์\nสุปาณี\nสุพพัต\nสุพรรณ\nสุพรรณบัฏ\nสุพรรณภาชน์\nสุพรรณราช\nสุพรรณศรี\nสุพรรณถัน\nสุพรรณิการ์\nสุภร\nสุภัค\nสุภา\nสุภาพ\nสุภาษิต\nสุม\nสุ่ม\nสุมทุม\nสุมน\nสุมนะ\nสุมนัส\nสุมนา\nสุ้มเสียง\nสุมะ\nสุมาลี\nสุเมธ\nสุเมรุ\nสุรคต\nสุรเชษฐ์\nสุรบดี\nสุรภาพ\nสุรโลก\nสุรสีหนาท\nสุรเสียง\nสุรงค์\nสุรังค์\nสุรภี\nสุรัติ\nสุรัสวดี\nสุรา\nสุรางค์จำเรียง\nสุรางคนา\nสุรางคนางค์\nสุรารักษ์\nสุราลัย\nสุรินทร์\nสุรินทราหู\nสุริยะ\nสุริยกันต์\nสุริยกานต์\nสุริยการ\nสุริยกาล\nสุริยคติ\nสุริยคราส\nสุริยมณฑล\nสุริยวงศ์\nสุริยง\nสุริยา\nสุริเยนทร์\nสุริเยศ\nสุริโย\nสุริยน\nสุริยัน\nสุริยุปราคา\nสุรีย์\nสุรุ่ยสุร่าย\nสุลต่าน\nสุวคนธ์\nสุวภาพ\nสุวรรณ\nสุวรรณภูมิ\nสุวะ\nสุวาน\nสุวินัย\nสุวิมล\nสุษิระ\nสุสาน\nสุหนัต\nสุหร่ง\nสุหร่าย\nสุหฤท\nสุหัท\nสุเหร่า\nสู\nสู่\nสู้\nสูง\nสูจิ\nสูจิบัตร\nสูญ\nสูด\nสูต\nสูตร\nสูติ\nสูติกรรม\nสูตินรีเวช\nสูติบัตร\nสูติแพทย์\nสูติศาสตร์\nสูท\nสูทกรรม\nสูทศาสตร์\nสูบ\nสูปะ\nสูร\nสูรย์\nสูรยกานต์\nสูริ\nสูสี\nเส\nเสก\nเสกขบุคคล\nเสกขะ\nเสขบุคคล\nเสขะ\nเส็ง\nเส้ง\nเส็งเคร็ง\nเสงี่ยม\nเสฏฐี\nเสณี\nเสด\nเสด็จ\nเสตุ\nเสถียร\nเสทะ\nเสโท\nเสน\nเส้น\nเสนง\nเสน่ง\nเสน่ห์\nเสนหา\nเสน่หา\nเสนอ\nเสนะ\nเสนา\nเสนาธิการ\nเสนาบดี\nเสน่า\nเสนากุฎ\nเสนางค์\nเสนางคนิกร\nเสนานี\nเสนาสนะ\nเสนาะ\nเสนี\nเสนีย์\nเสนียะ\nเสนียด\nเสบย\nเสบียง\nเสพ\nเสพย์\nเสเพล\nเสภา\nเสม็ด\nเสมหะ\nเสมอ\nเสมา\nเสมียน\nเสมือน\nเสย\nเสร็จ\nเสริด\nเสริม\nเสรี\nเสลด\nเสลบรรพต\nเสลา\nเสลี่ยง\nเสลือกสลน\nเสโล\nเสวก\nเสวกามาตย์\nเสวนะ\nเสวนา\nเสวย\nเสวียน\nเสสรวง\nเสสรวล\nเสา\nเส้า\nเสาร์\nเสารภย์\nเสารี\nเสาวคนธ์\nเสาวธาร\nเสาวภา\nเสาวภาคย์\nเสาวภาพ\nเสาวรภย์\nเสาวรส\nเสาวลักษณ์\nเสาวณิต\nเสาวนะ\nเสาวนา\nเสาวนีย์\nเสาหฤท\nเสาะ\nเสาะแสะ\nเสิร์จ\nเสิร์ฟ\nเสีย\nเสียง\nเสี่ยง\nเสียด\nเสี้ยน\nเสียบ\nเสียม\nเสี่ยม\nเสี้ยม\nเสียว\nเสี่ยว\nเสี้ยว\nเสือ\nเสื่อ\nเสื้อ\nเสือก\nเสื่อม\nแส\nแส่\nแส้\nแสก\nแสง\nแสด\nแสดง\nแสตมป์\nแสน\nแสนย์\nแสนยากร\nแสนยานุภาพ\nแสบ\nแสม\nแสยก\nแสยง\nแสยะ\nแสรก\nแสร้ง\nแสลง\nแสล้ม\nแสวง\nแสะ\nโสก\nโสกโดก\nโสกันต์\nโสโครก\nโสณฑ์\nโสณิ\nโสณี\nโสด\nโสดก\nโสดม\nโสดา\nโสดาบัน\nโสดาปัตติผล\nโสดาปัตติมรรค\nโสต\nโสตทัศนวัสดุ\nโสตทัศนอุปกรณ์\nโสตทัศนูปกรณ์\nโสตินทรีย์\nโสตถิ\nโสทก\nโสทร\nโสธก\nโสธนะ\nโสน\nโสภณ\nโสภา\nโสภี\nโสภิณี\nโสเภณี\nโสม\nโสมนัส\nโสมม\nโสมย์\nโสร่ง\nโสรจ\nโสรวาร\nโสโร\nโสวรรณ\nโสหุ้ย\nโสฬส\nใส\nใส่\nไส\nไส้\nไสย\nไสยา\nไสยาสน์\nไสร้\nไสว\nหก\nหกคะเมน\nหง\nหงก\nหงส์\nหงสบาท\nหงสรถ\nหงอ\nหงอก\nหง่อง\nหงองแหงง\nหงอด\nหงอน\nหง่อม\nหงอย\nหง่อย\nหงัก\nหงับ\nหง่าง\nหงาย\nหง่าว\nหงำ\nหงิก\nหงิง\nหงิม\nหงึก\nหงุงหงิง\nหงุดหงิด\nหงุบ\nหงุ่ย\nหญ้า\nหญ้าฝรั่น\nหญ้ายายเภา\nหญิง\nหญิบ\nหด\nหตะ\nหทัย\nหน\nหนวก\nหน่วง\nหนวด\nหน่วย\nหน่วยกิต\nหนอ\nหน่อ\nหนอก\nหนอง\nหนอน\nหนอนตายหยาก\nหน่อย\nหน็อยแน่\nหนัก\nหนัง\nหนังสติ๊ก\nหนังสือ\nหนั่น\nหนับ\nหนา\nหน้า\nหน่าง\nหนาด\nหนาน\nหนาม\nหน่าย\nหนาว\nหนำ\nหนำเลี้ยบ\nหนี\nหนี้\nหนีบ\nหนึก\nหนึ่ง\nหนึบ\nหนืด\nหนุ\nหนุน\nหนุบ\nหนุ่ม\nหนุ่ย\nหนู\nห่ม\nหมก\nหมด\nหม่น\nหมวก\nหมวด\nหมวน\nหมอ\nหม่อ\nหม้อ\nหมอก\nหมอง\nหม่อง\nหมอน\nหม่อน\nหมอบ\nหม่อม\nหมอย\nหม้อห้อม\nหมัก\nหมักหมม\nหมัด\nหมัน\nหมั่น\nหมั้น\nหมับ\nหมา\nหม่า\nหมาก\nหมากฮอส\nหมาง\nหมาด\nหมามุ่ย\nหมามุ้ย\nหมาย\nหม้าย\nหมาร่า\nหม่ำ\nหม้ำ\nหมิ่น\nหมี\nหมี่\nหมึก\nหมืน\nหมื่น\nหมุด\nหมุน\nหมุบ\nหมุบหมับ\nหมุบหมิบ\nหมุ่ย\nหมุยขาว\nหมู\nหมู่\nหมูหริ่ง\nหยก\nหย่ง\nหยด\nหยวก\nหยวบ\nหยอก\nหยอกเอิน\nหย็อกหย็อย\nหยอง\nหย็อง\nหย่อง\nหย็องกรอด\nหย็องแหย็ง\nหยอด\nหยอน\nหย่อน\nหย่อม\nหย็อมแหย็ม\nหย็อย\nหย่อย\nหยัก\nหยักไย่\nหยักเหยา\nหยัง\nหยั่ง\nหยังหยัง\nหยัด\nหยัน\nหยับ\nหยั่วเมือง\nหย่า\nหยากเยื่อ\nหยากไย่\nหยาด\nหยาบ\nหยาม\nหยาว\nหย้าว\nหยำเป\nหยำเหยอะ\nหยำแหยะ\nหยิก\nหยิ่ง\nหยิบ\nหยิม\nหยี\nหยี่\nหยุกหยิก\nหยุด\nหยุ่น\nหยุบ\nหยุมหยิม\nหยูกยา\nหโยดม\nหรคุณ\nหรณะ\nหรดาล\nหรดี\nหรรษ์\nหรรษา\nหรอ\nหรอก\nหร็อมแหร็ม\nหรอย\nหระ\nหรับ\nหรา\nหริ\nหริ่ง\nหริณะ\nหริต\nหริตกี\nหรีตกี\nหรี่\nหรีด\nหรือ\nหรุบ\nหรุบรู่\nหรุบหรู่\nหรุ่ม\nหรู\nหรูหรา\nหฤทัย\nหฤทย์\nหฤษฎ์\nหฤษฎี\nหฤหรรษ์\nหฤโหด\nหลง\nหลงใหล\nหลงจู๊\nหลด\nหลน\nหล่น\nหลบ\nหล่ม\nหลวง\nหลวม\nหลอ\nหล่อ\nหลอก\nหลอด\nหลอน\nหล็อน\nหล่อน\nหลอม\nหละ\nหละหลวม\nหลัก\nหลัง\nหลั่ง\nหลัด\nหลั่น\nหลับ\nหลัว\nหลา\nหล้า\nหลาก\nหลาน\nหลาบ\nหลาม\nหลาย\nหลาว\nหลิ่ง\nหลิท\nหลิน\nหลิม\nหลิว\nหลิ่ว\nหลี\nหลีก\nหลีโก\nหลีบ\nหลีฮื้อ\nหลืบ\nหลุกหลิก\nหลุด\nหลุน\nหลุบ\nหลุม\nหลุมพอ\nหลุมพี\nหลู่\nหวง\nห่วง\nห้วง\nหวด\nหวน\nห้วน\nหวย\nห้วย\nหวอ\nหวอด\nหวะ\nหวัง\nหวัด\nหวั่น\nหวันยิหวา\nหวัว\nหวัวร่อ\nหวัวเราะ\nหวา\nหว่า\nหว้า\nหวาก\nหว่าง\nหวาด\nหวาน\nหว่าน\nหวาม\nหวาย\nหวำ\nหวิด\nหวิว\nหวี\nหวี่\nหวีด\nหวือ\nหวุดหวิด\nหวูด\nหอ\nห่อ\nห้อ\nหอก\nหอง\nห้อง\nหอน\nห่อน\nหอบ\nหอม\nห้อม\nหอย\nห้อย\nหะ\nหะยี\nหะหาย\nหัก\nหัจญ์\nหัจญี\nหัช\nหัฏฐะ\nหัด\nหัต\nหัตถ์\nหัตถกรรม\nหัตถการ\nหัตถกิจ\nหัตถบาส\nหัตถพันธ์\nหัตถาภรณ์\nหัตถศาสตร์\nหัตถศิลป์\nหัตถศึกษา\nหัตถาจารย์\nหัตถินี\nหัตถี\nหัน\nหั่น\nหั้น\nหันตรา\nหับ\nหัย\nหัว\nหัวร่อ\nหัวเราะ\nหัส\nหัสดิน\nหัสดี\nหัสต์\nหัสตะ\nหา\nห่า\nห้า\nหาก\nหาง\nห่าง\nห้าง\nหาญ\nหาด\nห่าน\nหาบ\nหาม\nห่าม\nห้าม\nหาย\nหายใจ\nหายนะ\nหาร\nหารือ\nหาว\nห้าว\nหาสะ\nหำ\nห้ำ\nหิ้ง\nหิงคุ\nหิงสา\nหิงห้อย\nหิ่งห้อย\nหิ่งหาย\nหิด\nหิต\nหิตานุหิตประโยชน์\nหิน\nหิมพาน\nหิมพานต์\nหิมวัต\nหิมวันต์\nหิมวา\nหิมะ\nหิมาลัย\nหิรัญ\nหิรัญญิการ์\nหิรัญบัฏ\nหิรัณย์\nหิรัณยรัศมี\nหิริ\nหิว\nหิ้ว\nหี\nหีด\nหีนยาน\nหีบ\nหึ\nหึง\nหึ่ง\nหึงสา\nหืด\nหืน\nหื่น\nหือ\nหื้อ\nหุง\nหุน\nหุ่น\nหุ้น\nหุนหัน\nหุบ\nหุ้ม\nหุยฮา\nหู\nหู่\nหูก\nหูด\nเห\nเห่\nเหง\nเหง่ง\nเหงา\nเหง้า\nเหงื่อ\nเหงือก\nเห็จ\nเห็ด\nเหติ\nเหตุ\nเห็น\nเหน่ง\nเหนงนายพราน\nเหน็ดเหนื่อย\nเหน็บ\nเหน่อ\nเห็นอ้ม\nเหนอะ\nเหนอะหนะ\nเหน้า\nเหนาะ\nเหนียง\nเหนี่ยง\nเหนี่ยน\nเหนียม\nเหนียว\nเหนี่ยว\nเหนือ\nเหนื่อย\nเห็บ\nเหม\nเหม่\nเหม็ง\nเหม่ง\nเหม็น\nเหมวดี\nเหม่อ\nเหมันต์\nเหมันตฤดู\nเหมา\nเหมายัน\nเหมาะ\nเหมียว\nเหมี่ยว\nเหมือง\nเหมือด\nเหมือน\nเหมื่อย\nเหย\nเหยง\nเหย่อย\nเหยา\nเหย่า\nเหย้า\nเหยาะ\nเหยาะแหยะ\nเหยิง\nเหยิบ\nเหยียด\nเหยียบ\nเหยี่ยว\nเหยื่อ\nเหยือก\nเหรอ\nเหรอะ\nเหรัญญิก\nเหรา\nเหราะ\nเหรียญ\nเหล่\nเหล็ก\nเหลน\nเหลว\nเหลอ\nเหลา\nเหล่า\nเหล้า\nเหลาะแหละ\nเหลิง\nเหลิงเจิ้ง\nเหลียน\nเหลี่ยม\nเหลียว\nเหลือ\nเหลือก\nเหลือง\nเหลือบ\nเหลือม\nเหลื่อม\nเหว\nเหว่\nเหวง\nเหวย\nเหวอะ\nเหวอะหวะ\nเหวี่ยง\nเห่อ\nเหอะ\nเหะ\nเหะหะ\nเหา\nเห่า\nเหาะ\nเหิน\nเหิม\nเหี้ย\nเหียง\nเหียน\nเหี้ยน\nเหี้ยม\nเหี่ยว\nเหื่อ\nเหือด\nแห\nแห่\nแห้\nแหก\nแหง\nแหง่\nแห่ง\nแห้ง\nแหงแก๋\nแหง่ง\nแหงน\nแหน\nแห้น\nแหนง\nแหนบ\nแหนม\nแหบ\nแหม\nแหม่\nแหม่ม\nแหมะ\nแหย\nแหย่\nแหยง\nแหย่ง\nแหยม\nแหย็ม\nแหยะ\nแหล่\nแหลก\nแหล่ง\nแหลน\nแหลม\nแหละ\nแหว\nแห้ว\nแหวก\nแหว่ง\nแหวด\nแหวน\nแหวะ\nแหะ\nโห่\nโหง\nโหด\nโหน\nโหนก\nโหน่ง\nโหม\nโหม่ง\nโหมด\nโหย\nโหยกเหยก\nโหยง\nโหย่ง\nโหร\nโหรง\nโหรงเหรง\nโหรดาจารย์\nโหระพา\nโหรา\nโหราจารย์\nโหราศาสตร์\nโหล\nโหล่\nโหลงโจ้ง\nโหว\nโหว่\nโหว้\nโหวกเหวก\nโหวง\nโหวด\nโหวต\nให้\nใหญ่\nใหม่\nไห\nไห่\nไห้\nไหน\nไหม\nไหม้\nไหรณย์\nไหล\nไหล่\nไหว\nไหว้\nไหหลำ\nอก\nอกตเวทิตา\nอกตเวที\nอกตัญญุตา\nอกตัญญู\nอกนิษฐ์\nอกรณีย์\nอกรรมกริยา\nอกัปปิยวัตถุ\nอกัปปิยะ\nอกุศล\nอคติ\nอคาธ\nอโฆษะ\nองก์\nองค์\nองคชาต\nองคมนตรี\nองครักษ์\nองคาพยพ\nองคุลี\nองศ์\nองศา\nองอาจ\nองุ่น\nอจลา\nอจินตา\nอจินไตย\nอจิระ\nอเจลก\nอเจละ\nอชะ\nอชิน\nอชินี\nอชิระ\nอฏวี\nอณิ\nอณู\nอโณทัย\nอด\nอดิถี\nอดิเทพ\nอดิเรก\nอดิศร\nอดิศวร\nอดิศัย\nอดีต\nอดุล\nอดุลย์\nอติ\nอติชาต\nอติมานะ\nอติราช\nอติเรก\nอติสาร\nอถรรพเวท\nอาถรรพเวท\nอทระ\nอทินนาทาน\nอธรรม\nอธิ\nอธิกมาส\nอธิกรณ์\nอธิกวาร\nอธิกสุรทิน\nอธิการ\nอธิคม\nอธิฏฐาน\nอธิบดี\nอธิบาย\nอธิป\nอธิปไตย\nอธิมาตร\nอธิมุตติ\nอธิโมกข์\nอธิราช\nอธิวาส\nอธิวาสนะ\nอธิศีล\nอธิษฐาน\nอธึก\nอ้น\nอนงค์\nอนงคณะ\nอนงคเลขา\nอนธการ\nอนนต์\nอนยะ\nอนรรฆ\nอนรรถ\nอนล\nอนวัช\nอนัญ\nอนัตตา\nอนันต์\nอนันตริยกรรม\nอนัม\nอนาคต\nอนาคามิผล\nอนาคามิมรรค\nอนาคามี\nอนาจาร\nอนาถ\nอนาถา\nอนาทร\nอนาธิปไตย\nอนามัย\nอนามิกา\nอนารยชน\nอนารยธรรม\nอนารยะ\nอนาลัย\nอนำ\nอนิจ\nอนิจจัง\nอนิจจา\nอนิฏฐารมณ์\nอนิยต\nอนิยม\nอนิล\nอนิวรรต\nอนิวรรตน์\nอนีกะ\nอนีจะ\nอนึก\nอนึ่ง\nอนุ\nอนุกร\nอนุกรม\nอนุกรรมการ\nอนุกระเบียด\nอนุกาชาด\nอนุการ\nอนุกูล\nอนุคามิก\nอนุเคราะห์\nอนุจร\nอนุช\nอนุชน\nอนุชา\nอนุชาต\nอนุชิต\nอนุญาต\nอนุญาโตตุลาการ\nอนุตร\nอนุเถระ\nอนุทิน\nอนุบท\nอนุบาล\nอนุประโยค\nอนุปริญญา\nอนุปสัมบัน\nอนุปัสนา\nอนุพงศ์\nอนุพัทธ์\nอนุพันธ์\nอนุโพธ\nอนุภรรยา\nอนุภริยา\nอนุภาค\nอนุภาษ\nอนุมัติ\nอนุมาตรา\nอนุมาน\nอนุมูล\nอนุโมทนา\nอนุโยค\nอนุรักษ์\nอนุรักษนิยม\nอนุราช\nอนุราธ\nอนุราธะ\nอนุราธา\nอนุรูป\nอนุโลม\nอนุวงศ์\nอนุวรรตน์\nอนุวัต\nอนุวัตน์\nอนุวัตร\nอนุวัติ\nอนุวาต\nอนุศาสก\nอนุศาสน์\nอนุศาสนาจารย์\nอนุศิษฏ์\nอนุสติ\nอนุสนธิ\nอนุสร\nอนุสรณ์\nอนุสัญญา\nอนุสัย\nอนุสาวรีย์\nอนุสาสนี\nอเนก\nอเนกคุณ\nอเนกรรถประโยค\nอเนจอนาถ\nอโนชา\nอโนดาต\nอบ\nอบเชย\nอบาย\nอปจายนธรรม\nอปจายนมัย\nอปมงคล\nอปยศ\nอประไมย\nอปรัณณชาติ\nอปรา\nอปราชัย\nอปราชิต\nอปริมาณ\nอปลักษณ์\nอปโลกน์\nอปวาท\nอเปหิ\nอพพะ\nอพยพ\nอภัพ\nอภัย\nอภิ\nอภิฆาต\nอภิชฌา\nอภิชน\nอภิชนาธิปไตย\nอภิชัย\nอภิชาต\nอภิชิต\nอภิญญา\nอภิญญาณ\nอภิธรรม\nอภิธาน\nอภิไธย\nอภินันท์\nอภินันทนาการ\nอภินัย\nอภินิหาร\nอภิเนษกรมณ์\nอภิบาล\nอภิปรัชญา\nอภิปราย\nอภิมหาอำนาจ\nอภิมานะ\nอภิมุข\nอภิรดี\nอภิรติ\nอภิรมย์\nอภิรักษ์\nอภิราม\nอภิรุต\nอภิรุม\nอภิรูป\nอภิลักขิต\nอภิลักขิตสมัย\nอภิเลปน์\nอภิวันท์\nอภิวาท\nอภิวาทน์\nอภิเษก\nอภิสมโพธิ\nอภิสมัย\nอภิสมาจาร\nอภิสัมโพธิ\nอภิสัมโพธิญาณ\nอภิสิต\nอภิสิทธิ์\nอภูตะ\nอม\nอมพะนำ\nอ้ม\nอมตธรรม\nอมตบท\nอมตะ\nอมนุษย์\nอมร\nอมรา\nอมราวดี\nอมรินทร์\nอมเรนทร์\nอมเรศ\nอมฤต\nอมัตร\nอมาตย์\nอมาวสี\nอมาวสุ\nอมาวาสี\nอมิตร\nอเมริกัน\nอย่า\nอยาก\nอย่าง\nอยุทธ์\nอยู่\nอร\nอรชร\nอรชุน\nอรดี\nอรติ\nอรทัย\nอรไท\nอรนุช\nอรพินท์\nอรพิม\nอรรค\nอรรฆ\nอรรฆย์\nอรรจน์\nอรรณพ\nอรรถ\nอรรถกถา\nอรรถกถาจารย์\nอรรถาธิบาย\nอรรธ\nอรสุม\nอรหะ\nอรหัง\nอรหัต\nอรหัตผล\nอรหัตมรรค\nอรหัน\nอรหันต์\nอรหันตฆาต\nอร่อย\nอรัญ\nอรัญญิก\nอรัญวาส\nอรัญวาสี\nอรัณย์\nอราดี\nอร่าม\nอริ\nอรินทร์\nอริน\nอริยกะ\nอริยทรัพย์\nอริยบุคคล\nอริยผล\nอริยมรรค\nอริยสั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nอำพล\nอำพะนำ\nอำพัน\nอำไพ\nอำเภอ\nอำมร\nอำมฤคโชค\nอำมฤต\nอำมหิต\nอำมาตย์\nอำมาตยาธิปไตย\nอำยวน\nอำรุง\nอำลา\nอำอวม\nอ้ำอึ้ง\nอิก\nอิง\nอิงค์\nอิงอร\nอิจฉา\nอิฉัน\nอิชยา\nอิฏฐารมณ์\nอิฐ\nอิฐผล\nอิด\nอิตถี\nอิตเทรียม\nอิตเทอร์เบียม\nอิติวุตตกะ\nอิทธิ\nอิน\nอินซูลิน\nอินเดีย\nอินเดียนแดง\nอินเดียม\nอินท์\nอินทขีล\nอินทนิล\nอินทผลัม\nอินทร์\nอินทรธนู\nอินทรวงศ์\nอินทรวิเชียร\nอินทรศักดิ์\nอินทราณี\nอินทราภิเษก\nอินทรายุธ\nอินทรี\nอินทรีย์\nอินทรียสังวร\nอินทีวร\nอินทุ\nอินฟราเรด\nอินัง\nอิ่ม\nอิมัลชัน\nอิริเดียม\nอิริยา\nอิริยาบถ\nอิรุพเพท\nอิเล็กตรอน\nอิเล็กทรอนิกส์\nอิเล็กโทน\nอิศร\nอิศวร\nอิษฏ์\nอิษฏี\nอิส\nอิสตรี\nอิสัตรี\nอิสรภาพ\nอิสระ\nอิสริยยศ\nอิสริยะ\nอิสริยาภรณ์\nอิสลาม\nอิสสา\nอิสิ\nอิสี\nอิหม่าม\nอิหลักอิเหลื่อ\nอี\nอี่\nอี้\nอี๊\nอี๋\nอี๋อ๋อ\nอีก\nอีก้อ\nอีก๋อย\nอีโก้ง\nอีจู้\nอี๊ด\nอีเต้อ\nอีโต้\nอีทุบ\nอีเทอร์\nอีนุงตุงนัง\nอีนูน\nอีโน\nอีแปะ\nอีโปง\nอีเพา\nอีมู\nอีรม\nอีลุ้ม\nอีลุ่ยฉุยแฉก\nอีเลิ้ง\nอีศ\nอีศวร\nอีส\nอีสาน\nอีสุกอีใส\nอีหรอบ\nอีหลักอีเหลื่อ\nอีหลี\nอีหลุกขลุกขลัก\nอีหลุกขลุกขลุ่ย\nอีเห็น\nอีเหน็บ\nอีเหนียว\nอีเหละเขละขละ\nอีเหละเขะขะ\nอีโหน่อีเหน่\nอีโหลกโขลกเขลก\nอีแอ่น\nอึ\nอึก\nอึ้ก\nอึ๊ก\nอึกทึก\nอึกอัก\nอึง\nอึ่ง\nอึ้ง\nอึด\nอึดตะปือ\nอึ้ดทึ่ด\nอึน\nอืด\nอื่น\nอื้น\nอือ\nอื้อ\nอื้อฮือ\nอุ\nอุก\nอุกกา\nอุกกาบาต\nอุกฤษฏ์\nอุกลาบาต\nอุค\nอุคระ\nอุคหนิมิต\nอุโฆษ\nอุ้ง\nอุจ\nอุจจาระ\nอุจฉุ\nอุจเฉท\nอุจเฉททิฐิ\nอุจาด\nอุชุ\nอุฏฐาการ\nอุณหภูมิ\nอุณหะ\nอุณหาหาร\nอุณหิส\nอุณา\nอุณาโลม\nอุด\nอุดเตา\nอุดม\nอุดมการณ์\nอุดมคติ\nอุดมศึกษา\nอุดร\nอุดหนุน\nอุตดม\nอุตตมะ\nอุตมภาพ\nอุตมางค์\nอุตดร\nอุตรกุรุทวีป\nอุตตรายัน\nอุตรนิกาย\nอุตรผลคุนี\nอุตตรผลคุนี\nอุตรภัทรบท\nอุตตรภัทรบท\nอุตตรภัททะ\nอุตราภิมุข\nอุตราวรรต\nอุตราวัฏ\nอุตราษาฒ\nอุตตราสาฬหะ\nอุตราสงค์\nอุตตานภาพ\nอุตพิด\nอุตรา\nอุตริ\nอุตริมนุสธรรม\nอุตลุด\nอุตส่าห์\nอุตสาหกรรม\nอุตสาหะ\nอุตุ\nอุตุนิยม\nอุตุนิยมวิทยา\nอุทก\nอุทกธาร\nอุทกธารา\nอุทกภัย\nอุทกวิทยา\nอุทกศาสตร์\nอุทธรณ์\nอุทธัจ\nอุทยาน\nอุทร\nอุทริยะ\nอุทลุม\nอุทัช\nอุทัย\nอุทาน\nอุทาร\nอุทาหรณ์\nอุทิศ\nอุทุมพร\nอุเทศ\nอุเทสิกเจดีย์\nอุธัจ\nอุ่น\nอุบ\nอุบล\nอุบะ\nอุบ๊ะ\nอุบัติ\nอุบาท\nอุบาทว์\nอุบาย\nอุบาสก\nอุบาสิกา\nอุเบกขา\nอุโบสถ\nอุปกรณ์\nอุปกรม\nอุปการ\nอุปการะ\nอุปการี\nอุปกิเลส\nอุปจาร\nอุปถัมภ์\nอุปถัมภก\nอุปทม\nอุปทูต\nอุปเทศ\nอุปเท่ห์\nอุปธิ\nอุปนัย\nอุปนิกขิต\nอุปนิษัท\nอุปนิสัย\nอุปบัติ\nอุปปาติกะ\nอุปพัทธ์\nอุปพันธ์\nอุปโภค\nอุปมา\nอุปมาน\nอุปไมย\nอุปยุวราช\nอุปรากร\nอุปราคา\nอุปราช\nอุปริ\nอุปริมปริยาย\nอุปโลกน์\nอุปเวท\nอุปสมบท\nอุปสมบัน\nอุปสัมบัน\nอุปสรรค\nอุปสัมปทา\nอุปฮาด\nอุปัชฌาย์\nอุปัชฌายวัตร\nอุปัชฌายะ\nอุปัฏฐาก\nอุปัฏฐานะ\nอุปัทวะ\nอุปัทวันตราย\nอุปาทาน\nอุปาหนา\nอุภัย\nอุ้ม\nอุมงค์\nอุโมงค์\nอุย\nอุ่ย\nอุ้ย\nอุ๊ย\nอุยยาน\nอุยยาม\nอุรณะ\nอุรพี\nอุระ\nอุรังอุตัง\nอุรัจฉัท\nอุรัจฉทะ\nอุรา\nอุรุ\nอุไร\nอุลกมณี\nอุลโลจ\nอุลามก\nอุลิด\nอุโลก\nอุแว้\nอุษณกร\nอุษณกาล\nอุษณรัศมี\nอุษณรุจี\nอุษณาการ\nอุษณีษ์\nอุษมะ\nอุษมัน\nอุษา\nอุษาโยค\nอุสภ\nอุสส่าห์\nอุสสาหะ\nอุสา\nอุสุ\nอุสุภ\nอุสุภราช\nอุสุม\nอุหรับ\nอุหลบ\nอุเหม่\nอุฬาร\nอู\nอู่\nอู้\nอูฐ\nอูด\nอูม\nอูย\nอูรุ\nอู๋อี๋\nเอ\nเอ้\nเอ๊\nเอก\nเอกเขนก\nเอกซเรย์\nเอกรรถประโยค\nเอกัคตา\nเอกังสพยากรณ์\nเอกังสวาที\nเอกา\nเอ้กา\nเอกาธิปไตย\nเอเคอร์\nเอง\nเอ็ง\nเอ๋ง\nเอ็ด\nเอ็ดตะโร\nเอดส์\nเอตทัคคะ\nเอ้เต\nเอทิล\nเอน\nเอ็น\nเอนไซม์\nเอ็นดู\nเอ็นอ่อน\nเอม\nเอ็มบริโอ\nเอย\nเอ่ย\nเอ๊ย\nเอ๋ย\nเอร็ดอร่อย\nเอราวัณ\nเอว\nเอ๊ว\nเอวัง\nเอฬกะ\nเอฬา\nเออ\nเอ่อ\nเออร์เบียม\nเอ้อระเหย\nเอ้อเร้อ\nเอ้อเฮอ\nเอ๊ะ\nเอะใจ\nเอะอะ\nเอะอะมะเทิ่ง\nเอา\nเอาทาร\nเอาทารย์\nเอารส\nเอาฬาร\nเอาะลาย\nเอิก\nเอิกเกริก\nเอิ้น\nเอิบ\nเอียง\nเอี้ยง\nเอียด\nเอี๊ยด\nเอียน\nเอี่ยน\nเอี่ยม\nเอี๊ยม\nเอี้ยมจุ๊น\nเอี้ยมเฟี้ยม\nเอี่ยว\nเอี้ยว\nเอื้อ\nเอื๊อก\nเอื้อง\nเอือด\nเอือน\nเอื้อน\nเอือม\nเอื้อม\nเอื่อย\nเอื้อย\nแอ\nแอ้\nแอ๋\nแอก\nแอกทิเนียม\nแอ่ง\nแอ้งแม้ง\nแอด\nแอ้ด\nแอ๊ด\nแอ่น\nแอนติเจน\nแอนติบอดี\nแอนติอิเล็กตรอน\nแอโนด\nแอบ\nแอม\nแอ้ม\nแอมแปร์\nแอมมิเตอร์\nแอมโมเนีย\nแอร่ม\nแอลกอฮอล์\nแอลฟา\nแอว\nแอ่ว\nแอ้วแซ่ว\nแอสทาทีน\nแอสไพริน\nแอสฟัลต์\nแอหนัง\nแออัด\nโอ\nโอ่\nโอ้\nโอ๋\nโอก\nโอ้ก\nโอ้กอ้าก\nโอ๊ก\nโอกาส\nโอฆชล\nโอฆสงสาร\nโอฆะ\nโอ่ง\nโองการ\nโองโขดง\nโอชะ\nโอชา\nโอโซน\nโอฐ\nโอด\nโอ๊ต\nโอตตัปปะ\nโอทนะ\nโอน\nโอบ\nโอปปาติกะ\nโอภา\nโอภาส\nโอม\nโอย\nโอ๊ย\nโอรส\nโอละพ่อ\nโอลิมปิก\nโอวาท\nโอษฐ์\nโอษฐชะ\nโอษฐภัย\nโอสถ\nโอห์ม\nโอหัง\nโอฬาร\nโอฬาริก\nโอฬารึก\nโอ้เอ้\nโอเอซิส\nโอ้โฮ\nไอ\nไอ้\nไอโซโทป\nไอน์สไตเนียม\nไอยรา\nไอยเรศ\nไอราพต\nไอราวัณ\nไอราวัต\nไอศกรีม\nไอศวรรย์\nไอศุริยสมบัติ\nไอศูรย์\nไอออน\nไอโอดีน\nฮกเกี้ยน\nฮด\nฮวงซุ้ย\nฮวน\nฮ้วนหมู\nฮวบ\nฮ่อ\nฮ้อ\nฮอกกี้\nฮอด\nฮ่อม\nฮ่อยจ๊อ\nฮอร์โมน\nฮอลแลนด์\nฮอลันดา\nฮะ\nฮะเบส\nฮะเรีย\nฮัก\nฮังเล\nฮัจญ์\nฮัจญะฮ์\nฮัจญี\nฮั่น\nฮั้ว\nฮา\nฮ้า\nฮ่างหลวง\nฮาจญ์\nฮาม\nฮาเร็ม\nฮาห์เนียม\nฮิจเราะห์\nฮินดู\nฮิปโปโปเตมัส\nฮิสทีเรีย\nฮีเลียม\nฮึ\nฮึก\nฮึด\nฮึดฮัด\nฮึ่ม\nฮึย\nฮึ่ย\nฮืดฮาด\nฮือ\nฮื่อ\nฮื้อ\nฮื้อฉี่\nฮุด\nฮุบ\nฮุยเลฮุย\nฮู้\nฮูก\nฮูม\nเฮ\nเฮฮา\nเฮกตาร์\nเฮกโตกรัม\nเฮกโตเมตร\nเฮกโตลิตร\nเฮง\nเฮ็ด\nเฮโมโกลบิน\nเฮย\nเฮ่ย\nเฮ้ย\nเฮโรอีน\nเฮลิคอปเตอร์\nเฮโล\nเฮละโล\nเฮ้ว\nเฮอ\nเฮ่อ\nเฮ้อ\nเฮอริเคน\nเฮิรตซ์\nเฮี้ยน\nเฮี้ยบ\nเฮี้ยว\nเฮือก\nเฮือน\nแฮ\nแฮ่\nแฮก\nแฮ่กึ๊น\nแฮนด์บอล\nแฮฟเนียม\nแฮม\nแฮะ\nโฮ\nโฮก\nโฮ่ง\nโฮ้ง\nโฮเต็ล\nโฮลเมียม\nโฮะ\nไฮ้\nไฮโกรมิเตอร์\nไฮดรา\nไฮโดร\nไฮโดรคาร์บอน\nไฮโดรเจน\nไฮโดรมิเตอร์\nไฮไฟ\nไฮโล\nไฮฮี\n"
               .split(/[\r\n]+/)
               .filter(function (w) {
                 return w.length > 1;
diff --git a/docs/site/assets/javascripts/workers/search.b8dbb3d2.min.js.map b/docs/site/assets/javascripts/workers/search.b8dbb3d2.min.js.map
deleted file mode 100644
index 7ef0846..0000000
--- a/docs/site/assets/javascripts/workers/search.b8dbb3d2.min.js.map
+++ /dev/null
@@ -1,7 +0,0 @@
-{
-  "version": 3,
-  "sources": ["node_modules/lunr/lunr.js", "src/templates/assets/javascripts/integrations/search/worker/main/index.ts", "src/templates/assets/javascripts/browser/element/_/index.ts", "src/templates/assets/javascripts/polyfills/index.ts", "src/templates/assets/javascripts/integrations/search/config/index.ts", "src/templates/assets/javascripts/integrations/search/internal/_/index.ts", "src/templates/assets/javascripts/integrations/search/internal/extract/index.ts", "src/templates/assets/javascripts/integrations/search/internal/highlight/index.ts", "src/templates/assets/javascripts/integrations/search/internal/tokenize/index.ts", "src/templates/assets/javascripts/integrations/search/query/transform/index.ts", "src/templates/assets/javascripts/integrations/search/query/_/index.ts", "src/templates/assets/javascripts/integrations/search/query/segment/index.ts", "src/templates/assets/javascripts/integrations/search/_/index.ts"],
-  "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n *   this.field('title')\n *   this.field('body')\n *   this.ref('id')\n *\n *   documents.forEach(function (doc) {\n *     this.add(doc)\n *   }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n  var builder = new lunr.Builder\n\n  builder.pipeline.add(\n    lunr.trimmer,\n    lunr.stopWordFilter,\n    lunr.stemmer\n  )\n\n  builder.searchPipeline.add(\n    lunr.stemmer\n  )\n\n  config.call(builder, builder)\n  return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n  /* eslint-disable no-console */\n  return function (message) {\n    if (global.console && console.warn) {\n      console.warn(message)\n    }\n  }\n  /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n  if (obj === void 0 || obj === null) {\n    return \"\"\n  } else {\n    return obj.toString()\n  }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n  if (obj === null || obj === undefined) {\n    return obj\n  }\n\n  var clone = Object.create(null),\n      keys = Object.keys(obj)\n\n  for (var i = 0; i < keys.length; i++) {\n    var key = keys[i],\n        val = obj[key]\n\n    if (Array.isArray(val)) {\n      clone[key] = val.slice()\n      continue\n    }\n\n    if (typeof val === 'string' ||\n        typeof val === 'number' ||\n        typeof val === 'boolean') {\n      clone[key] = val\n      continue\n    }\n\n    throw new TypeError(\"clone is not deep and does not support nested objects\")\n  }\n\n  return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n  this.docRef = docRef\n  this.fieldName = fieldName\n  this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n  var n = s.indexOf(lunr.FieldRef.joiner)\n\n  if (n === -1) {\n    throw \"malformed field ref string\"\n  }\n\n  var fieldRef = s.slice(0, n),\n      docRef = s.slice(n + 1)\n\n  return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n  if (this._stringValue == undefined) {\n    this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n  }\n\n  return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n  this.elements = Object.create(null)\n\n  if (elements) {\n    this.length = elements.length\n\n    for (var i = 0; i < this.length; i++) {\n      this.elements[elements[i]] = true\n    }\n  } else {\n    this.length = 0\n  }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n  intersect: function (other) {\n    return other\n  },\n\n  union: function () {\n    return this\n  },\n\n  contains: function () {\n    return true\n  }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n  intersect: function () {\n    return this\n  },\n\n  union: function (other) {\n    return other\n  },\n\n  contains: function () {\n    return false\n  }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n  return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n  var a, b, elements, intersection = []\n\n  if (other === lunr.Set.complete) {\n    return this\n  }\n\n  if (other === lunr.Set.empty) {\n    return other\n  }\n\n  if (this.length < other.length) {\n    a = this\n    b = other\n  } else {\n    a = other\n    b = this\n  }\n\n  elements = Object.keys(a.elements)\n\n  for (var i = 0; i < elements.length; i++) {\n    var element = elements[i]\n    if (element in b.elements) {\n      intersection.push(element)\n    }\n  }\n\n  return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n  if (other === lunr.Set.complete) {\n    return lunr.Set.complete\n  }\n\n  if (other === lunr.Set.empty) {\n    return this\n  }\n\n  return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n  var documentsWithTerm = 0\n\n  for (var fieldName in posting) {\n    if (fieldName == '_index') continue // Ignore the term index, its not a field\n    documentsWithTerm += Object.keys(posting[fieldName]).length\n  }\n\n  var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n  return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n  this.str = str || \"\"\n  this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n  return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n *   return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n  this.str = fn(this.str, this.metadata)\n  return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n  fn = fn || function (s) { return s }\n  return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n  if (obj == null || obj == undefined) {\n    return []\n  }\n\n  if (Array.isArray(obj)) {\n    return obj.map(function (t) {\n      return new lunr.Token(\n        lunr.utils.asString(t).toLowerCase(),\n        lunr.utils.clone(metadata)\n      )\n    })\n  }\n\n  var str = obj.toString().toLowerCase(),\n      len = str.length,\n      tokens = []\n\n  for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n    var char = str.charAt(sliceEnd),\n        sliceLength = sliceEnd - sliceStart\n\n    if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n      if (sliceLength > 0) {\n        var tokenMetadata = lunr.utils.clone(metadata) || {}\n        tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n        tokenMetadata[\"index\"] = tokens.length\n\n        tokens.push(\n          new lunr.Token (\n            str.slice(sliceStart, sliceEnd),\n            tokenMetadata\n          )\n        )\n      }\n\n      sliceStart = sliceEnd + 1\n    }\n\n  }\n\n  return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n  this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n  if (label in this.registeredFunctions) {\n    lunr.utils.warn('Overwriting existing registered function: ' + label)\n  }\n\n  fn.label = label\n  lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n  var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n  if (!isRegistered) {\n    lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n  }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n  var pipeline = new lunr.Pipeline\n\n  serialised.forEach(function (fnName) {\n    var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n    if (fn) {\n      pipeline.add(fn)\n    } else {\n      throw new Error('Cannot load unregistered function: ' + fnName)\n    }\n  })\n\n  return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n  var fns = Array.prototype.slice.call(arguments)\n\n  fns.forEach(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n    this._stack.push(fn)\n  }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  pos = pos + 1\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n  var pos = this._stack.indexOf(fn)\n  if (pos == -1) {\n    return\n  }\n\n  this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n  var stackLength = this._stack.length\n\n  for (var i = 0; i < stackLength; i++) {\n    var fn = this._stack[i]\n    var memo = []\n\n    for (var j = 0; j < tokens.length; j++) {\n      var result = fn(tokens[j], j, tokens)\n\n      if (result === null || result === void 0 || result === '') continue\n\n      if (Array.isArray(result)) {\n        for (var k = 0; k < result.length; k++) {\n          memo.push(result[k])\n        }\n      } else {\n        memo.push(result)\n      }\n    }\n\n    tokens = memo\n  }\n\n  return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n  var token = new lunr.Token (str, metadata)\n\n  return this.run([token]).map(function (t) {\n    return t.toString()\n  })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n  this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n  return this._stack.map(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n    return fn.label\n  })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n  this._magnitude = 0\n  this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n  // For an empty vector the tuple can be inserted at the beginning\n  if (this.elements.length == 0) {\n    return 0\n  }\n\n  var start = 0,\n      end = this.elements.length / 2,\n      sliceLength = end - start,\n      pivotPoint = Math.floor(sliceLength / 2),\n      pivotIndex = this.elements[pivotPoint * 2]\n\n  while (sliceLength > 1) {\n    if (pivotIndex < index) {\n      start = pivotPoint\n    }\n\n    if (pivotIndex > index) {\n      end = pivotPoint\n    }\n\n    if (pivotIndex == index) {\n      break\n    }\n\n    sliceLength = end - start\n    pivotPoint = start + Math.floor(sliceLength / 2)\n    pivotIndex = this.elements[pivotPoint * 2]\n  }\n\n  if (pivotIndex == index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex > index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex < index) {\n    return (pivotPoint + 1) * 2\n  }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n  this.upsert(insertIdx, val, function () {\n    throw \"duplicate index\"\n  })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n  this._magnitude = 0\n  var position = this.positionForIndex(insertIdx)\n\n  if (this.elements[position] == insertIdx) {\n    this.elements[position + 1] = fn(this.elements[position + 1], val)\n  } else {\n    this.elements.splice(position, 0, insertIdx, val)\n  }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n  if (this._magnitude) return this._magnitude\n\n  var sumOfSquares = 0,\n      elementsLength = this.elements.length\n\n  for (var i = 1; i < elementsLength; i += 2) {\n    var val = this.elements[i]\n    sumOfSquares += val * val\n  }\n\n  return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n  var dotProduct = 0,\n      a = this.elements, b = otherVector.elements,\n      aLen = a.length, bLen = b.length,\n      aVal = 0, bVal = 0,\n      i = 0, j = 0\n\n  while (i < aLen && j < bLen) {\n    aVal = a[i], bVal = b[j]\n    if (aVal < bVal) {\n      i += 2\n    } else if (aVal > bVal) {\n      j += 2\n    } else if (aVal == bVal) {\n      dotProduct += a[i + 1] * b[j + 1]\n      i += 2\n      j += 2\n    }\n  }\n\n  return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n  return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n  var output = new Array (this.elements.length / 2)\n\n  for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n    output[j] = this.elements[i]\n  }\n\n  return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n  return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n  var step2list = {\n      \"ational\" : \"ate\",\n      \"tional\" : \"tion\",\n      \"enci\" : \"ence\",\n      \"anci\" : \"ance\",\n      \"izer\" : \"ize\",\n      \"bli\" : \"ble\",\n      \"alli\" : \"al\",\n      \"entli\" : \"ent\",\n      \"eli\" : \"e\",\n      \"ousli\" : \"ous\",\n      \"ization\" : \"ize\",\n      \"ation\" : \"ate\",\n      \"ator\" : \"ate\",\n      \"alism\" : \"al\",\n      \"iveness\" : \"ive\",\n      \"fulness\" : \"ful\",\n      \"ousness\" : \"ous\",\n      \"aliti\" : \"al\",\n      \"iviti\" : \"ive\",\n      \"biliti\" : \"ble\",\n      \"logi\" : \"log\"\n    },\n\n    step3list = {\n      \"icate\" : \"ic\",\n      \"ative\" : \"\",\n      \"alize\" : \"al\",\n      \"iciti\" : \"ic\",\n      \"ical\" : \"ic\",\n      \"ful\" : \"\",\n      \"ness\" : \"\"\n    },\n\n    c = \"[^aeiou]\",          // consonant\n    v = \"[aeiouy]\",          // vowel\n    C = c + \"[^aeiouy]*\",    // consonant sequence\n    V = v + \"[aeiou]*\",      // vowel sequence\n\n    mgr0 = \"^(\" + C + \")?\" + V + C,               // [C]VC... is m>0\n    meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\",  // [C]VC[V] is m=1\n    mgr1 = \"^(\" + C + \")?\" + V + C + V + C,       // [C]VCVC... is m>1\n    s_v = \"^(\" + C + \")?\" + v;                   // vowel in stem\n\n  var re_mgr0 = new RegExp(mgr0);\n  var re_mgr1 = new RegExp(mgr1);\n  var re_meq1 = new RegExp(meq1);\n  var re_s_v = new RegExp(s_v);\n\n  var re_1a = /^(.+?)(ss|i)es$/;\n  var re2_1a = /^(.+?)([^s])s$/;\n  var re_1b = /^(.+?)eed$/;\n  var re2_1b = /^(.+?)(ed|ing)$/;\n  var re_1b_2 = /.$/;\n  var re2_1b_2 = /(at|bl|iz)$/;\n  var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n  var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var re_1c = /^(.+?[^aeiou])y$/;\n  var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n  var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n  var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n  var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n  var re_5 = /^(.+?)e$/;\n  var re_5_1 = /ll$/;\n  var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var porterStemmer = function porterStemmer(w) {\n    var stem,\n      suffix,\n      firstch,\n      re,\n      re2,\n      re3,\n      re4;\n\n    if (w.length < 3) { return w; }\n\n    firstch = w.substr(0,1);\n    if (firstch == \"y\") {\n      w = firstch.toUpperCase() + w.substr(1);\n    }\n\n    // Step 1a\n    re = re_1a\n    re2 = re2_1a;\n\n    if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n    else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n    // Step 1b\n    re = re_1b;\n    re2 = re2_1b;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      re = re_mgr0;\n      if (re.test(fp[1])) {\n        re = re_1b_2;\n        w = w.replace(re,\"\");\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1];\n      re2 = re_s_v;\n      if (re2.test(stem)) {\n        w = stem;\n        re2 = re2_1b_2;\n        re3 = re3_1b_2;\n        re4 = re4_1b_2;\n        if (re2.test(w)) { w = w + \"e\"; }\n        else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n        else if (re4.test(w)) { w = w + \"e\"; }\n      }\n    }\n\n    // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n    re = re_1c;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      w = stem + \"i\";\n    }\n\n    // Step 2\n    re = re_2;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step2list[suffix];\n      }\n    }\n\n    // Step 3\n    re = re_3;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step3list[suffix];\n      }\n    }\n\n    // Step 4\n    re = re_4;\n    re2 = re2_4;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      if (re.test(stem)) {\n        w = stem;\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1] + fp[2];\n      re2 = re_mgr1;\n      if (re2.test(stem)) {\n        w = stem;\n      }\n    }\n\n    // Step 5\n    re = re_5;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      re2 = re_meq1;\n      re3 = re3_5;\n      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n        w = stem;\n      }\n    }\n\n    re = re_5_1;\n    re2 = re_mgr1;\n    if (re.test(w) && re2.test(w)) {\n      re = re_1b_2;\n      w = w.replace(re,\"\");\n    }\n\n    // and turn initial Y back to y\n\n    if (firstch == \"y\") {\n      w = firstch.toLowerCase() + w.substr(1);\n    }\n\n    return w;\n  };\n\n  return function (token) {\n    return token.update(porterStemmer);\n  }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n  var words = stopWords.reduce(function (memo, stopWord) {\n    memo[stopWord] = stopWord\n    return memo\n  }, {})\n\n  return function (token) {\n    if (token && words[token.toString()] !== token.toString()) return token\n  }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n  'a',\n  'able',\n  'about',\n  'across',\n  'after',\n  'all',\n  'almost',\n  'also',\n  'am',\n  'among',\n  'an',\n  'and',\n  'any',\n  'are',\n  'as',\n  'at',\n  'be',\n  'because',\n  'been',\n  'but',\n  'by',\n  'can',\n  'cannot',\n  'could',\n  'dear',\n  'did',\n  'do',\n  'does',\n  'either',\n  'else',\n  'ever',\n  'every',\n  'for',\n  'from',\n  'get',\n  'got',\n  'had',\n  'has',\n  'have',\n  'he',\n  'her',\n  'hers',\n  'him',\n  'his',\n  'how',\n  'however',\n  'i',\n  'if',\n  'in',\n  'into',\n  'is',\n  'it',\n  'its',\n  'just',\n  'least',\n  'let',\n  'like',\n  'likely',\n  'may',\n  'me',\n  'might',\n  'most',\n  'must',\n  'my',\n  'neither',\n  'no',\n  'nor',\n  'not',\n  'of',\n  'off',\n  'often',\n  'on',\n  'only',\n  'or',\n  'other',\n  'our',\n  'own',\n  'rather',\n  'said',\n  'say',\n  'says',\n  'she',\n  'should',\n  'since',\n  'so',\n  'some',\n  'than',\n  'that',\n  'the',\n  'their',\n  'them',\n  'then',\n  'there',\n  'these',\n  'they',\n  'this',\n  'tis',\n  'to',\n  'too',\n  'twas',\n  'us',\n  'wants',\n  'was',\n  'we',\n  'were',\n  'what',\n  'when',\n  'where',\n  'which',\n  'while',\n  'who',\n  'whom',\n  'why',\n  'will',\n  'with',\n  'would',\n  'yet',\n  'you',\n  'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n  return token.update(function (s) {\n    return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n  })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n  this.final = false\n  this.edges = {}\n  this.id = lunr.TokenSet._nextId\n  lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n  var builder = new lunr.TokenSet.Builder\n\n  for (var i = 0, len = arr.length; i < len; i++) {\n    builder.insert(arr[i])\n  }\n\n  builder.finish()\n  return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n  if ('editDistance' in clause) {\n    return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n  } else {\n    return lunr.TokenSet.fromString(clause.term)\n  }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n  var root = new lunr.TokenSet\n\n  var stack = [{\n    node: root,\n    editsRemaining: editDistance,\n    str: str\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop()\n\n    // no edit\n    if (frame.str.length > 0) {\n      var char = frame.str.charAt(0),\n          noEditNode\n\n      if (char in frame.node.edges) {\n        noEditNode = frame.node.edges[char]\n      } else {\n        noEditNode = new lunr.TokenSet\n        frame.node.edges[char] = noEditNode\n      }\n\n      if (frame.str.length == 1) {\n        noEditNode.final = true\n      }\n\n      stack.push({\n        node: noEditNode,\n        editsRemaining: frame.editsRemaining,\n        str: frame.str.slice(1)\n      })\n    }\n\n    if (frame.editsRemaining == 0) {\n      continue\n    }\n\n    // insertion\n    if (\"*\" in frame.node.edges) {\n      var insertionNode = frame.node.edges[\"*\"]\n    } else {\n      var insertionNode = new lunr.TokenSet\n      frame.node.edges[\"*\"] = insertionNode\n    }\n\n    if (frame.str.length == 0) {\n      insertionNode.final = true\n    }\n\n    stack.push({\n      node: insertionNode,\n      editsRemaining: frame.editsRemaining - 1,\n      str: frame.str\n    })\n\n    // deletion\n    // can only do a deletion if we have enough edits remaining\n    // and if there are characters left to delete in the string\n    if (frame.str.length > 1) {\n      stack.push({\n        node: frame.node,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // deletion\n    // just removing the last character from the str\n    if (frame.str.length == 1) {\n      frame.node.final = true\n    }\n\n    // substitution\n    // can only do a substitution if we have enough edits remaining\n    // and if there are characters left to substitute\n    if (frame.str.length >= 1) {\n      if (\"*\" in frame.node.edges) {\n        var substitutionNode = frame.node.edges[\"*\"]\n      } else {\n        var substitutionNode = new lunr.TokenSet\n        frame.node.edges[\"*\"] = substitutionNode\n      }\n\n      if (frame.str.length == 1) {\n        substitutionNode.final = true\n      }\n\n      stack.push({\n        node: substitutionNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // transposition\n    // can only do a transposition if there are edits remaining\n    // and there are enough characters to transpose\n    if (frame.str.length > 1) {\n      var charA = frame.str.charAt(0),\n          charB = frame.str.charAt(1),\n          transposeNode\n\n      if (charB in frame.node.edges) {\n        transposeNode = frame.node.edges[charB]\n      } else {\n        transposeNode = new lunr.TokenSet\n        frame.node.edges[charB] = transposeNode\n      }\n\n      if (frame.str.length == 1) {\n        transposeNode.final = true\n      }\n\n      stack.push({\n        node: transposeNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: charA + frame.str.slice(2)\n      })\n    }\n  }\n\n  return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n  var node = new lunr.TokenSet,\n      root = node\n\n  /*\n   * Iterates through all characters within the passed string\n   * appending a node for each character.\n   *\n   * When a wildcard character is found then a self\n   * referencing edge is introduced to continually match\n   * any number of any characters.\n   */\n  for (var i = 0, len = str.length; i < len; i++) {\n    var char = str[i],\n        final = (i == len - 1)\n\n    if (char == \"*\") {\n      node.edges[char] = node\n      node.final = final\n\n    } else {\n      var next = new lunr.TokenSet\n      next.final = final\n\n      node.edges[char] = next\n      node = next\n    }\n  }\n\n  return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n  var words = []\n\n  var stack = [{\n    prefix: \"\",\n    node: this\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop(),\n        edges = Object.keys(frame.node.edges),\n        len = edges.length\n\n    if (frame.node.final) {\n      /* In Safari, at this point the prefix is sometimes corrupted, see:\n       * https://github.com/olivernn/lunr.js/issues/279 Calling any\n       * String.prototype method forces Safari to \"cast\" this string to what\n       * it's supposed to be, fixing the bug. */\n      frame.prefix.charAt(0)\n      words.push(frame.prefix)\n    }\n\n    for (var i = 0; i < len; i++) {\n      var edge = edges[i]\n\n      stack.push({\n        prefix: frame.prefix.concat(edge),\n        node: frame.node.edges[edge]\n      })\n    }\n  }\n\n  return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n  // NOTE: Using Object.keys here as this.edges is very likely\n  // to enter 'hash-mode' with many keys being added\n  //\n  // avoiding a for-in loop here as it leads to the function\n  // being de-optimised (at least in V8). From some simple\n  // benchmarks the performance is comparable, but allowing\n  // V8 to optimize may mean easy performance wins in the future.\n\n  if (this._str) {\n    return this._str\n  }\n\n  var str = this.final ? '1' : '0',\n      labels = Object.keys(this.edges).sort(),\n      len = labels.length\n\n  for (var i = 0; i < len; i++) {\n    var label = labels[i],\n        node = this.edges[label]\n\n    str = str + label + node.id\n  }\n\n  return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n  var output = new lunr.TokenSet,\n      frame = undefined\n\n  var stack = [{\n    qNode: b,\n    output: output,\n    node: this\n  }]\n\n  while (stack.length) {\n    frame = stack.pop()\n\n    // NOTE: As with the #toString method, we are using\n    // Object.keys and a for loop instead of a for-in loop\n    // as both of these objects enter 'hash' mode, causing\n    // the function to be de-optimised in V8\n    var qEdges = Object.keys(frame.qNode.edges),\n        qLen = qEdges.length,\n        nEdges = Object.keys(frame.node.edges),\n        nLen = nEdges.length\n\n    for (var q = 0; q < qLen; q++) {\n      var qEdge = qEdges[q]\n\n      for (var n = 0; n < nLen; n++) {\n        var nEdge = nEdges[n]\n\n        if (nEdge == qEdge || qEdge == '*') {\n          var node = frame.node.edges[nEdge],\n              qNode = frame.qNode.edges[qEdge],\n              final = node.final && qNode.final,\n              next = undefined\n\n          if (nEdge in frame.output.edges) {\n            // an edge already exists for this character\n            // no need to create a new node, just set the finality\n            // bit unless this node is already final\n            next = frame.output.edges[nEdge]\n            next.final = next.final || final\n\n          } else {\n            // no edge exists yet, must create one\n            // set the finality bit and insert it\n            // into the output\n            next = new lunr.TokenSet\n            next.final = final\n            frame.output.edges[nEdge] = next\n          }\n\n          stack.push({\n            qNode: qNode,\n            output: next,\n            node: node\n          })\n        }\n      }\n    }\n  }\n\n  return output\n}\nlunr.TokenSet.Builder = function () {\n  this.previousWord = \"\"\n  this.root = new lunr.TokenSet\n  this.uncheckedNodes = []\n  this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n  var node,\n      commonPrefix = 0\n\n  if (word < this.previousWord) {\n    throw new Error (\"Out of order word insertion\")\n  }\n\n  for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n    if (word[i] != this.previousWord[i]) break\n    commonPrefix++\n  }\n\n  this.minimize(commonPrefix)\n\n  if (this.uncheckedNodes.length == 0) {\n    node = this.root\n  } else {\n    node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n  }\n\n  for (var i = commonPrefix; i < word.length; i++) {\n    var nextNode = new lunr.TokenSet,\n        char = word[i]\n\n    node.edges[char] = nextNode\n\n    this.uncheckedNodes.push({\n      parent: node,\n      char: char,\n      child: nextNode\n    })\n\n    node = nextNode\n  }\n\n  node.final = true\n  this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n  this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n  for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n    var node = this.uncheckedNodes[i],\n        childKey = node.child.toString()\n\n    if (childKey in this.minimizedNodes) {\n      node.parent.edges[node.char] = this.minimizedNodes[childKey]\n    } else {\n      // Cache the key for this node since\n      // we know it can't change anymore\n      node.child._str = childKey\n\n      this.minimizedNodes[childKey] = node.child\n    }\n\n    this.uncheckedNodes.pop()\n  }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object<string, lunr.Vector>} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n  this.invertedIndex = attrs.invertedIndex\n  this.fieldVectors = attrs.fieldVectors\n  this.tokenSet = attrs.tokenSet\n  this.fields = attrs.fields\n  this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example <caption>Simple single term query</caption>\n * hello\n * @example <caption>Multiple term query</caption>\n * hello world\n * @example <caption>term scoped to a field</caption>\n * title:hello\n * @example <caption>term with a boost of 10</caption>\n * hello^10\n * @example <caption>term with an edit distance of 2</caption>\n * hello~2\n * @example <caption>terms with presence modifiers</caption>\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first.  For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n  return this.query(function (query) {\n    var parser = new lunr.QueryParser(queryString, query)\n    parser.parse()\n  })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n  // for each query clause\n  // * process terms\n  // * expand terms from token set\n  // * find matching documents and metadata\n  // * get document vectors\n  // * score documents\n\n  var query = new lunr.Query(this.fields),\n      matchingFields = Object.create(null),\n      queryVectors = Object.create(null),\n      termFieldCache = Object.create(null),\n      requiredMatches = Object.create(null),\n      prohibitedMatches = Object.create(null)\n\n  /*\n   * To support field level boosts a query vector is created per\n   * field. An empty vector is eagerly created to support negated\n   * queries.\n   */\n  for (var i = 0; i < this.fields.length; i++) {\n    queryVectors[this.fields[i]] = new lunr.Vector\n  }\n\n  fn.call(query, query)\n\n  for (var i = 0; i < query.clauses.length; i++) {\n    /*\n     * Unless the pipeline has been disabled for this term, which is\n     * the case for terms with wildcards, we need to pass the clause\n     * term through the search pipeline. A pipeline returns an array\n     * of processed terms. Pipeline functions may expand the passed\n     * term, which means we may end up performing multiple index lookups\n     * for a single query term.\n     */\n    var clause = query.clauses[i],\n        terms = null,\n        clauseMatches = lunr.Set.empty\n\n    if (clause.usePipeline) {\n      terms = this.pipeline.runString(clause.term, {\n        fields: clause.fields\n      })\n    } else {\n      terms = [clause.term]\n    }\n\n    for (var m = 0; m < terms.length; m++) {\n      var term = terms[m]\n\n      /*\n       * Each term returned from the pipeline needs to use the same query\n       * clause object, e.g. the same boost and or edit distance. The\n       * simplest way to do this is to re-use the clause object but mutate\n       * its term property.\n       */\n      clause.term = term\n\n      /*\n       * From the term in the clause we create a token set which will then\n       * be used to intersect the indexes token set to get a list of terms\n       * to lookup in the inverted index\n       */\n      var termTokenSet = lunr.TokenSet.fromClause(clause),\n          expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n      /*\n       * If a term marked as required does not exist in the tokenSet it is\n       * impossible for the search to return any matches. We set all the field\n       * scoped required matches set to empty and stop examining any further\n       * clauses.\n       */\n      if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n        for (var k = 0; k < clause.fields.length; k++) {\n          var field = clause.fields[k]\n          requiredMatches[field] = lunr.Set.empty\n        }\n\n        break\n      }\n\n      for (var j = 0; j < expandedTerms.length; j++) {\n        /*\n         * For each term get the posting and termIndex, this is required for\n         * building the query vector.\n         */\n        var expandedTerm = expandedTerms[j],\n            posting = this.invertedIndex[expandedTerm],\n            termIndex = posting._index\n\n        for (var k = 0; k < clause.fields.length; k++) {\n          /*\n           * For each field that this query term is scoped by (by default\n           * all fields are in scope) we need to get all the document refs\n           * that have this term in that field.\n           *\n           * The posting is the entry in the invertedIndex for the matching\n           * term from above.\n           */\n          var field = clause.fields[k],\n              fieldPosting = posting[field],\n              matchingDocumentRefs = Object.keys(fieldPosting),\n              termField = expandedTerm + \"/\" + field,\n              matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n          /*\n           * if the presence of this term is required ensure that the matching\n           * documents are added to the set of required matches for this clause.\n           *\n           */\n          if (clause.presence == lunr.Query.presence.REQUIRED) {\n            clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n            if (requiredMatches[field] === undefined) {\n              requiredMatches[field] = lunr.Set.complete\n            }\n          }\n\n          /*\n           * if the presence of this term is prohibited ensure that the matching\n           * documents are added to the set of prohibited matches for this field,\n           * creating that set if it does not yet exist.\n           */\n          if (clause.presence == lunr.Query.presence.PROHIBITED) {\n            if (prohibitedMatches[field] === undefined) {\n              prohibitedMatches[field] = lunr.Set.empty\n            }\n\n            prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n            /*\n             * Prohibited matches should not be part of the query vector used for\n             * similarity scoring and no metadata should be extracted so we continue\n             * to the next field\n             */\n            continue\n          }\n\n          /*\n           * The query field vector is populated using the termIndex found for\n           * the term and a unit value with the appropriate boost applied.\n           * Using upsert because there could already be an entry in the vector\n           * for the term we are working with. In that case we just add the scores\n           * together.\n           */\n          queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n          /**\n           * If we've already seen this term, field combo then we've already collected\n           * the matching documents and metadata, no need to go through all that again\n           */\n          if (termFieldCache[termField]) {\n            continue\n          }\n\n          for (var l = 0; l < matchingDocumentRefs.length; l++) {\n            /*\n             * All metadata for this term/field/document triple\n             * are then extracted and collected into an instance\n             * of lunr.MatchData ready to be returned in the query\n             * results\n             */\n            var matchingDocumentRef = matchingDocumentRefs[l],\n                matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n                metadata = fieldPosting[matchingDocumentRef],\n                fieldMatch\n\n            if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n              matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n            } else {\n              fieldMatch.add(expandedTerm, field, metadata)\n            }\n\n          }\n\n          termFieldCache[termField] = true\n        }\n      }\n    }\n\n    /**\n     * If the presence was required we need to update the requiredMatches field sets.\n     * We do this after all fields for the term have collected their matches because\n     * the clause terms presence is required in _any_ of the fields not _all_ of the\n     * fields.\n     */\n    if (clause.presence === lunr.Query.presence.REQUIRED) {\n      for (var k = 0; k < clause.fields.length; k++) {\n        var field = clause.fields[k]\n        requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n      }\n    }\n  }\n\n  /**\n   * Need to combine the field scoped required and prohibited\n   * matching documents into a global set of required and prohibited\n   * matches\n   */\n  var allRequiredMatches = lunr.Set.complete,\n      allProhibitedMatches = lunr.Set.empty\n\n  for (var i = 0; i < this.fields.length; i++) {\n    var field = this.fields[i]\n\n    if (requiredMatches[field]) {\n      allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n    }\n\n    if (prohibitedMatches[field]) {\n      allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n    }\n  }\n\n  var matchingFieldRefs = Object.keys(matchingFields),\n      results = [],\n      matches = Object.create(null)\n\n  /*\n   * If the query is negated (contains only prohibited terms)\n   * we need to get _all_ fieldRefs currently existing in the\n   * index. This is only done when we know that the query is\n   * entirely prohibited terms to avoid any cost of getting all\n   * fieldRefs unnecessarily.\n   *\n   * Additionally, blank MatchData must be created to correctly\n   * populate the results.\n   */\n  if (query.isNegated()) {\n    matchingFieldRefs = Object.keys(this.fieldVectors)\n\n    for (var i = 0; i < matchingFieldRefs.length; i++) {\n      var matchingFieldRef = matchingFieldRefs[i]\n      var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n      matchingFields[matchingFieldRef] = new lunr.MatchData\n    }\n  }\n\n  for (var i = 0; i < matchingFieldRefs.length; i++) {\n    /*\n     * Currently we have document fields that match the query, but we\n     * need to return documents. The matchData and scores are combined\n     * from multiple fields belonging to the same document.\n     *\n     * Scores are calculated by field, using the query vectors created\n     * above, and combined into a final document score using addition.\n     */\n    var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n        docRef = fieldRef.docRef\n\n    if (!allRequiredMatches.contains(docRef)) {\n      continue\n    }\n\n    if (allProhibitedMatches.contains(docRef)) {\n      continue\n    }\n\n    var fieldVector = this.fieldVectors[fieldRef],\n        score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n        docMatch\n\n    if ((docMatch = matches[docRef]) !== undefined) {\n      docMatch.score += score\n      docMatch.matchData.combine(matchingFields[fieldRef])\n    } else {\n      var match = {\n        ref: docRef,\n        score: score,\n        matchData: matchingFields[fieldRef]\n      }\n      matches[docRef] = match\n      results.push(match)\n    }\n  }\n\n  /*\n   * Sort the results objects by score, highest first.\n   */\n  return results.sort(function (a, b) {\n    return b.score - a.score\n  })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n  var invertedIndex = Object.keys(this.invertedIndex)\n    .sort()\n    .map(function (term) {\n      return [term, this.invertedIndex[term]]\n    }, this)\n\n  var fieldVectors = Object.keys(this.fieldVectors)\n    .map(function (ref) {\n      return [ref, this.fieldVectors[ref].toJSON()]\n    }, this)\n\n  return {\n    version: lunr.version,\n    fields: this.fields,\n    fieldVectors: fieldVectors,\n    invertedIndex: invertedIndex,\n    pipeline: this.pipeline.toJSON()\n  }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n  var attrs = {},\n      fieldVectors = {},\n      serializedVectors = serializedIndex.fieldVectors,\n      invertedIndex = Object.create(null),\n      serializedInvertedIndex = serializedIndex.invertedIndex,\n      tokenSetBuilder = new lunr.TokenSet.Builder,\n      pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n  if (serializedIndex.version != lunr.version) {\n    lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n  }\n\n  for (var i = 0; i < serializedVectors.length; i++) {\n    var tuple = serializedVectors[i],\n        ref = tuple[0],\n        elements = tuple[1]\n\n    fieldVectors[ref] = new lunr.Vector(elements)\n  }\n\n  for (var i = 0; i < serializedInvertedIndex.length; i++) {\n    var tuple = serializedInvertedIndex[i],\n        term = tuple[0],\n        posting = tuple[1]\n\n    tokenSetBuilder.insert(term)\n    invertedIndex[term] = posting\n  }\n\n  tokenSetBuilder.finish()\n\n  attrs.fields = serializedIndex.fields\n\n  attrs.fieldVectors = fieldVectors\n  attrs.invertedIndex = invertedIndex\n  attrs.tokenSet = tokenSetBuilder.root\n  attrs.pipeline = pipeline\n\n  return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n  this._ref = \"id\"\n  this._fields = Object.create(null)\n  this._documents = Object.create(null)\n  this.invertedIndex = Object.create(null)\n  this.fieldTermFrequencies = {}\n  this.fieldLengths = {}\n  this.tokenizer = lunr.tokenizer\n  this.pipeline = new lunr.Pipeline\n  this.searchPipeline = new lunr.Pipeline\n  this.documentCount = 0\n  this._b = 0.75\n  this._k1 = 1.2\n  this.termIndex = 0\n  this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n  this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example <caption>Extracting a nested field</caption>\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n  if (/\\//.test(fieldName)) {\n    throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n  }\n\n  this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n  if (number < 0) {\n    this._b = 0\n  } else if (number > 1) {\n    this._b = 1\n  } else {\n    this._b = number\n  }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n  this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n  var docRef = doc[this._ref],\n      fields = Object.keys(this._fields)\n\n  this._documents[docRef] = attributes || {}\n  this.documentCount += 1\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i],\n        extractor = this._fields[fieldName].extractor,\n        field = extractor ? extractor(doc) : doc[fieldName],\n        tokens = this.tokenizer(field, {\n          fields: [fieldName]\n        }),\n        terms = this.pipeline.run(tokens),\n        fieldRef = new lunr.FieldRef (docRef, fieldName),\n        fieldTerms = Object.create(null)\n\n    this.fieldTermFrequencies[fieldRef] = fieldTerms\n    this.fieldLengths[fieldRef] = 0\n\n    // store the length of this field for this document\n    this.fieldLengths[fieldRef] += terms.length\n\n    // calculate term frequencies for this field\n    for (var j = 0; j < terms.length; j++) {\n      var term = terms[j]\n\n      if (fieldTerms[term] == undefined) {\n        fieldTerms[term] = 0\n      }\n\n      fieldTerms[term] += 1\n\n      // add to inverted index\n      // create an initial posting if one doesn't exist\n      if (this.invertedIndex[term] == undefined) {\n        var posting = Object.create(null)\n        posting[\"_index\"] = this.termIndex\n        this.termIndex += 1\n\n        for (var k = 0; k < fields.length; k++) {\n          posting[fields[k]] = Object.create(null)\n        }\n\n        this.invertedIndex[term] = posting\n      }\n\n      // add an entry for this term/fieldName/docRef to the invertedIndex\n      if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n        this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n      }\n\n      // store all whitelisted metadata about this token in the\n      // inverted index\n      for (var l = 0; l < this.metadataWhitelist.length; l++) {\n        var metadataKey = this.metadataWhitelist[l],\n            metadata = term.metadata[metadataKey]\n\n        if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n          this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n        }\n\n        this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n      }\n    }\n\n  }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n  var fieldRefs = Object.keys(this.fieldLengths),\n      numberOfFields = fieldRefs.length,\n      accumulator = {},\n      documentsWithField = {}\n\n  for (var i = 0; i < numberOfFields; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        field = fieldRef.fieldName\n\n    documentsWithField[field] || (documentsWithField[field] = 0)\n    documentsWithField[field] += 1\n\n    accumulator[field] || (accumulator[field] = 0)\n    accumulator[field] += this.fieldLengths[fieldRef]\n  }\n\n  var fields = Object.keys(this._fields)\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i]\n    accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n  }\n\n  this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n  var fieldVectors = {},\n      fieldRefs = Object.keys(this.fieldTermFrequencies),\n      fieldRefsLength = fieldRefs.length,\n      termIdfCache = Object.create(null)\n\n  for (var i = 0; i < fieldRefsLength; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        fieldName = fieldRef.fieldName,\n        fieldLength = this.fieldLengths[fieldRef],\n        fieldVector = new lunr.Vector,\n        termFrequencies = this.fieldTermFrequencies[fieldRef],\n        terms = Object.keys(termFrequencies),\n        termsLength = terms.length\n\n\n    var fieldBoost = this._fields[fieldName].boost || 1,\n        docBoost = this._documents[fieldRef.docRef].boost || 1\n\n    for (var j = 0; j < termsLength; j++) {\n      var term = terms[j],\n          tf = termFrequencies[term],\n          termIndex = this.invertedIndex[term]._index,\n          idf, score, scoreWithPrecision\n\n      if (termIdfCache[term] === undefined) {\n        idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n        termIdfCache[term] = idf\n      } else {\n        idf = termIdfCache[term]\n      }\n\n      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n      score *= fieldBoost\n      score *= docBoost\n      scoreWithPrecision = Math.round(score * 1000) / 1000\n      // Converts 1.23456789 to 1.234.\n      // Reducing the precision so that the vectors take up less\n      // space when serialised. Doing it now so that they behave\n      // the same before and after serialisation. Also, this is\n      // the fastest approach to reducing a number's precision in\n      // JavaScript.\n\n      fieldVector.insert(termIndex, scoreWithPrecision)\n    }\n\n    fieldVectors[fieldRef] = fieldVector\n  }\n\n  this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n  this.tokenSet = lunr.TokenSet.fromArray(\n    Object.keys(this.invertedIndex).sort()\n  )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n  this.calculateAverageFieldLengths()\n  this.createFieldVectors()\n  this.createTokenSet()\n\n  return new lunr.Index({\n    invertedIndex: this.invertedIndex,\n    fieldVectors: this.fieldVectors,\n    tokenSet: this.tokenSet,\n    fields: Object.keys(this._fields),\n    pipeline: this.searchPipeline\n  })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n  var args = Array.prototype.slice.call(arguments, 1)\n  args.unshift(this)\n  fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n  var clonedMetadata = Object.create(null),\n      metadataKeys = Object.keys(metadata || {})\n\n  // Cloning the metadata to prevent the original\n  // being mutated during match data combination.\n  // Metadata is kept in an array within the inverted\n  // index so cloning the data can be done with\n  // Array#slice\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n    clonedMetadata[key] = metadata[key].slice()\n  }\n\n  this.metadata = Object.create(null)\n\n  if (term !== undefined) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = clonedMetadata\n  }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n  var terms = Object.keys(otherMatchData.metadata)\n\n  for (var i = 0; i < terms.length; i++) {\n    var term = terms[i],\n        fields = Object.keys(otherMatchData.metadata[term])\n\n    if (this.metadata[term] == undefined) {\n      this.metadata[term] = Object.create(null)\n    }\n\n    for (var j = 0; j < fields.length; j++) {\n      var field = fields[j],\n          keys = Object.keys(otherMatchData.metadata[term][field])\n\n      if (this.metadata[term][field] == undefined) {\n        this.metadata[term][field] = Object.create(null)\n      }\n\n      for (var k = 0; k < keys.length; k++) {\n        var key = keys[k]\n\n        if (this.metadata[term][field][key] == undefined) {\n          this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n        } else {\n          this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n        }\n\n      }\n    }\n  }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n  if (!(term in this.metadata)) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  if (!(field in this.metadata[term])) {\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  var metadataKeys = Object.keys(metadata)\n\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n\n    if (key in this.metadata[term][field]) {\n      this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n    } else {\n      this.metadata[term][field][key] = metadata[key]\n    }\n  }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n  this.clauses = []\n  this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with trailing wildcard</caption>\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example <caption>query term with leading and trailing wildcard</caption>\n * query.term('foo', {\n *   wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with required presence</caption>\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n  /**\n   * Term's presence in a document is optional, this is the default value.\n   */\n  OPTIONAL: 1,\n\n  /**\n   * Term's presence in a document is required, documents that do not contain\n   * this term will not be returned.\n   */\n  REQUIRED: 2,\n\n  /**\n   * Term's presence in a document is prohibited, documents that do contain\n   * this term will not be returned.\n   */\n  PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n  if (!('fields' in clause)) {\n    clause.fields = this.allFields\n  }\n\n  if (!('boost' in clause)) {\n    clause.boost = 1\n  }\n\n  if (!('usePipeline' in clause)) {\n    clause.usePipeline = true\n  }\n\n  if (!('wildcard' in clause)) {\n    clause.wildcard = lunr.Query.wildcard.NONE\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n    clause.term = \"*\" + clause.term\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n    clause.term = \"\" + clause.term + \"*\"\n  }\n\n  if (!('presence' in clause)) {\n    clause.presence = lunr.Query.presence.OPTIONAL\n  }\n\n  this.clauses.push(clause)\n\n  return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n  for (var i = 0; i < this.clauses.length; i++) {\n    if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n      return false\n    }\n  }\n\n  return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example <caption>adding a single term to a query</caption>\n * query.term(\"foo\")\n * @example <caption>adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard</caption>\n * query.term(\"foo\", {\n *   fields: [\"title\"],\n *   boost: 10,\n *   wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example <caption>using lunr.tokenizer to convert a string to tokens before using them as terms</caption>\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n  if (Array.isArray(term)) {\n    term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n    return this\n  }\n\n  var clause = options || {}\n  clause.term = term.toString()\n\n  this.clause(clause)\n\n  return this\n}\nlunr.QueryParseError = function (message, start, end) {\n  this.name = \"QueryParseError\"\n  this.message = message\n  this.start = start\n  this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n  this.lexemes = []\n  this.str = str\n  this.length = str.length\n  this.pos = 0\n  this.start = 0\n  this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n  var state = lunr.QueryLexer.lexText\n\n  while (state) {\n    state = state(this)\n  }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n  var subSlices = [],\n      sliceStart = this.start,\n      sliceEnd = this.pos\n\n  for (var i = 0; i < this.escapeCharPositions.length; i++) {\n    sliceEnd = this.escapeCharPositions[i]\n    subSlices.push(this.str.slice(sliceStart, sliceEnd))\n    sliceStart = sliceEnd + 1\n  }\n\n  subSlices.push(this.str.slice(sliceStart, this.pos))\n  this.escapeCharPositions.length = 0\n\n  return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n  this.lexemes.push({\n    type: type,\n    str: this.sliceString(),\n    start: this.start,\n    end: this.pos\n  })\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n  this.escapeCharPositions.push(this.pos - 1)\n  this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n  if (this.pos >= this.length) {\n    return lunr.QueryLexer.EOS\n  }\n\n  var char = this.str.charAt(this.pos)\n  this.pos += 1\n  return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n  return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n  if (this.start == this.pos) {\n    this.pos += 1\n  }\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n  this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n  var char, charCode\n\n  do {\n    char = this.next()\n    charCode = char.charCodeAt(0)\n  } while (charCode > 47 && charCode < 58)\n\n  if (char != lunr.QueryLexer.EOS) {\n    this.backup()\n  }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n  return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n  lexer.backup()\n  lexer.emit(lunr.QueryLexer.FIELD)\n  lexer.ignore()\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n  if (lexer.width() > 1) {\n    lexer.backup()\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n\n  lexer.ignore()\n\n  if (lexer.more()) {\n    return lunr.QueryLexer.lexText\n  }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.BOOST)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n  if (lexer.width() > 0) {\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n  while (true) {\n    var char = lexer.next()\n\n    if (char == lunr.QueryLexer.EOS) {\n      return lunr.QueryLexer.lexEOS\n    }\n\n    // Escape character is '\\'\n    if (char.charCodeAt(0) == 92) {\n      lexer.escapeCharacter()\n      continue\n    }\n\n    if (char == \":\") {\n      return lunr.QueryLexer.lexField\n    }\n\n    if (char == \"~\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexEditDistance\n    }\n\n    if (char == \"^\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexBoost\n    }\n\n    // \"+\" indicates term presence is required\n    // checking for length to ensure that only\n    // leading \"+\" are considered\n    if (char == \"+\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    // \"-\" indicates term presence is prohibited\n    // checking for length to ensure that only\n    // leading \"-\" are considered\n    if (char == \"-\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    if (char.match(lunr.QueryLexer.termSeparator)) {\n      return lunr.QueryLexer.lexTerm\n    }\n  }\n}\n\nlunr.QueryParser = function (str, query) {\n  this.lexer = new lunr.QueryLexer (str)\n  this.query = query\n  this.currentClause = {}\n  this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n  this.lexer.run()\n  this.lexemes = this.lexer.lexemes\n\n  var state = lunr.QueryParser.parseClause\n\n  while (state) {\n    state = state(this)\n  }\n\n  return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n  return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n  var lexeme = this.peekLexeme()\n  this.lexemeIdx += 1\n  return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n  var completedClause = this.currentClause\n  this.query.clause(completedClause)\n  this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n  var lexeme = parser.peekLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.type) {\n    case lunr.QueryLexer.PRESENCE:\n      return lunr.QueryParser.parsePresence\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n      if (lexeme.str.length >= 1) {\n        errorMessage += \" with value '\" + lexeme.str + \"'\"\n      }\n\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.str) {\n    case \"-\":\n      parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n      break\n    case \"+\":\n      parser.currentClause.presence = lunr.Query.presence.REQUIRED\n      break\n    default:\n      var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term or field, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n    var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n        errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.fields = [lexeme.str]\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  parser.currentClause.term = lexeme.str.toLowerCase()\n\n  if (lexeme.str.indexOf(\"*\") != -1) {\n    parser.currentClause.usePipeline = false\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var editDistance = parseInt(lexeme.str, 10)\n\n  if (isNaN(editDistance)) {\n    var errorMessage = \"edit distance must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.editDistance = editDistance\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var boost = parseInt(lexeme.str, 10)\n\n  if (isNaN(boost)) {\n    var errorMessage = \"boost must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.boost = boost\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\n  /**\n   * export the module via AMD, CommonJS or as a browser global\n   * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n   */\n  ;(function (root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      // AMD. Register as an anonymous module.\n      define(factory)\n    } else if (typeof exports === 'object') {\n      /**\n       * Node. Does not work with strict CommonJS, but\n       * only CommonJS-like enviroments that support module.exports,\n       * like Node.\n       */\n      module.exports = factory()\n    } else {\n      // Browser globals (root is window)\n      root.lunr = factory()\n    }\n  }(this, function () {\n    /**\n     * Just return a value to define the module export.\n     * This example returns an object, but the module\n     * can return a function as the exported value.\n     */\n    return lunr\n  }))\n})();\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport { getElement } from \"~/browser/element/_\"\nimport \"~/polyfills\"\n\nimport { Search } from \"../../_\"\nimport { SearchConfig } from \"../../config\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for `iframe-worker` shim\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous shim implementation. The cool\n * thing is that awaiting a non-Promise will convert it into a Promise, so\n * extending the type definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n *\n * @param urls - Scripts to load\n *\n * @returns Promise resolving with no result\n */\ndeclare global {\n  function importScripts(...urls: string[]): Promise<void> | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages which are defined as part of the search configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n  config: SearchConfig\n): Promise<void> {\n  let base = \"../lunr\"\n\n  /* Detect `iframe-worker` and fix base URL */\n  if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n    const worker = getElement<HTMLScriptElement>(\"script[src]\")!\n    const [path] = worker.src.split(\"/worker\")\n\n    /* Prefix base with path */\n    base = base.replace(\"..\", path)\n  }\n\n  /* Add scripts for languages */\n  const scripts = []\n  for (const lang of config.lang) {\n    switch (lang) {\n\n      /* Add segmenter for Japanese */\n      case \"ja\":\n        scripts.push(`${base}/tinyseg.js`)\n        break\n\n      /* Add segmenter for Hindi and Thai */\n      case \"hi\":\n      case \"th\":\n        scripts.push(`${base}/wordcut.js`)\n        break\n    }\n\n    /* Add language support */\n    if (lang !== \"en\")\n      scripts.push(`${base}/min/lunr.${lang}.min.js`)\n  }\n\n  /* Add multi-language support */\n  if (config.lang.length > 1)\n    scripts.push(`${base}/min/lunr.multi.min.js`)\n\n  /* Load scripts synchronously */\n  if (scripts.length)\n    await importScripts(\n      `${base}/min/lunr.stemmer.support.min.js`,\n      ...scripts\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n  message: SearchMessage\n): Promise<SearchMessage> {\n  switch (message.type) {\n\n    /* Search setup message */\n    case SearchMessageType.SETUP:\n      await setupSearchLanguages(message.data.config)\n      index = new Search(message.data)\n      return {\n        type: SearchMessageType.READY\n      }\n\n    /* Search query message */\n    case SearchMessageType.QUERY:\n      const query = message.data\n      try {\n        return {\n          type: SearchMessageType.RESULT,\n          data: index.search(query)\n        }\n\n      /* Return empty result in case of error */\n      } catch (err) {\n        console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n        console.warn(err)\n        return {\n          type: SearchMessageType.RESULT,\n          data: { items: [] }\n        }\n      }\n\n    /* All other messages */\n    default:\n      throw new TypeError(\"Invalid message type\")\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* Expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n  postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return (\n    document.activeElement?.shadowRoot?.activeElement as HTMLElement ??\n    document.activeElement as HTMLElement ??\n    undefined\n  )\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search configuration\n */\nexport interface SearchConfig {\n  lang: string[]                       /* Search languages */\n  separator: string                    /* Search separator */\n  pipeline: SearchPipelineFn[]         /* Search pipeline */\n}\n\n/**\n * Search document\n */\nexport interface SearchDocument {\n  location: string                     /* Document location */\n  title: string                        /* Document title */\n  text: string                         /* Document text */\n  tags?: string[]                      /* Document tags */\n  boost?: number                       /* Document boost */\n  parent?: SearchDocument              /* Document parent */\n}\n\n/**\n * Search options\n */\nexport interface SearchOptions {\n  suggest: boolean                     /* Search suggestions */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport interface SearchIndex {\n  config: SearchConfig                 /* Search configuration */\n  docs: SearchDocument[]               /* Search documents */\n  options: SearchOptions               /* Search options */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search pipeline function\n */\ntype SearchPipelineFn =\n  | \"trimmer\"                          /* Trimmer */\n  | \"stopWordFilter\"                   /* Stop word filter */\n  | \"stemmer\"                          /* Stemmer */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document map\n *\n * This function creates a mapping of URLs (including anchors) to the actual\n * articles and sections. It relies on the invariant that the search index is\n * ordered with the main article appearing before all sections with anchors.\n * If this is not the case, the logic music be changed.\n *\n * @param docs - Search documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchDocument[]\n): Map<string, SearchDocument> {\n  const map = new Map<string, SearchDocument>()\n  for (const doc of docs) {\n    const [path] = doc.location.split(\"#\")\n\n    /* Add document article */\n    const article = map.get(path)\n    if (typeof article === \"undefined\") {\n      map.set(path, doc)\n\n      /* Add document section */\n    } else {\n      map.set(doc.location, doc)\n      doc.parent = article\n    }\n  }\n\n  /* Return search document map */\n  return map\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n  start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string using the given separator\n *\n * @param input - Input value\n * @param separator - Separator\n * @param fn - Visitor function\n */\nexport function split(\n  input: string, separator: RegExp, fn: VisitorFn\n): void {\n  separator = new RegExp(separator, \"g\")\n\n  /* Split string using separator */\n  let match: RegExpExecArray | null\n  let index = 0\n  do {\n    match = separator.exec(input)\n\n    /* Emit non-empty range */\n    const until = match?.index ?? input.length\n    if (index < until)\n      fn(index, until)\n\n    /* Update last index */\n    if (match) {\n      const [term] = match\n      index = match.index + term.length\n\n      /* Support zero-length lookaheads */\n      if (term.length === 0)\n        separator.lastIndex = match.index + 1\n    }\n  } while (match)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Extraction type\n *\n * This type defines the possible values that are encoded into the first two\n * bits of a section that is part of the blocks of a tokenization table. There\n * are three types of interest: HTML opening and closing tags, as well as the\n * actual text content we need to extract for indexing.\n */\nexport const enum Extract {\n  TAG_OPEN  = 0,                       /* HTML opening tag */\n  TEXT      = 1,                       /* Text content */\n  TAG_CLOSE = 2                        /* HTML closing tag */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param block - Block index\n * @param type - Extraction type\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n  block: number, type: Extract, start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string into markup and text sections\n *\n * This function scans a string and divides it up into sections of markup and\n * text. For each section, it invokes the given visitor function with the block\n * index, extraction type, as well as start and end offsets. Using a visitor\n * function (= streaming data) is ideal for minimizing pressure on the GC.\n *\n * @param input - Input value\n * @param fn - Visitor function\n */\nexport function extract(\n  input: string, fn: VisitorFn\n): void {\n\n  let block = 0                        /* Current block */\n  let start = 0                        /* Current start offset */\n  let end = 0                          /* Current end offset */\n\n  /* Split string into sections */\n  for (let stack = 0; end < input.length; end++) {\n\n    /* Opening tag after non-empty section */\n    if (input.charAt(end) === \"<\" && end > start) {\n      fn(block, Extract.TEXT, start, start = end)\n\n    /* Closing tag */\n    } else if (input.charAt(end) === \">\") {\n      if (input.charAt(start + 1) === \"/\") {\n        if (--stack === 0)\n          fn(block++, Extract.TAG_CLOSE, start, end + 1)\n\n      /* Tag is not self-closing */\n      } else if (input.charAt(end - 1) !== \"/\") {\n        if (stack++ === 0)\n          fn(block, Extract.TAG_OPEN, start, end + 1)\n      }\n\n      /* New section */\n      start = end + 1\n    }\n  }\n\n  /* Add trailing section */\n  if (end > start)\n    fn(block, Extract.TEXT, start, end)\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Position table\n */\nexport type PositionTable = number[][]\n\n/**\n * Position\n */\nexport type Position = number\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Highlight all occurrences in a string\n *\n * This function receives a field's value (e.g. like `title` or `text`), it's\n * position table that was generated during indexing, and the positions found\n * when executing the query. It then highlights all occurrences, and returns\n * their concatenation. In case of multiple blocks, two are returned.\n *\n * @param input - Input value\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string value\n */\nexport function highlight(\n  input: string, table: PositionTable, positions: Position[], full = false\n): string {\n  return highlightAll([input], table, positions, full).pop()!\n}\n\n/**\n * Highlight all occurrences in a set of strings\n *\n * @param inputs - Input values\n * @param table - Table for indexing\n * @param positions - Occurrences\n * @param full - Full results\n *\n * @returns Highlighted string values\n */\nexport function highlightAll(\n  inputs: string[], table: PositionTable, positions: Position[], full = false\n): string[] {\n\n  /* Map blocks to input values */\n  const mapping = [0]\n  for (let t = 1; t < table.length; t++) {\n    const prev = table[t - 1]\n    const next = table[t]\n\n    /* Check if table points to new block */\n    const p = prev[prev.length - 1] >>> 2 & 0x3FF\n    const q = next[0]               >>> 12\n\n    /* Add block to mapping */\n    mapping.push(+(p > q) + mapping[mapping.length - 1])\n  }\n\n  /* Highlight strings one after another */\n  return inputs.map((input, i) => {\n    let cursor = 0\n\n    /* Map occurrences to blocks */\n    const blocks = new Map<number, number[]>()\n    for (const p of positions.sort((a, b) => a - b)) {\n      const index = p & 0xFFFFF\n      const block = p >>> 20\n      if (mapping[block] !== i)\n        continue\n\n      /* Ensure presence of block group */\n      let group = blocks.get(block)\n      if (typeof group === \"undefined\")\n        blocks.set(block, group = [])\n\n      /* Add index to group */\n      group.push(index)\n    }\n\n    /* Just return string, if no occurrences */\n    if (blocks.size === 0)\n      return input\n\n    /* Compute slices */\n    const slices: string[] = []\n    for (const [block, indexes] of blocks) {\n      const t = table[block]\n\n      /* Extract positions and length */\n      const start  = t[0]            >>> 12\n      const end    = t[t.length - 1] >>> 12\n      const length = t[t.length - 1] >>> 2 & 0x3FF\n\n      /* Add prefix, if full results are desired */\n      if (full && start > cursor)\n        slices.push(input.slice(cursor, start))\n\n      /* Extract and highlight slice */\n      let slice = input.slice(start, end + length)\n      for (const j of indexes.sort((a, b) => b - a)) {\n\n        /* Retrieve offset and length of match */\n        const p = (t[j] >>> 12) - start\n        const q = (t[j] >>> 2 & 0x3FF) + p\n\n        /* Wrap occurrence */\n        slice = [\n          slice.slice(0, p),\n          \"<mark>\",\n          slice.slice(p, q),\n          \"</mark>\",\n          slice.slice(q)\n        ].join(\"\")\n      }\n\n      /* Update cursor */\n      cursor = end + length\n\n      /* Append slice and abort if we have two */\n      if (slices.push(slice) === 2)\n        break\n    }\n\n    /* Add suffix, if full results are desired */\n    if (full && cursor < input.length)\n      slices.push(input.slice(cursor))\n\n    /* Return highlighted slices */\n    return slices.join(\"\")\n  })\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../_\"\nimport {\n  Extract,\n  extract\n} from \"../extract\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string or set of strings into tokens\n *\n * This tokenizer supersedes the default tokenizer that is provided by Lunr.js,\n * as it is aware of HTML tags and allows for multi-character splitting.\n *\n * It takes the given inputs, splits each of them into markup and text sections,\n * tokenizes and segments (if necessary) each of them, and then indexes them in\n * a table by using a compact bit representation. Bitwise techniques are used\n * to write and read from the table during indexing and querying.\n *\n * @see https://bit.ly/3W3Xw4J - Search: better, faster, smaller\n *\n * @param input - Input value(s)\n *\n * @returns Tokens\n */\nexport function tokenize(\n  input?: string | string[]\n): lunr.Token[] {\n  const tokens: lunr.Token[] = []\n  if (typeof input === \"undefined\")\n    return tokens\n\n  /* Tokenize strings one after another */\n  const inputs = Array.isArray(input) ? input : [input]\n  for (let i = 0; i < inputs.length; i++) {\n    const table = lunr.tokenizer.table\n    const total = table.length\n\n    /* Split string into sections and tokenize content blocks */\n    extract(inputs[i], (block, type, start, end) => {\n      table[block += total] ||= []\n      switch (type) {\n\n        /* Handle markup */\n        case Extract.TAG_OPEN:\n        case Extract.TAG_CLOSE:\n          table[block].push(\n            start       << 12 |\n            end - start <<  2 |\n            type\n          )\n          break\n\n        /* Handle text content */\n        case Extract.TEXT:\n          const section = inputs[i].slice(start, end)\n          split(section, lunr.tokenizer.separator, (index, until) => {\n\n            /**\n             * Apply segmenter after tokenization. Note that the segmenter will\n             * also split words at word boundaries, which is not what we want,\n             * so we need to check if we can somehow mitigate this behavior.\n             */\n            if (typeof lunr.segmenter !== \"undefined\") {\n              const subsection = section.slice(index, until)\n              if (/^[MHIK]$/.test(lunr.segmenter.ctype_(subsection))) {\n                const segments = lunr.segmenter.segment(subsection)\n                for (let s = 0, l = 0; s < segments.length; s++) {\n\n                  /* Add block to section */\n                  table[block] ||= []\n                  table[block].push(\n                    start + index + l  << 12 |\n                    segments[s].length <<  2 |\n                    type\n                  )\n\n                  /* Add token with position */\n                  tokens.push(new lunr.Token(\n                    segments[s].toLowerCase(), {\n                      position: block << 20 | table[block].length - 1\n                    }\n                  ))\n\n                  /* Keep track of length */\n                  l += segments[s].length\n                }\n                return\n              }\n            }\n\n            /* Add block to section */\n            table[block].push(\n              start + index << 12 |\n              until - index <<  2 |\n              type\n            )\n\n            /* Add token with position */\n            tokens.push(new lunr.Token(\n              section.slice(index, until).toLowerCase(), {\n                position: block << 20 | table[block].length - 1\n              }\n            ))\n          })\n      }\n    })\n  }\n\n  /* Return tokens */\n  return tokens\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param value - String value\n *\n * @returns String term(s)\n */\ntype VisitorFn = (\n  value: string\n) => string | string[]\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Trim excess whitespace from left and right.\n *\n * 2. Search for parts in quotation marks and prepend a `+` modifier to denote\n *    that the resulting document must contain all parts, converting the query\n *    to an `AND` query (as opposed to the default `OR` behavior). While users\n *    may expect parts enclosed in quotation marks to map to span queries, i.e.\n *    for which order is important, Lunr.js doesn't support them, so the best\n *    we can do is to convert the parts to an `AND` query.\n *\n * 3. Replace control characters which are not located at the beginning of the\n *    query or preceded by white space, or are not followed by a non-whitespace\n *    character or are at the end of the query string. Furthermore, filter\n *    unmatched quotation marks.\n *\n * 4. Split the query string at whitespace, then pass each part to the visitor\n *    function for tokenization, and append a wildcard to every resulting term\n *    that is not explicitly marked with a `+`, `-`, `~` or `^` modifier, since\n *    it ensures consistent and stable ranking when multiple terms are entered.\n *    Also, if a fuzzy or boost modifier are given, but no numeric value has\n *    been entered, default to 1 to not induce a query error.\n *\n * @param query - Query value\n * @param fn - Visitor function\n *\n * @returns Transformed query value\n */\nexport function transform(\n  query: string, fn: VisitorFn = term => term\n): string {\n  return query\n\n    /* => 1 */\n    .trim()\n\n    /* => 2 */\n    .split(/\"([^\"]+)\"/g)\n      .map((parts, index) => index & 1\n        ? parts.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n        : parts\n      )\n      .join(\"\")\n\n    /* => 3 */\n    .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\")\n\n    /* => 4 */\n    .split(/\\s+/g)\n      .reduce((prev, term) => {\n        const next = fn(term)\n        return [...prev, ...Array.isArray(next) ? next : [next]]\n      }, [] as string[])\n      .map(term => /([~^]$)/.test(term) ? `${term}1` : term)\n      .map(term => /(^[+-]|[~^]\\d+$)/.test(term) ? term : `${term}*`)\n      .join(\" \")\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../../internal\"\nimport { transform } from \"../transform\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n  presence: lunr.Query.presence        /* Clause presence */\n  term: string                         /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record<string, boolean>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Transform search query\n *\n * This function lexes the given search query and applies the transformation\n * function to each term, preserving markup like `+` and `-` modifiers.\n *\n * @param query - Search query\n *\n * @returns Search query\n */\nexport function transformSearchQuery(\n  query: string\n): string {\n\n  /* Split query terms with tokenizer */\n  return transform(query, part => {\n    const terms: string[] = []\n\n    /* Initialize lexer and analyze part */\n    const lexer = new lunr.QueryLexer(part)\n    lexer.run()\n\n    /* Extract and tokenize term from lexeme */\n    for (const { type, str: term, start, end } of lexer.lexemes)\n      switch (type) {\n\n        /* Hack: remove colon - see https://bit.ly/3wD3T3I */\n        case \"FIELD\":\n          if (![\"title\", \"text\", \"tags\"].includes(term))\n            part = [\n              part.slice(0, end),\n              \" \",\n              part.slice(end + 1)\n            ].join(\"\")\n          break\n\n        /* Tokenize term */\n        case \"TERM\":\n          split(term, lunr.tokenizer.separator, (...range) => {\n            terms.push([\n              part.slice(0, start),\n              term.slice(...range),\n              part.slice(end)\n            ].join(\"\"))\n          })\n      }\n\n    /* Return terms */\n    return terms\n  })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * Lunr.js itself has a bug where it doesn't detect or remove wildcards for\n * query clauses, so we must do this here.\n *\n * @see https://bit.ly/3DpTGtz - GitHub issue\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n  value: string\n): SearchQueryClause[] {\n  const query  = new lunr.Query([\"title\", \"text\", \"tags\"])\n  const parser = new lunr.QueryParser(value, query)\n\n  /* Parse Search query */\n  parser.parse()\n  for (const clause of query.clauses) {\n    clause.usePipeline = true\n\n    /* Handle leading wildcard */\n    if (clause.term.startsWith(\"*\")) {\n      clause.wildcard = lunr.Query.wildcard.LEADING\n      clause.term = clause.term.slice(1)\n    }\n\n    /* Handle trailing wildcard */\n    if (clause.term.endsWith(\"*\")) {\n      clause.wildcard = lunr.Query.wildcard.TRAILING\n      clause.term = clause.term.slice(0, -1)\n    }\n  }\n\n  /* Return query clauses */\n  return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n  query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n  const clauses = new Set<SearchQueryClause>(query)\n\n  /* Match query clauses against terms */\n  const result: SearchQueryTerms = {}\n  for (let t = 0; t < terms.length; t++)\n    for (const clause of clauses)\n      if (terms[t].startsWith(clause.term)) {\n        result[clause.term] = true\n        clauses.delete(clause)\n      }\n\n  /* Annotate unmatched non-stopword query clauses */\n  for (const clause of clauses)\n    if (lunr.stopWordFilter?.(clause.term))\n      result[clause.term] = false\n\n  /* Return query terms */\n  return result\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Segment a search query using the inverted index\n *\n * This function implements a clever approach to text segmentation for Asian\n * languages, as it used the information already available in the search index.\n * The idea is to greedily segment the search query based on the tokens that are\n * already part of the index, as described in the linked issue.\n *\n * @see https://bit.ly/3lwjrk7 - GitHub issue\n *\n * @param query - Query value\n * @param index - Inverted index\n *\n * @returns Segmented query value\n */\nexport function segment(\n  query: string, index: object\n): Iterable<string> {\n  const segments = new Set<string>()\n\n  /* Segment search query */\n  const wordcuts = new Uint16Array(query.length)\n  for (let i = 0; i < query.length; i++)\n    for (let j = i + 1; j < query.length; j++) {\n      const value = query.slice(i, j)\n      if (value in index)\n        wordcuts[i] = j - i\n    }\n\n  /* Compute longest matches with minimum overlap */\n  const stack = [0]\n  for (let s = stack.length; s > 0;) {\n    const p = stack[--s]\n    for (let q = 1; q < wordcuts[p]; q++)\n      if (wordcuts[p + q] > wordcuts[p] - q) {\n        segments.add(query.slice(p, p + q))\n        stack[s++] = p + q\n      }\n\n    /* Continue at end of query string */\n    const q = p + wordcuts[p]\n    if (wordcuts[q] && q < query.length - 1)\n      stack[s++] = q\n\n    /* Add current segment */\n    segments.add(query.slice(p, q))\n  }\n\n  // @todo fix this case in the code block above, this is a hotfix\n  if (segments.has(\"\"))\n    return new Set([query])\n\n  /* Return segmented query value */\n  return segments\n}\n", "/*\n * Copyright (c) 2016-2024 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  SearchDocument,\n  SearchIndex,\n  SearchOptions,\n  setupSearchDocumentMap\n} from \"../config\"\nimport {\n  Position,\n  PositionTable,\n  highlight,\n  highlightAll,\n  tokenize\n} from \"../internal\"\nimport {\n  SearchQueryTerms,\n  getSearchQueryTerms,\n  parseSearchQuery,\n  segment,\n  transformSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search item\n */\nexport interface SearchItem\n  extends SearchDocument\n{\n  score: number                        /* Score (relevance) */\n  terms: SearchQueryTerms              /* Search query terms */\n}\n\n/**\n * Search result\n */\nexport interface SearchResult {\n  items: SearchItem[][]                /* Search items */\n  suggest?: string[]                   /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create field extractor factory\n *\n * @param table - Position table map\n *\n * @returns Extractor factory\n */\nfunction extractor(table: Map<string, PositionTable>) {\n  return (name: keyof SearchDocument) => {\n    return (doc: SearchDocument) => {\n      if (typeof doc[name] === \"undefined\")\n        return undefined\n\n      /* Compute identifier and initialize table */\n      const id = [doc.location, name].join(\":\")\n      table.set(id, lunr.tokenizer.table = [])\n\n      /* Return field value */\n      return doc[name]\n    }\n  }\n}\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n  const [x, y] = [new Set(a), new Set(b)]\n  return [\n    ...new Set([...x].filter(value => !y.has(value)))\n  ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n  /**\n   * Search document map\n   */\n  protected map: Map<string, SearchDocument>\n\n  /**\n   * Search options\n   */\n  protected options: SearchOptions\n\n  /**\n   * The underlying Lunr.js search index\n   */\n  protected index: lunr.Index\n\n  /**\n   * Internal position table map\n   */\n  protected table: Map<string, PositionTable>\n\n  /**\n   * Create the search integration\n   *\n   * @param data - Search index\n   */\n  public constructor({ config, docs, options }: SearchIndex) {\n    const field = extractor(this.table = new Map())\n\n    /* Set up document map and options */\n    this.map = setupSearchDocumentMap(docs)\n    this.options = options\n\n    /* Set up document index */\n    this.index = lunr(function () {\n      this.metadataWhitelist = [\"position\"]\n      this.b(0)\n\n      /* Set up (multi-)language support */\n      if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n        // @ts-expect-error - namespace indexing not supported\n        this.use(lunr[config.lang[0]])\n      } else if (config.lang.length > 1) {\n        this.use(lunr.multiLanguage(...config.lang))\n      }\n\n      /* Set up custom tokenizer (must be after language setup) */\n      this.tokenizer = tokenize as typeof lunr.tokenizer\n      lunr.tokenizer.separator = new RegExp(config.separator)\n\n      /* Set up custom segmenter, if loaded */\n      lunr.segmenter = \"TinySegmenter\" in lunr\n        ? new lunr.TinySegmenter()\n        : undefined\n\n      /* Compute functions to be removed from the pipeline */\n      const fns = difference([\n        \"trimmer\", \"stopWordFilter\", \"stemmer\"\n      ], config.pipeline)\n\n      /* Remove functions from the pipeline for registered languages */\n      for (const lang of config.lang.map(language => (\n        // @ts-expect-error - namespace indexing not supported\n        language === \"en\" ? lunr : lunr[language]\n      )))\n        for (const fn of fns) {\n          this.pipeline.remove(lang[fn])\n          this.searchPipeline.remove(lang[fn])\n        }\n\n      /* Set up index reference */\n      this.ref(\"location\")\n\n      /* Set up index fields */\n      this.field(\"title\", { boost: 1e3, extractor: field(\"title\") })\n      this.field(\"text\",  { boost: 1e0, extractor: field(\"text\") })\n      this.field(\"tags\",  { boost: 1e6, extractor: field(\"tags\") })\n\n      /* Add documents to index */\n      for (const doc of docs)\n        this.add(doc, { boost: doc.boost })\n    })\n  }\n\n  /**\n   * Search for matching documents\n   *\n   * @param query - Search query\n   *\n   * @returns Search result\n   */\n  public search(query: string): SearchResult {\n\n    // Experimental Chinese segmentation\n    query = query.replace(/\\p{sc=Han}+/gu, value => {\n      return [...segment(value, this.index.invertedIndex)]\n        .join(\"* \")\n    })\n\n    // @todo: move segmenter (above) into transformSearchQuery\n    query = transformSearchQuery(query)\n    if (!query)\n      return { items: [] }\n\n    /* Parse query to extract clauses for analysis */\n    const clauses = parseSearchQuery(query)\n      .filter(clause => (\n        clause.presence !== lunr.Query.presence.PROHIBITED\n      ))\n\n    /* Perform search and post-process results */\n    const groups = this.index.search(query)\n\n      /* Apply post-query boosts based on title and search query terms */\n      .reduce<SearchItem[]>((item, { ref, score, matchData }) => {\n        let doc = this.map.get(ref)\n        if (typeof doc !== \"undefined\") {\n\n          /* Shallow copy document */\n          doc = { ...doc }\n          if (doc.tags)\n            doc.tags = [...doc.tags]\n\n          /* Compute and analyze search query terms */\n          const terms = getSearchQueryTerms(\n            clauses,\n            Object.keys(matchData.metadata)\n          )\n\n          /* Highlight matches in fields */\n          for (const field of this.index.fields) {\n            if (typeof doc[field] === \"undefined\")\n              continue\n\n            /* Collect positions from matches */\n            const positions: Position[] = []\n            for (const match of Object.values(matchData.metadata))\n              if (typeof match[field] !== \"undefined\")\n                positions.push(...match[field].position)\n\n            /* Skip highlighting, if no positions were collected */\n            if (!positions.length)\n              continue\n\n            /* Load table and determine highlighting method */\n            const table = this.table.get([doc.location, field].join(\":\"))!\n            const fn = Array.isArray(doc[field])\n              ? highlightAll\n              : highlight\n\n            // @ts-expect-error - stop moaning, TypeScript!\n            doc[field] = fn(doc[field], table, positions, field !== \"text\")\n          }\n\n          /* Highlight title and text and apply post-query boosts */\n          const boost = +!doc.parent +\n            Object.values(terms)\n              .filter(t => t).length /\n            Object.keys(terms).length\n\n          /* Append item */\n          item.push({\n            ...doc,\n            score: score * (1 + boost ** 2),\n            terms\n          })\n        }\n        return item\n      }, [])\n\n      /* Sort search results again after applying boosts */\n      .sort((a, b) => b.score - a.score)\n\n      /* Group search results by article */\n      .reduce((items, result) => {\n        const doc = this.map.get(result.location)\n        if (typeof doc !== \"undefined\") {\n          const ref = doc.parent\n            ? doc.parent.location\n            : doc.location\n          items.set(ref, [...items.get(ref) || [], result])\n        }\n        return items\n      }, new Map<string, SearchItem[]>())\n\n    /* Ensure that every item set has an article */\n    for (const [ref, items] of groups)\n      if (!items.find(item => item.location === ref)) {\n        const doc = this.map.get(ref)!\n        items.push({ ...doc, score: 0, terms: {} })\n      }\n\n    /* Generate search suggestions, if desired */\n    let suggest: string[] | undefined\n    if (this.options.suggest) {\n      const titles = this.index.query(builder => {\n        for (const clause of clauses)\n          builder.term(clause.term, {\n            fields: [\"title\"],\n            presence: lunr.Query.presence.REQUIRED,\n            wildcard: lunr.Query.wildcard.TRAILING\n          })\n      })\n\n      /* Retrieve suggestions for best match */\n      suggest = titles.length\n        ? Object.keys(titles[0].matchData.metadata)\n        : []\n    }\n\n    /* Return search result */\n    return {\n      items: [...groups.values()],\n      ...typeof suggest !== \"undefined\" && { suggest }\n    }\n  }\n}\n"],
-  "mappings": 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diff --git a/docs/site/assets/javascripts/workers/search.b8dbb3d2.min.js b/docs/site/assets/javascripts/workers/search.b97dbffb.min.js
similarity index 59%
rename from docs/site/assets/javascripts/workers/search.b8dbb3d2.min.js
rename to docs/site/assets/javascripts/workers/search.b97dbffb.min.js
index c8a1ec8..8201c5e 100644
--- a/docs/site/assets/javascripts/workers/search.b8dbb3d2.min.js
+++ b/docs/site/assets/javascripts/workers/search.b97dbffb.min.js
@@ -1,4 +1,4 @@
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  * lunr.Vector
  * Copyright (C) 2020 Oliver Nightingale
- */t.Vector=function(e){this._magnitude=0,this.elements=e||[]},t.Vector.prototype.positionForIndex=function(e){if(this.elements.length==0)return 0;for(var r=0,n=this.elements.length/2,i=n-r,s=Math.floor(i/2),o=this.elements[s*2];i>1&&(o<e&&(r=s),o>e&&(n=s),o!=e);)i=n-r,s=r+Math.floor(i/2),o=this.elements[s*2];if(o==e||o>e)return s*2;if(o<e)return(s+1)*2},t.Vector.prototype.insert=function(e,r){this.upsert(e,r,function(){throw"duplicate index"})},t.Vector.prototype.upsert=function(e,r,n){this._magnitude=0;var i=this.positionForIndex(e);this.elements[i]==e?this.elements[i+1]=n(this.elements[i+1],r):this.elements.splice(i,0,e,r)},t.Vector.prototype.magnitude=function(){if(this._magnitude)return this._magnitude;for(var e=0,r=this.elements.length,n=1;n<r;n+=2){var i=this.elements[n];e+=i*i}return this._magnitude=Math.sqrt(e)},t.Vector.prototype.dot=function(e){for(var r=0,n=this.elements,i=e.elements,s=n.length,o=i.length,a=0,u=0,c=0,f=0;c<s&&f<o;)a=n[c],u=i[f],a<u?c+=2:a>u?f+=2:a==u&&(r+=n[c+1]*i[f+1],c+=2,f+=2);return r},t.Vector.prototype.similarity=function(e){return this.dot(e)/this.magnitude()||0},t.Vector.prototype.toArray=function(){for(var e=new Array(this.elements.length/2),r=1,n=0;r<this.elements.length;r+=2,n++)e[n]=this.elements[r];return e},t.Vector.prototype.toJSON=function(){return this.elements};/*!
+ */t.Vector=function(e){this._magnitude=0,this.elements=e||[]},t.Vector.prototype.positionForIndex=function(e){if(this.elements.length==0)return 0;for(var r=0,n=this.elements.length/2,i=n-r,s=Math.floor(i/2),o=this.elements[s*2];i>1&&(o<e&&(r=s),o>e&&(n=s),o!=e);)i=n-r,s=r+Math.floor(i/2),o=this.elements[s*2];if(o==e||o>e)return s*2;if(o<e)return(s+1)*2},t.Vector.prototype.insert=function(e,r){this.upsert(e,r,function(){throw"duplicate index"})},t.Vector.prototype.upsert=function(e,r,n){this._magnitude=0;var i=this.positionForIndex(e);this.elements[i]==e?this.elements[i+1]=n(this.elements[i+1],r):this.elements.splice(i,0,e,r)},t.Vector.prototype.magnitude=function(){if(this._magnitude)return this._magnitude;for(var e=0,r=this.elements.length,n=1;n<r;n+=2){var i=this.elements[n];e+=i*i}return this._magnitude=Math.sqrt(e)},t.Vector.prototype.dot=function(e){for(var r=0,n=this.elements,i=e.elements,s=n.length,o=i.length,a=0,u=0,c=0,h=0;c<s&&h<o;)a=n[c],u=i[h],a<u?c+=2:a>u?h+=2:a==u&&(r+=n[c+1]*i[h+1],c+=2,h+=2);return r},t.Vector.prototype.similarity=function(e){return this.dot(e)/this.magnitude()||0},t.Vector.prototype.toArray=function(){for(var e=new Array(this.elements.length/2),r=1,n=0;r<this.elements.length;r+=2,n++)e[n]=this.elements[r];return e},t.Vector.prototype.toJSON=function(){return this.elements};/*!
  * lunr.stemmer
  * Copyright (C) 2020 Oliver Nightingale
  * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
- */t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,u="^("+s+")?"+o+s+"("+o+")?$",c="^("+s+")?"+o+s+o+s,f="^("+s+")?"+i,g=new RegExp(a),l=new RegExp(c),m=new RegExp(u),x=new RegExp(f),v=/^(.+?)(ss|i)es$/,d=/^(.+?)([^s])s$/,y=/^(.+?)eed$/,b=/^(.+?)(ed|ing)$/,E=/.$/,w=/(at|bl|iz)$/,R=new RegExp("([^aeiouylsz])\\1$"),j=new RegExp("^"+s+i+"[^aeiouwxy]$"),_=/^(.+?[^aeiou])y$/,D=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,N=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,C=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,V=/^(.+?)(s|t)(ion)$/,P=/^(.+?)e$/,z=/ll$/,$=new RegExp("^"+s+i+"[^aeiouwxy]$"),M=function(h){var S,k,L,p,T,O,F;if(h.length<3)return h;if(L=h.substr(0,1),L=="y"&&(h=L.toUpperCase()+h.substr(1)),p=v,T=d,p.test(h)?h=h.replace(p,"$1$2"):T.test(h)&&(h=h.replace(T,"$1$2")),p=y,T=b,p.test(h)){var Q=p.exec(h);p=g,p.test(Q[1])&&(p=E,h=h.replace(p,""))}else if(T.test(h)){var Q=T.exec(h);S=Q[1],T=x,T.test(S)&&(h=S,T=w,O=R,F=j,T.test(h)?h=h+"e":O.test(h)?(p=E,h=h.replace(p,"")):F.test(h)&&(h=h+"e"))}if(p=_,p.test(h)){var Q=p.exec(h);S=Q[1],h=S+"i"}if(p=D,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+e[k])}if(p=N,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+r[k])}if(p=C,T=V,p.test(h)){var Q=p.exec(h);S=Q[1],p=l,p.test(S)&&(h=S)}else if(T.test(h)){var Q=T.exec(h);S=Q[1]+Q[2],T=l,T.test(S)&&(h=S)}if(p=P,p.test(h)){var Q=p.exec(h);S=Q[1],p=l,T=m,O=$,(p.test(S)||T.test(S)&&!O.test(S))&&(h=S)}return p=z,T=l,p.test(h)&&T.test(h)&&(p=E,h=h.replace(p,"")),L=="y"&&(h=L.toLowerCase()+h.substr(1)),h};return function(I){return I.update(M)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");/*!
+ */t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,u="^("+s+")?"+o+s+"("+o+")?$",c="^("+s+")?"+o+s+o+s,h="^("+s+")?"+i,y=new RegExp(a),g=new RegExp(c),b=new RegExp(u),m=new RegExp(h),Q=/^(.+?)(ss|i)es$/,p=/^(.+?)([^s])s$/,d=/^(.+?)eed$/,w=/^(.+?)(ed|ing)$/,L=/.$/,k=/(at|bl|iz)$/,I=new RegExp("([^aeiouylsz])\\1$"),j=new RegExp("^"+s+i+"[^aeiouwxy]$"),F=/^(.+?[^aeiou])y$/,A=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,N=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,C=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,$=/^(.+?)(s|t)(ion)$/,T=/^(.+?)e$/,V=/ll$/,B=new RegExp("^"+s+i+"[^aeiouwxy]$"),D=function(l){var v,P,E,f,x,R,_;if(l.length<3)return l;if(E=l.substr(0,1),E=="y"&&(l=E.toUpperCase()+l.substr(1)),f=Q,x=p,f.test(l)?l=l.replace(f,"$1$2"):x.test(l)&&(l=l.replace(x,"$1$2")),f=d,x=w,f.test(l)){var S=f.exec(l);f=y,f.test(S[1])&&(f=L,l=l.replace(f,""))}else if(x.test(l)){var S=x.exec(l);v=S[1],x=m,x.test(v)&&(l=v,x=k,R=I,_=j,x.test(l)?l=l+"e":R.test(l)?(f=L,l=l.replace(f,"")):_.test(l)&&(l=l+"e"))}if(f=F,f.test(l)){var S=f.exec(l);v=S[1],l=v+"i"}if(f=A,f.test(l)){var S=f.exec(l);v=S[1],P=S[2],f=y,f.test(v)&&(l=v+e[P])}if(f=N,f.test(l)){var S=f.exec(l);v=S[1],P=S[2],f=y,f.test(v)&&(l=v+r[P])}if(f=C,x=$,f.test(l)){var S=f.exec(l);v=S[1],f=g,f.test(v)&&(l=v)}else if(x.test(l)){var S=x.exec(l);v=S[1]+S[2],x=g,x.test(v)&&(l=v)}if(f=T,f.test(l)){var S=f.exec(l);v=S[1],f=g,x=b,R=B,(f.test(v)||x.test(v)&&!R.test(v))&&(l=v)}return f=V,x=g,f.test(l)&&x.test(l)&&(f=L,l=l.replace(f,"")),E=="y"&&(l=E.toLowerCase()+l.substr(1)),l};return function(O){return O.update(D)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");/*!
  * lunr.stopWordFilter
  * Copyright (C) 2020 Oliver Nightingale
  */t.generateStopWordFilter=function(e){var r=e.reduce(function(n,i){return n[i]=i,n},{});return function(n){if(n&&r[n.toString()]!==n.toString())return n}},t.stopWordFilter=t.generateStopWordFilter(["a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"]),t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter");/*!
@@ -31,12 +31,18 @@
  */t.trimmer=function(e){return e.update(function(r){return r.replace(/^\W+/,"").replace(/\W+$/,"")})},t.Pipeline.registerFunction(t.trimmer,"trimmer");/*!
  * lunr.TokenSet
  * Copyright (C) 2020 Oliver Nightingale
- */t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}if(s.str.length==0&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var c=s.node.edges["*"];else{var c=new t.TokenSet;s.node.edges["*"]=c}s.str.length==1&&(c.final=!0),i.push({node:c,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var f=s.str.charAt(0),g=s.str.charAt(1),l;g in s.node.edges?l=s.node.edges[g]:(l=new t.TokenSet,s.node.edges[g]=l),s.str.length==1&&(l.final=!0),i.push({node:l,editsRemaining:s.editsRemaining-1,str:f+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var u=new t.TokenSet;u.final=a,r.edges[o]=u,r=u}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),u=a.length,c=0;c<o;c++)for(var f=s[c],g=0;g<u;g++){var l=a[g];if(l==f||f=="*"){var m=n.node.edges[l],x=n.qNode.edges[f],v=m.final&&x.final,d=void 0;l in n.output.edges?(d=n.output.edges[l],d.final=d.final||v):(d=new t.TokenSet,d.final=v,n.output.edges[l]=d),i.push({qNode:x,output:d,node:m})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};/*!
+ */t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}if(s.str.length==0&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var c=s.node.edges["*"];else{var c=new t.TokenSet;s.node.edges["*"]=c}s.str.length==1&&(c.final=!0),i.push({node:c,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var h=s.str.charAt(0),y=s.str.charAt(1),g;y in s.node.edges?g=s.node.edges[y]:(g=new t.TokenSet,s.node.edges[y]=g),s.str.length==1&&(g.final=!0),i.push({node:g,editsRemaining:s.editsRemaining-1,str:h+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var u=new t.TokenSet;u.final=a,r.edges[o]=u,r=u}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),u=a.length,c=0;c<o;c++)for(var h=s[c],y=0;y<u;y++){var g=a[y];if(g==h||h=="*"){var b=n.node.edges[g],m=n.qNode.edges[h],Q=b.final&&m.final,p=void 0;g in n.output.edges?(p=n.output.edges[g],p.final=p.final||Q):(p=new t.TokenSet,p.final=Q,n.output.edges[g]=p),i.push({qNode:m,output:p,node:b})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};/*!
  * lunr.Index
  * Copyright (C) 2020 Oliver Nightingale
- */t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(r,r);for(var u=0;u<r.clauses.length;u++){var c=r.clauses[u],f=null,g=t.Set.empty;c.usePipeline?f=this.pipeline.runString(c.term,{fields:c.fields}):f=[c.term];for(var l=0;l<f.length;l++){var m=f[l];c.term=m;var x=t.TokenSet.fromClause(c),v=this.tokenSet.intersect(x).toArray();if(v.length===0&&c.presence===t.Query.presence.REQUIRED){for(var d=0;d<c.fields.length;d++){var y=c.fields[d];o[y]=t.Set.empty}break}for(var b=0;b<v.length;b++)for(var E=v[b],w=this.invertedIndex[E],R=w._index,d=0;d<c.fields.length;d++){var y=c.fields[d],j=w[y],_=Object.keys(j),D=E+"/"+y,N=new t.Set(_);if(c.presence==t.Query.presence.REQUIRED&&(g=g.union(N),o[y]===void 0&&(o[y]=t.Set.complete)),c.presence==t.Query.presence.PROHIBITED){a[y]===void 0&&(a[y]=t.Set.empty),a[y]=a[y].union(N);continue}if(i[y].upsert(R,c.boost,function(ye,me){return ye+me}),!s[D]){for(var C=0;C<_.length;C++){var V=_[C],P=new t.FieldRef(V,y),z=j[V],$;($=n[P])===void 0?n[P]=new t.MatchData(E,y,z):$.add(E,y,z)}s[D]=!0}}}if(c.presence===t.Query.presence.REQUIRED)for(var d=0;d<c.fields.length;d++){var y=c.fields[d];o[y]=o[y].intersect(g)}}for(var M=t.Set.complete,I=t.Set.empty,u=0;u<this.fields.length;u++){var y=this.fields[u];o[y]&&(M=M.intersect(o[y])),a[y]&&(I=I.union(a[y]))}var h=Object.keys(n),S=[],k=Object.create(null);if(r.isNegated()){h=Object.keys(this.fieldVectors);for(var u=0;u<h.length;u++){var P=h[u],L=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var u=0;u<h.length;u++){var L=t.FieldRef.fromString(h[u]),p=L.docRef;if(M.contains(p)&&!I.contains(p)){var T=this.fieldVectors[L],O=i[L.fieldName].similarity(T),F;if((F=k[p])!==void 0)F.score+=O,F.matchData.combine(n[L]);else{var Q={ref:p,score:O,matchData:n[L]};k[p]=Q,S.push(Q)}}}return S.sort(function(pe,ge){return ge.score-pe.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var c=0;c<i.length;c++){var f=i[c],g=f[0],l=f[1];n[g]=new t.Vector(l)}for(var c=0;c<o.length;c++){var f=o[c],m=f[0],x=f[1];a.insert(m),s[m]=x}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=u,new t.Index(r)};/*!
+ */t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(r,r);for(var u=0;u<r.clauses.length;u++){var c=r.clauses[u],h=null,y=t.Set.empty;c.usePipeline?h=this.pipeline.runString(c.term,{fields:c.fields}):h=[c.term];for(var g=0;g<h.length;g++){var b=h[g];c.term=b;var m=t.TokenSet.fromClause(c),Q=this.tokenSet.intersect(m).toArray();if(Q.length===0&&c.presence===t.Query.presence.REQUIRED){for(var p=0;p<c.fields.length;p++){var d=c.fields[p];o[d]=t.Set.empty}break}for(var w=0;w<Q.length;w++)for(var L=Q[w],k=this.invertedIndex[L],I=k._index,p=0;p<c.fields.length;p++){var d=c.fields[p],j=k[d],F=Object.keys(j),A=L+"/"+d,N=new t.Set(F);if(c.presence==t.Query.presence.REQUIRED&&(y=y.union(N),o[d]===void 0&&(o[d]=t.Set.complete)),c.presence==t.Query.presence.PROHIBITED){a[d]===void 0&&(a[d]=t.Set.empty),a[d]=a[d].union(N);continue}if(i[d].upsert(I,c.boost,function(pe,de){return pe+de}),!s[A]){for(var C=0;C<F.length;C++){var $=F[C],T=new t.FieldRef($,d),V=j[$],B;(B=n[T])===void 0?n[T]=new t.MatchData(L,d,V):B.add(L,d,V)}s[A]=!0}}}if(c.presence===t.Query.presence.REQUIRED)for(var p=0;p<c.fields.length;p++){var d=c.fields[p];o[d]=o[d].intersect(y)}}for(var D=t.Set.complete,O=t.Set.empty,u=0;u<this.fields.length;u++){var d=this.fields[u];o[d]&&(D=D.intersect(o[d])),a[d]&&(O=O.union(a[d]))}var l=Object.keys(n),v=[],P=Object.create(null);if(r.isNegated()){l=Object.keys(this.fieldVectors);for(var u=0;u<l.length;u++){var T=l[u],E=t.FieldRef.fromString(T);n[T]=new t.MatchData}}for(var u=0;u<l.length;u++){var E=t.FieldRef.fromString(l[u]),f=E.docRef;if(!!D.contains(f)&&!O.contains(f)){var x=this.fieldVectors[E],R=i[E.fieldName].similarity(x),_;if((_=P[f])!==void 0)_.score+=R,_.matchData.combine(n[E]);else{var S={ref:f,score:R,matchData:n[E]};P[f]=S,v.push(S)}}}return v.sort(function(he,fe){return fe.score-he.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var c=0;c<i.length;c++){var h=i[c],y=h[0],g=h[1];n[y]=new t.Vector(g)}for(var c=0;c<o.length;c++){var h=o[c],b=h[0],m=h[1];a.insert(b),s[b]=m}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=u,new t.Index(r)};/*!
  * lunr.Builder
  * Copyright (C) 2020 Oliver Nightingale
- */t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],c=this.tokenizer(u,{fields:[o]}),f=this.pipeline.run(c),g=new t.FieldRef(n,o),l=Object.create(null);this.fieldTermFrequencies[g]=l,this.fieldLengths[g]=0,this.fieldLengths[g]+=f.length;for(var m=0;m<f.length;m++){var x=f[m];if(l[x]==null&&(l[x]=0),l[x]+=1,this.invertedIndex[x]==null){var v=Object.create(null);v._index=this.termIndex,this.termIndex+=1;for(var d=0;d<i.length;d++)v[i[d]]=Object.create(null);this.invertedIndex[x]=v}this.invertedIndex[x][o][n]==null&&(this.invertedIndex[x][o][n]=Object.create(null));for(var y=0;y<this.metadataWhitelist.length;y++){var b=this.metadataWhitelist[y],E=x.metadata[b];this.invertedIndex[x][o][n][b]==null&&(this.invertedIndex[x][o][n][b]=[]),this.invertedIndex[x][o][n][b].push(E)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var c=u[s];n[c]=n[c]/i[c]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,u=this.fieldLengths[o],c=new t.Vector,f=this.fieldTermFrequencies[o],g=Object.keys(f),l=g.length,m=this._fields[a].boost||1,x=this._documents[o.docRef].boost||1,v=0;v<l;v++){var d=g[v],y=f[d],b=this.invertedIndex[d]._index,E,w,R;i[d]===void 0?(E=t.idf(this.invertedIndex[d],this.documentCount),i[d]=E):E=i[d],w=E*((this._k1+1)*y)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+y),w*=m,w*=x,R=Math.round(w*1e3)/1e3,c.insert(b,R)}e[o]=c}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var c=0;c<u.length;c++){var f=u[c];this.metadata[i][a][f]==null?this.metadata[i][a][f]=e.metadata[i][a][f]:this.metadata[i][a][f]=this.metadata[i][a][f].concat(e.metadata[i][a][f])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,r){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(r))},this),this;var n=r||{};return n.term=e.toString(),this.clause(n),this},t.QueryParseError=function(e,r,n){this.name="QueryParseError",this.message=e,this.start=r,this.end=n},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do e=this.next(),r=e.charCodeAt(0);while(r>47&&r<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return this.pos<this.length},t.QueryLexer.EOS="EOS",t.QueryLexer.FIELD="FIELD",t.QueryLexer.TERM="TERM",t.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",t.QueryLexer.BOOST="BOOST",t.QueryLexer.PRESENCE="PRESENCE",t.QueryLexer.lexField=function(e){return e.backup(),e.emit(t.QueryLexer.FIELD),e.ignore(),t.QueryLexer.lexText},t.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case 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t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var c=0;c<u.length;c++){var 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e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do e=this.next(),r=e.charCodeAt(0);while(r>47&&r<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return this.pos<this.length},t.QueryLexer.EOS="EOS",t.QueryLexer.FIELD="FIELD",t.QueryLexer.TERM="TERM",t.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",t.QueryLexer.BOOST="BOOST",t.QueryLexer.PRESENCE="PRESENCE",t.QueryLexer.lexField=function(e){return e.backup(),e.emit(t.QueryLexer.FIELD),e.ignore(),t.QueryLexer.lexText},t.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof ee=="object"?te.exports=r():e.lunr=r()}(this,function(){return t})})()});var q=K((Re,ne)=>{"use strict";/*!
+ * escape-html
+ * Copyright(c) 2012-2013 TJ Holowaychuk
+ * Copyright(c) 2015 Andreas Lubbe
+ * Copyright(c) 2015 Tiancheng "Timothy" Gu
+ * MIT Licensed
+ */var Le=/["'&<>]/;ne.exports=we;function we(t){var e=""+t,r=Le.exec(e);if(!r)return e;var n,i="",s=0,o=0;for(s=r.index;s<e.length;s++){switch(e.charCodeAt(s)){case 34:n="&quot;";break;case 38:n="&amp;";break;case 39:n="&#39;";break;case 60:n="&lt;";break;case 62:n="&gt;";break;default:continue}o!==s&&(i+=e.substring(o,s)),o=s+1,i+=n}return o!==s?i+e.substring(o,s):i}});var le=H(re());Object.entries||(Object.entries=function(t){let e=[];for(let r of Object.keys(t))e.push([r,t[r]]);return e});Object.values||(Object.values=function(t){let e=[];for(let r of Object.keys(t))e.push(t[r]);return e});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(t,e){typeof t=="object"?(this.scrollLeft=t.left,this.scrollTop=t.top):(this.scrollLeft=t,this.scrollTop=e)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...t){let e=this.parentNode;if(e){t.length===0&&e.removeChild(this);for(let r=t.length-1;r>=0;r--){let n=t[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?e.insertBefore(this.previousSibling,n):e.replaceChild(n,this)}}}));var ie=H(q());function se(t){let e=new Map,r=new Set;for(let n of t){let[i,s]=n.location.split("#"),o=n.location,a=n.title,u=n.tags,c=(0,ie.default)(n.text).replace(/\s+(?=[,.:;!?])/g,"").replace(/\s+/g," ");if(s){let h=e.get(i);r.has(h)?e.set(o,{location:o,title:a,text:c,parent:h}):(h.title=n.title,h.text=c,r.add(h))}else e.set(o,M({location:o,title:a,text:c},u&&{tags:u}))}return e}var oe=H(q());function ae(t,e){let r=new RegExp(t.separator,"img"),n=(i,s,o)=>`${s}<mark data-md-highlight>${o}</mark>`;return i=>{i=i.replace(/[\s*+\-:~^]+/g," ").trim();let s=new RegExp(`(^|${t.separator})(${i.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return o=>(e?(0,oe.default)(o):o).replace(s,n).replace(/<\/mark>(\s+)<mark[^>]*>/img,"$1")}}function ue(t){let e=new lunr.Query(["title","text"]);return new lunr.QueryParser(t,e).parse(),e.clauses}function ce(t,e){var i;let r=new Set(t),n={};for(let s=0;s<e.length;s++)for(let o of r)e[s].startsWith(o.term)&&(n[o.term]=!0,r.delete(o));for(let s of r)(i=lunr.stopWordFilter)!=null&&i.call(lunr,s.term)&&(n[s.term]=!1);return n}function Ee(t,e){let[r,n]=[new Set(t),new Set(e)];return[...new Set([...r].filter(i=>!n.has(i)))]}var U=class{constructor({config:e,docs:r,options:n}){this.options=n,this.documents=se(r),this.highlight=ae(e,!1),lunr.tokenizer.separator=new RegExp(e.separator),this.index=lunr(function(){e.lang.length===1&&e.lang[0]!=="en"?this.use(lunr[e.lang[0]]):e.lang.length>1&&this.use(lunr.multiLanguage(...e.lang));let i=Ee(["trimmer","stopWordFilter","stemmer"],n.pipeline);for(let s of e.lang.map(o=>o==="en"?lunr:lunr[o]))for(let o of i)this.pipeline.remove(s[o]),this.searchPipeline.remove(s[o]);this.ref("location"),this.field("title",{boost:1e3}),this.field("text"),this.field("tags",{boost:1e6,extractor:s=>{let{tags:o=[]}=s;return o.reduce((a,u)=>[...a,...lunr.tokenizer(u)],[])}});for(let s of r)this.add(s,{boost:s.boost})})}search(e){if(e)try{let r=this.highlight(e),n=ue(e).filter(o=>o.presence!==lunr.Query.presence.PROHIBITED),i=this.index.search(`${e}*`).reduce((o,{ref:a,score:u,matchData:c})=>{let h=this.documents.get(a);if(typeof h!="undefined"){let{location:y,title:g,text:b,tags:m,parent:Q}=h,p=ce(n,Object.keys(c.metadata)),d=+!Q+ +Object.values(p).every(w=>w);o.push(Z(M({location:y,title:r(g),text:r(b)},m&&{tags:m.map(r)}),{score:u*(1+d),terms:p}))}return o},[]).sort((o,a)=>a.score-o.score).reduce((o,a)=>{let u=this.documents.get(a.location);if(typeof u!="undefined"){let c="parent"in u?u.parent.location:u.location;o.set(c,[...o.get(c)||[],a])}return o},new Map),s;if(this.options.suggestions){let o=this.index.query(a=>{for(let u of n)a.term(u.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});s=o.length?Object.keys(o[0].matchData.metadata):[]}return M({items:[...i.values()]},typeof s!="undefined"&&{suggestions:s})}catch(r){console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`)}return{items:[]}}};var Y;function ke(t){return z(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=document.querySelector("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Te(t){return z(this,null,function*(){switch(t.type){case 0:return yield ke(t.data.config),Y=new U(t.data),{type:1};case 2:return{type:3,data:Y?Y.search(t.data):{items:[]}};default:throw new TypeError("Invalid message type")}})}self.lunr=le.default;addEventListener("message",t=>z(void 0,null,function*(){postMessage(yield Te(t.data))}));})();
+//# sourceMappingURL=search.b97dbffb.min.js.map
 
diff --git a/docs/site/assets/javascripts/workers/search.b97dbffb.min.js.map b/docs/site/assets/javascripts/workers/search.b97dbffb.min.js.map
new file mode 100644
index 0000000..5764b1b
--- /dev/null
+++ b/docs/site/assets/javascripts/workers/search.b97dbffb.min.js.map
@@ -0,0 +1,8 @@
+{
+  "version": 3,
+  "sources": ["node_modules/lunr/lunr.js", "node_modules/escape-html/index.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"],
+  "sourceRoot": "../../../..",
+  "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n *   this.field('title')\n *   this.field('body')\n *   this.ref('id')\n *\n *   documents.forEach(function (doc) {\n *     this.add(doc)\n *   }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n  var builder = new lunr.Builder\n\n  builder.pipeline.add(\n    lunr.trimmer,\n    lunr.stopWordFilter,\n    lunr.stemmer\n  )\n\n  builder.searchPipeline.add(\n    lunr.stemmer\n  )\n\n  config.call(builder, builder)\n  return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n  /* eslint-disable no-console */\n  return function (message) {\n    if (global.console && console.warn) {\n      console.warn(message)\n    }\n  }\n  /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n  if (obj === void 0 || obj === null) {\n    return \"\"\n  } else {\n    return obj.toString()\n  }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n  if (obj === null || obj === undefined) {\n    return obj\n  }\n\n  var clone = Object.create(null),\n      keys = Object.keys(obj)\n\n  for (var i = 0; i < keys.length; i++) {\n    var key = keys[i],\n        val = obj[key]\n\n    if (Array.isArray(val)) {\n      clone[key] = val.slice()\n      continue\n    }\n\n    if (typeof val === 'string' ||\n        typeof val === 'number' ||\n        typeof val === 'boolean') {\n      clone[key] = val\n      continue\n    }\n\n    throw new TypeError(\"clone is not deep and does not support nested objects\")\n  }\n\n  return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n  this.docRef = docRef\n  this.fieldName = fieldName\n  this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n  var n = s.indexOf(lunr.FieldRef.joiner)\n\n  if (n === -1) {\n    throw \"malformed field ref string\"\n  }\n\n  var fieldRef = s.slice(0, n),\n      docRef = s.slice(n + 1)\n\n  return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n  if (this._stringValue == undefined) {\n    this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n  }\n\n  return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n  this.elements = Object.create(null)\n\n  if (elements) {\n    this.length = elements.length\n\n    for (var i = 0; i < this.length; i++) {\n      this.elements[elements[i]] = true\n    }\n  } else {\n    this.length = 0\n  }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n  intersect: function (other) {\n    return other\n  },\n\n  union: function () {\n    return this\n  },\n\n  contains: function () {\n    return true\n  }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n  intersect: function () {\n    return this\n  },\n\n  union: function (other) {\n    return other\n  },\n\n  contains: function () {\n    return false\n  }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n  return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n  var a, b, elements, intersection = []\n\n  if (other === lunr.Set.complete) {\n    return this\n  }\n\n  if (other === lunr.Set.empty) {\n    return other\n  }\n\n  if (this.length < other.length) {\n    a = this\n    b = other\n  } else {\n    a = other\n    b = this\n  }\n\n  elements = Object.keys(a.elements)\n\n  for (var i = 0; i < elements.length; i++) {\n    var element = elements[i]\n    if (element in b.elements) {\n      intersection.push(element)\n    }\n  }\n\n  return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n  if (other === lunr.Set.complete) {\n    return lunr.Set.complete\n  }\n\n  if (other === lunr.Set.empty) {\n    return this\n  }\n\n  return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n  var documentsWithTerm = 0\n\n  for (var fieldName in posting) {\n    if (fieldName == '_index') continue // Ignore the term index, its not a field\n    documentsWithTerm += Object.keys(posting[fieldName]).length\n  }\n\n  var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n  return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n  this.str = str || \"\"\n  this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n  return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n *   return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n  this.str = fn(this.str, this.metadata)\n  return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n  fn = fn || function (s) { return s }\n  return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n  if (obj == null || obj == undefined) {\n    return []\n  }\n\n  if (Array.isArray(obj)) {\n    return obj.map(function (t) {\n      return new lunr.Token(\n        lunr.utils.asString(t).toLowerCase(),\n        lunr.utils.clone(metadata)\n      )\n    })\n  }\n\n  var str = obj.toString().toLowerCase(),\n      len = str.length,\n      tokens = []\n\n  for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n    var char = str.charAt(sliceEnd),\n        sliceLength = sliceEnd - sliceStart\n\n    if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n      if (sliceLength > 0) {\n        var tokenMetadata = lunr.utils.clone(metadata) || {}\n        tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n        tokenMetadata[\"index\"] = tokens.length\n\n        tokens.push(\n          new lunr.Token (\n            str.slice(sliceStart, sliceEnd),\n            tokenMetadata\n          )\n        )\n      }\n\n      sliceStart = sliceEnd + 1\n    }\n\n  }\n\n  return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n  this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n  if (label in this.registeredFunctions) {\n    lunr.utils.warn('Overwriting existing registered function: ' + label)\n  }\n\n  fn.label = label\n  lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n  var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n  if (!isRegistered) {\n    lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n  }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n  var pipeline = new lunr.Pipeline\n\n  serialised.forEach(function (fnName) {\n    var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n    if (fn) {\n      pipeline.add(fn)\n    } else {\n      throw new Error('Cannot load unregistered function: ' + fnName)\n    }\n  })\n\n  return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n  var fns = Array.prototype.slice.call(arguments)\n\n  fns.forEach(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n    this._stack.push(fn)\n  }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  pos = pos + 1\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n  var pos = this._stack.indexOf(fn)\n  if (pos == -1) {\n    return\n  }\n\n  this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n  var stackLength = this._stack.length\n\n  for (var i = 0; i < stackLength; i++) {\n    var fn = this._stack[i]\n    var memo = []\n\n    for (var j = 0; j < tokens.length; j++) {\n      var result = fn(tokens[j], j, tokens)\n\n      if (result === null || result === void 0 || result === '') continue\n\n      if (Array.isArray(result)) {\n        for (var k = 0; k < result.length; k++) {\n          memo.push(result[k])\n        }\n      } else {\n        memo.push(result)\n      }\n    }\n\n    tokens = memo\n  }\n\n  return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n  var token = new lunr.Token (str, metadata)\n\n  return this.run([token]).map(function (t) {\n    return t.toString()\n  })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n  this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n  return this._stack.map(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n    return fn.label\n  })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n  this._magnitude = 0\n  this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n  // For an empty vector the tuple can be inserted at the beginning\n  if (this.elements.length == 0) {\n    return 0\n  }\n\n  var start = 0,\n      end = this.elements.length / 2,\n      sliceLength = end - start,\n      pivotPoint = Math.floor(sliceLength / 2),\n      pivotIndex = this.elements[pivotPoint * 2]\n\n  while (sliceLength > 1) {\n    if (pivotIndex < index) {\n      start = pivotPoint\n    }\n\n    if (pivotIndex > index) {\n      end = pivotPoint\n    }\n\n    if (pivotIndex == index) {\n      break\n    }\n\n    sliceLength = end - start\n    pivotPoint = start + Math.floor(sliceLength / 2)\n    pivotIndex = this.elements[pivotPoint * 2]\n  }\n\n  if (pivotIndex == index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex > index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex < index) {\n    return (pivotPoint + 1) * 2\n  }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n  this.upsert(insertIdx, val, function () {\n    throw \"duplicate index\"\n  })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n  this._magnitude = 0\n  var position = this.positionForIndex(insertIdx)\n\n  if (this.elements[position] == insertIdx) {\n    this.elements[position + 1] = fn(this.elements[position + 1], val)\n  } else {\n    this.elements.splice(position, 0, insertIdx, val)\n  }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n  if (this._magnitude) return this._magnitude\n\n  var sumOfSquares = 0,\n      elementsLength = this.elements.length\n\n  for (var i = 1; i < elementsLength; i += 2) {\n    var val = this.elements[i]\n    sumOfSquares += val * val\n  }\n\n  return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n  var dotProduct = 0,\n      a = this.elements, b = otherVector.elements,\n      aLen = a.length, bLen = b.length,\n      aVal = 0, bVal = 0,\n      i = 0, j = 0\n\n  while (i < aLen && j < bLen) {\n    aVal = a[i], bVal = b[j]\n    if (aVal < bVal) {\n      i += 2\n    } else if (aVal > bVal) {\n      j += 2\n    } else if (aVal == bVal) {\n      dotProduct += a[i + 1] * b[j + 1]\n      i += 2\n      j += 2\n    }\n  }\n\n  return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n  return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n  var output = new Array (this.elements.length / 2)\n\n  for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n    output[j] = this.elements[i]\n  }\n\n  return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n  return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n  var step2list = {\n      \"ational\" : \"ate\",\n      \"tional\" : \"tion\",\n      \"enci\" : \"ence\",\n      \"anci\" : \"ance\",\n      \"izer\" : \"ize\",\n      \"bli\" : \"ble\",\n      \"alli\" : \"al\",\n      \"entli\" : \"ent\",\n      \"eli\" : \"e\",\n      \"ousli\" : \"ous\",\n      \"ization\" : \"ize\",\n      \"ation\" : \"ate\",\n      \"ator\" : \"ate\",\n      \"alism\" : \"al\",\n      \"iveness\" : \"ive\",\n      \"fulness\" : \"ful\",\n      \"ousness\" : \"ous\",\n      \"aliti\" : \"al\",\n      \"iviti\" : \"ive\",\n      \"biliti\" : \"ble\",\n      \"logi\" : \"log\"\n    },\n\n    step3list = {\n      \"icate\" : \"ic\",\n      \"ative\" : \"\",\n      \"alize\" : \"al\",\n      \"iciti\" : \"ic\",\n      \"ical\" : \"ic\",\n      \"ful\" : \"\",\n      \"ness\" : \"\"\n    },\n\n    c = \"[^aeiou]\",          // consonant\n    v = \"[aeiouy]\",          // vowel\n    C = c + \"[^aeiouy]*\",    // consonant sequence\n    V = v + \"[aeiou]*\",      // vowel sequence\n\n    mgr0 = \"^(\" + C + \")?\" + V + C,               // [C]VC... is m>0\n    meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\",  // [C]VC[V] is m=1\n    mgr1 = \"^(\" + C + \")?\" + V + C + V + C,       // [C]VCVC... is m>1\n    s_v = \"^(\" + C + \")?\" + v;                   // vowel in stem\n\n  var re_mgr0 = new RegExp(mgr0);\n  var re_mgr1 = new RegExp(mgr1);\n  var re_meq1 = new RegExp(meq1);\n  var re_s_v = new RegExp(s_v);\n\n  var re_1a = /^(.+?)(ss|i)es$/;\n  var re2_1a = /^(.+?)([^s])s$/;\n  var re_1b = /^(.+?)eed$/;\n  var re2_1b = /^(.+?)(ed|ing)$/;\n  var re_1b_2 = /.$/;\n  var re2_1b_2 = /(at|bl|iz)$/;\n  var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n  var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var re_1c = /^(.+?[^aeiou])y$/;\n  var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n  var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n  var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n  var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n  var re_5 = /^(.+?)e$/;\n  var re_5_1 = /ll$/;\n  var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var porterStemmer = function porterStemmer(w) {\n    var stem,\n      suffix,\n      firstch,\n      re,\n      re2,\n      re3,\n      re4;\n\n    if (w.length < 3) { return w; }\n\n    firstch = w.substr(0,1);\n    if (firstch == \"y\") {\n      w = firstch.toUpperCase() + w.substr(1);\n    }\n\n    // Step 1a\n    re = re_1a\n    re2 = re2_1a;\n\n    if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n    else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n    // Step 1b\n    re = re_1b;\n    re2 = re2_1b;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      re = re_mgr0;\n      if (re.test(fp[1])) {\n        re = re_1b_2;\n        w = w.replace(re,\"\");\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1];\n      re2 = re_s_v;\n      if (re2.test(stem)) {\n        w = stem;\n        re2 = re2_1b_2;\n        re3 = re3_1b_2;\n        re4 = re4_1b_2;\n        if (re2.test(w)) { w = w + \"e\"; }\n        else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n        else if (re4.test(w)) { w = w + \"e\"; }\n      }\n    }\n\n    // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n    re = re_1c;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      w = stem + \"i\";\n    }\n\n    // Step 2\n    re = re_2;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step2list[suffix];\n      }\n    }\n\n    // Step 3\n    re = re_3;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step3list[suffix];\n      }\n    }\n\n    // Step 4\n    re = re_4;\n    re2 = re2_4;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      if (re.test(stem)) {\n        w = stem;\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1] + fp[2];\n      re2 = re_mgr1;\n      if (re2.test(stem)) {\n        w = stem;\n      }\n    }\n\n    // Step 5\n    re = re_5;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      re2 = re_meq1;\n      re3 = re3_5;\n      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n        w = stem;\n      }\n    }\n\n    re = re_5_1;\n    re2 = re_mgr1;\n    if (re.test(w) && re2.test(w)) {\n      re = re_1b_2;\n      w = w.replace(re,\"\");\n    }\n\n    // and turn initial Y back to y\n\n    if (firstch == \"y\") {\n      w = firstch.toLowerCase() + w.substr(1);\n    }\n\n    return w;\n  };\n\n  return function (token) {\n    return token.update(porterStemmer);\n  }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n  var words = stopWords.reduce(function (memo, stopWord) {\n    memo[stopWord] = stopWord\n    return memo\n  }, {})\n\n  return function (token) {\n    if (token && words[token.toString()] !== token.toString()) return token\n  }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n  'a',\n  'able',\n  'about',\n  'across',\n  'after',\n  'all',\n  'almost',\n  'also',\n  'am',\n  'among',\n  'an',\n  'and',\n  'any',\n  'are',\n  'as',\n  'at',\n  'be',\n  'because',\n  'been',\n  'but',\n  'by',\n  'can',\n  'cannot',\n  'could',\n  'dear',\n  'did',\n  'do',\n  'does',\n  'either',\n  'else',\n  'ever',\n  'every',\n  'for',\n  'from',\n  'get',\n  'got',\n  'had',\n  'has',\n  'have',\n  'he',\n  'her',\n  'hers',\n  'him',\n  'his',\n  'how',\n  'however',\n  'i',\n  'if',\n  'in',\n  'into',\n  'is',\n  'it',\n  'its',\n  'just',\n  'least',\n  'let',\n  'like',\n  'likely',\n  'may',\n  'me',\n  'might',\n  'most',\n  'must',\n  'my',\n  'neither',\n  'no',\n  'nor',\n  'not',\n  'of',\n  'off',\n  'often',\n  'on',\n  'only',\n  'or',\n  'other',\n  'our',\n  'own',\n  'rather',\n  'said',\n  'say',\n  'says',\n  'she',\n  'should',\n  'since',\n  'so',\n  'some',\n  'than',\n  'that',\n  'the',\n  'their',\n  'them',\n  'then',\n  'there',\n  'these',\n  'they',\n  'this',\n  'tis',\n  'to',\n  'too',\n  'twas',\n  'us',\n  'wants',\n  'was',\n  'we',\n  'were',\n  'what',\n  'when',\n  'where',\n  'which',\n  'while',\n  'who',\n  'whom',\n  'why',\n  'will',\n  'with',\n  'would',\n  'yet',\n  'you',\n  'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n  return token.update(function (s) {\n    return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n  })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n  this.final = false\n  this.edges = {}\n  this.id = lunr.TokenSet._nextId\n  lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n  var builder = new lunr.TokenSet.Builder\n\n  for (var i = 0, len = arr.length; i < len; i++) {\n    builder.insert(arr[i])\n  }\n\n  builder.finish()\n  return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n  if ('editDistance' in clause) {\n    return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n  } else {\n    return lunr.TokenSet.fromString(clause.term)\n  }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n  var root = new lunr.TokenSet\n\n  var stack = [{\n    node: root,\n    editsRemaining: editDistance,\n    str: str\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop()\n\n    // no edit\n    if (frame.str.length > 0) {\n      var char = frame.str.charAt(0),\n          noEditNode\n\n      if (char in frame.node.edges) {\n        noEditNode = frame.node.edges[char]\n      } else {\n        noEditNode = new lunr.TokenSet\n        frame.node.edges[char] = noEditNode\n      }\n\n      if (frame.str.length == 1) {\n        noEditNode.final = true\n      }\n\n      stack.push({\n        node: noEditNode,\n        editsRemaining: frame.editsRemaining,\n        str: frame.str.slice(1)\n      })\n    }\n\n    if (frame.editsRemaining == 0) {\n      continue\n    }\n\n    // insertion\n    if (\"*\" in frame.node.edges) {\n      var insertionNode = frame.node.edges[\"*\"]\n    } else {\n      var insertionNode = new lunr.TokenSet\n      frame.node.edges[\"*\"] = insertionNode\n    }\n\n    if (frame.str.length == 0) {\n      insertionNode.final = true\n    }\n\n    stack.push({\n      node: insertionNode,\n      editsRemaining: frame.editsRemaining - 1,\n      str: frame.str\n    })\n\n    // deletion\n    // can only do a deletion if we have enough edits remaining\n    // and if there are characters left to delete in the string\n    if (frame.str.length > 1) {\n      stack.push({\n        node: frame.node,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // deletion\n    // just removing the last character from the str\n    if (frame.str.length == 1) {\n      frame.node.final = true\n    }\n\n    // substitution\n    // can only do a substitution if we have enough edits remaining\n    // and if there are characters left to substitute\n    if (frame.str.length >= 1) {\n      if (\"*\" in frame.node.edges) {\n        var substitutionNode = frame.node.edges[\"*\"]\n      } else {\n        var substitutionNode = new lunr.TokenSet\n        frame.node.edges[\"*\"] = substitutionNode\n      }\n\n      if (frame.str.length == 1) {\n        substitutionNode.final = true\n      }\n\n      stack.push({\n        node: substitutionNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // transposition\n    // can only do a transposition if there are edits remaining\n    // and there are enough characters to transpose\n    if (frame.str.length > 1) {\n      var charA = frame.str.charAt(0),\n          charB = frame.str.charAt(1),\n          transposeNode\n\n      if (charB in frame.node.edges) {\n        transposeNode = frame.node.edges[charB]\n      } else {\n        transposeNode = new lunr.TokenSet\n        frame.node.edges[charB] = transposeNode\n      }\n\n      if (frame.str.length == 1) {\n        transposeNode.final = true\n      }\n\n      stack.push({\n        node: transposeNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: charA + frame.str.slice(2)\n      })\n    }\n  }\n\n  return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n  var node = new lunr.TokenSet,\n      root = node\n\n  /*\n   * Iterates through all characters within the passed string\n   * appending a node for each character.\n   *\n   * When a wildcard character is found then a self\n   * referencing edge is introduced to continually match\n   * any number of any characters.\n   */\n  for (var i = 0, len = str.length; i < len; i++) {\n    var char = str[i],\n        final = (i == len - 1)\n\n    if (char == \"*\") {\n      node.edges[char] = node\n      node.final = final\n\n    } else {\n      var next = new lunr.TokenSet\n      next.final = final\n\n      node.edges[char] = next\n      node = next\n    }\n  }\n\n  return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n  var words = []\n\n  var stack = [{\n    prefix: \"\",\n    node: this\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop(),\n        edges = Object.keys(frame.node.edges),\n        len = edges.length\n\n    if (frame.node.final) {\n      /* In Safari, at this point the prefix is sometimes corrupted, see:\n       * https://github.com/olivernn/lunr.js/issues/279 Calling any\n       * String.prototype method forces Safari to \"cast\" this string to what\n       * it's supposed to be, fixing the bug. */\n      frame.prefix.charAt(0)\n      words.push(frame.prefix)\n    }\n\n    for (var i = 0; i < len; i++) {\n      var edge = edges[i]\n\n      stack.push({\n        prefix: frame.prefix.concat(edge),\n        node: frame.node.edges[edge]\n      })\n    }\n  }\n\n  return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n  // NOTE: Using Object.keys here as this.edges is very likely\n  // to enter 'hash-mode' with many keys being added\n  //\n  // avoiding a for-in loop here as it leads to the function\n  // being de-optimised (at least in V8). From some simple\n  // benchmarks the performance is comparable, but allowing\n  // V8 to optimize may mean easy performance wins in the future.\n\n  if (this._str) {\n    return this._str\n  }\n\n  var str = this.final ? '1' : '0',\n      labels = Object.keys(this.edges).sort(),\n      len = labels.length\n\n  for (var i = 0; i < len; i++) {\n    var label = labels[i],\n        node = this.edges[label]\n\n    str = str + label + node.id\n  }\n\n  return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n  var output = new lunr.TokenSet,\n      frame = undefined\n\n  var stack = [{\n    qNode: b,\n    output: output,\n    node: this\n  }]\n\n  while (stack.length) {\n    frame = stack.pop()\n\n    // NOTE: As with the #toString method, we are using\n    // Object.keys and a for loop instead of a for-in loop\n    // as both of these objects enter 'hash' mode, causing\n    // the function to be de-optimised in V8\n    var qEdges = Object.keys(frame.qNode.edges),\n        qLen = qEdges.length,\n        nEdges = Object.keys(frame.node.edges),\n        nLen = nEdges.length\n\n    for (var q = 0; q < qLen; q++) {\n      var qEdge = qEdges[q]\n\n      for (var n = 0; n < nLen; n++) {\n        var nEdge = nEdges[n]\n\n        if (nEdge == qEdge || qEdge == '*') {\n          var node = frame.node.edges[nEdge],\n              qNode = frame.qNode.edges[qEdge],\n              final = node.final && qNode.final,\n              next = undefined\n\n          if (nEdge in frame.output.edges) {\n            // an edge already exists for this character\n            // no need to create a new node, just set the finality\n            // bit unless this node is already final\n            next = frame.output.edges[nEdge]\n            next.final = next.final || final\n\n          } else {\n            // no edge exists yet, must create one\n            // set the finality bit and insert it\n            // into the output\n            next = new lunr.TokenSet\n            next.final = final\n            frame.output.edges[nEdge] = next\n          }\n\n          stack.push({\n            qNode: qNode,\n            output: next,\n            node: node\n          })\n        }\n      }\n    }\n  }\n\n  return output\n}\nlunr.TokenSet.Builder = function () {\n  this.previousWord = \"\"\n  this.root = new lunr.TokenSet\n  this.uncheckedNodes = []\n  this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n  var node,\n      commonPrefix = 0\n\n  if (word < this.previousWord) {\n    throw new Error (\"Out of order word insertion\")\n  }\n\n  for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n    if (word[i] != this.previousWord[i]) break\n    commonPrefix++\n  }\n\n  this.minimize(commonPrefix)\n\n  if (this.uncheckedNodes.length == 0) {\n    node = this.root\n  } else {\n    node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n  }\n\n  for (var i = commonPrefix; i < word.length; i++) {\n    var nextNode = new lunr.TokenSet,\n        char = word[i]\n\n    node.edges[char] = nextNode\n\n    this.uncheckedNodes.push({\n      parent: node,\n      char: char,\n      child: nextNode\n    })\n\n    node = nextNode\n  }\n\n  node.final = true\n  this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n  this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n  for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n    var node = this.uncheckedNodes[i],\n        childKey = node.child.toString()\n\n    if (childKey in this.minimizedNodes) {\n      node.parent.edges[node.char] = this.minimizedNodes[childKey]\n    } else {\n      // Cache the key for this node since\n      // we know it can't change anymore\n      node.child._str = childKey\n\n      this.minimizedNodes[childKey] = node.child\n    }\n\n    this.uncheckedNodes.pop()\n  }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object<string, lunr.Vector>} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n  this.invertedIndex = attrs.invertedIndex\n  this.fieldVectors = attrs.fieldVectors\n  this.tokenSet = attrs.tokenSet\n  this.fields = attrs.fields\n  this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example <caption>Simple single term query</caption>\n * hello\n * @example <caption>Multiple term query</caption>\n * hello world\n * @example <caption>term scoped to a field</caption>\n * title:hello\n * @example <caption>term with a boost of 10</caption>\n * hello^10\n * @example <caption>term with an edit distance of 2</caption>\n * hello~2\n * @example <caption>terms with presence modifiers</caption>\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first.  For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n  return this.query(function (query) {\n    var parser = new lunr.QueryParser(queryString, query)\n    parser.parse()\n  })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n  // for each query clause\n  // * process terms\n  // * expand terms from token set\n  // * find matching documents and metadata\n  // * get document vectors\n  // * score documents\n\n  var query = new lunr.Query(this.fields),\n      matchingFields = Object.create(null),\n      queryVectors = Object.create(null),\n      termFieldCache = Object.create(null),\n      requiredMatches = Object.create(null),\n      prohibitedMatches = Object.create(null)\n\n  /*\n   * To support field level boosts a query vector is created per\n   * field. An empty vector is eagerly created to support negated\n   * queries.\n   */\n  for (var i = 0; i < this.fields.length; i++) {\n    queryVectors[this.fields[i]] = new lunr.Vector\n  }\n\n  fn.call(query, query)\n\n  for (var i = 0; i < query.clauses.length; i++) {\n    /*\n     * Unless the pipeline has been disabled for this term, which is\n     * the case for terms with wildcards, we need to pass the clause\n     * term through the search pipeline. A pipeline returns an array\n     * of processed terms. Pipeline functions may expand the passed\n     * term, which means we may end up performing multiple index lookups\n     * for a single query term.\n     */\n    var clause = query.clauses[i],\n        terms = null,\n        clauseMatches = lunr.Set.empty\n\n    if (clause.usePipeline) {\n      terms = this.pipeline.runString(clause.term, {\n        fields: clause.fields\n      })\n    } else {\n      terms = [clause.term]\n    }\n\n    for (var m = 0; m < terms.length; m++) {\n      var term = terms[m]\n\n      /*\n       * Each term returned from the pipeline needs to use the same query\n       * clause object, e.g. the same boost and or edit distance. The\n       * simplest way to do this is to re-use the clause object but mutate\n       * its term property.\n       */\n      clause.term = term\n\n      /*\n       * From the term in the clause we create a token set which will then\n       * be used to intersect the indexes token set to get a list of terms\n       * to lookup in the inverted index\n       */\n      var termTokenSet = lunr.TokenSet.fromClause(clause),\n          expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n      /*\n       * If a term marked as required does not exist in the tokenSet it is\n       * impossible for the search to return any matches. We set all the field\n       * scoped required matches set to empty and stop examining any further\n       * clauses.\n       */\n      if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n        for (var k = 0; k < clause.fields.length; k++) {\n          var field = clause.fields[k]\n          requiredMatches[field] = lunr.Set.empty\n        }\n\n        break\n      }\n\n      for (var j = 0; j < expandedTerms.length; j++) {\n        /*\n         * For each term get the posting and termIndex, this is required for\n         * building the query vector.\n         */\n        var expandedTerm = expandedTerms[j],\n            posting = this.invertedIndex[expandedTerm],\n            termIndex = posting._index\n\n        for (var k = 0; k < clause.fields.length; k++) {\n          /*\n           * For each field that this query term is scoped by (by default\n           * all fields are in scope) we need to get all the document refs\n           * that have this term in that field.\n           *\n           * The posting is the entry in the invertedIndex for the matching\n           * term from above.\n           */\n          var field = clause.fields[k],\n              fieldPosting = posting[field],\n              matchingDocumentRefs = Object.keys(fieldPosting),\n              termField = expandedTerm + \"/\" + field,\n              matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n          /*\n           * if the presence of this term is required ensure that the matching\n           * documents are added to the set of required matches for this clause.\n           *\n           */\n          if (clause.presence == lunr.Query.presence.REQUIRED) {\n            clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n            if (requiredMatches[field] === undefined) {\n              requiredMatches[field] = lunr.Set.complete\n            }\n          }\n\n          /*\n           * if the presence of this term is prohibited ensure that the matching\n           * documents are added to the set of prohibited matches for this field,\n           * creating that set if it does not yet exist.\n           */\n          if (clause.presence == lunr.Query.presence.PROHIBITED) {\n            if (prohibitedMatches[field] === undefined) {\n              prohibitedMatches[field] = lunr.Set.empty\n            }\n\n            prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n            /*\n             * Prohibited matches should not be part of the query vector used for\n             * similarity scoring and no metadata should be extracted so we continue\n             * to the next field\n             */\n            continue\n          }\n\n          /*\n           * The query field vector is populated using the termIndex found for\n           * the term and a unit value with the appropriate boost applied.\n           * Using upsert because there could already be an entry in the vector\n           * for the term we are working with. In that case we just add the scores\n           * together.\n           */\n          queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n          /**\n           * If we've already seen this term, field combo then we've already collected\n           * the matching documents and metadata, no need to go through all that again\n           */\n          if (termFieldCache[termField]) {\n            continue\n          }\n\n          for (var l = 0; l < matchingDocumentRefs.length; l++) {\n            /*\n             * All metadata for this term/field/document triple\n             * are then extracted and collected into an instance\n             * of lunr.MatchData ready to be returned in the query\n             * results\n             */\n            var matchingDocumentRef = matchingDocumentRefs[l],\n                matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n                metadata = fieldPosting[matchingDocumentRef],\n                fieldMatch\n\n            if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n              matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n            } else {\n              fieldMatch.add(expandedTerm, field, metadata)\n            }\n\n          }\n\n          termFieldCache[termField] = true\n        }\n      }\n    }\n\n    /**\n     * If the presence was required we need to update the requiredMatches field sets.\n     * We do this after all fields for the term have collected their matches because\n     * the clause terms presence is required in _any_ of the fields not _all_ of the\n     * fields.\n     */\n    if (clause.presence === lunr.Query.presence.REQUIRED) {\n      for (var k = 0; k < clause.fields.length; k++) {\n        var field = clause.fields[k]\n        requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n      }\n    }\n  }\n\n  /**\n   * Need to combine the field scoped required and prohibited\n   * matching documents into a global set of required and prohibited\n   * matches\n   */\n  var allRequiredMatches = lunr.Set.complete,\n      allProhibitedMatches = lunr.Set.empty\n\n  for (var i = 0; i < this.fields.length; i++) {\n    var field = this.fields[i]\n\n    if (requiredMatches[field]) {\n      allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n    }\n\n    if (prohibitedMatches[field]) {\n      allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n    }\n  }\n\n  var matchingFieldRefs = Object.keys(matchingFields),\n      results = [],\n      matches = Object.create(null)\n\n  /*\n   * If the query is negated (contains only prohibited terms)\n   * we need to get _all_ fieldRefs currently existing in the\n   * index. This is only done when we know that the query is\n   * entirely prohibited terms to avoid any cost of getting all\n   * fieldRefs unnecessarily.\n   *\n   * Additionally, blank MatchData must be created to correctly\n   * populate the results.\n   */\n  if (query.isNegated()) {\n    matchingFieldRefs = Object.keys(this.fieldVectors)\n\n    for (var i = 0; i < matchingFieldRefs.length; i++) {\n      var matchingFieldRef = matchingFieldRefs[i]\n      var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n      matchingFields[matchingFieldRef] = new lunr.MatchData\n    }\n  }\n\n  for (var i = 0; i < matchingFieldRefs.length; i++) {\n    /*\n     * Currently we have document fields that match the query, but we\n     * need to return documents. The matchData and scores are combined\n     * from multiple fields belonging to the same document.\n     *\n     * Scores are calculated by field, using the query vectors created\n     * above, and combined into a final document score using addition.\n     */\n    var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n        docRef = fieldRef.docRef\n\n    if (!allRequiredMatches.contains(docRef)) {\n      continue\n    }\n\n    if (allProhibitedMatches.contains(docRef)) {\n      continue\n    }\n\n    var fieldVector = this.fieldVectors[fieldRef],\n        score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n        docMatch\n\n    if ((docMatch = matches[docRef]) !== undefined) {\n      docMatch.score += score\n      docMatch.matchData.combine(matchingFields[fieldRef])\n    } else {\n      var match = {\n        ref: docRef,\n        score: score,\n        matchData: matchingFields[fieldRef]\n      }\n      matches[docRef] = match\n      results.push(match)\n    }\n  }\n\n  /*\n   * Sort the results objects by score, highest first.\n   */\n  return results.sort(function (a, b) {\n    return b.score - a.score\n  })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n  var invertedIndex = Object.keys(this.invertedIndex)\n    .sort()\n    .map(function (term) {\n      return [term, this.invertedIndex[term]]\n    }, this)\n\n  var fieldVectors = Object.keys(this.fieldVectors)\n    .map(function (ref) {\n      return [ref, this.fieldVectors[ref].toJSON()]\n    }, this)\n\n  return {\n    version: lunr.version,\n    fields: this.fields,\n    fieldVectors: fieldVectors,\n    invertedIndex: invertedIndex,\n    pipeline: this.pipeline.toJSON()\n  }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n  var attrs = {},\n      fieldVectors = {},\n      serializedVectors = serializedIndex.fieldVectors,\n      invertedIndex = Object.create(null),\n      serializedInvertedIndex = serializedIndex.invertedIndex,\n      tokenSetBuilder = new lunr.TokenSet.Builder,\n      pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n  if (serializedIndex.version != lunr.version) {\n    lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n  }\n\n  for (var i = 0; i < serializedVectors.length; i++) {\n    var tuple = serializedVectors[i],\n        ref = tuple[0],\n        elements = tuple[1]\n\n    fieldVectors[ref] = new lunr.Vector(elements)\n  }\n\n  for (var i = 0; i < serializedInvertedIndex.length; i++) {\n    var tuple = serializedInvertedIndex[i],\n        term = tuple[0],\n        posting = tuple[1]\n\n    tokenSetBuilder.insert(term)\n    invertedIndex[term] = posting\n  }\n\n  tokenSetBuilder.finish()\n\n  attrs.fields = serializedIndex.fields\n\n  attrs.fieldVectors = fieldVectors\n  attrs.invertedIndex = invertedIndex\n  attrs.tokenSet = tokenSetBuilder.root\n  attrs.pipeline = pipeline\n\n  return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n  this._ref = \"id\"\n  this._fields = Object.create(null)\n  this._documents = Object.create(null)\n  this.invertedIndex = Object.create(null)\n  this.fieldTermFrequencies = {}\n  this.fieldLengths = {}\n  this.tokenizer = lunr.tokenizer\n  this.pipeline = new lunr.Pipeline\n  this.searchPipeline = new lunr.Pipeline\n  this.documentCount = 0\n  this._b = 0.75\n  this._k1 = 1.2\n  this.termIndex = 0\n  this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n  this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example <caption>Extracting a nested field</caption>\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n  if (/\\//.test(fieldName)) {\n    throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n  }\n\n  this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n  if (number < 0) {\n    this._b = 0\n  } else if (number > 1) {\n    this._b = 1\n  } else {\n    this._b = number\n  }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n  this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n  var docRef = doc[this._ref],\n      fields = Object.keys(this._fields)\n\n  this._documents[docRef] = attributes || {}\n  this.documentCount += 1\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i],\n        extractor = this._fields[fieldName].extractor,\n        field = extractor ? extractor(doc) : doc[fieldName],\n        tokens = this.tokenizer(field, {\n          fields: [fieldName]\n        }),\n        terms = this.pipeline.run(tokens),\n        fieldRef = new lunr.FieldRef (docRef, fieldName),\n        fieldTerms = Object.create(null)\n\n    this.fieldTermFrequencies[fieldRef] = fieldTerms\n    this.fieldLengths[fieldRef] = 0\n\n    // store the length of this field for this document\n    this.fieldLengths[fieldRef] += terms.length\n\n    // calculate term frequencies for this field\n    for (var j = 0; j < terms.length; j++) {\n      var term = terms[j]\n\n      if (fieldTerms[term] == undefined) {\n        fieldTerms[term] = 0\n      }\n\n      fieldTerms[term] += 1\n\n      // add to inverted index\n      // create an initial posting if one doesn't exist\n      if (this.invertedIndex[term] == undefined) {\n        var posting = Object.create(null)\n        posting[\"_index\"] = this.termIndex\n        this.termIndex += 1\n\n        for (var k = 0; k < fields.length; k++) {\n          posting[fields[k]] = Object.create(null)\n        }\n\n        this.invertedIndex[term] = posting\n      }\n\n      // add an entry for this term/fieldName/docRef to the invertedIndex\n      if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n        this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n      }\n\n      // store all whitelisted metadata about this token in the\n      // inverted index\n      for (var l = 0; l < this.metadataWhitelist.length; l++) {\n        var metadataKey = this.metadataWhitelist[l],\n            metadata = term.metadata[metadataKey]\n\n        if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n          this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n        }\n\n        this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n      }\n    }\n\n  }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n  var fieldRefs = Object.keys(this.fieldLengths),\n      numberOfFields = fieldRefs.length,\n      accumulator = {},\n      documentsWithField = {}\n\n  for (var i = 0; i < numberOfFields; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        field = fieldRef.fieldName\n\n    documentsWithField[field] || (documentsWithField[field] = 0)\n    documentsWithField[field] += 1\n\n    accumulator[field] || (accumulator[field] = 0)\n    accumulator[field] += this.fieldLengths[fieldRef]\n  }\n\n  var fields = Object.keys(this._fields)\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i]\n    accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n  }\n\n  this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n  var fieldVectors = {},\n      fieldRefs = Object.keys(this.fieldTermFrequencies),\n      fieldRefsLength = fieldRefs.length,\n      termIdfCache = Object.create(null)\n\n  for (var i = 0; i < fieldRefsLength; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        fieldName = fieldRef.fieldName,\n        fieldLength = this.fieldLengths[fieldRef],\n        fieldVector = new lunr.Vector,\n        termFrequencies = this.fieldTermFrequencies[fieldRef],\n        terms = Object.keys(termFrequencies),\n        termsLength = terms.length\n\n\n    var fieldBoost = this._fields[fieldName].boost || 1,\n        docBoost = this._documents[fieldRef.docRef].boost || 1\n\n    for (var j = 0; j < termsLength; j++) {\n      var term = terms[j],\n          tf = termFrequencies[term],\n          termIndex = this.invertedIndex[term]._index,\n          idf, score, scoreWithPrecision\n\n      if (termIdfCache[term] === undefined) {\n        idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n        termIdfCache[term] = idf\n      } else {\n        idf = termIdfCache[term]\n      }\n\n      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n      score *= fieldBoost\n      score *= docBoost\n      scoreWithPrecision = Math.round(score * 1000) / 1000\n      // Converts 1.23456789 to 1.234.\n      // Reducing the precision so that the vectors take up less\n      // space when serialised. Doing it now so that they behave\n      // the same before and after serialisation. Also, this is\n      // the fastest approach to reducing a number's precision in\n      // JavaScript.\n\n      fieldVector.insert(termIndex, scoreWithPrecision)\n    }\n\n    fieldVectors[fieldRef] = fieldVector\n  }\n\n  this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n  this.tokenSet = lunr.TokenSet.fromArray(\n    Object.keys(this.invertedIndex).sort()\n  )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n  this.calculateAverageFieldLengths()\n  this.createFieldVectors()\n  this.createTokenSet()\n\n  return new lunr.Index({\n    invertedIndex: this.invertedIndex,\n    fieldVectors: this.fieldVectors,\n    tokenSet: this.tokenSet,\n    fields: Object.keys(this._fields),\n    pipeline: this.searchPipeline\n  })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n  var args = Array.prototype.slice.call(arguments, 1)\n  args.unshift(this)\n  fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n  var clonedMetadata = Object.create(null),\n      metadataKeys = Object.keys(metadata || {})\n\n  // Cloning the metadata to prevent the original\n  // being mutated during match data combination.\n  // Metadata is kept in an array within the inverted\n  // index so cloning the data can be done with\n  // Array#slice\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n    clonedMetadata[key] = metadata[key].slice()\n  }\n\n  this.metadata = Object.create(null)\n\n  if (term !== undefined) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = clonedMetadata\n  }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n  var terms = Object.keys(otherMatchData.metadata)\n\n  for (var i = 0; i < terms.length; i++) {\n    var term = terms[i],\n        fields = Object.keys(otherMatchData.metadata[term])\n\n    if (this.metadata[term] == undefined) {\n      this.metadata[term] = Object.create(null)\n    }\n\n    for (var j = 0; j < fields.length; j++) {\n      var field = fields[j],\n          keys = Object.keys(otherMatchData.metadata[term][field])\n\n      if (this.metadata[term][field] == undefined) {\n        this.metadata[term][field] = Object.create(null)\n      }\n\n      for (var k = 0; k < keys.length; k++) {\n        var key = keys[k]\n\n        if (this.metadata[term][field][key] == undefined) {\n          this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n        } else {\n          this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n        }\n\n      }\n    }\n  }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n  if (!(term in this.metadata)) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  if (!(field in this.metadata[term])) {\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  var metadataKeys = Object.keys(metadata)\n\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n\n    if (key in this.metadata[term][field]) {\n      this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n    } else {\n      this.metadata[term][field][key] = metadata[key]\n    }\n  }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n  this.clauses = []\n  this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with trailing wildcard</caption>\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example <caption>query term with leading and trailing wildcard</caption>\n * query.term('foo', {\n *   wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with required presence</caption>\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n  /**\n   * Term's presence in a document is optional, this is the default value.\n   */\n  OPTIONAL: 1,\n\n  /**\n   * Term's presence in a document is required, documents that do not contain\n   * this term will not be returned.\n   */\n  REQUIRED: 2,\n\n  /**\n   * Term's presence in a document is prohibited, documents that do contain\n   * this term will not be returned.\n   */\n  PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n  if (!('fields' in clause)) {\n    clause.fields = this.allFields\n  }\n\n  if (!('boost' in clause)) {\n    clause.boost = 1\n  }\n\n  if (!('usePipeline' in clause)) {\n    clause.usePipeline = true\n  }\n\n  if (!('wildcard' in clause)) {\n    clause.wildcard = lunr.Query.wildcard.NONE\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n    clause.term = \"*\" + clause.term\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n    clause.term = \"\" + clause.term + \"*\"\n  }\n\n  if (!('presence' in clause)) {\n    clause.presence = lunr.Query.presence.OPTIONAL\n  }\n\n  this.clauses.push(clause)\n\n  return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n  for (var i = 0; i < this.clauses.length; i++) {\n    if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n      return false\n    }\n  }\n\n  return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example <caption>adding a single term to a query</caption>\n * query.term(\"foo\")\n * @example <caption>adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard</caption>\n * query.term(\"foo\", {\n *   fields: [\"title\"],\n *   boost: 10,\n *   wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example <caption>using lunr.tokenizer to convert a string to tokens before using them as terms</caption>\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n  if (Array.isArray(term)) {\n    term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n    return this\n  }\n\n  var clause = options || {}\n  clause.term = term.toString()\n\n  this.clause(clause)\n\n  return this\n}\nlunr.QueryParseError = function (message, start, end) {\n  this.name = \"QueryParseError\"\n  this.message = message\n  this.start = start\n  this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n  this.lexemes = []\n  this.str = str\n  this.length = str.length\n  this.pos = 0\n  this.start = 0\n  this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n  var state = lunr.QueryLexer.lexText\n\n  while (state) {\n    state = state(this)\n  }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n  var subSlices = [],\n      sliceStart = this.start,\n      sliceEnd = this.pos\n\n  for (var i = 0; i < this.escapeCharPositions.length; i++) {\n    sliceEnd = this.escapeCharPositions[i]\n    subSlices.push(this.str.slice(sliceStart, sliceEnd))\n    sliceStart = sliceEnd + 1\n  }\n\n  subSlices.push(this.str.slice(sliceStart, this.pos))\n  this.escapeCharPositions.length = 0\n\n  return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n  this.lexemes.push({\n    type: type,\n    str: this.sliceString(),\n    start: this.start,\n    end: this.pos\n  })\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n  this.escapeCharPositions.push(this.pos - 1)\n  this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n  if (this.pos >= this.length) {\n    return lunr.QueryLexer.EOS\n  }\n\n  var char = this.str.charAt(this.pos)\n  this.pos += 1\n  return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n  return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n  if (this.start == this.pos) {\n    this.pos += 1\n  }\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n  this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n  var char, charCode\n\n  do {\n    char = this.next()\n    charCode = char.charCodeAt(0)\n  } while (charCode > 47 && charCode < 58)\n\n  if (char != lunr.QueryLexer.EOS) {\n    this.backup()\n  }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n  return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n  lexer.backup()\n  lexer.emit(lunr.QueryLexer.FIELD)\n  lexer.ignore()\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n  if (lexer.width() > 1) {\n    lexer.backup()\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n\n  lexer.ignore()\n\n  if (lexer.more()) {\n    return lunr.QueryLexer.lexText\n  }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.BOOST)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n  if (lexer.width() > 0) {\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n  while (true) {\n    var char = lexer.next()\n\n    if (char == lunr.QueryLexer.EOS) {\n      return lunr.QueryLexer.lexEOS\n    }\n\n    // Escape character is '\\'\n    if (char.charCodeAt(0) == 92) {\n      lexer.escapeCharacter()\n      continue\n    }\n\n    if (char == \":\") {\n      return lunr.QueryLexer.lexField\n    }\n\n    if (char == \"~\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexEditDistance\n    }\n\n    if (char == \"^\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexBoost\n    }\n\n    // \"+\" indicates term presence is required\n    // checking for length to ensure that only\n    // leading \"+\" are considered\n    if (char == \"+\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    // \"-\" indicates term presence is prohibited\n    // checking for length to ensure that only\n    // leading \"-\" are considered\n    if (char == \"-\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    if (char.match(lunr.QueryLexer.termSeparator)) {\n      return lunr.QueryLexer.lexTerm\n    }\n  }\n}\n\nlunr.QueryParser = function (str, query) {\n  this.lexer = new lunr.QueryLexer (str)\n  this.query = query\n  this.currentClause = {}\n  this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n  this.lexer.run()\n  this.lexemes = this.lexer.lexemes\n\n  var state = lunr.QueryParser.parseClause\n\n  while (state) {\n    state = state(this)\n  }\n\n  return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n  return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n  var lexeme = this.peekLexeme()\n  this.lexemeIdx += 1\n  return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n  var completedClause = this.currentClause\n  this.query.clause(completedClause)\n  this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n  var lexeme = parser.peekLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.type) {\n    case lunr.QueryLexer.PRESENCE:\n      return lunr.QueryParser.parsePresence\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n      if (lexeme.str.length >= 1) {\n        errorMessage += \" with value '\" + lexeme.str + \"'\"\n      }\n\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.str) {\n    case \"-\":\n      parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n      break\n    case \"+\":\n      parser.currentClause.presence = lunr.Query.presence.REQUIRED\n      break\n    default:\n      var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term or field, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n    var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n        errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.fields = [lexeme.str]\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  parser.currentClause.term = lexeme.str.toLowerCase()\n\n  if (lexeme.str.indexOf(\"*\") != -1) {\n    parser.currentClause.usePipeline = false\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var editDistance = parseInt(lexeme.str, 10)\n\n  if (isNaN(editDistance)) {\n    var errorMessage = \"edit distance must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.editDistance = editDistance\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var boost = parseInt(lexeme.str, 10)\n\n  if (isNaN(boost)) {\n    var errorMessage = \"boost must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.boost = boost\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\n  /**\n   * export the module via AMD, CommonJS or as a browser global\n   * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n   */\n  ;(function (root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      // AMD. Register as an anonymous module.\n      define(factory)\n    } else if (typeof exports === 'object') {\n      /**\n       * Node. Does not work with strict CommonJS, but\n       * only CommonJS-like enviroments that support module.exports,\n       * like Node.\n       */\n      module.exports = factory()\n    } else {\n      // Browser globals (root is window)\n      root.lunr = factory()\n    }\n  }(this, function () {\n    /**\n     * Just return a value to define the module export.\n     * This example returns an object, but the module\n     * can return a function as the exported value.\n     */\n    return lunr\n  }))\n})();\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport \"~/polyfills\"\n\nimport { Search, SearchIndexConfig } from \"../../_\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for usage with `iframe-worker` polyfill\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous polyfilled implementation. The\n * cool thing is that awaiting a non-Promise is a noop, so extending the type\n * definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n */\ndeclare global {\n  function importScripts(...urls: string[]): Promise<void> | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages, which are defined through the search index configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search index configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n  config: SearchIndexConfig\n): Promise<void> {\n  let base = \"../lunr\"\n\n  /* Detect `iframe-worker` and fix base URL */\n  if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n    const worker = document.querySelector<HTMLScriptElement>(\"script[src]\")!\n    const [path] = worker.src.split(\"/worker\")\n\n    /* Prefix base with path */\n    base = base.replace(\"..\", path)\n  }\n\n  /* Add scripts for languages */\n  const scripts = []\n  for (const lang of config.lang) {\n    switch (lang) {\n\n      /* Add segmenter for Japanese */\n      case \"ja\":\n        scripts.push(`${base}/tinyseg.js`)\n        break\n\n      /* Add segmenter for Hindi and Thai */\n      case \"hi\":\n      case \"th\":\n        scripts.push(`${base}/wordcut.js`)\n        break\n    }\n\n    /* Add language support */\n    if (lang !== \"en\")\n      scripts.push(`${base}/min/lunr.${lang}.min.js`)\n  }\n\n  /* Add multi-language support */\n  if (config.lang.length > 1)\n    scripts.push(`${base}/min/lunr.multi.min.js`)\n\n  /* Load scripts synchronously */\n  if (scripts.length)\n    await importScripts(\n      `${base}/min/lunr.stemmer.support.min.js`,\n      ...scripts\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n  message: SearchMessage\n): Promise<SearchMessage> {\n  switch (message.type) {\n\n    /* Search setup message */\n    case SearchMessageType.SETUP:\n      await setupSearchLanguages(message.data.config)\n      index = new Search(message.data)\n      return {\n        type: SearchMessageType.READY\n      }\n\n    /* Search query message */\n    case SearchMessageType.QUERY:\n      return {\n        type: SearchMessageType.RESULT,\n        data: index ? index.search(message.data) : { items: [] }\n      }\n\n    /* All other messages */\n    default:\n      throw new TypeError(\"Invalid message type\")\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* @ts-expect-error - expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n  postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node !== \"object\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n  parent?: SearchIndexDocument         /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map<string, SearchDocument>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchIndexDocument[]\n): SearchDocumentMap {\n  const documents = new Map<string, SearchDocument>()\n  const parents   = new Set<SearchDocument>()\n  for (const doc of docs) {\n    const [path, hash] = doc.location.split(\"#\")\n\n    /* Extract location, title and tags */\n    const location = doc.location\n    const title    = doc.title\n    const tags     = doc.tags\n\n    /* Escape and cleanup text */\n    const text = escapeHTML(doc.text)\n      .replace(/\\s+(?=[,.:;!?])/g, \"\")\n      .replace(/\\s+/g, \" \")\n\n    /* Handle section */\n    if (hash) {\n      const parent = documents.get(path)!\n\n      /* Ignore first section, override article */\n      if (!parents.has(parent)) {\n        parent.title = doc.title\n        parent.text  = text\n\n        /* Remember that we processed the article */\n        parents.add(parent)\n\n      /* Add subsequent section */\n      } else {\n        documents.set(location, {\n          location,\n          title,\n          text,\n          parent\n        })\n      }\n\n    /* Add article */\n    } else {\n      documents.set(location, {\n        location,\n        title,\n        text,\n        ...tags && { tags }\n      })\n    }\n  }\n  return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n  const separator = new RegExp(config.separator, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator})(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => (\n      escape\n        ? escapeHTML(value)\n        : value\n      )\n        .replace(match, highlight)\n        .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n  presence: lunr.Query.presence        /* Clause presence */\n  term: string                         /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record<string, boolean>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n  value: string\n): SearchQueryClause[] {\n  const query  = new (lunr as any).Query([\"title\", \"text\"])\n  const parser = new (lunr as any).QueryParser(value, query)\n\n  /* Parse and return query clauses */\n  parser.parse()\n  return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n  query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n  const clauses = new Set<SearchQueryClause>(query)\n\n  /* Match query clauses against terms */\n  const result: SearchQueryTerms = {}\n  for (let t = 0; t < terms.length; t++)\n    for (const clause of clauses)\n      if (terms[t].startsWith(clause.term)) {\n        result[clause.term] = true\n        clauses.delete(clause)\n      }\n\n  /* Annotate unmatched non-stopword query clauses */\n  for (const clause of clauses)\n    if (lunr.stopWordFilter?.(clause.term as any))\n      result[clause.term] = false\n\n  /* Return query terms */\n  return result\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  SearchDocument,\n  SearchDocumentMap,\n  setupSearchDocumentMap\n} from \"../document\"\nimport {\n  SearchHighlightFactoryFn,\n  setupSearchHighlighter\n} from \"../highlighter\"\nimport { SearchOptions } from \"../options\"\nimport {\n  SearchQueryTerms,\n  getSearchQueryTerms,\n  parseSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index configuration\n */\nexport interface SearchIndexConfig {\n  lang: string[]                       /* Search languages */\n  separator: string                    /* Search separator */\n}\n\n/**\n * Search index document\n */\nexport interface SearchIndexDocument {\n  location: string                     /* Document location */\n  title: string                        /* Document title */\n  text: string                         /* Document text */\n  tags?: string[]                      /* Document tags */\n  boost?: number                       /* Document boost */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n *\n * This interfaces describes the format of the `search_index.json` file which\n * is automatically built by the MkDocs search plugin.\n */\nexport interface SearchIndex {\n  config: SearchIndexConfig            /* Search index configuration */\n  docs: SearchIndexDocument[]          /* Search index documents */\n  options: SearchOptions               /* Search options */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search metadata\n */\nexport interface SearchMetadata {\n  score: number                        /* Score (relevance) */\n  terms: SearchQueryTerms              /* Search query terms */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result document\n */\nexport type SearchResultDocument = SearchDocument & SearchMetadata\n\n/**\n * Search result item\n */\nexport type SearchResultItem = SearchResultDocument[]\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result\n */\nexport interface SearchResult {\n  items: SearchResultItem[]            /* Search result items */\n  suggestions?: string[]               /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n  const [x, y] = [new Set(a), new Set(b)]\n  return [\n    ...new Set([...x].filter(value => !y.has(value)))\n  ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n  /**\n   * Search document mapping\n   *\n   * A mapping of URLs (including hash fragments) to the actual articles and\n   * sections of the documentation. The search document mapping must be created\n   * regardless of whether the index was prebuilt or not, as Lunr.js itself\n   * only stores the actual index.\n   */\n  protected documents: SearchDocumentMap\n\n  /**\n   * Search highlight factory function\n   */\n  protected highlight: SearchHighlightFactoryFn\n\n  /**\n   * The underlying Lunr.js search index\n   */\n  protected index: lunr.Index\n\n  /**\n   * Search options\n   */\n  protected options: SearchOptions\n\n  /**\n   * Create the search integration\n   *\n   * @param data - Search index\n   */\n  public constructor({ config, docs, options }: SearchIndex) {\n    this.options = options\n\n    /* Set up document map and highlighter factory */\n    this.documents = setupSearchDocumentMap(docs)\n    this.highlight = setupSearchHighlighter(config, false)\n\n    /* Set separator for tokenizer */\n    lunr.tokenizer.separator = new RegExp(config.separator)\n\n    /* Create search index */\n    this.index = lunr(function () {\n\n      /* Set up multi-language support */\n      if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n        this.use((lunr as any)[config.lang[0]])\n      } else if (config.lang.length > 1) {\n        this.use((lunr as any).multiLanguage(...config.lang))\n      }\n\n      /* Compute functions to be removed from the pipeline */\n      const fns = difference([\n        \"trimmer\", \"stopWordFilter\", \"stemmer\"\n      ], options.pipeline)\n\n      /* Remove functions from the pipeline for registered languages */\n      for (const lang of config.lang.map(language => (\n        language === \"en\" ? lunr : (lunr as any)[language]\n      ))) {\n        for (const fn of fns) {\n          this.pipeline.remove(lang[fn])\n          this.searchPipeline.remove(lang[fn])\n        }\n      }\n\n      /* Set up reference */\n      this.ref(\"location\")\n\n      /* Set up fields */\n      this.field(\"title\", { boost: 1e3 })\n      this.field(\"text\")\n      this.field(\"tags\", { boost: 1e6, extractor: doc => {\n        const { tags = [] } = doc as SearchDocument\n        return tags.reduce((list, tag) => [\n          ...list,\n          ...lunr.tokenizer(tag)\n        ], [] as lunr.Token[])\n      } })\n\n      /* Index documents */\n      for (const doc of docs)\n        this.add(doc, { boost: doc.boost })\n    })\n  }\n\n  /**\n   * Search for matching documents\n   *\n   * The search index which MkDocs provides is divided up into articles, which\n   * contain the whole content of the individual pages, and sections, which only\n   * contain the contents of the subsections obtained by breaking the individual\n   * pages up at `h1` ... `h6`. As there may be many sections on different pages\n   * with identical titles (for example within this very project, e.g. \"Usage\"\n   * or \"Installation\"), they need to be put into the context of the containing\n   * page. For this reason, section results are grouped within their respective\n   * articles which are the top-level results that are returned.\n   *\n   * @param query - Query value\n   *\n   * @returns Search results\n   */\n  public search(query: string): SearchResult {\n    if (query) {\n      try {\n        const highlight = this.highlight(query)\n\n        /* Parse query to extract clauses for analysis */\n        const clauses = parseSearchQuery(query)\n          .filter(clause => (\n            clause.presence !== lunr.Query.presence.PROHIBITED\n          ))\n\n        /* Perform search and post-process results */\n        const groups = this.index.search(`${query}*`)\n\n          /* Apply post-query boosts based on title and search query terms */\n          .reduce<SearchResultItem>((item, { ref, score, matchData }) => {\n            const document = this.documents.get(ref)\n            if (typeof document !== \"undefined\") {\n              const { location, title, text, tags, parent } = document\n\n              /* Compute and analyze search query terms */\n              const terms = getSearchQueryTerms(\n                clauses,\n                Object.keys(matchData.metadata)\n              )\n\n              /* Highlight title and text and apply post-query boosts */\n              const boost = +!parent + +Object.values(terms).every(t => t)\n              item.push({\n                location,\n                title: highlight(title),\n                text:  highlight(text),\n                ...tags && { tags: tags.map(highlight) },\n                score: score * (1 + boost),\n                terms\n              })\n            }\n            return item\n          }, [])\n\n          /* Sort search results again after applying boosts */\n          .sort((a, b) => b.score - a.score)\n\n          /* Group search results by page */\n          .reduce((items, result) => {\n            const document = this.documents.get(result.location)\n            if (typeof document !== \"undefined\") {\n              const ref = \"parent\" in document\n                ? document.parent!.location\n                : document.location\n              items.set(ref, [...items.get(ref) || [], result])\n            }\n            return items\n          }, new Map<string, SearchResultItem>())\n\n        /* Generate search suggestions, if desired */\n        let suggestions: string[] | undefined\n        if (this.options.suggestions) {\n          const titles = this.index.query(builder => {\n            for (const clause of clauses)\n              builder.term(clause.term, {\n                fields: [\"title\"],\n                presence: lunr.Query.presence.REQUIRED,\n                wildcard: lunr.Query.wildcard.TRAILING\n              })\n          })\n\n          /* Retrieve suggestions for best match */\n          suggestions = titles.length\n            ? Object.keys(titles[0].matchData.metadata)\n            : []\n        }\n\n        /* Return items and suggestions */\n        return {\n          items: [...groups.values()],\n          ...typeof suggestions !== \"undefined\" && { suggestions }\n        }\n\n      /* Log errors to console (for now) */\n      } catch {\n        console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n      }\n    }\n\n    /* Return nothing in case of error or empty query */\n    return { items: [] }\n  }\n}\n"],
+  "mappings": 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+}
diff --git a/docs/site/assets/stylesheets/main.1d29e8d0.min.css b/docs/site/assets/stylesheets/main.1d29e8d0.min.css
new file mode 100644
index 0000000..2730c64
--- /dev/null
+++ b/docs/site/assets/stylesheets/main.1d29e8d0.min.css
@@ -0,0 +1 @@
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deleted file mode 100644
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diff --git a/docs/site/assets/stylesheets/palette.06af60db.min.css.map b/docs/site/assets/stylesheets/palette.06af60db.min.css.map
deleted file mode 100644
index efb568c..0000000
--- a/docs/site/assets/stylesheets/palette.06af60db.min.css.map
+++ /dev/null
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diff --git a/docs/site/assets/stylesheets/palette.cbb835fc.min.css b/docs/site/assets/stylesheets/palette.cbb835fc.min.css
new file mode 100644
index 0000000..30f9264
--- /dev/null
+++ b/docs/site/assets/stylesheets/palette.cbb835fc.min.css
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new file mode 100644
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diff --git a/docs/site/churn_pred/code_profiling.html b/docs/site/churn_pred/code_profiling.html
index ab9ab9e..34b1452 100644
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+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>code_profiling - ecovadis_assignment</title>
+        <title>code_profiling - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
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@@ -67,9 +56,12 @@
     
     
     
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+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
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@@ -90,22 +82,21 @@
 
   
 
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+<header class="md-header md-header--lifted" data-md-component="header">
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-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
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+            churnpred
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+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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+          
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+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
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@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
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+          <ol class="md-search-result__list"></ol>
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@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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-    ecovadis_assignment
+    churnpred
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@@ -204,41 +184,31 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
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-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
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-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
@@ -246,55 +216,30 @@
   
     
   
+
+
   
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-    
   
-  churn_pred
-
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-    
+    <li class="md-tabs__item">
+      <a href="code_profiling.html" class="md-tabs__link md-tabs__link--active">
+        churn_pred
+      </a>
+    </li>
   
 
       
         
   
   
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-          
-  
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-  
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     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
       </a>
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@@ -303,35 +248,37 @@
         
   
   
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-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
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-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
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-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
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@@ -356,7 +303,6 @@
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-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
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-    ecovadis_assignment
+    churnpred
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     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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-    ecovadis_assignment
+    churnpred
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@@ -388,19 +334,14 @@
     
       
       
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     <li class="md-nav__item">
       <a href="../index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,19 +349,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../installation.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    installation
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-  
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+        installation
       </a>
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@@ -428,92 +364,66 @@
     
       
       
+      
+
   
   
     
   
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+          churn_pred
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+          churn_pred
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-  
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+          code_profiling
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="code_profiling.html" class="md-nav__link md-nav__link--active">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+        code_profiling
       </a>
       
         
@@ -533,27 +443,7 @@
       
         <li class="md-nav__item">
   <a href="#churn_pred.code_profiling" class="md-nav__link">
-    <span class="md-ellipsis">
-      code_profiling
-    </span>
-  </a>
-  
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#churn_pred.code_profiling.ram_usage" class="md-nav__link">
-    <span class="md-ellipsis">
-      ram_usage
-    </span>
-  </a>
-  
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#churn_pred.code_profiling.timer" class="md-nav__link">
-    <span class="md-ellipsis">
-      timer
-    </span>
+    code_profiling
   </a>
   
 </li>
@@ -565,303 +455,214 @@
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="preprocessing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    preprocessing
-  </span>
-  
-
+        preprocessing
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="training.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    training
-  </span>
-  
-
+        training
       </a>
     </li>
   
 
-              
             
-          </ul>
-        </nav>
-      
+          
+        </ul>
+      </nav>
     </li>
   
 
     
       
       
+      
+
   
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--nested">
       
-        
       
-        
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" checked>
       
-        
       
-        
       
         
-      
+          
         
-      
+          
         
-      
-    
-    
-      
-      
-    
-    
-    <li class="md-nav__item md-nav__item--nested">
-      
+          
         
+          
         
           
         
-        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" type="checkbox" id="__nav_4" >
+          
         
           
-          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
-            
-  
-  <span class="md-ellipsis">
-    notebooks
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_4">
-            <span class="md-nav__icon md-icon"></span>
-            notebooks
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_4">
+          notebooks
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="notebooks" data-md-level="1">
+        <label class="md-nav__title" for="__nav_4">
+          <span class="md-nav__icon md-icon"></span>
+          notebooks
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
-  </span>
-  
-
+        TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../notebooks/00_auxiliary_features.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    00_auxiliary_features
-  </span>
-  
-
+        00_auxiliary_features
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../notebooks/01_feature_analysis.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    01_feature_analysis
-  </span>
-  
-
+        01_feature_analysis
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../notebooks/02_target_analysis.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    02_target_analysis
-  </span>
-  
-
+        02_target_analysis
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../notebooks/03_models.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    03_models
-  </span>
-  
-
+        03_models
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../notebooks/04_model_training_pipeline.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    04_model_training_pipeline
-  </span>
-  
-
+        04_model_training_pipeline
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../notebooks/05_model_analysis.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    05_model_analysis.ipynb
-  </span>
-  
-
+        05_model_analysis.ipynb
       </a>
     </li>
   
 
-              
             
-          </ul>
-        </nav>
-      
+          
+        </ul>
+      </nav>
     </li>
   
 
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../cicd.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    cicd
-  </span>
-  
-
+        cicd
       </a>
     </li>
   
@@ -869,19 +670,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code profiling
-  </span>
-  
-
+        code profiling
       </a>
     </li>
   
@@ -889,19 +685,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../contributing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    contributing
-  </span>
-  
-
+        contributing
       </a>
     </li>
   
@@ -915,14 +706,14 @@
             
             
           
-          
-            <div class="md-content" data-md-component="content">
-              <article class="md-content__inner md-typeset">
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
                 
-                  
+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/churn_pred/code_profiling.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  
-  
 
 
 <h1 id="code-profiling">Code profiling<a class="headerlink" href="#code-profiling" title="Permanent link">&para;</a></h1>
@@ -932,7 +723,12 @@ <h1 id="code-profiling">Code profiling<a class="headerlink" href="#code-profilin
 
 
 
-<a id="churn_pred.code_profiling"></a>
+<h2 id="churn_pred.code_profiling" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">code_profiling</span>
+
+
+<a href="#churn_pred.code_profiling" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -947,87 +743,33 @@ <h1 id="code-profiling">Code profiling<a class="headerlink" href="#code-profilin
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.code_profiling.ram_usage" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">ram_usage</span><span class="p">(</span><span class="n">func</span><span class="p">)</span></code>
+<h3 id="churn_pred.code_profiling.timer" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">timer</span>
 
-<a href="#churn_pred.code_profiling.ram_usage" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.code_profiling.timer" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">timer</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Wrapper to monitor and log RAM usage during the function execution.
-NOTE: can be applied to function but not to method of a class.
-Parameters:
-    func: function</p>
+      <p>Wrapper to time and log the function execution.</p>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/code_profiling.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">28</span>
-<span class="normal">29</span>
-<span class="normal">30</span>
-<span class="normal">31</span>
-<span class="normal">32</span>
-<span class="normal">33</span>
-<span class="normal">34</span>
-<span class="normal">35</span>
-<span class="normal">36</span>
-<span class="normal">37</span>
-<span class="normal">38</span>
-<span class="normal">39</span>
-<span class="normal">40</span>
-<span class="normal">41</span>
-<span class="normal">42</span>
-<span class="normal">43</span>
-<span class="normal">44</span>
-<span class="normal">45</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">ram_usage</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Wrapper to monitor and log RAM usage during the function execution.</span>
-<span class="sd">    NOTE: can be applied to function but not to method of a class.</span>
-<span class="sd">    Parameters:</span>
-<span class="sd">        func: function</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="nd">@wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
-    <span class="k">def</span> <span class="nf">ram_usage_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">ram</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">memory_usage</span><span class="p">(</span>
-            <span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="o">*</span><span class="n">kwargs</span><span class="p">),</span> <span class="n">interval</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">retval</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">max_usage</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span>
-        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
-            <span class="sa">f</span><span class="s2">&quot;Finished </span><span class="si">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span><span class="si">}</span><span class="s2">. Max RAM used </span><span class="si">{</span><span class="p">(</span><span class="n">ram</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="mi">1000</span><span class="p">)</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> GB.&quot;</span>
-        <span class="p">)</span>  <span class="c1"># noqa</span>
-        <span class="k">return</span> <span class="n">value</span>
-
-    <span class="k">return</span> <span class="n">ram_usage_func</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="churn_pred.code_profiling.timer" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">timer</span><span class="p">(</span><span class="n">func</span><span class="p">)</span></code>
-
-<a href="#churn_pred.code_profiling.timer" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Wrapper to time and log the function execution.
-Parameters:
-    func: function</p>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>func</b>
+        – <p>function</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/code_profiling.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 9</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/code_profiling.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 9</span>
 <span class="normal">10</span>
 <span class="normal">11</span>
 <span class="normal">12</span>
@@ -1044,7 +786,7 @@ <h2 id="churn_pred.code_profiling.timer" class="doc doc-heading">
 <span class="normal">23</span>
 <span class="normal">24</span>
 <span class="normal">25</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">timer</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Wrapper to time and log the function execution.</span>
+    <span class="sd">&quot;&quot;&quot;Wrapper to time and log the function execution.</span>
 <span class="sd">    Parameters:</span>
 <span class="sd">        func: function</span>
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -1055,70 +797,139 @@ <h2 id="churn_pred.code_profiling.timer" class="doc doc-heading">
         <span class="n">value</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="n">end_time</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
         <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
-            <span class="sa">f</span><span class="s2">&quot;Finished </span><span class="si">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> in </span><span class="si">{</span><span class="p">(</span><span class="n">end_time</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">start_time</span><span class="p">)</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> seconds.&quot;</span>  <span class="c1"># noqa</span>
+            <span class="sa">f</span><span class="s2">&quot;Finished </span><span class="si">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2"> in </span><span class="si">{</span><span class="p">(</span><span class="n">end_time</span> <span class="o">-</span> <span class="n">start_time</span><span class="p">)</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> seconds.&quot;</span>  <span class="c1"># noqa</span>
         <span class="p">)</span>
         <span class="k">return</span> <span class="n">value</span>
 
     <span class="k">return</span> <span class="n">timer_func</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
+<div class="doc doc-object doc-function">
 
 
-  </div>
 
-  </div>
-
-</div>
+<h3 id="churn_pred.code_profiling.ram_usage" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">ram_usage</span>
 
 
+<a href="#churn_pred.code_profiling.ram_usage" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">ram_usage</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
+</code></pre></div>
 
+  <div class="doc doc-contents ">
+  
+      <p>Wrapper to monitor and log RAM usage during the function execution.
+NOTE: can be applied to function but not to method of a class.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>func</b>
+        – <p>function</p>
+      </li>
+  </ul>
 
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/code_profiling.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">28</span>
+<span class="normal">29</span>
+<span class="normal">30</span>
+<span class="normal">31</span>
+<span class="normal">32</span>
+<span class="normal">33</span>
+<span class="normal">34</span>
+<span class="normal">35</span>
+<span class="normal">36</span>
+<span class="normal">37</span>
+<span class="normal">38</span>
+<span class="normal">39</span>
+<span class="normal">40</span>
+<span class="normal">41</span>
+<span class="normal">42</span>
+<span class="normal">43</span>
+<span class="normal">44</span>
+<span class="normal">45</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">ram_usage</span><span class="p">(</span><span class="n">func</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Wrapper to monitor and log RAM usage during the function execution.</span>
+<span class="sd">    NOTE: can be applied to function but not to method of a class.</span>
+<span class="sd">    Parameters:</span>
+<span class="sd">        func: function</span>
+<span class="sd">    &quot;&quot;&quot;</span>
 
+    <span class="nd">@wraps</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
+    <span class="k">def</span> <span class="nf">ram_usage_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ram</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">memory_usage</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="o">*</span><span class="n">kwargs</span><span class="p">),</span> <span class="n">interval</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">retval</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">max_usage</span><span class="o">=</span><span class="kc">True</span>
+        <span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Finished </span><span class="si">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span><span class="si">}</span><span class="s2">. Max RAM used </span><span class="si">{</span><span class="p">(</span><span class="n">ram</span> <span class="o">/</span> <span class="mi">1000</span><span class="p">)</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> GB.&quot;</span>
+        <span class="p">)</span>  <span class="c1"># noqa</span>
+        <span class="k">return</span> <span class="n">value</span>
 
+    <span class="k">return</span> <span class="n">ram_usage_func</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
 
-  <aside class="md-source-file">
-    
-    
-    
-      
-  
-  <span class="md-source-file__fact">
-    <span class="md-icon" title="Contributors">
-      
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 4a4 4 0 0 1 4 4 4 4 0 0 1-4 4 4 4 0 0 1-4-4 4 4 0 0 1 4-4m0 10c4.42 0 8 1.79 8 4v2H4v-2c0-2.21 3.58-4 8-4Z"/></svg>
-      
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-    <nav>
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-        <a href="mailto:mulinka.pavol@gmail.com">Pavol Mulinka</a>
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+</div>
 
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+  </div>
 
+  </div>
 
+</div>
 
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-            </div>
-          
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-<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
+              
+            </article>
+            
+          </div>
         </div>
         
       </main>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../installation.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: installation" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              installation
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="eda.html" class="md-footer__link md-footer__link--next" aria-label="Next: eda" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              eda
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -1143,12 +954,10 @@ <h2 id="churn_pred.code_profiling.timer" class="doc doc-heading">
     <div class="md-dialog" data-md-component="dialog">
       <div class="md-dialog__inner md-typeset"></div>
     </div>
+    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b97dbffb.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
-    
-    
-      <script src="../assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="../assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="../stylesheets/extra.js"></script>
       
diff --git a/docs/site/churn_pred/eda.html b/docs/site/churn_pred/eda.html
index d62974b..d687221 100644
--- a/docs/site/churn_pred/eda.html
+++ b/docs/site/churn_pred/eda.html
@@ -8,34 +8,24 @@
       
       
       
-      
-        <link rel="prev" href="code_profiling.html">
-      
-      
-        <link rel="next" href="preprocessing.html">
-      
-      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>eda - ecovadis_assignment</title>
+        <title>eda - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
-
-
     
     
-      
-    
     
       
         
@@ -50,13 +40,12 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
-    
   </head>
   
   
@@ -67,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -118,39 +109,31 @@
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     </div>
     
-      
-        <form class="md-header__option" data-md-component="palette">
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
-    
-      <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
-      </label>
-    
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
-    
-      <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
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-      </label>
-    
-  
-</form>
-      
-    
-    
-      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,41 +184,31 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
@@ -246,55 +216,30 @@
   
     
   
+
+
   
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="code_profiling.html" class="md-tabs__link md-tabs__link--active">
+        churn_pred
+      </a>
+    </li>
   
 
       
         
   
   
-  
-    
-    
-      <li class="md-tabs__item">
-        <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
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@@ -364,22 +310,22 @@
 
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+
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-    contributing
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+        contributing
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-          
-            <div class="md-content" data-md-component="content">
-              <article class="md-content__inner md-typeset">
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
                 
-                  
+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/churn_pred/eda.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  
-  
 
 
 <h1 id="preprocess-data">Preprocess data<a class="headerlink" href="#preprocess-data" title="Permanent link">&para;</a></h1>
@@ -1085,191 +758,43 @@ <h1 id="preprocess-data">Preprocess data<a class="headerlink" href="#preprocess-
 
 
 
-<a id="churn_pred.eda.features.analysis"></a>
-  <div class="doc doc-contents first">
-
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+<h2 id="churn_pred.eda.features.analysis" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">analysis</span>
 
 
+<a href="#churn_pred.eda.features.analysis" class="headerlink" title="Permanent link">&para;</a></h2>
 
+  <div class="doc doc-contents first">
 
-<div class="doc doc-object doc-function">
-
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-<h2 id="churn_pred.eda.features.analysis.entropy" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">entropy</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
-
-<a href="#churn_pred.eda.features.analysis.entropy" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
   
-      <p>Procedure to plot features sorted by their entropy.</p>
 
+  <div class="doc doc-children">
 
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
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-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
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-            <div class="doc-md-description">
-              <p>dataset</p>
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-              <em>required</em>
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-          <td><code>scale</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>y scale of the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;linear&#39;</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>plot</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether to output the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>plot with sorted entropy of the features</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">186</span>
-<span class="normal">187</span>
-<span class="normal">188</span>
-<span class="normal">189</span>
-<span class="normal">190</span>
-<span class="normal">191</span>
-<span class="normal">192</span>
-<span class="normal">193</span>
-<span class="normal">194</span>
-<span class="normal">195</span>
-<span class="normal">196</span>
-<span class="normal">197</span>
-<span class="normal">198</span>
-<span class="normal">199</span>
-<span class="normal">200</span>
-<span class="normal">201</span>
-<span class="normal">202</span>
-<span class="normal">203</span>
-<span class="normal">204</span>
-<span class="normal">205</span>
-<span class="normal">206</span>
-<span class="normal">207</span>
-<span class="normal">208</span>
-<span class="normal">209</span>
-<span class="normal">210</span>
-<span class="normal">211</span>
-<span class="normal">212</span>
-<span class="normal">213</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span>
-    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">scale</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="s2">&quot;linear&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Figure</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to plot features sorted by their entropy.</span>
 
-<span class="sd">    Args:</span>
-<span class="sd">        df (DataFrame): dataset</span>
-<span class="sd">        scale (str): y scale of the plot</span>
-<span class="sd">        plot (bool): whether to output the plot</span>
 
-<span class="sd">    Returns:</span>
-<span class="sd">        fig (Figure): plot with sorted entropy of the features</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 
-    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
-    <span class="n">col_entropies</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">entropy_calc</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">col_entropies</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
-    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-        <span class="n">bar_plot</span><span class="p">(</span>
-            <span class="n">col_entropies</span><span class="o">.</span><span class="n">dropna</span><span class="p">(),</span> <span class="n">ax</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Features entropies (NA are dropped)&quot;</span>
-        <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fig</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">scale</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">col_entropies</span><span class="p">,</span> <span class="n">fig</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
 
-</div>
 
 
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.features.analysis.init_check" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">init_check</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">identifier</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cat_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cont_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.features.analysis.init_check" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">init_check</span>
 
-<a href="#churn_pred.eda.features.analysis.init_check" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.features.analysis.init_check" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">init_check</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span>
+    <span class="n">identifier</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">cat_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">cont_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
@@ -1278,125 +803,49 @@ <h2 id="churn_pred.eda.features.analysis.init_check" class="doc doc-heading">
 * general stats of numerical features
 * general stats of categorical features</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>pandas dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>identifier</b>
+            (<code>str</code>)
+        – <p>column which identifies unique user IDs</p>
+      </li>
+      <li class="field-body">
+        <b>cat_cols</b>
+            (<code>list</code>)
+        – <p>categorical features in the dataset</p>
+      </li>
+      <li class="field-body">
+        <b>cont_cols</b>
+            (<code>list</code>)
+        – <p>numerical features in the dataset</p>
+      </li>
+  </ul>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>duplicated_ids</b>(            <code>int</code>
+)        – 
+      </li>
+      <li class="field-body">
+<b>cont_cols_desc</b>(            <code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>
+)        – 
+      </li>
+      <li class="field-body">
+<b>cat_cols_desc</b>(            <code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>
+)        – 
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pandas dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>identifier</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>column which identifies unique user IDs</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>cat_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>categorical features in the dataset</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>cont_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>numerical features in the dataset</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>duplicated_ids</code></td>          <td>
-                <code>int</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              
-            </div>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-<td><code>cont_cols_desc</code></td>          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              
-            </div>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-<td><code>cat_cols_desc</code></td>          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">11</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">11</span>
 <span class="normal">12</span>
 <span class="normal">13</span>
 <span class="normal">14</span>
@@ -1448,7 +897,7 @@ <h2 id="churn_pred.eda.features.analysis.init_check" class="doc doc-heading">
     <span class="n">cont_cols</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to check:</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to check:</span>
 <span class="sd">    * duplicated rows in teh dataset</span>
 <span class="sd">    * general stats of numerical features</span>
 <span class="sd">    * general stats of categorical features</span>
@@ -1488,122 +937,62 @@ <h2 id="churn_pred.eda.features.analysis.init_check" class="doc doc-heading">
                 <span class="nb">print</span><span class="p">(</span><span class="n">cat_cols_desc</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">duplicated_ids</span><span class="p">,</span> <span class="n">cont_cols_desc</span><span class="p">,</span> <span class="n">cat_cols_desc</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.features.analysis.missing" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">missing</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.features.analysis.missing" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">missing</span>
 
-<a href="#churn_pred.eda.features.analysis.missing" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.features.analysis.missing" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">missing</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Procedure to check fraction of missing values in the dataset.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>pandas dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>scale</b>
+            (<code>str</code>)
+        – <p>y scale of the plot</p>
+      </li>
+      <li class="field-body">
+        <b>plot</b>
+            (<code>bool</code>)
+        – <p>whether to output the plot</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pandas dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>scale</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>y scale of the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;linear&#39;</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>plot</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether to output the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>missing_val_frac</code></td>          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>sorted dataframe with fraction of missing values
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>missing_val_frac</b>(            <code>DataFrame</code>
+)        – <p>sorted dataframe with fraction of missing values
 per feature</p>
-            </div>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>plot with sorted fractions of missing values in each column</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">59</span>
+      </li>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>plot with sorted fractions of missing values in each column</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">59</span>
 <span class="normal">60</span>
 <span class="normal">61</span>
 <span class="normal">62</span>
@@ -1639,7 +1028,7 @@ <h2 id="churn_pred.eda.features.analysis.missing" class="doc doc-heading">
     <span class="n">scale</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="s2">&quot;linear&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;linear&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Figure</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to check fraction of missing values in the dataset.</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to check fraction of missing values in the dataset.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (DataFrame): pandas dataframe</span>
@@ -1667,111 +1056,168 @@ <h2 id="churn_pred.eda.features.analysis.missing" class="doc doc-heading">
     <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">scale</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">missing_val_frac</span><span class="p">,</span> <span class="n">fig</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
+<div class="doc doc-object doc-function">
+
+
+
+<h3 id="churn_pred.eda.features.analysis.zero" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">zero</span>
+
+
+<a href="#churn_pred.eda.features.analysis.zero" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">zero</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Procedure to check fraction of zero values in the dataset.</p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>pandas dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>scale</b>
+            (<code>str</code>)
+        – <p>y scale of the plot</p>
+      </li>
+      <li class="field-body">
+        <b>plot</b>
+            (<code>bool</code>)
+        – <p>whether to output the plot</p>
+      </li>
+  </ul>
+
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>zero_val_frac</b>(            <code>DataFrame</code>
+)        – <p>sorted dataframe with fraction of '0' values per
+feature</p>
+      </li>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>plot with sorted fractions of '0' values in each column</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 93</span>
+<span class="normal"> 94</span>
+<span class="normal"> 95</span>
+<span class="normal"> 96</span>
+<span class="normal"> 97</span>
+<span class="normal"> 98</span>
+<span class="normal"> 99</span>
+<span class="normal">100</span>
+<span class="normal">101</span>
+<span class="normal">102</span>
+<span class="normal">103</span>
+<span class="normal">104</span>
+<span class="normal">105</span>
+<span class="normal">106</span>
+<span class="normal">107</span>
+<span class="normal">108</span>
+<span class="normal">109</span>
+<span class="normal">110</span>
+<span class="normal">111</span>
+<span class="normal">112</span>
+<span class="normal">113</span>
+<span class="normal">114</span>
+<span class="normal">115</span>
+<span class="normal">116</span>
+<span class="normal">117</span>
+<span class="normal">118</span>
+<span class="normal">119</span>
+<span class="normal">120</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">zero</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="n">scale</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="s2">&quot;linear&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;linear&quot;</span><span class="p">,</span>
+    <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Figure</span><span class="p">]:</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to check fraction of zero values in the dataset.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        df (DataFrame): pandas dataframe</span>
+<span class="sd">        scale (str): y scale of the plot</span>
+<span class="sd">        plot (bool): whether to output the plot</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        zero_val_frac (DataFrame): sorted dataframe with fraction of &#39;0&#39; values per</span>
+<span class="sd">            feature</span>
+<span class="sd">        fig (Figure): plot with sorted fractions of &#39;0&#39; values in each column</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">zero_val_frac</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">zero_val_frac</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+        <span class="n">bar_plot</span><span class="p">(</span><span class="n">zero_val_frac</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Fraction of zero values in the dataset&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">scale</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">zero_val_frac</span><span class="p">,</span> <span class="n">fig</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
+
+</div>
 
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.features.analysis.nunique" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">nunique</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.features.analysis.nunique" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">nunique</span>
 
-<a href="#churn_pred.eda.features.analysis.nunique" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.features.analysis.nunique" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">nunique</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Procedure to plot features sorted by their number of unique values.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>dataset</p>
+      </li>
+      <li class="field-body">
+        <b>scale</b>
+            (<code>str</code>)
+        – <p>y scale of the plot</p>
+      </li>
+      <li class="field-body">
+        <b>plot</b>
+            (<code>bool</code>)
+        – <p>whether to output the plot</p>
+      </li>
+  </ul>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>plot with sorted number of unique values in features</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>scale</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>y scale of the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;linear&#39;</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>plot</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether to output the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>plot with sorted number of unique values in features</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">123</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">123</span>
 <span class="normal">124</span>
 <span class="normal">125</span>
 <span class="normal">126</span>
@@ -1806,7 +1252,7 @@ <h2 id="churn_pred.eda.features.analysis.nunique" class="doc doc-heading">
     <span class="n">scale</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="s2">&quot;linear&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;linear&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Figure</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to plot features sorted by their number of unique values.</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to plot features sorted by their number of unique values.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (DataFrame): dataset</span>
@@ -1833,111 +1279,57 @@ <h2 id="churn_pred.eda.features.analysis.nunique" class="doc doc-heading">
     <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">scale</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">data_nunique</span><span class="p">,</span> <span class="n">fig</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.features.analysis.std" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">std</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.features.analysis.std" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">std</span>
 
-<a href="#churn_pred.eda.features.analysis.std" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.features.analysis.std" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">std</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Procedure to plot features sorted by their variance.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>dataset</p>
+      </li>
+      <li class="field-body">
+        <b>scale</b>
+            (<code>str</code>)
+        – <p>y scale of the plot</p>
+      </li>
+      <li class="field-body">
+        <b>plot</b>
+            (<code>bool</code>)
+        – <p>whether to output the plot</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>scale</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>y scale of the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;linear&#39;</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>plot</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether to output the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>plot with sorted standard deviation of continuous features</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">156</span>
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>plot with sorted standard deviation of continuous features</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">156</span>
 <span class="normal">157</span>
 <span class="normal">158</span>
 <span class="normal">159</span>
@@ -1969,7 +1361,7 @@ <h2 id="churn_pred.eda.features.analysis.std" class="doc doc-heading">
     <span class="n">scale</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="s2">&quot;linear&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;linear&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Figure</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to plot features sorted by their variance.</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to plot features sorted by their variance.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (DataFrame): dataset</span>
@@ -1993,178 +1385,113 @@ <h2 id="churn_pred.eda.features.analysis.std" class="doc doc-heading">
     <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">scale</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">data_std</span><span class="p">,</span> <span class="n">fig</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.features.analysis.zero" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">zero</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.features.analysis.entropy" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">entropy</span>
 
-<a href="#churn_pred.eda.features.analysis.zero" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.features.analysis.entropy" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">entropy</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Procedure to check fraction of zero values in the dataset.</p>
+      <p>Procedure to plot features sorted by their entropy.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>dataset</p>
+      </li>
+      <li class="field-body">
+        <b>scale</b>
+            (<code>str</code>)
+        – <p>y scale of the plot</p>
+      </li>
+      <li class="field-body">
+        <b>plot</b>
+            (<code>bool</code>)
+        – <p>whether to output the plot</p>
+      </li>
+  </ul>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>plot with sorted entropy of the features</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pandas dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>scale</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>y scale of the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;linear&#39;</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>plot</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether to output the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>zero_val_frac</code></td>          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>sorted dataframe with fraction of '0' values per
-feature</p>
-            </div>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>plot with sorted fractions of '0' values in each column</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 93</span>
-<span class="normal"> 94</span>
-<span class="normal"> 95</span>
-<span class="normal"> 96</span>
-<span class="normal"> 97</span>
-<span class="normal"> 98</span>
-<span class="normal"> 99</span>
-<span class="normal">100</span>
-<span class="normal">101</span>
-<span class="normal">102</span>
-<span class="normal">103</span>
-<span class="normal">104</span>
-<span class="normal">105</span>
-<span class="normal">106</span>
-<span class="normal">107</span>
-<span class="normal">108</span>
-<span class="normal">109</span>
-<span class="normal">110</span>
-<span class="normal">111</span>
-<span class="normal">112</span>
-<span class="normal">113</span>
-<span class="normal">114</span>
-<span class="normal">115</span>
-<span class="normal">116</span>
-<span class="normal">117</span>
-<span class="normal">118</span>
-<span class="normal">119</span>
-<span class="normal">120</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">zero</span><span class="p">(</span>
-    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="n">scale</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="s2">&quot;linear&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;linear&quot;</span><span class="p">,</span>
-    <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/features/analysis.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">186</span>
+<span class="normal">187</span>
+<span class="normal">188</span>
+<span class="normal">189</span>
+<span class="normal">190</span>
+<span class="normal">191</span>
+<span class="normal">192</span>
+<span class="normal">193</span>
+<span class="normal">194</span>
+<span class="normal">195</span>
+<span class="normal">196</span>
+<span class="normal">197</span>
+<span class="normal">198</span>
+<span class="normal">199</span>
+<span class="normal">200</span>
+<span class="normal">201</span>
+<span class="normal">202</span>
+<span class="normal">203</span>
+<span class="normal">204</span>
+<span class="normal">205</span>
+<span class="normal">206</span>
+<span class="normal">207</span>
+<span class="normal">208</span>
+<span class="normal">209</span>
+<span class="normal">210</span>
+<span class="normal">211</span>
+<span class="normal">212</span>
+<span class="normal">213</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">scale</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="s2">&quot;linear&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Figure</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to check fraction of zero values in the dataset.</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to plot features sorted by their entropy.</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        df (DataFrame): pandas dataframe</span>
+<span class="sd">        df (DataFrame): dataset</span>
 <span class="sd">        scale (str): y scale of the plot</span>
 <span class="sd">        plot (bool): whether to output the plot</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        zero_val_frac (DataFrame): sorted dataframe with fraction of &#39;0&#39; values per</span>
-<span class="sd">            feature</span>
-<span class="sd">        fig (Figure): plot with sorted fractions of &#39;0&#39; values in each column</span>
+<span class="sd">        fig (Figure): plot with sorted entropy of the features</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">data</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">zero_val_frac</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">0</span><span class="p">])</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-    <span class="n">zero_val_frac</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
+    <span class="n">col_entropies</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">entropy_calc</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">col_entropies</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="n">plot</span><span class="p">:</span>
         <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
-        <span class="n">bar_plot</span><span class="p">(</span><span class="n">zero_val_frac</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Fraction of zero values in the dataset&quot;</span><span class="p">)</span>
+        <span class="n">bar_plot</span><span class="p">(</span>
+            <span class="n">col_entropies</span><span class="o">.</span><span class="n">dropna</span><span class="p">(),</span> <span class="n">ax</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Features entropies (NA are dropped)&quot;</span>
+        <span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">fig</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">scale</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">zero_val_frac</span><span class="p">,</span> <span class="n">fig</span>
+    <span class="k">return</span> <span class="n">col_entropies</span><span class="p">,</span> <span class="n">fig</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -2181,7 +1508,12 @@ <h2 id="churn_pred.eda.features.analysis.zero" class="doc doc-heading">
 
 
 
-<a id="churn_pred.eda.features.plotting"></a>
+<h2 id="churn_pred.eda.features.plotting" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">plotting</span>
+
+
+<a href="#churn_pred.eda.features.plotting" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -2196,107 +1528,53 @@ <h2 id="churn_pred.eda.features.analysis.zero" class="doc doc-heading">
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.features.plotting.cross_correlation" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">cross_correlation</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.features.plotting.cross_correlation" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">cross_correlation</span>
 
-<a href="#churn_pred.eda.features.plotting.cross_correlation" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.features.plotting.cross_correlation" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">cross_correlation</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Procedure to calculate and plot cross-correlation of features
 in the dataset.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>pandas dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>verbose</b>
+            (<code>bool</code>)
+        – <p>show n most correlated features</p>
+      </li>
+      <li class="field-body">
+        <b>n</b>
+            (<code>int</code>)
+        – <p>number correlated features in verbose output</p>
+      </li>
+  </ul>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>heatmap of continuous features cross correlations</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pandas dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>verbose</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>show n most correlated features</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>n</code></td>
-          <td>
-                <code>int</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>number correlated features in verbose output</p>
-            </div>
-          </td>
-          <td>
-                <code>10</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>heatmap of continuous features cross correlations</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/features/plotting.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">10</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/features/plotting.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">10</span>
 <span class="normal">11</span>
 <span class="normal">12</span>
 <span class="normal">13</span>
@@ -2342,7 +1620,7 @@ <h2 id="churn_pred.eda.features.plotting.cross_correlation" class="doc doc-headi
     <span class="n">n</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
     <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Figure</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to calculate and plot cross-correlation of features</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to calculate and plot cross-correlation of features</span>
 <span class="sd">    in the dataset.</span>
 
 <span class="sd">    Args:</span>
@@ -2380,21 +1658,24 @@ <h2 id="churn_pred.eda.features.plotting.cross_correlation" class="doc doc-headi
         <span class="nb">print</span><span class="p">(</span><span class="n">upper_stacked_sorted</span><span class="p">[:</span><span class="n">n</span><span class="p">])</span>
     <span class="k">return</span> <span class="n">fig</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.features.plotting.distributions" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">distributions</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">low_per_cut</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high_per_cut</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;box&#39;</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.features.plotting.distributions" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">distributions</span>
 
-<a href="#churn_pred.eda.features.plotting.distributions" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.features.plotting.distributions" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">distributions</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span> <span class="n">low_per_cut</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high_per_cut</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;box&quot;</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
@@ -2403,107 +1684,43 @@ <h2 id="churn_pred.eda.features.plotting.distributions" class="doc doc-heading">
 * <a href="https://stackoverflow.com/a/64787568/8147433">https://stackoverflow.com/a/64787568/8147433</a>
 DISCALIMER: for now the cont_cols NA vals are filled with 0</p>
 
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pandas dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>low_per_cut</code></td>
-          <td>
-                <code>float</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>lower percentile where to cut the plot for better
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>pandas dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>low_per_cut</b>
+            (<code>float</code>)
+        – <p>lower percentile where to cut the plot for better
 readability</p>
-            </div>
-          </td>
-          <td>
-                <code>0</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>high_per_cut</code></td>
-          <td>
-                <code>float</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>higher percentile where to cut the plot for better
+      </li>
+      <li class="field-body">
+        <b>high_per_cut</b>
+            (<code>float</code>)
+        – <p>higher percentile where to cut the plot for better
 readability</p>
-            </div>
-          </td>
-          <td>
-                <code>1</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>type</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>type of distribution plot</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;box&#39;</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>ditribution plot per each feature</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/features/plotting.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 54</span>
+      </li>
+      <li class="field-body">
+        <b>type</b>
+            (<code>str</code>)
+        – <p>type of distribution plot</p>
+      </li>
+  </ul>
+
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>ditribution plot per each feature</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/features/plotting.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 54</span>
 <span class="normal"> 55</span>
 <span class="normal"> 56</span>
 <span class="normal"> 57</span>
@@ -2558,7 +1775,7 @@ <h2 id="churn_pred.eda.features.plotting.distributions" class="doc doc-heading">
     <span class="n">high_per_cut</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
     <span class="nb">type</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;box&quot;</span><span class="p">,</span> <span class="s2">&quot;violin&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;box&quot;</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Figure</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to plot distributions of the features splitted</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to plot distributions of the features splitted</span>
 <span class="sd">    by column split_col using workaround for violinplots in seaborn:</span>
 <span class="sd">    * https://stackoverflow.com/a/64787568/8147433</span>
 <span class="sd">    DISCALIMER: for now the cont_cols NA vals are filled with 0</span>
@@ -2603,7 +1820,7 @@ <h2 id="churn_pred.eda.features.plotting.distributions" class="doc doc-heading">
     <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">fig</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -2620,7 +1837,12 @@ <h2 id="churn_pred.eda.features.plotting.distributions" class="doc doc-heading">
 
 
 
-<a id="churn_pred.eda.target.analysis"></a>
+<h2 id="churn_pred.eda.target.analysis" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">analysis</span>
+
+
+<a href="#churn_pred.eda.target.analysis" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -2635,132 +1857,63 @@ <h2 id="churn_pred.eda.features.plotting.distributions" class="doc doc-heading">
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.target.analysis.correlation" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">correlation</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.target.analysis.correlation" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">correlation</span>
 
-<a href="#churn_pred.eda.target.analysis.correlation" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.target.analysis.correlation" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">correlation</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Procedure to plot most correlated numerical features with
 target column.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>pandas dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>target</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>)
+        – <p>target values</p>
+      </li>
+      <li class="field-body">
+        <b>scale</b>
+            (<code>str</code>)
+        – <p>y scale of the plot</p>
+      </li>
+      <li class="field-body">
+        <b>plot</b>
+            (<code>bool</code>)
+        – <p>whether to output the plot</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pandas dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>target</code></td>
-          <td>
-                <code><span title="pandas.Series">Series</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>target values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>scale</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>y scale of the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;linear&#39;</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>plot</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether to output the plot</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>sorted_corr_cols</code></td>          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>sorted dataframe with feature and target value
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>sorted_corr_cols</b>(            <code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>
+)        – <p>sorted dataframe with feature and target value
 correlation</p>
-            </div>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>sorted bar plot with feature and target value correlation</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/target/analysis.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">10</span>
+      </li>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>sorted bar plot with feature and target value correlation</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/target/analysis.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">10</span>
 <span class="normal">11</span>
 <span class="normal">12</span>
 <span class="normal">13</span>
@@ -2802,7 +1955,7 @@ <h2 id="churn_pred.eda.target.analysis.correlation" class="doc doc-heading">
     <span class="n">scale</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;log&quot;</span><span class="p">,</span> <span class="s2">&quot;linear&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;linear&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Figure</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to plot most correlated numerical features with</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to plot most correlated numerical features with</span>
 <span class="sd">    target column.</span>
 
 <span class="sd">    Args:</span>
@@ -2834,7 +1987,7 @@ <h2 id="churn_pred.eda.target.analysis.correlation" class="doc doc-heading">
     <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="n">scale</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">sorted_corr_cols</span><span class="p">,</span> <span class="n">fig</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -2851,303 +2004,73 @@ <h2 id="churn_pred.eda.target.analysis.correlation" class="doc doc-heading">
 
 
 
-<a id="churn_pred.eda.target.plotting"></a>
-  <div class="doc doc-contents first">
-
-  
-
-  <div class="doc doc-children">
-
-
-
-
+<h2 id="churn_pred.eda.target.plotting" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">plotting</span>
 
 
+<a href="#churn_pred.eda.target.plotting" class="headerlink" title="Permanent link">&para;</a></h2>
 
+  <div class="doc doc-contents first">
 
+  
 
+  <div class="doc doc-children">
 
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="churn_pred.eda.target.plotting.distributions_in_binary_cls" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">distributions_in_binary_cls</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">low_per_cut</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high_per_cut</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></code>
-
-<a href="#churn_pred.eda.target.plotting.distributions_in_binary_cls" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Procedure to plot distributions of the features splitted
-by column split_col using workaround for violinplots in seaborn:
-* <a href="https://stackoverflow.com/a/64787568/8147433">https://stackoverflow.com/a/64787568/8147433</a>
-DISCALIMER: for now the cont_cols NA vals are filled with 0</p>
 
 
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pandas dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>target</code></td>
-          <td>
-                <code><span title="pandas.Series">Series</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>target values, i.e. binary classes</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>ditribution plot per each feature</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/target/plotting.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 84</span>
-<span class="normal"> 85</span>
-<span class="normal"> 86</span>
-<span class="normal"> 87</span>
-<span class="normal"> 88</span>
-<span class="normal"> 89</span>
-<span class="normal"> 90</span>
-<span class="normal"> 91</span>
-<span class="normal"> 92</span>
-<span class="normal"> 93</span>
-<span class="normal"> 94</span>
-<span class="normal"> 95</span>
-<span class="normal"> 96</span>
-<span class="normal"> 97</span>
-<span class="normal"> 98</span>
-<span class="normal"> 99</span>
-<span class="normal">100</span>
-<span class="normal">101</span>
-<span class="normal">102</span>
-<span class="normal">103</span>
-<span class="normal">104</span>
-<span class="normal">105</span>
-<span class="normal">106</span>
-<span class="normal">107</span>
-<span class="normal">108</span>
-<span class="normal">109</span>
-<span class="normal">110</span>
-<span class="normal">111</span>
-<span class="normal">112</span>
-<span class="normal">113</span>
-<span class="normal">114</span>
-<span class="normal">115</span>
-<span class="normal">116</span>
-<span class="normal">117</span>
-<span class="normal">118</span>
-<span class="normal">119</span>
-<span class="normal">120</span>
-<span class="normal">121</span>
-<span class="normal">122</span>
-<span class="normal">123</span>
-<span class="normal">124</span>
-<span class="normal">125</span>
-<span class="normal">126</span>
-<span class="normal">127</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">distributions_in_binary_cls</span><span class="p">(</span>
-    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="n">target</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span>
-    <span class="n">low_per_cut</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
-    <span class="n">high_per_cut</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Figure</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to plot distributions of the features splitted</span>
-<span class="sd">    by column split_col using workaround for violinplots in seaborn:</span>
-<span class="sd">    * https://stackoverflow.com/a/64787568/8147433</span>
-<span class="sd">    DISCALIMER: for now the cont_cols NA vals are filled with 0</span>
 
-<span class="sd">    Args:</span>
-<span class="sd">        df (DataFrame): pandas dataframe</span>
-<span class="sd">        target (pd.Series): target values, i.e. binary classes</span>
 
-<span class="sd">    Returns:</span>
-<span class="sd">        fig (Figure): ditribution plot per each feature</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">target</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">cols</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span>
-    <span class="n">data</span><span class="p">[</span><span class="s2">&quot;dummy&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="p">))</span>
-    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Distribution of feature values splitted by </span><span class="si">{</span><span class="n">target</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">cols</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">))):</span>
-        <span class="k">if</span> <span class="n">col</span> <span class="o">!=</span> <span class="n">target</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
-            <span class="n">sns</span><span class="o">.</span><span class="n">violinplot</span><span class="p">(</span>
-                <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
-                <span class="n">hue</span><span class="o">=</span><span class="n">target</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
-                <span class="n">x</span><span class="o">=</span><span class="n">col</span><span class="p">,</span>
-                <span class="n">y</span><span class="o">=</span><span class="s2">&quot;dummy&quot;</span><span class="p">,</span>
-                <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;h&quot;</span><span class="p">,</span>
-                <span class="n">cut</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
-                <span class="n">showextrem</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                <span class="n">split</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span>
-            <span class="p">)</span>
-            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
-            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span>
-                <span class="n">data</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">low_per_cut</span><span class="p">),</span> <span class="n">data</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">high_per_cut</span><span class="p">)</span>
-            <span class="p">)</span>
-            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-    <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mf">0.97</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">fig</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
 
-</div>
 
 
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.target.plotting.prob_distrib_per_class" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">prob_distrib_per_class</span><span class="p">(</span><span class="n">predicted_probs</span><span class="p">,</span> <span class="n">actual</span><span class="p">,</span> <span class="n">task</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.target.plotting.prob_distrib_per_class" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">prob_distrib_per_class</span>
 
-<a href="#churn_pred.eda.target.plotting.prob_distrib_per_class" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.target.plotting.prob_distrib_per_class" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">prob_distrib_per_class</span><span class="p">(</span><span class="n">predicted_probs</span><span class="p">,</span> <span class="n">actual</span><span class="p">,</span> <span class="n">task</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Procedure to plot probability density distributions per class from LightGBM
 predictions.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>predicted_probs</b>
+            (<code>ndarray</code>)
+        – <p>predicted probs</p>
+      </li>
+      <li class="field-body">
+        <b>actual</b>
+            (<code>ndarray</code>)
+        – <p>ground truth classes</p>
+      </li>
+      <li class="field-body">
+        <b>task</b>
+            (<code>str</code>)
+        – <p>type of task</p>
+      </li>
+  </ul>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>probability density ditributions plot per each class</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>predicted_probs</code></td>
-          <td>
-                <code>ndarray</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>predicted probs</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>actual</code></td>
-          <td>
-                <code>ndarray</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>ground truth classes</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>task</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>type of task</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>fig</code></td>          <td>
-                <code><span title="matplotlib.figure.Figure">Figure</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>probability density ditributions plot per each class</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/target/plotting.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">11</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/target/plotting.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">11</span>
 <span class="normal">12</span>
 <span class="normal">13</span>
 <span class="normal">14</span>
@@ -3222,7 +2145,7 @@ <h2 id="churn_pred.eda.target.plotting.prob_distrib_per_class" class="doc doc-he
     <span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
     <span class="n">task</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">],</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Figure</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Procedure to plot probability density distributions per class from LightGBM</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to plot probability density distributions per class from LightGBM</span>
 <span class="sd">    predictions.</span>
 
 <span class="sd">    Args:</span>
@@ -3289,7 +2212,145 @@ <h2 id="churn_pred.eda.target.plotting.prob_distrib_per_class" class="doc doc-he
             <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;probability_density&quot;</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">fig</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
+  </div>
+
+</div>
+
+<div class="doc doc-object doc-function">
+
+
+
+<h3 id="churn_pred.eda.target.plotting.distributions_in_binary_cls" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">distributions_in_binary_cls</span>
+
+
+<a href="#churn_pred.eda.target.plotting.distributions_in_binary_cls" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">distributions_in_binary_cls</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">low_per_cut</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">high_per_cut</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Procedure to plot distributions of the features splitted
+by column split_col using workaround for violinplots in seaborn:
+* <a href="https://stackoverflow.com/a/64787568/8147433">https://stackoverflow.com/a/64787568/8147433</a>
+DISCALIMER: for now the cont_cols NA vals are filled with 0</p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>pandas dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>target</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>)
+        – <p>target values, i.e. binary classes</p>
+      </li>
+  </ul>
+
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>fig</b>(            <code><span title="matplotlib.figure.Figure">Figure</span></code>
+)        – <p>ditribution plot per each feature</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/target/plotting.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 84</span>
+<span class="normal"> 85</span>
+<span class="normal"> 86</span>
+<span class="normal"> 87</span>
+<span class="normal"> 88</span>
+<span class="normal"> 89</span>
+<span class="normal"> 90</span>
+<span class="normal"> 91</span>
+<span class="normal"> 92</span>
+<span class="normal"> 93</span>
+<span class="normal"> 94</span>
+<span class="normal"> 95</span>
+<span class="normal"> 96</span>
+<span class="normal"> 97</span>
+<span class="normal"> 98</span>
+<span class="normal"> 99</span>
+<span class="normal">100</span>
+<span class="normal">101</span>
+<span class="normal">102</span>
+<span class="normal">103</span>
+<span class="normal">104</span>
+<span class="normal">105</span>
+<span class="normal">106</span>
+<span class="normal">107</span>
+<span class="normal">108</span>
+<span class="normal">109</span>
+<span class="normal">110</span>
+<span class="normal">111</span>
+<span class="normal">112</span>
+<span class="normal">113</span>
+<span class="normal">114</span>
+<span class="normal">115</span>
+<span class="normal">116</span>
+<span class="normal">117</span>
+<span class="normal">118</span>
+<span class="normal">119</span>
+<span class="normal">120</span>
+<span class="normal">121</span>
+<span class="normal">122</span>
+<span class="normal">123</span>
+<span class="normal">124</span>
+<span class="normal">125</span>
+<span class="normal">126</span>
+<span class="normal">127</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">distributions_in_binary_cls</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="n">target</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">,</span>
+    <span class="n">low_per_cut</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">high_per_cut</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Figure</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Procedure to plot distributions of the features splitted</span>
+<span class="sd">    by column split_col using workaround for violinplots in seaborn:</span>
+<span class="sd">    * https://stackoverflow.com/a/64787568/8147433</span>
+<span class="sd">    DISCALIMER: for now the cont_cols NA vals are filled with 0</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        df (DataFrame): pandas dataframe</span>
+<span class="sd">        target (pd.Series): target values, i.e. binary classes</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        fig (Figure): ditribution plot per each feature</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">target</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">cols</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span>
+    <span class="n">data</span><span class="p">[</span><span class="s2">&quot;dummy&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Distribution of feature values splitted by </span><span class="si">{</span><span class="n">target</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">cols</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cols</span><span class="p">))):</span>
+        <span class="k">if</span> <span class="n">col</span> <span class="o">!=</span> <span class="n">target</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
+            <span class="n">sns</span><span class="o">.</span><span class="n">violinplot</span><span class="p">(</span>
+                <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                <span class="n">hue</span><span class="o">=</span><span class="n">target</span><span class="o">.</span><span class="n">name</span><span class="p">,</span>
+                <span class="n">x</span><span class="o">=</span><span class="n">col</span><span class="p">,</span>
+                <span class="n">y</span><span class="o">=</span><span class="s2">&quot;dummy&quot;</span><span class="p">,</span>
+                <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;h&quot;</span><span class="p">,</span>
+                <span class="n">cut</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                <span class="n">showextrem</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">split</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                <span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get_legend</span><span class="p">()</span><span class="o">.</span><span class="n">remove</span><span class="p">()</span>
+            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span>
+                <span class="n">data</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">low_per_cut</span><span class="p">),</span> <span class="n">data</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">high_per_cut</span><span class="p">)</span>
+            <span class="p">)</span>
+            <span class="n">ax</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span> <span class="n">top</span><span class="o">=</span><span class="mf">0.97</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">fig</span>
+</code></pre></div></td></tr></table></div>
+      </details>
   </div>
 
 </div>
@@ -3306,7 +2367,12 @@ <h2 id="churn_pred.eda.target.plotting.prob_distrib_per_class" class="doc doc-he
 
 
 
-<a id="churn_pred.eda.plotting"></a>
+<h2 id="churn_pred.eda.plotting" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">plotting</span>
+
+
+<a href="#churn_pred.eda.plotting" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -3321,92 +2387,47 @@ <h2 id="churn_pred.eda.target.plotting.prob_distrib_per_class" class="doc doc-he
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.plotting.bar_plot" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">bar_plot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">title</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.plotting.bar_plot" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">bar_plot</span>
 
-<a href="#churn_pred.eda.plotting.bar_plot" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.plotting.bar_plot" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">bar_plot</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">title</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Helper method for unified bar plot</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code>DataFrame</code>)
+        – <p>dataframe to plot</p>
+      </li>
+      <li class="field-body">
+        <b>ax</b>
+            (<code><span title="matplotlib.axes.Axes">Axes</span></code>)
+        – <p>axes defining where to plot it</p>
+      </li>
+  </ul>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>adjusted_plot</b>(            <code><span title="matplotlib.axes.Axes">Axes</span></code>
+)        – <p>adjusted axes</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code>DataFrame</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>dataframe to plot</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>ax</code></td>
-          <td>
-                <code><span title="matplotlib.axes.Axes">Axes</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>axes defining where to plot it</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>adjusted_plot</code></td>          <td>
-                <code><span title="matplotlib.axes.Axes">Axes</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>adjusted axes</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/plotting.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">10</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/plotting.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">10</span>
 <span class="normal">11</span>
 <span class="normal">12</span>
 <span class="normal">13</span>
@@ -3421,7 +2442,7 @@ <h2 id="churn_pred.eda.plotting.bar_plot" class="doc doc-heading">
 <span class="normal">22</span>
 <span class="normal">23</span>
 <span class="normal">24</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">bar_plot</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">ax</span><span class="p">:</span> <span class="n">Axes</span><span class="p">,</span> <span class="n">title</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Axes</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper method for unified bar plot</span>
+    <span class="sd">&quot;&quot;&quot;Helper method for unified bar plot</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (DataFrame): dataframe to plot</span>
@@ -3436,7 +2457,7 @@ <h2 id="churn_pred.eda.plotting.bar_plot" class="doc doc-heading">
     <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;sorted features&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -3453,7 +2474,12 @@ <h2 id="churn_pred.eda.plotting.bar_plot" class="doc doc-heading">
 
 
 
-<a id="churn_pred.eda.general_utils"></a>
+<h2 id="churn_pred.eda.general_utils" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">general_utils</span>
+
+
+<a href="#churn_pred.eda.general_utils" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -3468,30 +2494,107 @@ <h2 id="churn_pred.eda.plotting.bar_plot" class="doc doc-heading">
 
 
 
+<div class="doc doc-object doc-function">
+
+
+
+<h3 id="churn_pred.eda.general_utils.intsec" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">intsec</span>
+
+
+<a href="#churn_pred.eda.general_utils.intsec" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">intsec</span><span class="p">(</span><span class="n">list1</span><span class="p">,</span> <span class="n">list2</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Simple intesection of two lists.</p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>list1</b>
+            (<code>list</code>)
+        – <p>list1</p>
+      </li>
+      <li class="field-body">
+        <b>list2</b>
+            (<code>list</code>)
+        – <p>list2</p>
+      </li>
+  </ul>
+
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>list</b>(            <code>list</code>
+)        – <p>intersection of lists</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/general_utils.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 6</span>
+<span class="normal"> 7</span>
+<span class="normal"> 8</span>
+<span class="normal"> 9</span>
+<span class="normal">10</span>
+<span class="normal">11</span>
+<span class="normal">12</span>
+<span class="normal">13</span>
+<span class="normal">14</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">intsec</span><span class="p">(</span><span class="n">list1</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">list2</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Simple intesection of two lists.</span>
+<span class="sd">    Args:</span>
+<span class="sd">        list1 (list): list1</span>
+<span class="sd">        list2 (list): list2</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        list (list): intersection of lists</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">list1</span><span class="p">),</span> <span class="nb">set</span><span class="p">(</span><span class="n">list2</span><span class="p">)))</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
+
+</div>
 
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.eda.general_utils.entropy_calc" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">entropy_calc</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">e</span><span class="p">)</span></code>
+<h3 id="churn_pred.eda.general_utils.entropy_calc" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">entropy_calc</span>
 
-<a href="#churn_pred.eda.general_utils.entropy_calc" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.eda.general_utils.entropy_calc" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">entropy_calc</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">base</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">e</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Computes entropy of both continuous and categorical features.
 Shamelessly stolen from :
-<a href="https://stackoverflow.com/a/45091961">https://stackoverflow.com/a/45091961</a>
-Args:
-    labels (list, ndarray, Series): list of values
-Returns:
-    ent (float): entropy of the list of values</p>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/general_utils.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">17</span>
+<a href="https://stackoverflow.com/a/45091961">https://stackoverflow.com/a/45091961</a></p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>labels</b>
+            (<code>list, ndarray, Series</code>)
+        – <p>list of values</p>
+      </li>
+  </ul>
+
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>ent</b>(            <code>float</code>
+)        – <p>entropy of the list of values</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/eda/general_utils.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">17</span>
 <span class="normal">18</span>
 <span class="normal">19</span>
 <span class="normal">20</span>
@@ -3510,7 +2613,7 @@ <h2 id="churn_pred.eda.general_utils.entropy_calc" class="doc doc-heading">
 <span class="normal">33</span>
 <span class="normal">34</span>
 <span class="normal">35</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">entropy_calc</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">base</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">e</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes entropy of both continuous and categorical features.</span>
+    <span class="sd">&quot;&quot;&quot;Computes entropy of both continuous and categorical features.</span>
 <span class="sd">    Shamelessly stolen from :</span>
 <span class="sd">    https://stackoverflow.com/a/45091961</span>
 <span class="sd">    Args:</span>
@@ -3529,52 +2632,7 @@ <h2 id="churn_pred.eda.general_utils.entropy_calc" class="doc doc-heading">
             <span class="n">ent</span> <span class="o">-=</span> <span class="n">i</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">base</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">ent</span>
 </code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="churn_pred.eda.general_utils.intsec" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">intsec</span><span class="p">(</span><span class="n">list1</span><span class="p">,</span> <span class="n">list2</span><span class="p">)</span></code>
-
-<a href="#churn_pred.eda.general_utils.intsec" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Simple intesection of two lists.
-Args:
-    list1 (list): list1
-    list2 (list): list2
-Returns:
-    list (list): intersection of lists</p>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/eda/general_utils.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 6</span>
-<span class="normal"> 7</span>
-<span class="normal"> 8</span>
-<span class="normal"> 9</span>
-<span class="normal">10</span>
-<span class="normal">11</span>
-<span class="normal">12</span>
-<span class="normal">13</span>
-<span class="normal">14</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">intsec</span><span class="p">(</span><span class="n">list1</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">list2</span><span class="p">:</span> <span class="nb">list</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Simple intesection of two lists.</span>
-<span class="sd">    Args:</span>
-<span class="sd">        list1 (list): list1</span>
-<span class="sd">        list2 (list): list2</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        list (list): intersection of lists</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">list1</span><span class="p">),</span> <span class="nb">set</span><span class="p">(</span><span class="n">list2</span><span class="p">)))</span>
-</code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -3587,50 +2645,51 @@ <h2 id="churn_pred.eda.general_utils.intsec" class="doc doc-heading">
 
 </div>
 
-
-
-
-
-
-
-
-  <aside class="md-source-file">
-    
-    
-    
+              
+            </article>
+            
+          </div>
+        </div>
+        
+      </main>
       
+        <footer class="md-footer">
   
-  <span class="md-source-file__fact">
-    <span class="md-icon" title="Contributors">
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
       
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 4a4 4 0 0 1 4 4 4 4 0 0 1-4 4 4 4 0 0 1-4-4 4 4 0 0 1 4-4m0 10c4.42 0 8 1.79 8 4v2H4v-2c0-2.21 3.58-4 8-4Z"/></svg>
+        
+        <a href="code_profiling.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: code_profiling" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              code_profiling
+            </div>
+          </div>
+        </a>
       
-    </span>
-    <nav>
       
-        <a href="mailto:mulinka.pavol@gmail.com">Pavol Mulinka</a>
-    </nav>
-  </span>
-
-    
-    
-  </aside>
-
-
-
-
-
-                
-              </article>
-            </div>
-          
-          
-<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
-        </div>
         
-      </main>
+        <a href="preprocessing.html" class="md-footer__link md-footer__link--next" aria-label="Next: preprocessing" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              preprocessing
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
       
-        <footer class="md-footer">
+    </nav>
   
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
@@ -3656,12 +2715,10 @@ <h2 id="churn_pred.eda.general_utils.intsec" class="doc doc-heading">
     <div class="md-dialog" data-md-component="dialog">
       <div class="md-dialog__inner md-typeset"></div>
     </div>
+    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b97dbffb.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
-    
-    
-      <script src="../assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="../assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="../stylesheets/extra.js"></script>
       
diff --git a/docs/site/churn_pred/preprocessing.html b/docs/site/churn_pred/preprocessing.html
index 03583db..af57161 100644
--- a/docs/site/churn_pred/preprocessing.html
+++ b/docs/site/churn_pred/preprocessing.html
@@ -8,34 +8,24 @@
       
       
       
-      
-        <link rel="prev" href="eda.html">
-      
-      
-        <link rel="next" href="training.html">
-      
-      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>preprocessing - ecovadis_assignment</title>
+        <title>preprocessing - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
-
-
     
     
-      
-    
     
       
         
@@ -50,13 +40,12 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
-    
   </head>
   
   
@@ -67,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
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@@ -118,39 +109,31 @@
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-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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-    
-      <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,41 +184,31 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
@@ -246,55 +216,30 @@
   
     
   
+
+
   
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="code_profiling.html" class="md-tabs__link md-tabs__link--active">
+        churn_pred
+      </a>
+    </li>
   
 
       
         
   
   
-  
-    
-    
-      <li class="md-tabs__item">
-        <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
     </div>
@@ -388,19 +334,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,19 +349,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../installation.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+        installation
       </a>
     </li>
   
@@ -428,134 +364,94 @@
     
       
       
+      
+
   
   
     
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-        
-      
-        
       
-        
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
       
-        
       
-    
-    
       
         
+          
         
-      
-      
-    
-    
-    <li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
-      
-        
+          
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
+          
         
           
-          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="">
-            
-  
-  <span class="md-ellipsis">
-    churn_pred
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_3">
-            <span class="md-nav__icon md-icon"></span>
-            churn_pred
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+        code_profiling
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
   
   
     
   
   
-  
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          
-  
-  <span class="md-ellipsis">
-    preprocessing
-  </span>
-  
-
+          preprocessing
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="preprocessing.html" class="md-nav__link md-nav__link--active">
-        
-  
-  <span class="md-ellipsis">
-    preprocessing
-  </span>
-  
-
+        preprocessing
       </a>
       
         
@@ -575,536 +471,233 @@
       
         <li class="md-nav__item">
   <a href="#churn_pred.preprocessing.auxiliary_data" class="md-nav__link">
-    <span class="md-ellipsis">
-      auxiliary_data
-    </span>
+    auxiliary_data
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#churn_pred.preprocessing.auxiliary_data.age_categories" class="md-nav__link">
-    <span class="md-ellipsis">
-      age_categories
-    </span>
+  <a href="#churn_pred.preprocessing.label_encoder" class="md-nav__link">
+    label_encoder
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#churn_pred.preprocessing.auxiliary_data.big_mac_index" class="md-nav__link">
-    <span class="md-ellipsis">
-      big_mac_index
-    </span>
+  <a href="#churn_pred.preprocessing.preprocess_data" class="md-nav__link">
+    preprocess_data
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#churn_pred.preprocessing.auxiliary_data.gdppc" class="md-nav__link">
-    <span class="md-ellipsis">
-      gdppc
-    </span>
+  <a href="#churn_pred.preprocessing.preprocess_text" class="md-nav__link">
+    preprocess_text
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#churn_pred.preprocessing.auxiliary_data.get_country_name" class="md-nav__link">
-    <span class="md-ellipsis">
-      get_country_name
-    </span>
+  <a href="#churn_pred.preprocessing.preprocess" class="md-nav__link">
+    preprocess
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#churn_pred.preprocessing.auxiliary_data.get_country_region_subregion" class="md-nav__link">
-    <span class="md-ellipsis">
-      get_country_region_subregion
-    </span>
+  <a href="#churn_pred.preprocessing.scaler" class="md-nav__link">
+    scaler
   </a>
   
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+        TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
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+        00_auxiliary_features
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+        01_feature_analysis
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+        04_model_training_pipeline
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-    05_model_analysis.ipynb
-  </span>
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+        05_model_analysis.ipynb
       </a>
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-        </nav>
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+          
+        </ul>
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-          
-            <div class="md-content" data-md-component="content">
-              <article class="md-content__inner md-typeset">
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
                 
-                  
+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/churn_pred/preprocessing.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  
-  
 
 
 <h1 id="preprocess-data">Preprocess data<a class="headerlink" href="#preprocess-data" title="Permanent link">&para;</a></h1>
@@ -1175,7 +758,12 @@ <h1 id="preprocess-data">Preprocess data<a class="headerlink" href="#preprocess-
 
 
 
-<a id="churn_pred.preprocessing.auxiliary_data"></a>
+<h2 id="churn_pred.preprocessing.auxiliary_data" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">auxiliary_data</span>
+
+
+<a href="#churn_pred.preprocessing.auxiliary_data" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -1190,30 +778,331 @@ <h1 id="preprocess-data">Preprocess data<a class="headerlink" href="#preprocess-
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.auxiliary_data.age_categories" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">age_categories</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">age_col</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.auxiliary_data.surname_origin_bert" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">surname_origin_bert</span>
 
-<a href="#churn_pred.preprocessing.auxiliary_data.age_categories" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.auxiliary_data.surname_origin_bert" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">surname_origin_bert</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">surname_col</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Returns string pd.DataFrame identifing different age classification
-categories according to the age:
-* working_class (<a href="https://ourworldindata.org/age-structure">https://ourworldindata.org/age-structure</a>)
-  * children_and_adolescents: &lt;0, 15)
-  * working_age: &lt;15, 65)
-  * elderly: &lt;65, inf)
-* stage_of_life (<a href="https://integrishealth.org/resources/on-your-health/2015/october/stages-of-life-health-for-every-age">https://integrishealth.org/resources/on-your-health/2015/october/stages-of-life-health-for-every-age</a>)
-  * infant: &lt;0, 2)
-  * toddler: &lt;2, 5)
-  * child: &lt;5, 13)
-  * teen: &lt;13, 20)
+      <p>THIS IS WORK IN PROGRESS!!!
+Surname classification that uses adjusted script
+scripts/surname_classification_with_bert.py) from:
+<a href="https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert">https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert</a>
+to train a bert model for surname classification</p>
+<p>additional data from:
+<a href="https://github.com/greenelab/wiki-nationality-estimate/tree/master">https://github.com/greenelab/wiki-nationality-estimate/tree/master</a></p>
+<p>and possibly data from:
+<a href="https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset">https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset</a></p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataset</p>
+      </li>
+      <li class="field-body">
+        <b>surname_col</b>
+            (<code>str</code>)
+        – <p>column name with surnames</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">19</span>
+<span class="normal">20</span>
+<span class="normal">21</span>
+<span class="normal">22</span>
+<span class="normal">23</span>
+<span class="normal">24</span>
+<span class="normal">25</span>
+<span class="normal">26</span>
+<span class="normal">27</span>
+<span class="normal">28</span>
+<span class="normal">29</span>
+<span class="normal">30</span>
+<span class="normal">31</span>
+<span class="normal">32</span>
+<span class="normal">33</span>
+<span class="normal">34</span>
+<span class="normal">35</span>
+<span class="normal">36</span>
+<span class="normal">37</span>
+<span class="normal">38</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">surname_origin_bert</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">surname_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    THIS IS WORK IN PROGRESS!!!</span>
+<span class="sd">    Surname classification that uses adjusted script</span>
+<span class="sd">    scripts/surname_classification_with_bert.py) from:</span>
+<span class="sd">    https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert</span>
+<span class="sd">    to train a bert model for surname classification</span>
+
+<span class="sd">    additional data from:</span>
+<span class="sd">    https://github.com/greenelab/wiki-nationality-estimate/tree/master</span>
+
+<span class="sd">    and possibly data from:</span>
+<span class="sd">    https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        df (pd.DataFrame): input dataset</span>
+<span class="sd">        surname_col (str): column name with surnames</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">dfc</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
+
+</div>
+
+<div class="doc doc-object doc-function">
+
+
+
+<h3 id="churn_pred.preprocessing.auxiliary_data.surname_origin" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">surname_origin</span>
+
+
+<a href="#churn_pred.preprocessing.auxiliary_data.surname_origin" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">surname_origin</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">surname_col</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Surname classification that uses db of names leaked from FB, see:
+<a href="https://github.com/philipperemy/name-dataset">https://github.com/philipperemy/name-dataset</a></p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataset</p>
+      </li>
+      <li class="field-body">
+        <b>surname_col</b>
+            (<code>str</code>)
+        – <p>column name with surnames</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">41</span>
+<span class="normal">42</span>
+<span class="normal">43</span>
+<span class="normal">44</span>
+<span class="normal">45</span>
+<span class="normal">46</span>
+<span class="normal">47</span>
+<span class="normal">48</span>
+<span class="normal">49</span>
+<span class="normal">50</span>
+<span class="normal">51</span>
+<span class="normal">52</span>
+<span class="normal">53</span>
+<span class="normal">54</span>
+<span class="normal">55</span>
+<span class="normal">56</span>
+<span class="normal">57</span>
+<span class="normal">58</span>
+<span class="normal">59</span>
+<span class="normal">60</span>
+<span class="normal">61</span>
+<span class="normal">62</span>
+<span class="normal">63</span>
+<span class="normal">64</span>
+<span class="normal">65</span>
+<span class="normal">66</span>
+<span class="normal">67</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">surname_origin</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">surname_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Surname classification that uses db of names leaked from FB, see:</span>
+<span class="sd">    https://github.com/philipperemy/name-dataset</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        df (pd.DataFrame): input dataset</span>
+<span class="sd">        surname_col (str): column name with surnames</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">nd</span> <span class="o">=</span> <span class="n">NameDataset</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_search_surname</span><span class="p">(</span><span class="n">surname</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">res</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">surname</span><span class="p">)</span>
+            <span class="n">origin_country</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span>
+                <span class="n">res</span><span class="p">[</span><span class="s2">&quot;last_name&quot;</span><span class="p">][</span><span class="s2">&quot;country&quot;</span><span class="p">],</span> <span class="n">key</span><span class="o">=</span><span class="n">res</span><span class="p">[</span><span class="s2">&quot;last_name&quot;</span><span class="p">][</span><span class="s2">&quot;country&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span>
+            <span class="p">)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">origin_country</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
+        <span class="k">return</span> <span class="n">origin_country</span>
+
+    <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
+    <span class="k">for</span> <span class="n">surname</span> <span class="ow">in</span> <span class="n">dfc</span><span class="p">[</span><span class="n">surname_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+        <span class="n">dfc</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dfc</span><span class="p">[</span><span class="n">surname_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">surname</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">_search_surname</span><span class="p">(</span><span class="n">surname</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
+
+</div>
+
+<div class="doc doc-object doc-function">
+
+
+
+<h3 id="churn_pred.preprocessing.auxiliary_data.hemisphere" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">hemisphere</span>
+
+
+<a href="#churn_pred.preprocessing.auxiliary_data.hemisphere" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">hemisphere</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">cc_col</span><span class="p">,</span> <span class="n">loc_db_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">city_col</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Returns string pd.Series identifing hemisphere: "northern" or "southern".</p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>city_col</b>
+            (<code>str</code>)
+        – <p>city name column</p>
+      </li>
+      <li class="field-body">
+        <b>cc_col</b>
+            (<code>str</code>)
+        – <p>country code column</p>
+      </li>
+      <li class="field-body">
+        <b>loc_db_df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>pandas dataframe with previously saved locations</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">138</span>
+<span class="normal">139</span>
+<span class="normal">140</span>
+<span class="normal">141</span>
+<span class="normal">142</span>
+<span class="normal">143</span>
+<span class="normal">144</span>
+<span class="normal">145</span>
+<span class="normal">146</span>
+<span class="normal">147</span>
+<span class="normal">148</span>
+<span class="normal">149</span>
+<span class="normal">150</span>
+<span class="normal">151</span>
+<span class="normal">152</span>
+<span class="normal">153</span>
+<span class="normal">154</span>
+<span class="normal">155</span>
+<span class="normal">156</span>
+<span class="normal">157</span>
+<span class="normal">158</span>
+<span class="normal">159</span>
+<span class="normal">160</span>
+<span class="normal">161</span>
+<span class="normal">162</span>
+<span class="normal">163</span>
+<span class="normal">164</span>
+<span class="normal">165</span>
+<span class="normal">166</span>
+<span class="normal">167</span>
+<span class="normal">168</span>
+<span class="normal">169</span>
+<span class="normal">170</span>
+<span class="normal">171</span>
+<span class="normal">172</span>
+<span class="normal">173</span>
+<span class="normal">174</span>
+<span class="normal">175</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">hemisphere</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="n">cc_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">loc_db_df</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">city_col</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Returns string pd.Series identifing hemisphere: &quot;northern&quot; or &quot;southern&quot;.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        df (pd.DataFrame): input dataframe</span>
+<span class="sd">        city_col (str): city name column</span>
+<span class="sd">        cc_col (str): country code column</span>
+<span class="sd">        loc_db_df (pd.DataFrame): pandas dataframe with previously saved locations</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">city_col</span><span class="p">:</span>
+        <span class="n">uq_locs</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">city_col</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="n">cc_col</span><span class="p">])</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">uq_locs</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot; ,&quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="n">cc_col</span><span class="p">])</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">geolocator</span> <span class="o">=</span> <span class="n">Nominatim</span><span class="p">(</span><span class="n">user_agent</span><span class="o">=</span><span class="s2">&quot;hemisphere_identification&quot;</span><span class="p">)</span>
+    <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;hemisphere&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;northern&quot;</span>
+
+    <span class="k">for</span> <span class="n">uq_loc</span> <span class="ow">in</span> <span class="n">uq_locs</span><span class="p">:</span>
+        <span class="k">if</span> <span class="p">(</span><span class="n">loc_db_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">uq_loc</span> <span class="ow">in</span> <span class="n">loc_db_df</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">):</span>
+            <span class="n">location</span> <span class="o">=</span> <span class="n">loc_db_df</span><span class="p">[</span><span class="n">loc_db_df</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">uq_loc</span><span class="p">][</span>
+                <span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">]</span>
+            <span class="p">]</span>
+            <span class="n">latitude</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">location</span><span class="o">.</span><span class="n">latitude</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">latitude</span> <span class="o">=</span> <span class="n">geolocator</span><span class="o">.</span><span class="n">geocode</span><span class="p">(</span><span class="n">uq_loc</span><span class="p">)</span><span class="o">.</span><span class="n">latitude</span>
+
+        <span class="k">if</span> <span class="n">latitude</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">city</span><span class="p">,</span> <span class="n">country</span> <span class="o">=</span> <span class="n">uq_loc</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
+            <span class="n">dfc</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
+                <span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="n">city_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">city</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="n">cc_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">country</span><span class="p">),</span> <span class="s2">&quot;hemisphere&quot;</span>
+            <span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;southern&quot;</span>
+
+    <span class="k">return</span> <span class="n">dfc</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
+
+</div>
+
+<div class="doc doc-object doc-function">
+
+
+
+<h3 id="churn_pred.preprocessing.auxiliary_data.age_categories" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">age_categories</span>
+
+
+<a href="#churn_pred.preprocessing.auxiliary_data.age_categories" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">age_categories</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">age_col</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Returns string pd.DataFrame identifing different age classification
+categories according to the age:
+* working_class (<a href="https://ourworldindata.org/age-structure">https://ourworldindata.org/age-structure</a>)
+  * children_and_adolescents: &lt;0, 15)
+  * working_age: &lt;15, 65)
+  * elderly: &lt;65, inf)
+* stage_of_life (<a href="https://integrishealth.org/resources/on-your-health/2015/october/stages-of-life-health-for-every-age">https://integrishealth.org/resources/on-your-health/2015/october/stages-of-life-health-for-every-age</a>)
+  * infant: &lt;0, 2)
+  * toddler: &lt;2, 5)
+  * child: &lt;5, 13)
+  * teen: &lt;13, 20)
   * adult: &lt;20, 40)
   * middle_age_adult: &lt;40, 60)
   * senior_adult: &lt;60, inf)
@@ -1227,53 +1116,23 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.age_categories" class="doc doc-h
   * ww2: &lt;97, 102)
   * vampire: &lt;102, inf)</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataset</p>
+      </li>
+      <li class="field-body">
+        <b>age_col</b>
+            (<code>str</code>)
+        – <p>age column</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>age_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>age column</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">178</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">178</span>
 <span class="normal">179</span>
 <span class="normal">180</span>
 <span class="normal">181</span>
@@ -1339,7 +1198,7 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.age_categories" class="doc doc-h
 <span class="normal">241</span>
 <span class="normal">242</span>
 <span class="normal">243</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">age_categories</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">age_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns string pd.DataFrame identifing different age classification</span>
+    <span class="sd">&quot;&quot;&quot;Returns string pd.DataFrame identifing different age classification</span>
 <span class="sd">    categories according to the age:</span>
 <span class="sd">    * working_class (https://ourworldindata.org/age-structure)</span>
 <span class="sd">      * children_and_adolescents: &lt;0, 15)</span>
@@ -1405,87 +1264,58 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.age_categories" class="doc doc-h
     <span class="p">)</span>
     <span class="k">return</span> <span class="n">dfc</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.auxiliary_data.big_mac_index" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">big_mac_index</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.auxiliary_data.big_mac_index" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">big_mac_index</span>
 
-<a href="#churn_pred.preprocessing.auxiliary_data.big_mac_index" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.auxiliary_data.big_mac_index" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">big_mac_index</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Returns dataframe with Big Max Index corresponding to country codes.
 Downloaded on 26/4/2024.</p>
 
-<details class="note" open>
+<details class="note">
   <summary>Note</summary>
   <p>Unfortunatelly Spain, France, Germany (unique companies in the dataset) are not included, other index to explore:
 <a href="https://en.wikipedia.org/wiki/Big_Mac_Index">https://en.wikipedia.org/wiki/Big_Mac_Index</a></p>
 </details>
-<details class="based-on" open>
+<details class="based-on">
   <summary>Based on</summary>
   
 </details>
-<details class="downloaded-from" open>
+<details class="downloaded-from">
   <summary>Downloaded from</summary>
   
 </details>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataset</p>
+      </li>
+      <li class="field-body">
+        <b>country_name_col</b>
+            (<code>str</code>)
+        – <p>column name with country names</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>country_name_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>column name with country names</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">246</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">246</span>
 <span class="normal">247</span>
 <span class="normal">248</span>
 <span class="normal">249</span>
@@ -1523,7 +1353,7 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.big_mac_index" class="doc doc-he
 <span class="normal">281</span>
 <span class="normal">282</span>
 <span class="normal">283</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">big_mac_index</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns dataframe with Big Max Index corresponding to country codes.</span>
 <span class="sd">    Downloaded on 26/4/2024.</span>
 
@@ -1561,21 +1391,22 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.big_mac_index" class="doc doc-he
     <span class="n">dfc</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;iso_a3&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">dfc</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.auxiliary_data.gdppc" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">gdppc</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.auxiliary_data.gdppc" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">gdppc</span>
 
-<a href="#churn_pred.preprocessing.auxiliary_data.gdppc" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.auxiliary_data.gdppc" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">gdppc</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
@@ -1584,65 +1415,39 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.gdppc" class="doc doc-heading">
 Last file update: 3/28/2024.
 Posprocessed file has initial 3 lines removed for easier processing using pandas df.</p>
 
-<details class="downloaded-from" open>
+<details class="downloaded-from">
   <summary>Downloaded from</summary>
   <ul>
 <li><a href="https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD">https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD</a></li>
 <li><a href="https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.PP.CD?downloadformat=csv">https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.PP.CD?downloadformat=csv</a></li>
 </ul>
-</details>      <p>Main file before preprocessing:
-    'data/gdpp/API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'
-Other included files:
-    'data/gdpp/Metadata_Country_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'
-    'data/gdpp/Metadata_Indicator_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>country_name_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>column name with country names</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+</details>
+<details class="main-file-before-preprocessing">
+  <summary>Main file before preprocessing</summary>
+  <p>'data/gdpp/API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'</p>
+</details>
+<details class="other-included-files">
+  <summary>Other included files</summary>
+  <p>'data/gdpp/Metadata_Country_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'
+'data/gdpp/Metadata_Indicator_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'</p>
+</details>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataset</p>
+      </li>
+      <li class="field-body">
+        <b>country_name_col</b>
+            (<code>str</code>)
+        – <p>column name with country names</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">286</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">286</span>
 <span class="normal">287</span>
 <span class="normal">288</span>
 <span class="normal">289</span>
@@ -1689,7 +1494,7 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.gdppc" class="doc doc-heading">
 <span class="normal">330</span>
 <span class="normal">331</span>
 <span class="normal">332</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">gdppc</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns dataframe with Gross Domestic Product Per Capita corresponding to country</span>
 <span class="sd">    codes. Uses information from worldbank API downloaded on 26/4/2024.</span>
 <span class="sd">    Last file update: 3/28/2024.</span>
@@ -1730,267 +1535,45 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.gdppc" class="doc doc-heading">
     <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;iso_a3&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">get_iso_a3</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">dfc</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="n">country_name_col</span><span class="p">)</span>
 
-    <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">gdp_per_capita</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;iso_a3&quot;</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">)</span>
-    <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">gdp_per_capita_ic</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;iso_a3&quot;</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">)</span>
-    <span class="n">dfc</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;iso_a3&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">dfc</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="churn_pred.preprocessing.auxiliary_data.get_country_name" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">get_country_name</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span></code>
-
-<a href="#churn_pred.preprocessing.auxiliary_data.get_country_name" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Returns pd.Series with mapped country iso_a2 to names.</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.Series">Series</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input country name pandas series</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">360</span>
-<span class="normal">361</span>
-<span class="normal">362</span>
-<span class="normal">363</span>
-<span class="normal">364</span>
-<span class="normal">365</span>
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-<span class="normal">381</span>
-<span class="normal">382</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_country_name</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns pd.Series with mapped country iso_a2 to names.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        df (pd.Series): input country name pandas series</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="nf">_get_country_name_map</span><span class="p">(</span><span class="n">country_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function for get_country_name&quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">result</span> <span class="o">=</span> <span class="n">pycountry</span><span class="o">.</span><span class="n">countries</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">alpha_2</span><span class="o">=</span><span class="n">country_name</span><span class="p">)</span>
-            <span class="n">name_str</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">name</span>
-        <span class="k">except</span><span class="p">:</span>
-            <span class="n">name_str</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
-        <span class="k">return</span> <span class="n">name_str</span>
-
-    <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
-    <span class="k">for</span> <span class="n">country_name</span> <span class="ow">in</span> <span class="n">dfc</span><span class="p">[</span><span class="n">country_name_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
-        <span class="n">dfc</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dfc</span><span class="p">[</span><span class="n">country_name_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">country_name</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">_get_country_name_map</span><span class="p">(</span>
-            <span class="n">country_name</span>
-        <span class="p">)</span>
-    <span class="k">return</span> <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="churn_pred.preprocessing.auxiliary_data.get_country_region_subregion" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">get_country_region_subregion</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span></code>
-
-<a href="#churn_pred.preprocessing.auxiliary_data.get_country_region_subregion" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Returns pd.Series with mapped country iso_a2 to names.</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.Series">Series</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input country name pandas series</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">385</span>
-<span class="normal">386</span>
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-<span class="normal">408</span>
-<span class="normal">409</span>
-<span class="normal">410</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_country_region_subregion</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns pd.Series with mapped country iso_a2 to names.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        df (pd.Series): input country name pandas series</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="k">def</span> <span class="nf">_get_country_region_subregion_map</span><span class="p">(</span><span class="n">country_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function for get_country_name&quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">result</span> <span class="o">=</span> <span class="n">CountryInfo</span><span class="p">(</span><span class="n">country_name</span><span class="p">)</span>
-            <span class="n">region_str</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">region</span><span class="p">()</span>
-            <span class="n">subregion_str</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">subregion</span><span class="p">()</span>
-        <span class="k">except</span><span class="p">:</span>
-            <span class="n">region_str</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
-            <span class="n">subregion_str</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">region_str</span><span class="p">,</span> <span class="n">subregion_str</span><span class="p">)</span>
+    <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">gdp_per_capita</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;iso_a3&quot;</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">)</span>
+    <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">gdp_per_capita_ic</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;iso_a3&quot;</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">)</span>
+    <span class="n">dfc</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;iso_a3&quot;</span><span class="p">],</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
-    <span class="n">dfc</span><span class="p">[[</span><span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_region&quot;</span><span class="p">,</span> <span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_subregion&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;NA&quot;</span><span class="p">,</span> <span class="s2">&quot;NA&quot;</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">country_name</span> <span class="ow">in</span> <span class="n">dfc</span><span class="p">[</span><span class="n">country_name_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
-        <span class="n">dfc</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-            <span class="n">dfc</span><span class="p">[</span><span class="n">country_name_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">country_name</span><span class="p">,</span>
-            <span class="p">[</span><span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_region&quot;</span><span class="p">,</span> <span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_subregion&quot;</span><span class="p">],</span>
-        <span class="p">]</span> <span class="o">=</span> <span class="n">_get_country_region_subregion_map</span><span class="p">(</span><span class="n">country_name</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">dfc</span><span class="p">[[</span><span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_region&quot;</span><span class="p">,</span> <span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_subregion&quot;</span><span class="p">]]</span>
+    <span class="k">return</span> <span class="n">dfc</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.auxiliary_data.get_iso_a3" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">get_iso_a3</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.auxiliary_data.get_iso_a3" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">get_iso_a3</span>
 
-<a href="#churn_pred.preprocessing.auxiliary_data.get_iso_a3" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.auxiliary_data.get_iso_a3" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">get_iso_a3</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Returns pd.Series with mapped country names to iso_a3.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>)
+        – <p>input country name pandas series</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.Series">Series</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input country name pandas series</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">335</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">335</span>
 <span class="normal">336</span>
 <span class="normal">337</span>
 <span class="normal">338</span>
@@ -2013,7 +1596,7 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.get_iso_a3" class="doc doc-headi
 <span class="normal">355</span>
 <span class="normal">356</span>
 <span class="normal">357</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_iso_a3</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns pd.Series with mapped country names to iso_a3.</span>
+    <span class="sd">&quot;&quot;&quot;Returns pd.Series with mapped country names to iso_a3.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (pd.Series): input country name pandas series</span>
@@ -2021,7 +1604,7 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.get_iso_a3" class="doc doc-headi
     <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 
     <span class="k">def</span> <span class="nf">_get_iso_a3_map</span><span class="p">(</span><span class="n">country_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Helper function for get_iso_a3&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Helper function for get_iso_a3&quot;&quot;&quot;</span>
         <span class="k">try</span><span class="p">:</span>
             <span class="n">result</span> <span class="o">=</span> <span class="n">pycountry</span><span class="o">.</span><span class="n">countries</span><span class="o">.</span><span class="n">search_fuzzy</span><span class="p">(</span><span class="n">country_name</span><span class="p">)</span>
             <span class="n">iso_a3_str</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">alpha_3</span>  <span class="c1"># type: ignore</span>
@@ -2036,412 +1619,169 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.get_iso_a3" class="doc doc-headi
         <span class="p">)</span>
     <span class="k">return</span> <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]</span>
 </code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="churn_pred.preprocessing.auxiliary_data.hemisphere" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">hemisphere</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">cc_col</span><span class="p">,</span> <span class="n">loc_db_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">city_col</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
-
-<a href="#churn_pred.preprocessing.auxiliary_data.hemisphere" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Returns string pd.Series identifing hemisphere: "northern" or "southern".</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>city_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>city name column</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>cc_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>country code column</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>loc_db_df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pandas dataframe with previously saved locations</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">138</span>
-<span class="normal">139</span>
-<span class="normal">140</span>
-<span class="normal">141</span>
-<span class="normal">142</span>
-<span class="normal">143</span>
-<span class="normal">144</span>
-<span class="normal">145</span>
-<span class="normal">146</span>
-<span class="normal">147</span>
-<span class="normal">148</span>
-<span class="normal">149</span>
-<span class="normal">150</span>
-<span class="normal">151</span>
-<span class="normal">152</span>
-<span class="normal">153</span>
-<span class="normal">154</span>
-<span class="normal">155</span>
-<span class="normal">156</span>
-<span class="normal">157</span>
-<span class="normal">158</span>
-<span class="normal">159</span>
-<span class="normal">160</span>
-<span class="normal">161</span>
-<span class="normal">162</span>
-<span class="normal">163</span>
-<span class="normal">164</span>
-<span class="normal">165</span>
-<span class="normal">166</span>
-<span class="normal">167</span>
-<span class="normal">168</span>
-<span class="normal">169</span>
-<span class="normal">170</span>
-<span class="normal">171</span>
-<span class="normal">172</span>
-<span class="normal">173</span>
-<span class="normal">174</span>
-<span class="normal">175</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">hemisphere</span><span class="p">(</span>
-    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="n">cc_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="n">loc_db_df</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="n">city_col</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns string pd.Series identifing hemisphere: &quot;northern&quot; or &quot;southern&quot;.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        df (pd.DataFrame): input dataframe</span>
-<span class="sd">        city_col (str): city name column</span>
-<span class="sd">        cc_col (str): country code column</span>
-<span class="sd">        loc_db_df (pd.DataFrame): pandas dataframe with previously saved locations</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">city_col</span><span class="p">:</span>
-        <span class="n">uq_locs</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">city_col</span><span class="p">]</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="n">cc_col</span><span class="p">])</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">uq_locs</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot; ,&quot;</span> <span class="o">+</span> <span class="n">df</span><span class="p">[</span><span class="n">cc_col</span><span class="p">])</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
-    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">geolocator</span> <span class="o">=</span> <span class="n">Nominatim</span><span class="p">(</span><span class="n">user_agent</span><span class="o">=</span><span class="s2">&quot;hemisphere_identification&quot;</span><span class="p">)</span>
-    <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;hemisphere&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;northern&quot;</span>
-
-    <span class="k">for</span> <span class="n">uq_loc</span> <span class="ow">in</span> <span class="n">uq_locs</span><span class="p">:</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">loc_db_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="n">uq_loc</span> <span class="ow">in</span> <span class="n">loc_db_df</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">):</span>
-            <span class="n">location</span> <span class="o">=</span> <span class="n">loc_db_df</span><span class="p">[</span><span class="n">loc_db_df</span><span class="p">[</span><span class="s2">&quot;location&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">uq_loc</span><span class="p">][</span>
-                <span class="p">[</span><span class="s2">&quot;latitude&quot;</span><span class="p">,</span> <span class="s2">&quot;longitude&quot;</span><span class="p">]</span>
-            <span class="p">]</span>
-            <span class="n">latitude</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">location</span><span class="o">.</span><span class="n">latitude</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">latitude</span> <span class="o">=</span> <span class="n">geolocator</span><span class="o">.</span><span class="n">geocode</span><span class="p">(</span><span class="n">uq_loc</span><span class="p">)</span><span class="o">.</span><span class="n">latitude</span>
-
-        <span class="k">if</span> <span class="n">latitude</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">city</span><span class="p">,</span> <span class="n">country</span> <span class="o">=</span> <span class="n">uq_loc</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>
-            <span class="n">dfc</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-                <span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="n">city_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">city</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="n">cc_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">country</span><span class="p">),</span> <span class="s2">&quot;hemisphere&quot;</span>
-            <span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;southern&quot;</span>
-
-    <span class="k">return</span> <span class="n">dfc</span>
-</code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.auxiliary_data.surname_origin" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">surname_origin</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">surname_col</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.auxiliary_data.get_country_name" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">get_country_name</span>
 
-<a href="#churn_pred.preprocessing.auxiliary_data.surname_origin" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.auxiliary_data.get_country_name" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">get_country_name</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Surname classification that uses db of names leaked from FB, see:
-<a href="https://github.com/philipperemy/name-dataset">https://github.com/philipperemy/name-dataset</a></p>
-
-
+      <p>Returns pd.Series with mapped country iso_a2 to names.</p>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>surname_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>column name with surnames</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>)
+        – <p>input country name pandas series</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">41</span>
-<span class="normal">42</span>
-<span class="normal">43</span>
-<span class="normal">44</span>
-<span class="normal">45</span>
-<span class="normal">46</span>
-<span class="normal">47</span>
-<span class="normal">48</span>
-<span class="normal">49</span>
-<span class="normal">50</span>
-<span class="normal">51</span>
-<span class="normal">52</span>
-<span class="normal">53</span>
-<span class="normal">54</span>
-<span class="normal">55</span>
-<span class="normal">56</span>
-<span class="normal">57</span>
-<span class="normal">58</span>
-<span class="normal">59</span>
-<span class="normal">60</span>
-<span class="normal">61</span>
-<span class="normal">62</span>
-<span class="normal">63</span>
-<span class="normal">64</span>
-<span class="normal">65</span>
-<span class="normal">66</span>
-<span class="normal">67</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">surname_origin</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">surname_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Surname classification that uses db of names leaked from FB, see:</span>
-<span class="sd">    https://github.com/philipperemy/name-dataset</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">360</span>
+<span class="normal">361</span>
+<span class="normal">362</span>
+<span class="normal">363</span>
+<span class="normal">364</span>
+<span class="normal">365</span>
+<span class="normal">366</span>
+<span class="normal">367</span>
+<span class="normal">368</span>
+<span class="normal">369</span>
+<span class="normal">370</span>
+<span class="normal">371</span>
+<span class="normal">372</span>
+<span class="normal">373</span>
+<span class="normal">374</span>
+<span class="normal">375</span>
+<span class="normal">376</span>
+<span class="normal">377</span>
+<span class="normal">378</span>
+<span class="normal">379</span>
+<span class="normal">380</span>
+<span class="normal">381</span>
+<span class="normal">382</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_country_name</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Returns pd.Series with mapped country iso_a2 to names.</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        df (pd.DataFrame): input dataset</span>
-<span class="sd">        surname_col (str): column name with surnames</span>
+<span class="sd">        df (pd.Series): input country name pandas series</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">nd</span> <span class="o">=</span> <span class="n">NameDataset</span><span class="p">()</span>
 
-    <span class="k">def</span> <span class="nf">_search_surname</span><span class="p">(</span><span class="n">surname</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">_get_country_name_map</span><span class="p">(</span><span class="n">country_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;Helper function for get_country_name&quot;&quot;&quot;</span>
         <span class="k">try</span><span class="p">:</span>
-            <span class="n">res</span> <span class="o">=</span> <span class="n">nd</span><span class="o">.</span><span class="n">search</span><span class="p">(</span><span class="n">surname</span><span class="p">)</span>
-            <span class="n">origin_country</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span>
-                <span class="n">res</span><span class="p">[</span><span class="s2">&quot;last_name&quot;</span><span class="p">][</span><span class="s2">&quot;country&quot;</span><span class="p">],</span> <span class="n">key</span><span class="o">=</span><span class="n">res</span><span class="p">[</span><span class="s2">&quot;last_name&quot;</span><span class="p">][</span><span class="s2">&quot;country&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span>
-            <span class="p">)</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">pycountry</span><span class="o">.</span><span class="n">countries</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">alpha_2</span><span class="o">=</span><span class="n">country_name</span><span class="p">)</span>
+            <span class="n">name_str</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">name</span>
         <span class="k">except</span><span class="p">:</span>
-            <span class="n">origin_country</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
-        <span class="k">return</span> <span class="n">origin_country</span>
+            <span class="n">name_str</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
+        <span class="k">return</span> <span class="n">name_str</span>
 
     <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
-    <span class="k">for</span> <span class="n">surname</span> <span class="ow">in</span> <span class="n">dfc</span><span class="p">[</span><span class="n">surname_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
-        <span class="n">dfc</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dfc</span><span class="p">[</span><span class="n">surname_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">surname</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">_search_surname</span><span class="p">(</span><span class="n">surname</span><span class="p">)</span>
-
+    <span class="k">for</span> <span class="n">country_name</span> <span class="ow">in</span> <span class="n">dfc</span><span class="p">[</span><span class="n">country_name_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+        <span class="n">dfc</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">dfc</span><span class="p">[</span><span class="n">country_name_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">country_name</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">_get_country_name_map</span><span class="p">(</span>
+            <span class="n">country_name</span>
+        <span class="p">)</span>
     <span class="k">return</span> <span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;res&quot;</span><span class="p">]</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.auxiliary_data.surname_origin_bert" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">surname_origin_bert</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">surname_col</span><span class="p">)</span></code>
-
-<a href="#churn_pred.preprocessing.auxiliary_data.surname_origin_bert" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>THIS IS WORK IN PROGRESS!!!
-Surname classification that uses adjusted script
-scripts/surname_classification_with_bert.py) from:
-<a href="https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert">https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert</a>
-to train a bert model for surname classification</p>
-<p>additional data from:
-<a href="https://github.com/greenelab/wiki-nationality-estimate/tree/master">https://github.com/greenelab/wiki-nationality-estimate/tree/master</a></p>
-<p>and possibly data from:
-<a href="https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset">https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset</a></p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>surname_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>column name with surnames</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">19</span>
-<span class="normal">20</span>
-<span class="normal">21</span>
-<span class="normal">22</span>
-<span class="normal">23</span>
-<span class="normal">24</span>
-<span class="normal">25</span>
-<span class="normal">26</span>
-<span class="normal">27</span>
-<span class="normal">28</span>
-<span class="normal">29</span>
-<span class="normal">30</span>
-<span class="normal">31</span>
-<span class="normal">32</span>
-<span class="normal">33</span>
-<span class="normal">34</span>
-<span class="normal">35</span>
-<span class="normal">36</span>
-<span class="normal">37</span>
-<span class="normal">38</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">surname_origin_bert</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">surname_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    THIS IS WORK IN PROGRESS!!!</span>
-<span class="sd">    Surname classification that uses adjusted script</span>
-<span class="sd">    scripts/surname_classification_with_bert.py) from:</span>
-<span class="sd">    https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert</span>
-<span class="sd">    to train a bert model for surname classification</span>
-
-<span class="sd">    additional data from:</span>
-<span class="sd">    https://github.com/greenelab/wiki-nationality-estimate/tree/master</span>
-
-<span class="sd">    and possibly data from:</span>
-<span class="sd">    https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset</span>
+<h3 id="churn_pred.preprocessing.auxiliary_data.get_country_region_subregion" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">get_country_region_subregion</span>
+
+
+<a href="#churn_pred.preprocessing.auxiliary_data.get_country_region_subregion" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">get_country_region_subregion</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Returns pd.Series with mapped country iso_a2 to names.</p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>)
+        – <p>input country name pandas series</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">385</span>
+<span class="normal">386</span>
+<span class="normal">387</span>
+<span class="normal">388</span>
+<span class="normal">389</span>
+<span class="normal">390</span>
+<span class="normal">391</span>
+<span class="normal">392</span>
+<span class="normal">393</span>
+<span class="normal">394</span>
+<span class="normal">395</span>
+<span class="normal">396</span>
+<span class="normal">397</span>
+<span class="normal">398</span>
+<span class="normal">399</span>
+<span class="normal">400</span>
+<span class="normal">401</span>
+<span class="normal">402</span>
+<span class="normal">403</span>
+<span class="normal">404</span>
+<span class="normal">405</span>
+<span class="normal">406</span>
+<span class="normal">407</span>
+<span class="normal">408</span>
+<span class="normal">409</span>
+<span class="normal">410</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_country_region_subregion</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">country_name_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Returns pd.Series with mapped country iso_a2 to names.</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        df (pd.DataFrame): input dataset</span>
-<span class="sd">        surname_col (str): column name with surnames</span>
+<span class="sd">        df (pd.Series): input country name pandas series</span>
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="k">return</span> <span class="n">dfc</span>
+
+    <span class="k">def</span> <span class="nf">_get_country_region_subregion_map</span><span class="p">(</span><span class="n">country_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">str</span><span class="p">]:</span>
+        <span class="sd">&quot;&quot;&quot;Helper function for get_country_name&quot;&quot;&quot;</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">CountryInfo</span><span class="p">(</span><span class="n">country_name</span><span class="p">)</span>
+            <span class="n">region_str</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">region</span><span class="p">()</span>
+            <span class="n">subregion_str</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">subregion</span><span class="p">()</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">region_str</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
+            <span class="n">subregion_str</span> <span class="o">=</span> <span class="s2">&quot;NA&quot;</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">region_str</span><span class="p">,</span> <span class="n">subregion_str</span><span class="p">)</span>
+
+    <span class="n">dfc</span><span class="p">[[</span><span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_region&quot;</span><span class="p">,</span> <span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_subregion&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;NA&quot;</span><span class="p">,</span> <span class="s2">&quot;NA&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">country_name</span> <span class="ow">in</span> <span class="n">dfc</span><span class="p">[</span><span class="n">country_name_col</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
+        <span class="n">dfc</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
+            <span class="n">dfc</span><span class="p">[</span><span class="n">country_name_col</span><span class="p">]</span> <span class="o">==</span> <span class="n">country_name</span><span class="p">,</span>
+            <span class="p">[</span><span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_region&quot;</span><span class="p">,</span> <span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_subregion&quot;</span><span class="p">],</span>
+        <span class="p">]</span> <span class="o">=</span> <span class="n">_get_country_region_subregion_map</span><span class="p">(</span><span class="n">country_name</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">dfc</span><span class="p">[[</span><span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_region&quot;</span><span class="p">,</span> <span class="n">country_name_col</span> <span class="o">+</span> <span class="s2">&quot;_subregion&quot;</span><span class="p">]]</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -2458,7 +1798,12 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.surname_origin_bert" class="doc
 
 
 
-<a id="churn_pred.preprocessing.label_encoder"></a>
+<h2 id="churn_pred.preprocessing.label_encoder" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">label_encoder</span>
+
+
+<a href="#churn_pred.preprocessing.label_encoder" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -2476,16 +1821,17 @@ <h2 id="churn_pred.preprocessing.auxiliary_data.surname_origin_bert" class="doc
 
 
 
-<h2 id="churn_pred.preprocessing.label_encoder.LabelEncoder" class="doc doc-heading">
-          <code>LabelEncoder</code>
-
+<h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder" class="doc doc-heading">
+        <span class="doc doc-object-name doc-class-name">LabelEncoder</span>
 
-<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">LabelEncoder</span><span class="p">(</span><span class="n">columns_to_encode</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
-          <p class="doc doc-class-bases">
-            Bases: <code>object</code></p>
+      <p class="doc doc-class-bases">
+        Bases: <code>object</code></p>
 
   
       <p>Label Encode categorical values for multiple columns at once</p>
@@ -2495,358 +1841,47 @@ <h2 id="churn_pred.preprocessing.label_encoder.LabelEncoder" class="doc doc-head
 LabelEncoder reserves 0 for <code>unseen</code> new categories. This is convenient
 when defining the embedding layers, since we can just set padding idx to 0.</p>
 
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>columns_to_encode</code></td>
-          <td>
-                <code>list, Optional, default = None</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>List of strings containing
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>columns_to_encode</b>
+            (<code>list, Optional, default = None</code>)
+        – <p>List of strings containing
 the names of the columns to encode. If <code>None</code> all columns of type <code>object</code>
 in the dataframe will be label encoded.</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Attributes:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code><span title="churn_pred.preprocessing.label_encoder.LabelEncoder.encoding_dict">encoding_dict</span></code></td>
-          <td>
-                <code>Dict</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Dictionary containing the encoding mappings in the format,
+      </li>
+  </ul>
+
+  <p>Attributes:</p>
+  <ul>
+      <li class="field-body">
+        <b>encoding_dict</b>
+            (<code>Dict</code>)
+        – <p>Dictionary containing the encoding mappings in the format,
 e.g. : <br/> <code>{'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...}</code>  # noqa</p>
-            </div>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code><span title="churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_encoding_dict(Dict)">inverse_encoding_dict(Dict)</span></code></td>
-          <td>
-                <code>Dict</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Dictionary containing the inverse encoding mappings
+      </li>
+      <li class="field-body">
+        <b>inverse_encoding_dict(Dict)</b>
+            (<code>Dict</code>)
+        – <p>Dictionary containing the inverse encoding mappings
 in the format, e.g. : <br/> <code>{'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...}</code>  # noqa</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-            <details class="quote">
-              <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
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-<span class="normal">145</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">LabelEncoder</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Label Encode categorical values for multiple columns at once</span>
-
-<span class="sd">    :information_source: **NOTE**:</span>
-<span class="sd">    Shamlessly copied from https://github.com/jrzaurin/pytorch-widedeep</span>
-
-<span class="sd">    :information_source: **NOTE**:</span>
-<span class="sd">    LabelEncoder reserves 0 for `unseen` new categories. This is convenient</span>
-<span class="sd">    when defining the embedding layers, since we can just set padding idx to 0.</span>
-
-<span class="sd">    Parameters:</span>
-<span class="sd">        columns_to_encode (list, Optional, default = None): List of strings containing</span>
-<span class="sd">            the names of the columns to encode. If `None` all columns of type `object`</span>
-<span class="sd">            in the dataframe will be label encoded.</span>
-
-<span class="sd">    Attributes:</span>
-<span class="sd">        encoding_dict (Dict): Dictionary containing the encoding mappings in the format,</span>
-<span class="sd">            e.g. : &lt;br/&gt; `{&#39;colname1&#39;: {&#39;cat1&#39;: 1, &#39;cat2&#39;: 2, ...}, &#39;colname2&#39;: {&#39;cat1&#39;: 1, &#39;cat2&#39;: 2, ...}, ...}`  # noqa</span>
-<span class="sd">        inverse_encoding_dict(Dict): Dictionary containing the inverse encoding mappings</span>
-<span class="sd">            in the format, e.g. : &lt;br/&gt; `{&#39;colname1&#39;: {1: &#39;cat1&#39;, 2: &#39;cat2&#39;, ...}, &#39;colname2&#39;: {1: &#39;cat1&#39;, 2: &#39;cat2&#39;, ...}, ...}`  # noqa</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">columns_to_encode</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="n">columns_to_encode</span>
-
-    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;LabelEncoder&quot;</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Creates encoding attributes</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            LabelEncoder: `LabelEncoder` fitted object</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">df_inp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-                <span class="n">df_inp</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;object&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span>
-            <span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
-            <span class="c1"># format</span>
-            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
-                <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>
-
-        <span class="n">unique_column_vals</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
-            <span class="n">unique_column_vals</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-
-        <span class="c1"># leave 0 for padding/&quot;unseen&quot; categories</span>
-        <span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unique_column_vals</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
-                <span class="n">o</span><span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">idx</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unique_column_vals</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-            <span class="p">}</span>
-            <span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
-                <span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-            <span class="p">}</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;unseen&quot;</span>
-
-        <span class="k">return</span> <span class="bp">self</span>
-
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Label Encoded the categories in `columns_to_encode`</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            pd.DataFrame: label-encoded dataframe</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span>
-        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span>
-                <span class="s2">&quot;This LabelEncoder instance is not fitted yet. &quot;</span>
-                <span class="s2">&quot;Call &#39;fit&#39; with appropriate arguments before using this LabelEncoder.&quot;</span>
-            <span class="p">)</span>
+      </li>
+  </ul>
 
-        <span class="n">df_inp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
-        <span class="c1"># format</span>
-        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>  <span class="c1"># type: ignore</span>
-            <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>
 
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">df_inp</span>
-
-    <span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Combines `fit` and `transform`</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            pd.DataFrame: label-encoded dataframe</span>
-
-<span class="sd">        Examples:</span>
-<span class="sd">            &gt;&gt;&gt; import pandas as pd</span>
-<span class="sd">            &gt;&gt;&gt; from churn_pred.preprocessing.label_encoder import LabelEncoder</span>
-<span class="sd">            &gt;&gt;&gt; df = pd.DataFrame({&#39;col1&#39;: [1,2,3], &#39;col2&#39;: [&#39;me&#39;, &#39;you&#39;, &#39;him&#39;]})</span>
-<span class="sd">            &gt;&gt;&gt; columns_to_encode = [&#39;col2&#39;]</span>
-<span class="sd">            &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)</span>
-<span class="sd">            &gt;&gt;&gt; encoder.fit_transform(df)</span>
-<span class="sd">               col1  col2</span>
-<span class="sd">            0     1     1</span>
-<span class="sd">            1     2     2</span>
-<span class="sd">            2     3     3</span>
-<span class="sd">            &gt;&gt;&gt; encoder.encoding_dict</span>
-<span class="sd">            {&#39;col2&#39;: {&#39;me&#39;: 1, &#39;you&#39;: 2, &#39;him&#39;: 3}}</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">inverse_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the original categories</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">            pd.DataFrame: label-encoded dataframe</span>
-
-<span class="sd">        Examples:</span>
-<span class="sd">            &gt;&gt;&gt; import pandas as pd</span>
-<span class="sd">            &gt;&gt;&gt; from churn_pred.preprocessing.label_encoder import LabelEncoder</span>
-<span class="sd">            &gt;&gt;&gt; df = pd.DataFrame({&#39;col1&#39;: [1,2,3], &#39;col2&#39;: [&#39;me&#39;, &#39;you&#39;, &#39;him&#39;]})</span>
-<span class="sd">            &gt;&gt;&gt; columns_to_encode = [&#39;col2&#39;]</span>
-<span class="sd">            &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)</span>
-<span class="sd">            &gt;&gt;&gt; df_enc = encoder.fit_transform(df)</span>
-<span class="sd">            &gt;&gt;&gt; encoder.inverse_transform(df_enc)</span>
-<span class="sd">               col1 col2</span>
-<span class="sd">            0     1   me</span>
-<span class="sd">            1     2  you</span>
-<span class="sd">            2     3  him</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">df</span>
+          <details class="quote">
+            <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">29</span>
+<span class="normal">30</span>
+<span class="normal">31</span>
+<span class="normal">32</span>
+<span class="normal">33</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+    <span class="bp">self</span><span class="p">,</span>
+    <span class="n">columns_to_encode</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="n">columns_to_encode</span>
 </code></pre></div></td></tr></table></div>
-            </details>
+          </details>
 
   
 
@@ -2860,48 +1895,33 @@ <h2 id="churn_pred.preprocessing.label_encoder.LabelEncoder" class="doc doc-head
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.fit" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
+<h4 id="churn_pred.preprocessing.label_encoder.LabelEncoder.fit" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">fit</span>
 
-<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder.fit" class="headerlink" title="Permanent link">&para;</a></h3>
 
+<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder.fit" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Creates encoding attributes</p>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>LabelEncoder</b>(            <code><a class="autorefs autorefs-internal" title="churn_pred.preprocessing.label_encoder.LabelEncoder" href="#churn_pred.preprocessing.label_encoder.LabelEncoder">LabelEncoder</a></code>
+)        – <p><code>LabelEncoder</code> fitted object</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>LabelEncoder</code></td>          <td>
-                <code><a class="autorefs autorefs-internal" title="churn_pred.preprocessing.label_encoder.LabelEncoder" href="#churn_pred.preprocessing.label_encoder.LabelEncoder">LabelEncoder</a></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p><code>LabelEncoder</code> fitted object</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">35</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">35</span>
 <span class="normal">36</span>
 <span class="normal">37</span>
 <span class="normal">38</span>
@@ -2942,94 +1962,156 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.fit" class="doc doc-
 <span class="normal">73</span>
 <span class="normal">74</span>
 <span class="normal">75</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;LabelEncoder&quot;</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates encoding attributes</span>
+    <span class="sd">&quot;&quot;&quot;Creates encoding attributes</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        LabelEncoder: `LabelEncoder` fitted object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">df_inp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
+            <span class="n">df_inp</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;object&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
+        <span class="c1"># format</span>
+        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
+            <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>
+
+    <span class="n">unique_column_vals</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
+        <span class="n">unique_column_vals</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+
+    <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+
+    <span class="c1"># leave 0 for padding/&quot;unseen&quot; categories</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unique_column_vals</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">o</span><span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">idx</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unique_column_vals</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+        <span class="p">}</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
+        <span class="p">}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;unseen&quot;</span>
+
+    <span class="k">return</span> <span class="bp">self</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
+
+</div>
+
+<div class="doc doc-object doc-function">
+
+
+
+<h4 id="churn_pred.preprocessing.label_encoder.LabelEncoder.transform" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">transform</span>
+
+
+<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder.transform" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Label Encoded the categories in <code>columns_to_encode</code></p>
+
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+            <code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>
+        – <p>pd.DataFrame: label-encoded dataframe</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 77</span>
+<span class="normal"> 78</span>
+<span class="normal"> 79</span>
+<span class="normal"> 80</span>
+<span class="normal"> 81</span>
+<span class="normal"> 82</span>
+<span class="normal"> 83</span>
+<span class="normal"> 84</span>
+<span class="normal"> 85</span>
+<span class="normal"> 86</span>
+<span class="normal"> 87</span>
+<span class="normal"> 88</span>
+<span class="normal"> 89</span>
+<span class="normal"> 90</span>
+<span class="normal"> 91</span>
+<span class="normal"> 92</span>
+<span class="normal"> 93</span>
+<span class="normal"> 94</span>
+<span class="normal"> 95</span>
+<span class="normal"> 96</span>
+<span class="normal"> 97</span>
+<span class="normal"> 98</span>
+<span class="normal"> 99</span>
+<span class="normal">100</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Label Encoded the categories in `columns_to_encode`</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        LabelEncoder: `LabelEncoder` fitted object</span>
+<span class="sd">        pd.DataFrame: label-encoded dataframe</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">df_inp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-            <span class="n">df_inp</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;object&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span>
+    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span>
+            <span class="s2">&quot;This LabelEncoder instance is not fitted yet. &quot;</span>
+            <span class="s2">&quot;Call &#39;fit&#39; with appropriate arguments before using this LabelEncoder.&quot;</span>
         <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
-        <span class="c1"># format</span>
-        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
-            <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>
-
-    <span class="n">unique_column_vals</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
-        <span class="n">unique_column_vals</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
 
-    <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-
-    <span class="c1"># leave 0 for padding/&quot;unseen&quot; categories</span>
-    <span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unique_column_vals</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="n">o</span><span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="n">idx</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unique_column_vals</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
-        <span class="p">}</span>
-        <span class="n">idx</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">df_inp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
+    <span class="c1"># format</span>
+    <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>  <span class="c1"># type: ignore</span>
+        <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>
 
-    <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">:</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()</span>
-        <span class="p">}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;unseen&quot;</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
 
-    <span class="k">return</span> <span class="bp">self</span>
+    <span class="k">return</span> <span class="n">df_inp</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.fit_transform" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
+<h4 id="churn_pred.preprocessing.label_encoder.LabelEncoder.fit_transform" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">fit_transform</span>
 
-<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder.fit_transform" class="headerlink" title="Permanent link">&para;</a></h3>
 
+<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder.fit_transform" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Combines <code>fit</code> and <code>transform</code></p>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+            <code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>
+        – <p>pd.DataFrame: label-encoded dataframe</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pd.DataFrame: label-encoded dataframe</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-<p><span class="doc-section-title">Examples:</span></p>
+<p><strong>Examples:</strong></p>
     <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">churn_pred.preprocessing.label_encoder</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;col1&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;col2&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;me&#39;</span><span class="p">,</span> <span class="s1">&#39;you&#39;</span><span class="p">,</span> <span class="s1">&#39;him&#39;</span><span class="p">]})</span>
@@ -3044,9 +2126,9 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.fit_transform" class
 <span class="go">{&#39;col2&#39;: {&#39;me&#39;: 1, &#39;you&#39;: 2, &#39;him&#39;: 3}}</span>
 </code></pre></div>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">102</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">102</span>
 <span class="normal">103</span>
 <span class="normal">104</span>
 <span class="normal">105</span>
@@ -3067,7 +2149,7 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.fit_transform" class
 <span class="normal">120</span>
 <span class="normal">121</span>
 <span class="normal">122</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Combines `fit` and `transform`</span>
+    <span class="sd">&quot;&quot;&quot;Combines `fit` and `transform`</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        pd.DataFrame: label-encoded dataframe</span>
@@ -3088,53 +2170,36 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.fit_transform" class
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_transform" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">inverse_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
+<h4 id="churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_transform" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">inverse_transform</span>
 
-<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_transform" class="headerlink" title="Permanent link">&para;</a></h3>
 
+<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_transform" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">inverse_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Returns the original categories</p>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+            <code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>
+        – <p>pd.DataFrame: label-encoded dataframe</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pd.DataFrame: label-encoded dataframe</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-<p><span class="doc-section-title">Examples:</span></p>
+<p><strong>Examples:</strong></p>
     <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">churn_pred.preprocessing.label_encoder</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;col1&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;col2&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;me&#39;</span><span class="p">,</span> <span class="s1">&#39;you&#39;</span><span class="p">,</span> <span class="s1">&#39;him&#39;</span><span class="p">]})</span>
@@ -3148,9 +2213,9 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_transform" c
 <span class="go">2     3  him</span>
 </code></pre></div>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">124</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">124</span>
 <span class="normal">125</span>
 <span class="normal">126</span>
 <span class="normal">127</span>
@@ -3172,7 +2237,7 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_transform" c
 <span class="normal">143</span>
 <span class="normal">144</span>
 <span class="normal">145</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">inverse_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the original categories</span>
+    <span class="sd">&quot;&quot;&quot;Returns the original categories</span>
 
 <span class="sd">    Returns:</span>
 <span class="sd">        pd.DataFrame: label-encoded dataframe</span>
@@ -3194,101 +2259,7 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_transform" c
         <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
     <span class="k">return</span> <span class="n">df</span>
 </code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.transform" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
-
-<a href="#churn_pred.preprocessing.label_encoder.LabelEncoder.transform" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Label Encoded the categories in <code>columns_to_encode</code></p>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>pd.DataFrame: label-encoded dataframe</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/label_encoder.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 77</span>
-<span class="normal"> 78</span>
-<span class="normal"> 79</span>
-<span class="normal"> 80</span>
-<span class="normal"> 81</span>
-<span class="normal"> 82</span>
-<span class="normal"> 83</span>
-<span class="normal"> 84</span>
-<span class="normal"> 85</span>
-<span class="normal"> 86</span>
-<span class="normal"> 87</span>
-<span class="normal"> 88</span>
-<span class="normal"> 89</span>
-<span class="normal"> 90</span>
-<span class="normal"> 91</span>
-<span class="normal"> 92</span>
-<span class="normal"> 93</span>
-<span class="normal"> 94</span>
-<span class="normal"> 95</span>
-<span class="normal"> 96</span>
-<span class="normal"> 97</span>
-<span class="normal"> 98</span>
-<span class="normal"> 99</span>
-<span class="normal">100</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Label Encoded the categories in `columns_to_encode`</span>
-
-<span class="sd">    Returns:</span>
-<span class="sd">        pd.DataFrame: label-encoded dataframe</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span>
-    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span>
-            <span class="s2">&quot;This LabelEncoder instance is not fitted yet. &quot;</span>
-            <span class="s2">&quot;Call &#39;fit&#39; with appropriate arguments before using this LabelEncoder.&quot;</span>
-        <span class="p">)</span>
-
-    <span class="n">df_inp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
-    <span class="c1"># format</span>
-    <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>  <span class="c1"># type: ignore</span>
-        <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">df_inp</span>
-</code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -3299,7 +2270,6 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.transform" class="do
 
   </div>
 
-
 </div>
 
 
@@ -3315,152 +2285,64 @@ <h3 id="churn_pred.preprocessing.label_encoder.LabelEncoder.transform" class="do
 
 
 
-<a id="churn_pred.preprocessing.preprocess_data"></a>
-  <div class="doc doc-contents first">
-
-  
-
-  <div class="doc doc-children">
-
-
-
-
-
-
+<h2 id="churn_pred.preprocessing.preprocess_data" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">preprocess_data</span>
 
 
+<a href="#churn_pred.preprocessing.preprocess_data" class="headerlink" title="Permanent link">&para;</a></h2>
 
+  <div class="doc doc-contents first">
 
-<div class="doc doc-object doc-function">
+  
 
+  <div class="doc doc-children">
 
 
-<h2 id="churn_pred.preprocessing.preprocess_data.drop_constant_cols" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">drop_constant_cols</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
-<a href="#churn_pred.preprocessing.preprocess_data.drop_constant_cols" class="headerlink" title="Permanent link">&para;</a></h2>
 
 
-  <div class="doc doc-contents ">
-  
-      <p>Returns dataframe without constant columns, i.e. those with just 1 unique
-value for all rows.</p>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">35</span>
-<span class="normal">36</span>
-<span class="normal">37</span>
-<span class="normal">38</span>
-<span class="normal">39</span>
-<span class="normal">40</span>
-<span class="normal">41</span>
-<span class="normal">42</span>
-<span class="normal">43</span>
-<span class="normal">44</span>
-<span class="normal">45</span>
-<span class="normal">46</span>
-<span class="normal">47</span>
-<span class="normal">48</span>
-<span class="normal">49</span>
-<span class="normal">50</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">drop_constant_cols</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns dataframe without constant columns, i.e. those with just 1 unique</span>
-<span class="sd">    value for all rows.&quot;&quot;&quot;</span>
-    <span class="n">nunique_per_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">nunique</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">const_cols</span> <span class="o">=</span> <span class="n">nunique_per_col</span><span class="p">[</span><span class="n">nunique_per_col</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span>
-    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">const_cols</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">            Dropped {} constant columns.</span>
-<span class="sd">            Affected columns: {}</span>
-<span class="sd">            &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="n">const_cols</span><span class="p">),</span> <span class="n">const_cols</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-    <span class="k">return</span> <span class="n">df</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
 
-</div>
 
 
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_data.drop_high_nan_cols" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">drop_high_nan_cols</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_data.drop_high_nan_cols" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">drop_high_nan_cols</span>
 
-<a href="#churn_pred.preprocessing.preprocess_data.drop_high_nan_cols" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_data.drop_high_nan_cols" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">drop_high_nan_cols</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Returns dataframe without columns that have ratio of missingness above threshold.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>threshold</b>
+            (<code>float = 0.8</code>)
+        – <p>ratio of missingness applied per column</p>
+      </li>
+      <li class="field-body">
+        <b>verbose</b>
+            (<code>bool</code>)
+        – <p>whether the output should be verbose</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>threshold</code></td>
-          <td>
-                <code>float = 0.8</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>ratio of missingness applied per column</p>
-            </div>
-          </td>
-          <td>
-                <code>0.8</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>verbose</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether the output should be verbose</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 7</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 7</span>
 <span class="normal"> 8</span>
 <span class="normal"> 9</span>
 <span class="normal">10</span>
@@ -3488,7 +2370,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.drop_high_nan_cols" class="doc
 <span class="normal">32</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">drop_high_nan_cols</span><span class="p">(</span>
     <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns dataframe without columns that have ratio of missingness above threshold.</span>
+    <span class="sd">&quot;&quot;&quot;Returns dataframe without columns that have ratio of missingness above threshold.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (pd.DataFrame): input dataframe</span>
@@ -3503,110 +2385,121 @@ <h2 id="churn_pred.preprocessing.preprocess_data.drop_high_nan_cols" class="doc
     <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">high_nan_percentage_cols</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="nb">print</span><span class="p">(</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">            Dropped {} columns with fraction of NaN above threshold = {}.</span>
+            <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">            Dropped {} columns with fraction of NaN above threshold = {}.</span>
+<span class="sd">            Affected columns: {}</span>
+<span class="sd">            &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="nb">len</span><span class="p">(</span><span class="n">high_nan_percentage_cols</span><span class="p">),</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">high_nan_percentage_cols</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="n">df</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
+
+</div>
+
+<div class="doc doc-object doc-function">
+
+
+
+<h3 id="churn_pred.preprocessing.preprocess_data.drop_constant_cols" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">drop_constant_cols</span>
+
+
+<a href="#churn_pred.preprocessing.preprocess_data.drop_constant_cols" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">drop_constant_cols</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Returns dataframe without constant columns, i.e. those with just 1 unique
+value for all rows.</p>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">35</span>
+<span class="normal">36</span>
+<span class="normal">37</span>
+<span class="normal">38</span>
+<span class="normal">39</span>
+<span class="normal">40</span>
+<span class="normal">41</span>
+<span class="normal">42</span>
+<span class="normal">43</span>
+<span class="normal">44</span>
+<span class="normal">45</span>
+<span class="normal">46</span>
+<span class="normal">47</span>
+<span class="normal">48</span>
+<span class="normal">49</span>
+<span class="normal">50</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">drop_constant_cols</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Returns dataframe without constant columns, i.e. those with just 1 unique</span>
+<span class="sd">    value for all rows.&quot;&quot;&quot;</span>
+    <span class="n">nunique_per_col</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">nunique</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">const_cols</span> <span class="o">=</span> <span class="n">nunique_per_col</span><span class="p">[</span><span class="n">nunique_per_col</span> <span class="o">==</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">const_cols</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">            Dropped {} constant columns.</span>
 <span class="sd">            Affected columns: {}</span>
 <span class="sd">            &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="nb">len</span><span class="p">(</span><span class="n">high_nan_percentage_cols</span><span class="p">),</span> <span class="n">threshold</span><span class="p">,</span> <span class="n">high_nan_percentage_cols</span>
+                <span class="nb">len</span><span class="p">(</span><span class="n">const_cols</span><span class="p">),</span> <span class="n">const_cols</span>
             <span class="p">)</span>
         <span class="p">)</span>
     <span class="k">return</span> <span class="n">df</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_data.drop_high_uq_cat_cols" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">drop_high_uq_cat_cols</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">cat_cols</span><span class="p">,</span> <span class="n">uq_val_count</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_data.drop_high_uq_cat_cols" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">drop_high_uq_cat_cols</span>
 
-<a href="#churn_pred.preprocessing.preprocess_data.drop_high_uq_cat_cols" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_data.drop_high_uq_cat_cols" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">drop_high_uq_cat_cols</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span> <span class="n">cat_cols</span><span class="p">,</span> <span class="n">uq_val_count</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Returns dataframe without categorical columns that have too many unique values</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>cat_cols</b>
+            (<code>list</code>)
+        – <p>list of categorical columns</p>
+      </li>
+      <li class="field-body">
+        <b>uq_val_count</b>
+            (<code>int</code>)
+        – <p>unique value count</p>
+      </li>
+      <li class="field-body">
+        <b>verbose</b>
+            (<code>bool</code>)
+        – <p>whether the output should be verbose</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>cat_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of categorical columns</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>uq_val_count</code></td>
-          <td>
-                <code>int</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>unique value count</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>verbose</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether the output should be verbose</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">53</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">53</span>
 <span class="normal">54</span>
 <span class="normal">55</span>
 <span class="normal">56</span>
@@ -3634,7 +2527,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.drop_high_uq_cat_cols" class="d
 <span class="normal">78</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">drop_high_uq_cat_cols</span><span class="p">(</span>
     <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">cat_cols</span><span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">uq_val_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns dataframe without categorical columns that have too many unique values</span>
+    <span class="sd">&quot;&quot;&quot;Returns dataframe without categorical columns that have too many unique values</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (pd.DataFrame): input dataframe</span>
@@ -3649,7 +2542,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.drop_high_uq_cat_cols" class="d
     <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">high_uq_value_cat_cols</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="nb">print</span><span class="p">(</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;</span>
+            <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">            Dropped {} high unique value columns.</span>
 <span class="sd">            Affected columns: {}</span>
 <span class="sd">            &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
@@ -3658,102 +2551,57 @@ <h2 id="churn_pred.preprocessing.preprocess_data.drop_high_uq_cat_cols" class="d
         <span class="p">)</span>
     <span class="k">return</span> <span class="n">df</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_data.drop_highly_correlated_columns" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">drop_highly_correlated_columns</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">cont_cols</span><span class="p">,</span> <span class="n">crosscorr_val</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_data.drop_highly_correlated_columns" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">drop_highly_correlated_columns</span>
 
-<a href="#churn_pred.preprocessing.preprocess_data.drop_highly_correlated_columns" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_data.drop_highly_correlated_columns" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">drop_highly_correlated_columns</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span> <span class="n">cont_cols</span><span class="p">,</span> <span class="n">crosscorr_val</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Returns dataframe without highly correlated columns, cross correlation is
 evaluated with crosscorr_val.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>cont_cols</b>
+            (<code>list</code>)
+        – <p>list of columns to evaluate correlation for</p>
+      </li>
+      <li class="field-body">
+        <b>crosscorr_val</b>
+            (<code>float = 0.95</code>)
+        – <p>threshold value of correlation</p>
+      </li>
+      <li class="field-body">
+        <b>verbose</b>
+            (<code>bool</code>)
+        – <p>whether the output should be verbose</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>cont_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of columns to evaluate correlation for</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>crosscorr_val</code></td>
-          <td>
-                <code>float = 0.95</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>threshold value of correlation</p>
-            </div>
-          </td>
-          <td>
-                <code>0.95</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>verbose</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether the output should be verbose</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 81</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 81</span>
 <span class="normal"> 82</span>
 <span class="normal"> 83</span>
 <span class="normal"> 84</span>
@@ -3788,7 +2636,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.drop_highly_correlated_columns"
     <span class="n">crosscorr_val</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.95</span><span class="p">,</span>
     <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns dataframe without highly correlated columns, cross correlation is</span>
+    <span class="sd">&quot;&quot;&quot;Returns dataframe without highly correlated columns, cross correlation is</span>
 <span class="sd">    evaluated with crosscorr_val.</span>
 
 <span class="sd">    Args:</span>
@@ -3804,7 +2652,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.drop_highly_correlated_columns"
     <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">to_drop</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="nb">print</span><span class="p">(</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;</span>
+            <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">            Dropped {} highly correlated columns.</span>
 <span class="sd">            Affected columns: {}</span>
 <span class="sd">            &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
@@ -3813,463 +2661,271 @@ <h2 id="churn_pred.preprocessing.preprocess_data.drop_highly_correlated_columns"
         <span class="p">)</span>
     <span class="k">return</span> <span class="n">df</span>
 </code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="churn_pred.preprocessing.preprocess_data.most_frequent_in_list_col" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">most_frequent_in_list_col</span><span class="p">(</span><span class="n">dfs</span><span class="p">)</span></code>
-
-<a href="#churn_pred.preprocessing.preprocess_data.most_frequent_in_list_col" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Returns pd.Series with the most frequent values in the lists.</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>dfs</code></td>
-          <td>
-                <code><span title="pandas.Series">Series</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input pandas series containing string list values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">206</span>
-<span class="normal">207</span>
-<span class="normal">208</span>
-<span class="normal">209</span>
-<span class="normal">210</span>
-<span class="normal">211</span>
-<span class="normal">212</span>
-<span class="normal">213</span>
-<span class="normal">214</span>
-<span class="normal">215</span>
-<span class="normal">216</span>
-<span class="normal">217</span>
-<span class="normal">218</span>
-<span class="normal">219</span>
-<span class="normal">220</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">most_frequent_in_list_col</span><span class="p">(</span><span class="n">dfs</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns pd.Series with the most frequent values in the lists.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        dfs (pd.Series): input pandas series containing string list values</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dfsc</span> <span class="o">=</span> <span class="n">dfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">dfsc</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
-        <span class="k">lambda</span> <span class="n">string</span><span class="p">:</span> <span class="p">(</span>
-            <span class="n">_most_frequent</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">string</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;[&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;]&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;, &quot;</span><span class="p">)))</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">string</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
-            <span class="k">else</span> <span class="n">string</span>
-        <span class="p">)</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">dfsc</span>
-</code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_data.nan_with_number_imputer" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">nan_with_number_imputer</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">fill_number</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_data.nan_with_unknown_imputer" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">nan_with_unknown_imputer</span>
 
-<a href="#churn_pred.preprocessing.preprocess_data.nan_with_number_imputer" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_data.nan_with_unknown_imputer" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">nan_with_unknown_imputer</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">fill_token</span><span class="o">=</span><span class="s2">&quot;unknown&quot;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Fills NAs with surrogate float, -1 is default value used, it can be customized.</p>
-
-
+      <p>Fills NAs with surrogate string, 'unknown' is default value used, it can be customized.</p>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>columns</code></td>
-          <td>
-                <code><span title="typing.List">List</span>[str]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of columns that will be filled</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>fill_number</code></td>
-          <td>
-                <code>float = -1</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>number used to replace NAs. Defaults to</p>
-            </div>
-          </td>
-          <td>
-                <code>-1.0</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>verbose</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether the output should be verbose</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>columns</b>
+            (<code><span title="typing.List">List</span>[str]</code>)
+        – <p>ist of columns that will be filled</p>
+      </li>
+      <li class="field-body">
+        <b>fill_token</b>
+            (<code>str = &#34;unknown&#34;</code>)
+        – <p>string used to replace NAs</p>
+      </li>
+      <li class="field-body">
+        <b>verbose</b>
+            (<code>bool</code>)
+        – <p>whether the output should be verbose</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">150</span>
-<span class="normal">151</span>
-<span class="normal">152</span>
-<span class="normal">153</span>
-<span class="normal">154</span>
-<span class="normal">155</span>
-<span class="normal">156</span>
-<span class="normal">157</span>
-<span class="normal">158</span>
-<span class="normal">159</span>
-<span class="normal">160</span>
-<span class="normal">161</span>
-<span class="normal">162</span>
-<span class="normal">163</span>
-<span class="normal">164</span>
-<span class="normal">165</span>
-<span class="normal">166</span>
-<span class="normal">167</span>
-<span class="normal">168</span>
-<span class="normal">169</span>
-<span class="normal">170</span>
-<span class="normal">171</span>
-<span class="normal">172</span>
-<span class="normal">173</span>
-<span class="normal">174</span>
-<span class="normal">175</span>
-<span class="normal">176</span>
-<span class="normal">177</span>
-<span class="normal">178</span>
-<span class="normal">179</span>
-<span class="normal">180</span>
-<span class="normal">181</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nan_with_number_imputer</span><span class="p">(</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">113</span>
+<span class="normal">114</span>
+<span class="normal">115</span>
+<span class="normal">116</span>
+<span class="normal">117</span>
+<span class="normal">118</span>
+<span class="normal">119</span>
+<span class="normal">120</span>
+<span class="normal">121</span>
+<span class="normal">122</span>
+<span class="normal">123</span>
+<span class="normal">124</span>
+<span class="normal">125</span>
+<span class="normal">126</span>
+<span class="normal">127</span>
+<span class="normal">128</span>
+<span class="normal">129</span>
+<span class="normal">130</span>
+<span class="normal">131</span>
+<span class="normal">132</span>
+<span class="normal">133</span>
+<span class="normal">134</span>
+<span class="normal">135</span>
+<span class="normal">136</span>
+<span class="normal">137</span>
+<span class="normal">138</span>
+<span class="normal">139</span>
+<span class="normal">140</span>
+<span class="normal">141</span>
+<span class="normal">142</span>
+<span class="normal">143</span>
+<span class="normal">144</span>
+<span class="normal">145</span>
+<span class="normal">146</span>
+<span class="normal">147</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nan_with_unknown_imputer</span><span class="p">(</span>
     <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
     <span class="n">columns</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
-    <span class="n">fill_number</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">,</span>
+    <span class="n">fill_token</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;unknown&quot;</span><span class="p">,</span>
     <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Fills NAs with surrogate float, -1 is default value used, it can be customized.</span>
+    <span class="sd">&quot;&quot;&quot;Fills NAs with surrogate string, &#39;unknown&#39; is default value used, it can be customized.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (pd.DataFrame): input dataframe</span>
-<span class="sd">        columns (List[str]): list of columns that will be filled</span>
-<span class="sd">        fill_number (float = -1): number used to replace NAs. Defaults to</span>
+<span class="sd">        columns (List[str]): ist of columns that will be filled</span>
+<span class="sd">        fill_token (str = &quot;unknown&quot;): string used to replace NAs</span>
 <span class="sd">        verbose (bool): whether the output should be verbose</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="n">sum_nan_vals</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
         <span class="n">nans_cols</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
             <span class="n">nans_cols</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">c</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
+    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
+        <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">object</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">fill_token</span><span class="p">)</span>
+
     <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">dfc</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">fill_number</span><span class="p">))</span>
+    <span class="n">dfc</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">object</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">fill_token</span><span class="p">))</span>
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="nb">print</span><span class="p">(</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;</span>
+            <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">            Imputed {} NaN values with {}.</span>
 <span class="sd">            Affected columns (col, num_NaNs): {}</span>
 <span class="sd">            &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="n">sum_nan_vals</span><span class="p">,</span> <span class="n">fill_number</span><span class="p">,</span> <span class="n">nans_cols</span>
+                <span class="n">sum_nan_vals</span><span class="p">,</span> <span class="n">fill_token</span><span class="p">,</span> <span class="n">nans_cols</span>
             <span class="p">)</span>
         <span class="p">)</span>
-    <span class="k">return</span> <span class="n">dfc</span>
+    <span class="k">return</span> <span class="n">df</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_data.nan_with_unknown_imputer" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">nan_with_unknown_imputer</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">fill_token</span><span class="o">=</span><span class="s1">&#39;unknown&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_data.nan_with_number_imputer" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">nan_with_number_imputer</span>
 
-<a href="#churn_pred.preprocessing.preprocess_data.nan_with_unknown_imputer" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_data.nan_with_number_imputer" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">nan_with_number_imputer</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span> <span class="n">columns</span><span class="p">,</span> <span class="n">fill_number</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Fills NAs with surrogate string, 'unknown' is default value used, it can be customized.</p>
-
-
+      <p>Fills NAs with surrogate float, -1 is default value used, it can be customized.</p>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>columns</code></td>
-          <td>
-                <code><span title="typing.List">List</span>[str]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>ist of columns that will be filled</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>fill_token</code></td>
-          <td>
-                <code>str = &#34;unknown&#34;</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>string used to replace NAs</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;unknown&#39;</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>verbose</code></td>
-          <td>
-                <code>bool</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>whether the output should be verbose</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>columns</b>
+            (<code><span title="typing.List">List</span>[str]</code>)
+        – <p>list of columns that will be filled</p>
+      </li>
+      <li class="field-body">
+        <b>fill_number</b>
+            (<code>float = -1</code>)
+        – <p>number used to replace NAs. Defaults to</p>
+      </li>
+      <li class="field-body">
+        <b>verbose</b>
+            (<code>bool</code>)
+        – <p>whether the output should be verbose</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">113</span>
-<span class="normal">114</span>
-<span class="normal">115</span>
-<span class="normal">116</span>
-<span class="normal">117</span>
-<span class="normal">118</span>
-<span class="normal">119</span>
-<span class="normal">120</span>
-<span class="normal">121</span>
-<span class="normal">122</span>
-<span class="normal">123</span>
-<span class="normal">124</span>
-<span class="normal">125</span>
-<span class="normal">126</span>
-<span class="normal">127</span>
-<span class="normal">128</span>
-<span class="normal">129</span>
-<span class="normal">130</span>
-<span class="normal">131</span>
-<span class="normal">132</span>
-<span class="normal">133</span>
-<span class="normal">134</span>
-<span class="normal">135</span>
-<span class="normal">136</span>
-<span class="normal">137</span>
-<span class="normal">138</span>
-<span class="normal">139</span>
-<span class="normal">140</span>
-<span class="normal">141</span>
-<span class="normal">142</span>
-<span class="normal">143</span>
-<span class="normal">144</span>
-<span class="normal">145</span>
-<span class="normal">146</span>
-<span class="normal">147</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nan_with_unknown_imputer</span><span class="p">(</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">150</span>
+<span class="normal">151</span>
+<span class="normal">152</span>
+<span class="normal">153</span>
+<span class="normal">154</span>
+<span class="normal">155</span>
+<span class="normal">156</span>
+<span class="normal">157</span>
+<span class="normal">158</span>
+<span class="normal">159</span>
+<span class="normal">160</span>
+<span class="normal">161</span>
+<span class="normal">162</span>
+<span class="normal">163</span>
+<span class="normal">164</span>
+<span class="normal">165</span>
+<span class="normal">166</span>
+<span class="normal">167</span>
+<span class="normal">168</span>
+<span class="normal">169</span>
+<span class="normal">170</span>
+<span class="normal">171</span>
+<span class="normal">172</span>
+<span class="normal">173</span>
+<span class="normal">174</span>
+<span class="normal">175</span>
+<span class="normal">176</span>
+<span class="normal">177</span>
+<span class="normal">178</span>
+<span class="normal">179</span>
+<span class="normal">180</span>
+<span class="normal">181</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nan_with_number_imputer</span><span class="p">(</span>
     <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
     <span class="n">columns</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
-    <span class="n">fill_token</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;unknown&quot;</span><span class="p">,</span>
+    <span class="n">fill_number</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="o">-</span><span class="mf">1.0</span><span class="p">,</span>
     <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Fills NAs with surrogate string, &#39;unknown&#39; is default value used, it can be customized.</span>
+    <span class="sd">&quot;&quot;&quot;Fills NAs with surrogate float, -1 is default value used, it can be customized.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (pd.DataFrame): input dataframe</span>
-<span class="sd">        columns (List[str]): ist of columns that will be filled</span>
-<span class="sd">        fill_token (str = &quot;unknown&quot;): string used to replace NAs</span>
+<span class="sd">        columns (List[str]): list of columns that will be filled</span>
+<span class="sd">        fill_number (float = -1): number used to replace NAs. Defaults to</span>
 <span class="sd">        verbose (bool): whether the output should be verbose</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="n">sum_nan_vals</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
         <span class="n">nans_cols</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
             <span class="n">nans_cols</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">c</span><span class="p">,</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()))</span>
-    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
-        <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">object</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">fill_token</span><span class="p">)</span>
-
     <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="n">dfc</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">object</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">fill_token</span><span class="p">))</span>
+    <span class="n">dfc</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="n">columns</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="n">fill_number</span><span class="p">))</span>
     <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
         <span class="nb">print</span><span class="p">(</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;</span>
+            <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">            Imputed {} NaN values with {}.</span>
 <span class="sd">            Affected columns (col, num_NaNs): {}</span>
 <span class="sd">            &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-                <span class="n">sum_nan_vals</span><span class="p">,</span> <span class="n">fill_token</span><span class="p">,</span> <span class="n">nans_cols</span>
+                <span class="n">sum_nan_vals</span><span class="p">,</span> <span class="n">fill_number</span><span class="p">,</span> <span class="n">nans_cols</span>
             <span class="p">)</span>
         <span class="p">)</span>
-    <span class="k">return</span> <span class="n">df</span>
+    <span class="k">return</span> <span class="n">dfc</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_data.nuq_in_list_col" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">nuq_in_list_col</span><span class="p">(</span><span class="n">dfs</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_data.nuq_in_list_col" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">nuq_in_list_col</span>
 
-<a href="#churn_pred.preprocessing.preprocess_data.nuq_in_list_col" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_data.nuq_in_list_col" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">nuq_in_list_col</span><span class="p">(</span><span class="n">dfs</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Returns pd.Series with the number of unique values in the lists.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>dfs</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>)
+        – <p>input pandas series containing string list values</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>dfs</code></td>
-          <td>
-                <code><span title="pandas.Series">Series</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input pandas series containing string list values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">184</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">184</span>
 <span class="normal">185</span>
 <span class="normal">186</span>
 <span class="normal">187</span>
@@ -4284,7 +2940,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.nuq_in_list_col" class="doc doc
 <span class="normal">196</span>
 <span class="normal">197</span>
 <span class="normal">198</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nuq_in_list_col</span><span class="p">(</span><span class="n">dfs</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns pd.Series with the number of unique values in the lists.</span>
+    <span class="sd">&quot;&quot;&quot;Returns pd.Series with the number of unique values in the lists.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        dfs (pd.Series): input pandas series containing string list values</span>
@@ -4299,126 +2955,131 @@ <h2 id="churn_pred.preprocessing.preprocess_data.nuq_in_list_col" class="doc doc
     <span class="p">)</span>
     <span class="k">return</span> <span class="n">dfsc</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
+<div class="doc doc-object doc-function">
+
+
+
+<h3 id="churn_pred.preprocessing.preprocess_data.most_frequent_in_list_col" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">most_frequent_in_list_col</span>
+
+
+<a href="#churn_pred.preprocessing.preprocess_data.most_frequent_in_list_col" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">most_frequent_in_list_col</span><span class="p">(</span><span class="n">dfs</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Returns pd.Series with the most frequent values in the lists.</p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>dfs</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.Series">Series</span></code>)
+        – <p>input pandas series containing string list values</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">206</span>
+<span class="normal">207</span>
+<span class="normal">208</span>
+<span class="normal">209</span>
+<span class="normal">210</span>
+<span class="normal">211</span>
+<span class="normal">212</span>
+<span class="normal">213</span>
+<span class="normal">214</span>
+<span class="normal">215</span>
+<span class="normal">216</span>
+<span class="normal">217</span>
+<span class="normal">218</span>
+<span class="normal">219</span>
+<span class="normal">220</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">most_frequent_in_list_col</span><span class="p">(</span><span class="n">dfs</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Returns pd.Series with the most frequent values in the lists.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        dfs (pd.Series): input pandas series containing string list values</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dfsc</span> <span class="o">=</span> <span class="n">dfs</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="n">dfsc</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
+        <span class="k">lambda</span> <span class="n">string</span><span class="p">:</span> <span class="p">(</span>
+            <span class="n">_most_frequent</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">string</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;[&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;]&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;, &quot;</span><span class="p">)))</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">string</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
+            <span class="k">else</span> <span class="n">string</span>
+        <span class="p">)</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">dfsc</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
+
+</div>
 
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_str_other" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">replace_rare_categories_with_str_other</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">categorical_cols</span><span class="p">,</span> <span class="n">quantile</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">surrogate_value</span><span class="o">=</span><span class="s1">&#39;other&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_str_other" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">replace_rare_categories_with_str_other</span>
 
-<a href="#churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_str_other" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_str_other" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">replace_rare_categories_with_str_other</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">,</span>
+    <span class="n">categorical_cols</span><span class="p">,</span>
+    <span class="n">quantile</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span>
+    <span class="n">surrogate_value</span><span class="o">=</span><span class="s2">&quot;other&quot;</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Replaces rare category value with surrogate string.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataframe</p>
+      </li>
+      <li class="field-body">
+        <b>categorical_cols</b>
+            (<code><span title="typing.List">List</span>[str]</code>)
+        – <p>list of columns in dataframe to process.</p>
+      </li>
+      <li class="field-body">
+        <b>quantile</b>
+            (<code>float = 0.05</code>)
+        – <p>determines what values are considered as rare</p>
+      </li>
+      <li class="field-body">
+        <b>surrogate_value</b>
+            (<code>str = &#34;other&#34;</code>)
+        – <p>string used to replace rare values</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataframe</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>categorical_cols</code></td>
-          <td>
-                <code><span title="typing.List">List</span>[str]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of columns in dataframe to process.</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>quantile</code></td>
-          <td>
-                <code>float = 0.05</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>determines what values are considered as rare</p>
-            </div>
-          </td>
-          <td>
-                <code>0.05</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>surrogate_value</code></td>
-          <td>
-                <code>str = &#34;other&#34;</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>string used to replace rare values</p>
-            </div>
-          </td>
-          <td>
-                <code>&#39;other&#39;</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td>
-                <code><span title="typing.Tuple">Tuple</span>[<span title="pandas.DataFrame">DataFrame</span>, <span title="typing.Dict">Dict</span>]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>Tuple[pd.DataFrame, Dict]:
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+            <code><span title="typing.Tuple">Tuple</span>[<span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span>, <span title="typing.Dict">Dict</span>]</code>
+        – <p>Tuple[pd.DataFrame, Dict]:
 New dataframe and a dict. with mapping between orig. and surrogate values.</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">228</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_data.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">228</span>
 <span class="normal">229</span>
 <span class="normal">230</span>
 <span class="normal">231</span>
@@ -4477,7 +3138,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_st
     <span class="n">surrogate_value</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;other&quot;</span><span class="p">,</span>
     <span class="n">verbose</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Dict</span><span class="p">]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Replaces rare category value with surrogate string.</span>
+    <span class="sd">&quot;&quot;&quot;Replaces rare category value with surrogate string.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df (pd.DataFrame): input dataframe</span>
@@ -4512,7 +3173,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_st
             <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">replace_dict_per_col</span>
         <span class="p">]</span>
         <span class="nb">print</span><span class="p">(</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;</span>
+            <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">            Replaced {} rare categories (val_count &lt; {}) with {}.</span>
 <span class="sd">            Affected columns (List[(col, rare_cats)]): {}</span>
 <span class="sd">            &quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
@@ -4524,7 +3185,7 @@ <h2 id="churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_st
         <span class="p">)</span>
     <span class="k">return</span> <span class="n">dfc</span><span class="p">,</span> <span class="n">replace_dict_per_col</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -4541,7 +3202,12 @@ <h2 id="churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_st
 
 
 
-<a id="churn_pred.preprocessing.preprocess_text"></a>
+<h2 id="churn_pred.preprocessing.preprocess_text" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">preprocess_text</span>
+
+
+<a href="#churn_pred.preprocessing.preprocess_text" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -4556,24 +3222,25 @@ <h2 id="churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_st
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_text.language_detection" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">language_detection</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">text_col</span><span class="p">,</span> <span class="n">model_type</span><span class="o">=</span><span class="s1">&#39;fasttext&#39;</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_text.language_detection" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">language_detection</span>
 
-<a href="#churn_pred.preprocessing.preprocess_text.language_detection" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_text.language_detection" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">language_detection</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">text_col</span><span class="p">,</span> <span class="n">model_type</span><span class="o">=</span><span class="s1">&#39;fasttext&#39;</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_text.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_text.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">13</span>
 <span class="normal">14</span>
 <span class="normal">15</span>
 <span class="normal">16</span>
@@ -4602,265 +3269,73 @@ <h2 id="churn_pred.preprocessing.preprocess_text.language_detection" class="doc
 <span class="normal">39</span>
 <span class="normal">40</span>
 <span class="normal">41</span>
-<span class="normal">42</span>
-<span class="normal">43</span>
-<span class="normal">44</span>
-<span class="normal">45</span>
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-<span class="normal">49</span>
-<span class="normal">50</span>
-<span class="normal">51</span>
-<span class="normal">52</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">language_detection</span><span class="p">(</span>
-    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="n">text_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="n">model_type</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;roberta&quot;</span><span class="p">,</span> <span class="s2">&quot;fasttext&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;fasttext&quot;</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    https://huggingface.co/papluca/xlm-roberta-base-language-detection</span>
-<span class="sd">    https://spacy.io/universe/project/spacy_fastlang</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="k">if</span> <span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;fasttext&quot;</span><span class="p">:</span>
-
-        <span class="k">def</span> <span class="nf">_get_language</span><span class="p">(</span><span class="n">token</span><span class="p">:</span> <span class="n">Doc</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">token</span><span class="o">.</span><span class="n">_</span><span class="o">.</span><span class="n">language</span>
-
-        <span class="k">def</span> <span class="nf">_get_language_score</span><span class="p">(</span><span class="n">token</span><span class="p">:</span> <span class="n">Doc</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">token</span><span class="o">.</span><span class="n">_</span><span class="o">.</span><span class="n">language_score</span>
-
-        <span class="n">nlp</span> <span class="o">=</span> <span class="n">spacy</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;en_core_web_sm&quot;</span><span class="p">)</span>
-        <span class="n">nlp</span><span class="o">.</span><span class="n">add_pipe</span><span class="p">(</span><span class="s2">&quot;language_detector&quot;</span><span class="p">)</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">text_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">nlp</span><span class="p">)</span>
-        <span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_language&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_get_language</span><span class="p">)</span>
-        <span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_language_score&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_get_language_score</span><span class="p">)</span>
-    <span class="k">elif</span> <span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;roberta&quot;</span><span class="p">:</span>
-
-        <span class="k">def</span> <span class="nf">_get_language</span><span class="p">(</span><span class="n">token</span><span class="p">:</span> <span class="n">Doc</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">token</span><span class="o">.</span><span class="n">_</span><span class="o">.</span><span class="n">language</span>
-
-        <span class="k">def</span> <span class="nf">_get_language_score</span><span class="p">(</span><span class="n">token</span><span class="p">:</span> <span class="n">Doc</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">token</span><span class="o">.</span><span class="n">_</span><span class="o">.</span><span class="n">language_score</span>
-
-        <span class="n">pipe</span> <span class="o">=</span> <span class="n">pipeline</span><span class="p">(</span>
-            <span class="s2">&quot;text-classification&quot;</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="s2">&quot;papluca/xlm-roberta-base-language-detection&quot;</span>
-        <span class="p">)</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="n">pipe</span><span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
-        <span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_language&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pipe</span><span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">))</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-    <span class="k">return</span> <span class="n">dfc</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h2 id="churn_pred.preprocessing.preprocess_text.sentiment_analysis" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">sentiment_analysis</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">text_col</span><span class="p">,</span> <span class="n">sentiment_depth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span></code>
-
-<a href="#churn_pred.preprocessing.preprocess_text.sentiment_analysis" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Returns dataframe with new column that analysis sentintent in the text_col.</p>
-<p>initial idea:
-Inspired by <a href="https://www.nature.com/articles/s41598-024-60210-7">https://www.nature.com/articles/s41598-024-60210-7</a> I also used
-the 3 most popular models with voting:
-1. cardiffnlp/twitter-roberta-base-sentiment-latest
-2. nlptown/bert-base-multilingual-uncased-sentiment
-3. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis
-4. lxyuan/distilbert-base-multilingual-cased-sentiments-student
-5. finiteautomata/bertweet-base-sentiment-analysis</p>
-<p>issues:
-1. 3(pos, neutral, neg) vs 5(1-5 stars) sentiments; models 1,3 vs 2
-2. maximum sequence length models 4-5</p>
-<p>final idea:
-Either 3 sentimnets(model 1) or 5 stars (model 2)</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>text_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>column name with text</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>sentiment_depth</code></td>
-          <td>
-                <code>[3, 5]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>depth of sentiment analysis</p>
-            </div>
-          </td>
-          <td>
-                <code>3</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_text.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 82</span>
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-<span class="normal">130</span>
-<span class="normal">131</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">sentiment_analysis</span><span class="p">(</span>
-    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">text_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">sentiment_depth</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="normal">42</span>
+<span class="normal">43</span>
+<span class="normal">44</span>
+<span class="normal">45</span>
+<span class="normal">46</span>
+<span class="normal">47</span>
+<span class="normal">48</span>
+<span class="normal">49</span>
+<span class="normal">50</span>
+<span class="normal">51</span>
+<span class="normal">52</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">language_detection</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="n">text_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">model_type</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;roberta&quot;</span><span class="p">,</span> <span class="s2">&quot;fasttext&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;fasttext&quot;</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Returns dataframe with new column that analysis sentintent in the text_col.</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    https://huggingface.co/papluca/xlm-roberta-base-language-detection</span>
+<span class="sd">    https://spacy.io/universe/project/spacy_fastlang</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 
-<span class="sd">    initial idea:</span>
-<span class="sd">    Inspired by https://www.nature.com/articles/s41598-024-60210-7 I also used</span>
-<span class="sd">    the 3 most popular models with voting:</span>
-<span class="sd">    1. cardiffnlp/twitter-roberta-base-sentiment-latest</span>
-<span class="sd">    2. nlptown/bert-base-multilingual-uncased-sentiment</span>
-<span class="sd">    3. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis</span>
-<span class="sd">    4. lxyuan/distilbert-base-multilingual-cased-sentiments-student</span>
-<span class="sd">    5. finiteautomata/bertweet-base-sentiment-analysis</span>
+    <span class="k">if</span> <span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;fasttext&quot;</span><span class="p">:</span>
 
-<span class="sd">    issues:</span>
-<span class="sd">    1. 3(pos, neutral, neg) vs 5(1-5 stars) sentiments; models 1,3 vs 2</span>
-<span class="sd">    2. maximum sequence length models 4-5</span>
+        <span class="k">def</span> <span class="nf">_get_language</span><span class="p">(</span><span class="n">token</span><span class="p">:</span> <span class="n">Doc</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">token</span><span class="o">.</span><span class="n">_</span><span class="o">.</span><span class="n">language</span>
 
-<span class="sd">    final idea:</span>
-<span class="sd">    Either 3 sentimnets(model 1) or 5 stars (model 2)</span>
+        <span class="k">def</span> <span class="nf">_get_language_score</span><span class="p">(</span><span class="n">token</span><span class="p">:</span> <span class="n">Doc</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">token</span><span class="o">.</span><span class="n">_</span><span class="o">.</span><span class="n">language_score</span>
+
+        <span class="n">nlp</span> <span class="o">=</span> <span class="n">spacy</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;en_core_web_sm&quot;</span><span class="p">)</span>
+        <span class="n">nlp</span><span class="o">.</span><span class="n">add_pipe</span><span class="p">(</span><span class="s2">&quot;language_detector&quot;</span><span class="p">)</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">text_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">nlp</span><span class="p">)</span>
+        <span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_language&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_get_language</span><span class="p">)</span>
+        <span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_language_score&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">_get_language_score</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">model_type</span> <span class="o">==</span> <span class="s2">&quot;roberta&quot;</span><span class="p">:</span>
+
+        <span class="k">def</span> <span class="nf">_get_language</span><span class="p">(</span><span class="n">token</span><span class="p">:</span> <span class="n">Doc</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">token</span><span class="o">.</span><span class="n">_</span><span class="o">.</span><span class="n">language</span>
+
+        <span class="k">def</span> <span class="nf">_get_language_score</span><span class="p">(</span><span class="n">token</span><span class="p">:</span> <span class="n">Doc</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">token</span><span class="o">.</span><span class="n">_</span><span class="o">.</span><span class="n">language_score</span>
 
-<span class="sd">    Args:</span>
-<span class="sd">        df (pd.DataFrame): input dataset</span>
-<span class="sd">        text_col (str): column name with text</span>
-<span class="sd">        sentiment_depth ([3, 5]): depth of sentiment analysis</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">sentiment_depth</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="n">pipe</span> <span class="o">=</span> <span class="n">pipeline</span><span class="p">(</span>
-            <span class="s2">&quot;text-classification&quot;</span><span class="p">,</span>
-            <span class="n">model</span><span class="o">=</span><span class="s2">&quot;cardiffnlp/twitter-roberta-base-sentiment-latest&quot;</span><span class="p">,</span>
-            <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">),</span>
-        <span class="p">)</span>
-    <span class="k">elif</span> <span class="n">sentiment_depth</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
         <span class="n">pipe</span> <span class="o">=</span> <span class="n">pipeline</span><span class="p">(</span>
-            <span class="s2">&quot;text-classification&quot;</span><span class="p">,</span>
-            <span class="n">model</span><span class="o">=</span><span class="s2">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span><span class="p">,</span>
-            <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">),</span>
+            <span class="s2">&quot;text-classification&quot;</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="s2">&quot;papluca/xlm-roberta-base-language-detection&quot;</span>
         <span class="p">)</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">pipe</span><span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+        <span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_language&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">pipe</span><span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">))</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-    <span class="n">res</span> <span class="o">=</span> <span class="n">pipe</span><span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
-    <span class="n">res</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">res</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
-        <span class="n">columns</span><span class="o">=</span><span class="p">{</span>
-            <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_sentiment&quot;</span><span class="p">,</span>
-            <span class="s2">&quot;score&quot;</span><span class="p">:</span> <span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_sentiment_score&quot;</span><span class="p">,</span>
-        <span class="p">}</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">dfc</span><span class="p">,</span> <span class="n">res</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">dfc</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.preprocess_text.text_cleaning" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">text_cleaning</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">text_col</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.preprocess_text.text_cleaning" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">text_cleaning</span>
 
-<a href="#churn_pred.preprocessing.preprocess_text.text_cleaning" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.preprocess_text.text_cleaning" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">text_cleaning</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">text_col</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
@@ -4868,58 +3343,28 @@ <h2 id="churn_pred.preprocessing.preprocess_text.text_cleaning" class="doc doc-h
 Spacy-cleaner that uses spacy functionalities.
 <a href="https://spacy.io/universe/project/spacy-cleaner">https://spacy.io/universe/project/spacy-cleaner</a></p>
 
-<details class="note" open>
+<details class="note">
   <summary>Note</summary>
   <p>The spacy-cleaner library does not do much.
 Future task - develop own cleaner</p>
 </details>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataset</p>
+      </li>
+      <li class="field-body">
+        <b>text_col</b>
+            (<code>str</code>)
+        – <p>column name with text</p>
+      </li>
+  </ul>
 
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>input dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>text_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>column name with text</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess_text.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">55</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_text.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">55</span>
 <span class="normal">56</span>
 <span class="normal">57</span>
 <span class="normal">58</span>
@@ -4944,7 +3389,7 @@ <h2 id="churn_pred.preprocessing.preprocess_text.text_cleaning" class="doc doc-h
 <span class="normal">77</span>
 <span class="normal">78</span>
 <span class="normal">79</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">text_cleaning</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">text_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Returns dataframe with preprocessed/cleaned text column.</span>
 <span class="sd">    Spacy-cleaner that uses spacy functionalities.</span>
 <span class="sd">    https://spacy.io/universe/project/spacy-cleaner</span>
@@ -4969,466 +3414,302 @@ <h2 id="churn_pred.preprocessing.preprocess_text.text_cleaning" class="doc doc-h
 
     <span class="k">return</span> <span class="n">dfc</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
+<div class="doc doc-object doc-function">
 
 
-  </div>
-
-  </div>
-
-</div>
-
-<div class="doc doc-object doc-module">
 
+<h3 id="churn_pred.preprocessing.preprocess_text.sentiment_analysis" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">sentiment_analysis</span>
 
 
-<a id="churn_pred.preprocessing.preprocess"></a>
-  <div class="doc doc-contents first">
+<a href="#churn_pred.preprocessing.preprocess_text.sentiment_analysis" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">sentiment_analysis</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">text_col</span><span class="p">,</span> <span class="n">sentiment_depth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+</code></pre></div>
 
+  <div class="doc doc-contents ">
   
+      <p>Returns dataframe with new column that analysis sentintent in the text_col.</p>
+<p>initial idea:
+Inspired by <a href="https://www.nature.com/articles/s41598-024-60210-7">https://www.nature.com/articles/s41598-024-60210-7</a> I also used
+the 3 most popular models with voting:
+1. cardiffnlp/twitter-roberta-base-sentiment-latest
+2. nlptown/bert-base-multilingual-uncased-sentiment
+3. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis
+4. lxyuan/distilbert-base-multilingual-cased-sentiments-student
+5. finiteautomata/bertweet-base-sentiment-analysis</p>
+<p>issues:
+1. 3(pos, neutral, neg) vs 5(1-5 stars) sentiments; models 1,3 vs 2
+2. maximum sequence length models 4-5</p>
+<p>final idea:
+Either 3 sentimnets(model 1) or 5 stars (model 2)</p>
 
-  <div class="doc doc-children">
-
-
-
-
-
-
-
-
-<div class="doc doc-object doc-class">
-
-
-
-<h2 id="churn_pred.preprocessing.preprocess.PreprocessData" class="doc doc-heading">
-          <code>PreprocessData</code>
-
-
-<a href="#churn_pred.preprocessing.preprocess.PreprocessData" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>input dataset</p>
+      </li>
+      <li class="field-body">
+        <b>text_col</b>
+            (<code>str</code>)
+        – <p>column name with text</p>
+      </li>
+      <li class="field-body">
+        <b>sentiment_depth</b>
+            (<code>[3, 5]</code>)
+        – <p>depth of sentiment analysis</p>
+      </li>
+  </ul>
 
-  
-      <p>Object to preprocess the dataset.
-Args:
-    target_col (str): target column name
-    id_cols (List[str]): id columns
-    cat_cols (Optional[List[str]]): list of categorical column names
-    cont_cols (Optional[List[str]]): list of continuous column names</p>
-
-            <details class="quote">
-              <summary>Source code in <code>churn_pred/preprocessing/preprocess.py</code></summary>
-              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 19</span>
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+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess_text.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 82</span>
 <span class="normal"> 83</span>
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-<span class="normal">191</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">PreprocessData</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Object to preprocess the dataset.</span>
-<span class="sd">    Args:</span>
-<span class="sd">        target_col (str): target column name</span>
-<span class="sd">        id_cols (List[str]): id columns</span>
-<span class="sd">        cat_cols (Optional[List[str]]): list of categorical column names</span>
-<span class="sd">        cont_cols (Optional[List[str]]): list of continuous column names</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">target_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-        <span class="n">id_cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-        <span class="n">cat_cols</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">cont_cols</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">target_col</span> <span class="o">=</span> <span class="n">target_col</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span> <span class="o">=</span> <span class="n">id_cols</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">=</span> <span class="n">cat_cols</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cont_cols</span> <span class="o">=</span> <span class="n">cont_cols</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">is_fitted</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit peprocessor and transform dataset in training step.&quot;&quot;&quot;</span>
-
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">pipe</span><span class="p">(</span><span class="n">drop_constant_cols</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">=</span> <span class="n">intsec</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">dfc</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_infere_cat_cols</span><span class="p">(</span><span class="n">dfc</span><span class="p">)</span>
-            <span class="n">dfc_cat_cols</span> <span class="o">=</span> <span class="n">drop_high_nan_cols</span><span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">])</span>
-            <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">)</span>
-            <span class="n">dfc</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">dfc</span><span class="p">,</span> <span class="n">dfc_cat_cols</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">=</span> <span class="n">dfc_cat_cols</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cont_cols</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">init_cont_cols</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_infere_cont_cols</span><span class="p">(</span><span class="n">dfc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">)</span>
-            <span class="n">dfc_init_cont_cols</span> <span class="o">=</span> <span class="n">drop_high_nan_cols</span><span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">init_cont_cols</span><span class="p">])</span>
-            <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">init_cont_cols</span><span class="p">)</span>
-            <span class="n">dfc</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">dfc</span><span class="p">,</span> <span class="n">dfc_init_cont_cols</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">init_cont_cols</span> <span class="o">=</span> <span class="n">dfc_init_cont_cols</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-
-            <span class="n">dfc</span> <span class="o">=</span> <span class="n">nan_with_number_imputer</span><span class="p">(</span><span class="n">dfc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">init_cont_cols</span><span class="p">,</span> <span class="o">-</span><span class="mf">9999.0</span><span class="p">)</span>
-
-            <span class="p">(</span>
-                <span class="n">dfc</span><span class="p">,</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span><span class="p">,</span>
-            <span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_drop_highly_corr_cols_and_get_final_cont_cols</span><span class="p">(</span>
-                <span class="n">dfc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">init_cont_cols</span>
-            <span class="p">)</span>
+<span class="normal"> 84</span>
+<span class="normal"> 85</span>
+<span class="normal"> 86</span>
+<span class="normal"> 87</span>
+<span class="normal"> 88</span>
+<span class="normal"> 89</span>
+<span class="normal"> 90</span>
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+<span class="normal"> 94</span>
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+<span class="normal"> 96</span>
+<span class="normal"> 97</span>
+<span class="normal"> 98</span>
+<span class="normal"> 99</span>
+<span class="normal">100</span>
+<span class="normal">101</span>
+<span class="normal">102</span>
+<span class="normal">103</span>
+<span class="normal">104</span>
+<span class="normal">105</span>
+<span class="normal">106</span>
+<span class="normal">107</span>
+<span class="normal">108</span>
+<span class="normal">109</span>
+<span class="normal">110</span>
+<span class="normal">111</span>
+<span class="normal">112</span>
+<span class="normal">113</span>
+<span class="normal">114</span>
+<span class="normal">115</span>
+<span class="normal">116</span>
+<span class="normal">117</span>
+<span class="normal">118</span>
+<span class="normal">119</span>
+<span class="normal">120</span>
+<span class="normal">121</span>
+<span class="normal">122</span>
+<span class="normal">123</span>
+<span class="normal">124</span>
+<span class="normal">125</span>
+<span class="normal">126</span>
+<span class="normal">127</span>
+<span class="normal">128</span>
+<span class="normal">129</span>
+<span class="normal">130</span>
+<span class="normal">131</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">sentiment_analysis</span><span class="p">(</span>
+    <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">text_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">sentiment_depth</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns dataframe with new column that analysis sentintent in the text_col.</span>
 
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
-                <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cont_cols</span><span class="p">)</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">dfc</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
-            <span class="p">)</span>
+<span class="sd">    initial idea:</span>
+<span class="sd">    Inspired by https://www.nature.com/articles/s41598-024-60210-7 I also used</span>
+<span class="sd">    the 3 most popular models with voting:</span>
+<span class="sd">    1. cardiffnlp/twitter-roberta-base-sentiment-latest</span>
+<span class="sd">    2. nlptown/bert-base-multilingual-uncased-sentiment</span>
+<span class="sd">    3. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis</span>
+<span class="sd">    4. lxyuan/distilbert-base-multilingual-cased-sentiments-student</span>
+<span class="sd">    5. finiteautomata/bertweet-base-sentiment-analysis</span>
 
-            <span class="n">dfc</span> <span class="o">=</span> <span class="n">nan_with_number_imputer</span><span class="p">(</span><span class="n">dfc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span><span class="p">,</span> <span class="o">-</span><span class="mf">9999.0</span><span class="p">)</span>
+<span class="sd">    issues:</span>
+<span class="sd">    1. 3(pos, neutral, neg) vs 5(1-5 stars) sentiments; models 1,3 vs 2</span>
+<span class="sd">    2. maximum sequence length models 4-5</span>
 
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">nan_with_unknown_imputer</span><span class="p">(</span><span class="n">dfc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">)</span>
+<span class="sd">    final idea:</span>
+<span class="sd">    Either 3 sentimnets(model 1) or 5 stars (model 2)</span>
 
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]]</span>
+<span class="sd">    Args:</span>
+<span class="sd">        df (pd.DataFrame): input dataset</span>
+<span class="sd">        text_col (str): column name with text</span>
+<span class="sd">        sentiment_depth ([3, 5]): depth of sentiment analysis</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">sentiment_depth</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">pipe</span> <span class="o">=</span> <span class="n">pipeline</span><span class="p">(</span>
+            <span class="s2">&quot;text-classification&quot;</span><span class="p">,</span>
+            <span class="n">model</span><span class="o">=</span><span class="s2">&quot;cardiffnlp/twitter-roberta-base-sentiment-latest&quot;</span><span class="p">,</span>
+            <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">),</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="n">sentiment_depth</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
+        <span class="n">pipe</span> <span class="o">=</span> <span class="n">pipeline</span><span class="p">(</span>
+            <span class="s2">&quot;text-classification&quot;</span><span class="p">,</span>
+            <span class="n">model</span><span class="o">=</span><span class="s2">&quot;nlptown/bert-base-multilingual-uncased-sentiment&quot;</span><span class="p">,</span>
+            <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">),</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+    <span class="n">res</span> <span class="o">=</span> <span class="n">pipe</span><span class="p">(</span><span class="n">dfc</span><span class="p">[</span><span class="n">text_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span>
+    <span class="n">res</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">res</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
+        <span class="n">columns</span><span class="o">=</span><span class="p">{</span>
+            <span class="s2">&quot;label&quot;</span><span class="p">:</span> <span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_sentiment&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;score&quot;</span><span class="p">:</span> <span class="n">text_col</span> <span class="o">+</span> <span class="s2">&quot;_sentiment_score&quot;</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">dfc</span><span class="p">,</span> <span class="n">res</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">)</span>
-        <span class="n">dfc_le</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dfc</span><span class="p">)</span>
+</div>
 
-        <span class="n">dfc_le</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_change_int_float_types</span><span class="p">(</span><span class="n">dfc_le</span><span class="p">)</span>
 
-        <span class="n">dfc_le</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">]</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">is_fitted</span> <span class="o">=</span> <span class="kc">True</span>
+  </div>
 
-        <span class="k">return</span> <span class="n">dfc_le</span>
+  </div>
 
-    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Transform dataset in inference step.&quot;&quot;&quot;</span>
+</div>
 
-        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_fitted</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span>
-<span class="w">                </span><span class="sd">&quot;&quot;&quot;This instance of &#39;PreprocessData&#39; has not been fitted yet.</span>
-<span class="sd">                Please, run &#39;fit&#39; first&quot;&quot;&quot;</span>
-            <span class="p">)</span>
+<div class="doc doc-object doc-module">
 
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]]</span>
-        <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
-            <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span><span class="p">]</span>
 
-        <span class="c1"># dfc = dfc.replace(self.replace_rare_categories_dict)</span>
+<h2 id="churn_pred.preprocessing.preprocess" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">preprocess</span>
 
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">nan_with_unknown_imputer</span><span class="p">(</span><span class="n">dfc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">)</span>
 
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">nan_with_number_imputer</span><span class="p">(</span>
-            <span class="n">dfc</span><span class="p">,</span>
-            <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span><span class="p">)),</span>
-            <span class="o">-</span><span class="mf">9999.0</span><span class="p">,</span>  <span class="c1"># noqa</span>
-        <span class="p">)</span>
+<a href="#churn_pred.preprocessing.preprocess" class="headerlink" title="Permanent link">&para;</a></h2>
 
-        <span class="n">dfc_le</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dfc</span><span class="p">)</span>
+  <div class="doc doc-contents first">
 
-        <span class="n">dfc_le</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_change_int_float_types</span><span class="p">(</span><span class="n">dfc_le</span><span class="p">)</span>
+  
 
-        <span class="n">dfc_le</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">]</span>
+  <div class="doc doc-children">
 
-        <span class="k">return</span> <span class="n">dfc_le</span>
 
-    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Just to keep familiar naming convention with sklearn.&quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">_drop_highly_corr_cols_and_get_final_cont_cols</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">cont_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Drop highly correlated columns to decrease the size of the dataset</span>
-<span class="sd">        by dropping redundant information.&quot;&quot;&quot;</span>
 
-        <span class="n">df</span> <span class="o">=</span> <span class="n">drop_highly_correlated_columns</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">cont_cols</span><span class="p">)</span>
 
-        <span class="n">final_cont_cols</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span>
-        <span class="p">]</span>
 
-        <span class="k">return</span> <span class="n">df</span><span class="p">,</span> <span class="n">final_cont_cols</span>
-
-    <span class="k">def</span> <span class="nf">_infere_cat_cols</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Guess the categorical columns by excluding clearly continuous</span>
-<span class="sd">        columns - int, float&quot;&quot;&quot;</span>
-
-        <span class="n">cat_cols</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-            <span class="k">if</span> <span class="p">(</span>
-                <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;int32&quot;</span><span class="p">,</span> <span class="s2">&quot;int64&quot;</span><span class="p">]</span>
-                <span class="ow">and</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;float32&quot;</span><span class="p">,</span> <span class="s2">&quot;float64&quot;</span><span class="p">]</span>
-                <span class="ow">and</span> <span class="n">col</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span>
-            <span class="p">):</span>
-                <span class="n">cat_cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">cat_cols</span>
-
-    <span class="k">def</span> <span class="nf">_infere_cont_cols</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">cat_cols</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span>
-    <span class="p">):</span>  <span class="c1"># noqa</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Guess the continuous columns by excluding clearly categorical,</span>
-<span class="sd">        and target_col and including only int or float.&quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="n">cat_cols</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">cont_cols</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">cat_cols</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">cont_cols</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-                <span class="k">if</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="s2">&quot;int&quot;</span> <span class="ow">or</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="s2">&quot;float&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="n">col</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">target_col</span>
-                <span class="p">]:</span>
-                    <span class="n">cont_cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">cont_cols</span>
-
-    <span class="k">def</span> <span class="nf">_change_int_float_types</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Change int to float as int causes data type issues in some ml</span>
-<span class="sd">        methods, eg. lightgbm.&quot;&quot;&quot;</span>
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span>
-            <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">dfc</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-        <span class="p">)</span>  <span class="c1"># noqa</span>
-        <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span>
-            <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">dfc</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-        <span class="p">)</span>
 
-        <span class="k">return</span> <span class="n">dfc</span>
-</code></pre></div></td></tr></table></div>
-            </details>
 
-  
+<div class="doc doc-object doc-class">
 
-  <div class="doc doc-children">
 
 
+<h3 id="churn_pred.preprocessing.preprocess.PreprocessData" class="doc doc-heading">
+        <span class="doc doc-object-name doc-class-name">PreprocessData</span>
 
 
+<a href="#churn_pred.preprocessing.preprocess.PreprocessData" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">PreprocessData</span><span class="p">(</span>
+    <span class="n">target_col</span><span class="p">,</span> <span class="n">id_cols</span><span class="p">,</span> <span class="n">cat_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cont_cols</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">)</span>
+</code></pre></div>
 
+  <div class="doc doc-contents ">
 
+  
+      <p>Object to preprocess the dataset.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>target_col</b>
+            (<code>str</code>)
+        – <p>target column name</p>
+      </li>
+      <li class="field-body">
+        <b>id_cols</b>
+            (<code><span title="typing.List">List</span>[str]</code>)
+        – <p>id columns</p>
+      </li>
+      <li class="field-body">
+        <b>cat_cols</b>
+            (<code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[str]]</code>)
+        – <p>list of categorical column names</p>
+      </li>
+      <li class="field-body">
+        <b>cont_cols</b>
+            (<code><span title="typing.Optional">Optional</span>[<span title="typing.List">List</span>[str]]</code>)
+        – <p>list of continuous column names</p>
+      </li>
+  </ul>
 
 
+          <details class="quote">
+            <summary>Source code in <code>churn_pred/preprocessing/preprocess.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">28</span>
+<span class="normal">29</span>
+<span class="normal">30</span>
+<span class="normal">31</span>
+<span class="normal">32</span>
+<span class="normal">33</span>
+<span class="normal">34</span>
+<span class="normal">35</span>
+<span class="normal">36</span>
+<span class="normal">37</span>
+<span class="normal">38</span>
+<span class="normal">39</span>
+<span class="normal">40</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+    <span class="bp">self</span><span class="p">,</span>
+    <span class="n">target_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">id_cols</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">cat_cols</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">cont_cols</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">target_col</span> <span class="o">=</span> <span class="n">target_col</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span> <span class="o">=</span> <span class="n">id_cols</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">=</span> <span class="n">cat_cols</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">cont_cols</span> <span class="o">=</span> <span class="n">cont_cols</span>
+
+    <span class="bp">self</span><span class="o">.</span><span class="n">is_fitted</span> <span class="o">=</span> <span class="kc">False</span>
+</code></pre></div></td></tr></table></div>
+          </details>
 
-<div class="doc doc-object doc-function">
+  
 
+  <div class="doc doc-children">
 
 
-<h3 id="churn_pred.preprocessing.preprocess.PreprocessData.fit" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
 
-<a href="#churn_pred.preprocessing.preprocess.PreprocessData.fit" class="headerlink" title="Permanent link">&para;</a></h3>
 
 
-  <div class="doc doc-contents ">
-  
-      <p>Just to keep familiar naming convention with sklearn.</p>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">130</span>
-<span class="normal">131</span>
-<span class="normal">132</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Just to keep familiar naming convention with sklearn.&quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
 
-</div>
 
 
 <div class="doc doc-object doc-function">
 
 
 
-<h3 id="churn_pred.preprocessing.preprocess.PreprocessData.fit_transform" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
+<h4 id="churn_pred.preprocessing.preprocess.PreprocessData.fit_transform" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">fit_transform</span>
 
-<a href="#churn_pred.preprocessing.preprocess.PreprocessData.fit_transform" class="headerlink" title="Permanent link">&para;</a></h3>
 
+<a href="#churn_pred.preprocessing.preprocess.PreprocessData.fit_transform" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Fit peprocessor and transform dataset in training step.</p>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">42</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">42</span>
 <span class="normal">43</span>
 <span class="normal">44</span>
 <span class="normal">45</span>
@@ -5481,7 +3762,7 @@ <h3 id="churn_pred.preprocessing.preprocess.PreprocessData.fit_transform" class=
 <span class="normal">92</span>
 <span class="normal">93</span>
 <span class="normal">94</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Fit peprocessor and transform dataset in training step.&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;Fit peprocessor and transform dataset in training step.&quot;&quot;&quot;</span>
 
     <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 
@@ -5534,29 +3815,30 @@ <h3 id="churn_pred.preprocessing.preprocess.PreprocessData.fit_transform" class=
 
     <span class="k">return</span> <span class="n">dfc_le</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h3 id="churn_pred.preprocessing.preprocess.PreprocessData.transform" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
+<h4 id="churn_pred.preprocessing.preprocess.PreprocessData.transform" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">transform</span>
 
-<a href="#churn_pred.preprocessing.preprocess.PreprocessData.transform" class="headerlink" title="Permanent link">&para;</a></h3>
 
+<a href="#churn_pred.preprocessing.preprocess.PreprocessData.transform" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Transform dataset in inference step.</p>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/preprocess.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 96</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 96</span>
 <span class="normal"> 97</span>
 <span class="normal"> 98</span>
 <span class="normal"> 99</span>
@@ -5588,17 +3870,25 @@ <h3 id="churn_pred.preprocessing.preprocess.PreprocessData.transform" class="doc
 <span class="normal">125</span>
 <span class="normal">126</span>
 <span class="normal">127</span>
-<span class="normal">128</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Transform dataset in inference step.&quot;&quot;&quot;</span>
+<span class="normal">128</span>
+<span class="normal">129</span>
+<span class="normal">130</span>
+<span class="normal">131</span>
+<span class="normal">132</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Transform dataset in inference step.&quot;&quot;&quot;</span>
 
     <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_fitted</span><span class="p">:</span>
         <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span>
-<span class="w">            </span><span class="sd">&quot;&quot;&quot;This instance of &#39;PreprocessData&#39; has not been fitted yet.</span>
+            <span class="sd">&quot;&quot;&quot;This instance of &#39;PreprocessData&#39; has not been fitted yet.</span>
 <span class="sd">            Please, run &#39;fit&#39; first&quot;&quot;&quot;</span>
         <span class="p">)</span>
 
     <span class="n">dfc</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">)</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 
+    <span class="c1"># added as mlflow inference is receiving all the data as objects</span>
+    <span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
+    <span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
+
     <span class="k">try</span><span class="p">:</span>
         <span class="n">dfc</span> <span class="o">=</span> <span class="n">dfc</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">final_cont_cols</span> <span class="o">+</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]]</span>
     <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
@@ -5622,17 +3912,45 @@ <h3 id="churn_pred.preprocessing.preprocess.PreprocessData.transform" class="doc
 
     <span class="k">return</span> <span class="n">dfc_le</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
+<div class="doc doc-object doc-function">
+
+
+
+<h4 id="churn_pred.preprocessing.preprocess.PreprocessData.fit" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">fit</span>
 
 
+<a href="#churn_pred.preprocessing.preprocess.PreprocessData.fit" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Just to keep familiar naming convention with sklearn.</p>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/preprocess.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">134</span>
+<span class="normal">135</span>
+<span class="normal">136</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Just to keep familiar naming convention with sklearn.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
+</code></pre></div></td></tr></table></div>
+      </details>
   </div>
 
+</div>
+
+
+
   </div>
 
+  </div>
 
 </div>
 
@@ -5649,7 +3967,12 @@ <h3 id="churn_pred.preprocessing.preprocess.PreprocessData.transform" class="doc
 
 
 
-<a id="churn_pred.preprocessing.scaler"></a>
+<h2 id="churn_pred.preprocessing.scaler" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">scaler</span>
+
+
+<a href="#churn_pred.preprocessing.scaler" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -5664,16 +3987,19 @@ <h3 id="churn_pred.preprocessing.preprocess.PreprocessData.transform" class="doc
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.preprocessing.scaler.scaler_mapper" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">scaler_mapper</span><span class="p">(</span><span class="n">cont_cols</span><span class="p">,</span> <span class="n">cat_cols</span><span class="p">,</span> <span class="n">id_cols</span><span class="p">,</span> <span class="n">scaler_mapper_def</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+<h3 id="churn_pred.preprocessing.scaler.scaler_mapper" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">scaler_mapper</span>
 
-<a href="#churn_pred.preprocessing.scaler.scaler_mapper" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.preprocessing.scaler.scaler_mapper" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">scaler_mapper</span><span class="p">(</span>
+    <span class="n">cont_cols</span><span class="p">,</span> <span class="n">cat_cols</span><span class="p">,</span> <span class="n">id_cols</span><span class="p">,</span> <span class="n">scaler_mapper_def</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
@@ -5682,84 +4008,44 @@ <h2 id="churn_pred.preprocessing.scaler.scaler_mapper" class="doc doc-heading">
 identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers.
 Only columns defined in mapper object will be present in the transformed dataset.</p>
 
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>cont_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of continuous feature columns in the dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>cat_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of categorical feature columns in the dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>id_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>identifier columns</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>scaler_mapper_def</code></td>
-          <td>
-                <code>dict</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>optional dictionary that contains keys
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>cont_cols</b>
+            (<code>list</code>)
+        – <p>list of continuous feature columns in the dataset</p>
+      </li>
+      <li class="field-body">
+        <b>cat_cols</b>
+            (<code>list</code>)
+        – <p>list of categorical feature columns in the dataset</p>
+      </li>
+      <li class="field-body">
+        <b>id_cols</b>
+            (<code>list</code>)
+        – <p>identifier columns</p>
+      </li>
+      <li class="field-body">
+        <b>scaler_mapper_def</b>
+            (<code>dict</code>)
+        – <p>optional dictionary that contains keys
 ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding
 scalers (defined by names, not instantiated) from sklearn library</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/preprocessing/scaler.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 6</span>
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>scaler</b>(            <code><span title="sklearn_pandas.DataFrameMapper">DataFrameMapper</span></code>
+)        – <p>scaler object mapping sklearn scalers to columns in
+pandas dataframe</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/preprocessing/scaler.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 6</span>
 <span class="normal"> 7</span>
 <span class="normal"> 8</span>
 <span class="normal"> 9</span>
@@ -5818,7 +4104,7 @@ <h2 id="churn_pred.preprocessing.scaler.scaler_mapper" class="doc doc-heading">
     <span class="n">id_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
     <span class="n">scaler_mapper_def</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataFrameMapper</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Function that maps scaler functions to appropriate columns.</span>
+    <span class="sd">&quot;&quot;&quot;Function that maps scaler functions to appropriate columns.</span>
 
 <span class="sd">    By default does not assign any scaler to continuous, categorical or</span>
 <span class="sd">    identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers.</span>
@@ -5867,7 +4153,7 @@ <h2 id="churn_pred.preprocessing.scaler.scaler_mapper" class="doc doc-heading">
     <span class="n">scaler</span> <span class="o">=</span> <span class="n">DataFrameMapper</span><span class="p">(</span><span class="n">cont_cols_def</span> <span class="o">+</span> <span class="n">cat_cols_def</span> <span class="o">+</span> <span class="n">id_cols_def</span><span class="p">,</span> <span class="n">df_out</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">scaler</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -5880,50 +4166,51 @@ <h2 id="churn_pred.preprocessing.scaler.scaler_mapper" class="doc doc-heading">
 
 </div>
 
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-        </div>
         
-      </main>
+        <a href="training.html" class="md-footer__link md-footer__link--next" aria-label="Next: training" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              training
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
       
-        <footer class="md-footer">
+    </nav>
   
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
@@ -5949,12 +4236,10 @@ <h2 id="churn_pred.preprocessing.scaler.scaler_mapper" class="doc doc-heading">
     <div class="md-dialog" data-md-component="dialog">
       <div class="md-dialog__inner md-typeset"></div>
     </div>
+    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b97dbffb.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
-    
-    
-      <script src="../assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="../assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="../stylesheets/extra.js"></script>
       
diff --git a/docs/site/churn_pred/training.html b/docs/site/churn_pred/training.html
index 3c6b9b3..2a0e08f 100644
--- a/docs/site/churn_pred/training.html
+++ b/docs/site/churn_pred/training.html
@@ -8,34 +8,24 @@
       
       
       
-      
-        <link rel="prev" href="preprocessing.html">
-      
-      
-        <link rel="next" href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html">
-      
-      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>training - ecovadis_assignment</title>
+        <title>training - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
-
-
     
     
-      
-    
     
       
         
@@ -50,13 +40,12 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
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@@ -67,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
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     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            ecovadis_assignment
+            churnpred
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@@ -118,39 +109,31 @@
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+      <form class="md-header__option" data-md-component="palette">
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+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
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       <label class="md-search__icon md-icon" for="__search">
-        
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       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
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 </a>
       </div>
@@ -204,41 +184,31 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
@@ -246,55 +216,30 @@
   
     
   
+
+
   
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="code_profiling.html" class="md-tabs__link md-tabs__link--active">
+        churn_pred
+      </a>
+    </li>
   
 
       
         
   
   
-  
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-    
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-        <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
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-      <a href="../code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
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-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
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-    ecovadis_assignment
+    churnpred
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-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
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-    ecovadis_assignment
+    churnpred
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+      
+
   
   
   
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+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
       
       
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-    <span class="md-ellipsis">
-      metrics
-    </span>
+    metrics
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-  <a href="#churn_pred.training.metrics.aiqc" class="md-nav__link">
-    <span class="md-ellipsis">
-      aiqc
-    </span>
+  <a href="#churn_pred.training.optuna_optimizer" class="md-nav__link">
+    optuna_optimizer
   </a>
   
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-  <a href="#churn_pred.training.metrics.nacil" class="md-nav__link">
-    <span class="md-ellipsis">
-      nacil
-    </span>
+  <a href="#churn_pred.training.trainer" class="md-nav__link">
+    trainer
   </a>
   
 </li>
       
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-  <a href="#churn_pred.training.metrics.ndcg" class="md-nav__link">
-    <span class="md-ellipsis">
-      ndcg
-    </span>
+  <a href="#churn_pred.training.utils" class="md-nav__link">
+    utils
   </a>
   
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-        <li class="md-nav__item">
-  <a href="#churn_pred.training.metrics.rmse" class="md-nav__link">
-    <span class="md-ellipsis">
-      rmse
-    </span>
-  </a>
+    </ul>
   
-</li>
+</nav>
       
-        <li class="md-nav__item">
-  <a href="#churn_pred.training.metrics.smape" class="md-nav__link">
-    <span class="md-ellipsis">
-      smape
-    </span>
-  </a>
+    </li>
   
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-      
-        <li class="md-nav__item">
-  <a href="#churn_pred.training.optuna_optimizer" class="md-nav__link">
-    <span class="md-ellipsis">
-      optuna_optimizer
-    </span>
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+
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+    </li>
   
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+
+    
       
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-  <a href="#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer" class="md-nav__link">
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-    </span>
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-  <a href="#churn_pred.training.trainer.Trainer" class="md-nav__link">
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+
   
-</li>
-      
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-  <a href="#churn_pred.training.utils" class="md-nav__link">
-    <span class="md-ellipsis">
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-  <a href="#churn_pred.training.utils.get_feature_importance" class="md-nav__link">
-    <span class="md-ellipsis">
-      get_feature_importance
-    </span>
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-  <a href="#churn_pred.training.utils.predict_cls_lgbm_from_raw" class="md-nav__link">
-    <span class="md-ellipsis">
-      predict_cls_lgbm_from_raw
-    </span>
-  </a>
-  
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-  <a href="#churn_pred.training.utils.predict_proba_lgbm_from_raw" class="md-nav__link">
-    <span class="md-ellipsis">
-      predict_proba_lgbm_from_raw
-    </span>
-  </a>
-  
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-  <a href="#churn_pred.training.utils.to_lgbdataset" class="md-nav__link">
-    <span class="md-ellipsis">
-      to_lgbdataset
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+          
         
           
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+      
+      
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+          notebooks
+          <span class="md-nav__icon md-icon"></span>
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+      <nav class="md-nav" aria-label="notebooks" data-md-level="1">
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+        TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
       </a>
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-  </span>
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+        00_auxiliary_features
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-  </span>
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+        01_feature_analysis
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-  </span>
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+        02_target_analysis
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-    04_model_training_pipeline
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+        04_model_training_pipeline
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-    05_model_analysis.ipynb
-  </span>
-  
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+        05_model_analysis.ipynb
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+          
+        </ul>
+      </nav>
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+        cicd
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+
   
   
   
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-    code profiling
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+        code profiling
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+        contributing
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-          
-            <div class="md-content" data-md-component="content">
-              <article class="md-content__inner md-typeset">
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
                 
-                  
+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/churn_pred/training.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  
-  
 
 
 <h1 id="training">Training<a class="headerlink" href="#training" title="Permanent link">&para;</a></h1>
@@ -1040,7 +744,12 @@ <h1 id="training">Training<a class="headerlink" href="#training" title="Permanen
 
 
 
-<a id="churn_pred.training.metrics"></a>
+<h2 id="churn_pred.training.metrics" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">metrics</span>
+
+
+<a href="#churn_pred.training.metrics" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -1055,500 +764,370 @@ <h1 id="training">Training<a class="headerlink" href="#training" title="Permanen
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.metrics.aiqc" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">aiqc</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">high_quantile</span><span class="p">,</span> <span class="n">low_quantile</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.metrics.smape" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">smape</span>
 
-<a href="#churn_pred.training.metrics.aiqc" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.metrics.smape" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">smape</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Average InterQuantile Coverage for Quantile Regression.
-Check if the interquantile coverage is close to the coverage of in the test dataset.</p>
-
+      <p>Symmetric Mean Absolute Percentage Error
+<a href="https://vedexcel.com/how-to-calculate-smape-in-python/">https://vedexcel.com/how-to-calculate-smape-in-python/</a></p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>actual</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>actual values</p>
+      </li>
+      <li class="field-body">
+        <b>predicted</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>predicted values</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>actual</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>actual values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>high_quantile</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>high quantile predicted values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>low_quantile</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>low quantile predicted values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>smape</b>(            <code>float</code>
+)        – <p>symmetric mean absolute percentage error</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">77</span>
-<span class="normal">78</span>
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-<span class="normal">84</span>
-<span class="normal">85</span>
-<span class="normal">86</span>
-<span class="normal">87</span>
-<span class="normal">88</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">aiqc</span><span class="p">(</span><span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">high_quantile</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">low_quantile</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Average InterQuantile Coverage for Quantile Regression.</span>
-<span class="sd">    Check if the interquantile coverage is close to the coverage of in the test dataset.</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">15</span>
+<span class="normal">16</span>
+<span class="normal">17</span>
+<span class="normal">18</span>
+<span class="normal">19</span>
+<span class="normal">20</span>
+<span class="normal">21</span>
+<span class="normal">22</span>
+<span class="normal">23</span>
+<span class="normal">24</span>
+<span class="normal">25</span>
+<span class="normal">26</span>
+<span class="normal">27</span>
+<span class="normal">28</span>
+<span class="normal">29</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">smape</span><span class="p">(</span><span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">predicted</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Symmetric Mean Absolute Percentage Error</span>
+<span class="sd">    https://vedexcel.com/how-to-calculate-smape-in-python/</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        actual (np.ndarray): actual values</span>
-<span class="sd">        high_quantile (np.ndarray): high quantile predicted values</span>
-<span class="sd">        low_quantile (np.ndarray): low quantile predicted values</span>
+<span class="sd">        predicted (np.ndarray): predicted values</span>
 <span class="sd">    Returns:</span>
-<span class="sd">        aiqc (float): average interquantile coverage</span>
+<span class="sd">        smape (float): symmetric mean absolute percentage error</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">actual</span> <span class="o">&lt;</span> <span class="n">high_quantile</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">actual</span> <span class="o">&gt;</span> <span class="n">low_quantile</span><span class="p">))</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">actual</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="mi">100</span>
+        <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">actual</span><span class="p">)</span>
+        <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">predicted</span> <span class="o">-</span> <span class="n">actual</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">actual</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">predicted</span><span class="p">)))</span>
+    <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.metrics.nacil" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">nacil</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">high_quantile_predicted</span><span class="p">,</span> <span class="n">low_quantile_predicted</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.metrics.ndcg" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">ndcg</span>
 
-<a href="#churn_pred.training.metrics.nacil" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.metrics.ndcg" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">ndcg</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Normalized Average Confidence Interval Length for Quantile Regression.
-The value is normalized to actual(expected) value.</p>
+      <p>Normalized Discounted Cumulative Gain</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>actual</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>actual values</p>
+      </li>
+      <li class="field-body">
+        <b>predicted</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>predicted values</p>
+      </li>
+  </ul>
 
+<details class="return">
+  <summary>Return</summary>
+  <p>ndcg (float): normalized discounted cumulative gain</p>
+</details>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">32</span>
+<span class="normal">33</span>
+<span class="normal">34</span>
+<span class="normal">35</span>
+<span class="normal">36</span>
+<span class="normal">37</span>
+<span class="normal">38</span>
+<span class="normal">39</span>
+<span class="normal">40</span>
+<span class="normal">41</span>
+<span class="normal">42</span>
+<span class="normal">43</span>
+<span class="normal">44</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">ndcg</span><span class="p">(</span><span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">predicted</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Normalized Discounted Cumulative Gain</span>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>actual</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>actual values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>high_quantile_predicted</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>high quantile predicted values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>low_quantile_predicted</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>low quantile predicted values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">59</span>
-<span class="normal">60</span>
-<span class="normal">61</span>
-<span class="normal">62</span>
-<span class="normal">63</span>
-<span class="normal">64</span>
-<span class="normal">65</span>
-<span class="normal">66</span>
-<span class="normal">67</span>
-<span class="normal">68</span>
-<span class="normal">69</span>
-<span class="normal">70</span>
-<span class="normal">71</span>
-<span class="normal">72</span>
-<span class="normal">73</span>
-<span class="normal">74</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nacil</span><span class="p">(</span>
-    <span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
-    <span class="n">high_quantile_predicted</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
-    <span class="n">low_quantile_predicted</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Normalized Average Confidence Interval Length for Quantile Regression.</span>
-<span class="sd">    The value is normalized to actual(expected) value.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        actual (np.ndarray): actual values</span>
-<span class="sd">        high_quantile_predicted (np.ndarray): high quantile predicted values</span>
-<span class="sd">        low_quantile_predicted (np.ndarray): low quantile predicted values</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        nacil (float): normalized average confidence interval length</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">high_quantile_predicted</span> <span class="o">-</span> <span class="n">low_quantile_predicted</span><span class="p">)</span> <span class="o">/</span> <span class="n">actual</span><span class="p">)</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
+<span class="sd">    Args:</span>
+<span class="sd">        actual (np.ndarray): actual values</span>
+<span class="sd">        predicted (np.ndarray): predicted values</span>
+<span class="sd">    Return:</span>
+<span class="sd">        ndcg (float): normalized discounted cumulative gain</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">ndcg_score</span><span class="p">(</span>
+        <span class="p">[</span><span class="n">rankdata</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;ordinal&quot;</span><span class="p">)],</span>
+        <span class="p">[</span><span class="n">rankdata</span><span class="p">(</span><span class="n">predicted</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;ordinal&quot;</span><span class="p">)],</span>
+    <span class="p">)</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.metrics.ndcg" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">ndcg</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.metrics.rmse" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">rmse</span>
 
-<a href="#churn_pred.training.metrics.ndcg" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.metrics.rmse" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">rmse</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Normalized Discounted Cumulative Gain</p>
-
+      <p>Root Mean Squared Error</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>actual</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>actual values</p>
+      </li>
+      <li class="field-body">
+        <b>predicted</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>predicted values</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>actual</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>actual values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>predicted</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>predicted values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>rmse</b>(            <code>float</code>
+)        – <p>root mean square error</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">32</span>
-<span class="normal">33</span>
-<span class="normal">34</span>
-<span class="normal">35</span>
-<span class="normal">36</span>
-<span class="normal">37</span>
-<span class="normal">38</span>
-<span class="normal">39</span>
-<span class="normal">40</span>
-<span class="normal">41</span>
-<span class="normal">42</span>
-<span class="normal">43</span>
-<span class="normal">44</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">ndcg</span><span class="p">(</span><span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">predicted</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Normalized Discounted Cumulative Gain</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">47</span>
+<span class="normal">48</span>
+<span class="normal">49</span>
+<span class="normal">50</span>
+<span class="normal">51</span>
+<span class="normal">52</span>
+<span class="normal">53</span>
+<span class="normal">54</span>
+<span class="normal">55</span>
+<span class="normal">56</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">predicted</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Root Mean Squared Error</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        actual (np.ndarray): actual values</span>
 <span class="sd">        predicted (np.ndarray): predicted values</span>
-<span class="sd">    Return:</span>
-<span class="sd">        ndcg (float): normalized discounted cumulative gain</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        rmse (float): root mean square error</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">ndcg_score</span><span class="p">(</span>
-        <span class="p">[</span><span class="n">rankdata</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;ordinal&quot;</span><span class="p">)],</span>
-        <span class="p">[</span><span class="n">rankdata</span><span class="p">(</span><span class="n">predicted</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;ordinal&quot;</span><span class="p">)],</span>
-    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">predicted</span><span class="p">,</span> <span class="n">squared</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.metrics.rmse" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">rmse</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.metrics.nacil" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">nacil</span>
 
-<a href="#churn_pred.training.metrics.rmse" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.metrics.nacil" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">nacil</span><span class="p">(</span>
+    <span class="n">actual</span><span class="p">,</span> <span class="n">high_quantile_predicted</span><span class="p">,</span> <span class="n">low_quantile_predicted</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Root Mean Squared Error</p>
-
+      <p>Normalized Average Confidence Interval Length for Quantile Regression.
+The value is normalized to actual(expected) value.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>actual</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>actual values</p>
+      </li>
+      <li class="field-body">
+        <b>high_quantile_predicted</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>high quantile predicted values</p>
+      </li>
+      <li class="field-body">
+        <b>low_quantile_predicted</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>low quantile predicted values</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>actual</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>actual values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>predicted</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>predicted values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>nacil</b>(            <code>float</code>
+)        – <p>normalized average confidence interval length</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">47</span>
-<span class="normal">48</span>
-<span class="normal">49</span>
-<span class="normal">50</span>
-<span class="normal">51</span>
-<span class="normal">52</span>
-<span class="normal">53</span>
-<span class="normal">54</span>
-<span class="normal">55</span>
-<span class="normal">56</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">predicted</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Root Mean Squared Error</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">59</span>
+<span class="normal">60</span>
+<span class="normal">61</span>
+<span class="normal">62</span>
+<span class="normal">63</span>
+<span class="normal">64</span>
+<span class="normal">65</span>
+<span class="normal">66</span>
+<span class="normal">67</span>
+<span class="normal">68</span>
+<span class="normal">69</span>
+<span class="normal">70</span>
+<span class="normal">71</span>
+<span class="normal">72</span>
+<span class="normal">73</span>
+<span class="normal">74</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">nacil</span><span class="p">(</span>
+    <span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">high_quantile_predicted</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">low_quantile_predicted</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Normalized Average Confidence Interval Length for Quantile Regression.</span>
+<span class="sd">    The value is normalized to actual(expected) value.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        actual (np.ndarray): actual values</span>
-<span class="sd">        predicted (np.ndarray): predicted values</span>
+<span class="sd">        high_quantile_predicted (np.ndarray): high quantile predicted values</span>
+<span class="sd">        low_quantile_predicted (np.ndarray): low quantile predicted values</span>
 <span class="sd">    Returns:</span>
-<span class="sd">        rmse (float): root mean square error</span>
+<span class="sd">        nacil (float): normalized average confidence interval length</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">predicted</span><span class="p">,</span> <span class="n">squared</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">((</span><span class="n">high_quantile_predicted</span> <span class="o">-</span> <span class="n">low_quantile_predicted</span><span class="p">)</span> <span class="o">/</span> <span class="n">actual</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.metrics.smape" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">smape</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">predicted</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.metrics.aiqc" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">aiqc</span>
 
-<a href="#churn_pred.training.metrics.smape" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.metrics.aiqc" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">aiqc</span><span class="p">(</span><span class="n">actual</span><span class="p">,</span> <span class="n">high_quantile</span><span class="p">,</span> <span class="n">low_quantile</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Symmetric Mean Absolute Percentage Error
-<a href="https://vedexcel.com/how-to-calculate-smape-in-python/">https://vedexcel.com/how-to-calculate-smape-in-python/</a></p>
-
+      <p>Average InterQuantile Coverage for Quantile Regression.
+Check if the interquantile coverage is close to the coverage of in the test dataset.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>actual</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>actual values</p>
+      </li>
+      <li class="field-body">
+        <b>high_quantile</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>high quantile predicted values</p>
+      </li>
+      <li class="field-body">
+        <b>low_quantile</b>
+            (<code><span title="numpy">np</span>.<span title="numpy.ndarray">ndarray</span></code>)
+        – <p>low quantile predicted values</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>actual</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>actual values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>predicted</code></td>
-          <td>
-                <code><span title="numpy.ndarray">ndarray</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>predicted values</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>aiqc</b>(            <code>float</code>
+)        – <p>average interquantile coverage</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">15</span>
-<span class="normal">16</span>
-<span class="normal">17</span>
-<span class="normal">18</span>
-<span class="normal">19</span>
-<span class="normal">20</span>
-<span class="normal">21</span>
-<span class="normal">22</span>
-<span class="normal">23</span>
-<span class="normal">24</span>
-<span class="normal">25</span>
-<span class="normal">26</span>
-<span class="normal">27</span>
-<span class="normal">28</span>
-<span class="normal">29</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">smape</span><span class="p">(</span><span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">predicted</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Symmetric Mean Absolute Percentage Error</span>
-<span class="sd">    https://vedexcel.com/how-to-calculate-smape-in-python/</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/metrics.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">77</span>
+<span class="normal">78</span>
+<span class="normal">79</span>
+<span class="normal">80</span>
+<span class="normal">81</span>
+<span class="normal">82</span>
+<span class="normal">83</span>
+<span class="normal">84</span>
+<span class="normal">85</span>
+<span class="normal">86</span>
+<span class="normal">87</span>
+<span class="normal">88</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">aiqc</span><span class="p">(</span><span class="n">actual</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">high_quantile</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">low_quantile</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Average InterQuantile Coverage for Quantile Regression.</span>
+<span class="sd">    Check if the interquantile coverage is close to the coverage of in the test dataset.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        actual (np.ndarray): actual values</span>
-<span class="sd">        predicted (np.ndarray): predicted values</span>
+<span class="sd">        high_quantile (np.ndarray): high quantile predicted values</span>
+<span class="sd">        low_quantile (np.ndarray): low quantile predicted values</span>
 <span class="sd">    Returns:</span>
-<span class="sd">        smape (float): symmetric mean absolute percentage error</span>
+<span class="sd">        aiqc (float): average interquantile coverage</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="p">(</span>
-        <span class="mi">100</span>
-        <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">actual</span><span class="p">)</span>
-        <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">predicted</span> <span class="o">-</span> <span class="n">actual</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">actual</span><span class="p">)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">predicted</span><span class="p">)))</span>
-    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">actual</span> <span class="o">&lt;</span> <span class="n">high_quantile</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">actual</span> <span class="o">&gt;</span> <span class="n">low_quantile</span><span class="p">))</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">actual</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -1565,7 +1144,12 @@ <h2 id="churn_pred.training.metrics.smape" class="doc doc-heading">
 
 
 
-<a id="churn_pred.training.optuna_optimizer"></a>
+<h2 id="churn_pred.training.optuna_optimizer" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">optuna_optimizer</span>
+
+
+<a href="#churn_pred.training.optuna_optimizer" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -1583,22 +1167,40 @@ <h2 id="churn_pred.training.metrics.smape" class="doc doc-heading">
 
 
 
-<h2 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer" class="doc doc-heading">
-          <code>LGBOptunaOptimizer</code>
+<h3 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer" class="doc doc-heading">
+        <span class="doc doc-object-name doc-class-name">LGBOptunaOptimizer</span>
 
 
-<a href="#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer" class="headerlink" title="Permanent link">&para;</a></h2>
-
+<a href="#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">LGBOptunaOptimizer</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">n_class</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
-          <p class="doc doc-class-bases">
-            Bases: <code><span title="churn_pred.training._base.BaseOptimizer">BaseOptimizer</span></code></p>
+      <p class="doc doc-class-bases">
+        Bases: <code><span title="churn_pred.training._base.BaseOptimizer">BaseOptimizer</span></code></p>
 
 
-            <details class="quote">
-              <summary>Source code in <code>churn_pred/training/optuna_optimizer.py</code></summary>
-              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">15</span>
-<span class="normal">16</span>
+  
+      <p>Fallback/backup Optuna optimizer. Development is focused on Raytune.
+Kepping this code as backup.</p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>objective</b>
+            (<code>str</code>)
+        – <p>objective of the model</p>
+      </li>
+      <li class="field-body">
+        <b>n_class</b>
+            (<code>int</code>)
+        – <p>number of classes in the dataset</p>
+      </li>
+  </ul>
+
+          <details class="quote">
+            <summary>Source code in <code>churn_pred/training/optuna_optimizer.py</code></summary>
+            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">16</span>
 <span class="normal">17</span>
 <span class="normal">18</span>
 <span class="normal">19</span>
@@ -1615,81 +1217,26 @@ <h2 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer" class="doc doc-
 <span class="normal">30</span>
 <span class="normal">31</span>
 <span class="normal">32</span>
-<span class="normal">33</span>
-<span class="normal">34</span>
-<span class="normal">35</span>
-<span class="normal">36</span>
-<span class="normal">37</span>
-<span class="normal">38</span>
-<span class="normal">39</span>
-<span class="normal">40</span>
-<span class="normal">41</span>
-<span class="normal">42</span>
-<span class="normal">43</span>
-<span class="normal">44</span>
-<span class="normal">45</span>
-<span class="normal">46</span>
-<span class="normal">47</span>
-<span class="normal">48</span>
-<span class="normal">49</span>
-<span class="normal">50</span>
-<span class="normal">51</span>
-<span class="normal">52</span>
-<span class="normal">53</span>
-<span class="normal">54</span>
-<span class="normal">55</span>
-<span class="normal">56</span>
-<span class="normal">57</span>
-<span class="normal">58</span>
-<span class="normal">59</span>
-<span class="normal">60</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">LGBOptunaOptimizer</span><span class="p">(</span><span class="n">BaseOptimizer</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">objective</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">,</span> <span class="s2">&quot;regression&quot;</span><span class="p">],</span>
-        <span class="n">n_class</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fallback/backup Optuna optimizer. Development is focused on Raytune.</span>
-<span class="sd">        Kepping this code as backup.</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            objective (str): objective of the model</span>
-<span class="sd">            n_class (int): number of classes in the dataset</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># Optuna does not support original lighgbm implemenattion and with a workaroud it</span>
-        <span class="c1"># is possible to get the binarry classfier with focal_loss(any custom loss)</span>
-        <span class="c1"># running but the multiclass requires changes to Optuna code, eg. this post:</span>
-        <span class="c1"># https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm  # noqa</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">LGBOptunaOptimizer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">n_class</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_params</span>
-
-    <span class="k">def</span> <span class="nf">optimize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtrain</span><span class="p">:</span> <span class="n">lgbDataset</span><span class="p">,</span> <span class="n">deval</span><span class="p">:</span> <span class="n">lgbDataset</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Optimize LGBM model on provided datasets.</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            dtrain (lgbDataset): training lgb dataset</span>
-<span class="sd">            deval (lgbDataset): evaluation lgb dataset</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">dtrain_copy</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">dtrain</span><span class="p">)</span>
-        <span class="n">deval_copy</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">deval</span><span class="p">)</span> <span class="k">if</span> <span class="n">deval</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
-
-        <span class="n">tuner</span> <span class="o">=</span> <span class="n">LightGBMTuner</span><span class="p">(</span>
-            <span class="n">params</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="p">,</span>
-            <span class="n">train_set</span><span class="o">=</span><span class="n">dtrain_copy</span><span class="p">,</span>
-            <span class="n">valid_sets</span><span class="o">=</span><span class="n">deval_copy</span><span class="p">,</span>
-            <span class="n">num_boost_round</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-            <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">lgb</span><span class="o">.</span><span class="n">early_stopping</span><span class="p">(</span><span class="n">stopping_rounds</span><span class="o">=</span><span class="mi">50</span><span class="p">)],</span>
-            <span class="n">feval</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">feval</span><span class="p">,</span>
-            <span class="c1"># fobj=self.fobj,</span>
-        <span class="p">)</span>
-        <span class="n">tuner</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+<span class="normal">33</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+    <span class="bp">self</span><span class="p">,</span>
+    <span class="n">objective</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">,</span> <span class="s2">&quot;regression&quot;</span><span class="p">],</span>
+    <span class="n">n_class</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Fallback/backup Optuna optimizer. Development is focused on Raytune.</span>
+<span class="sd">    Kepping this code as backup.</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">best</span> <span class="o">=</span> <span class="n">tuner</span><span class="o">.</span><span class="n">best_params</span>
-        <span class="c1"># since n_estimators is not among the params that Optuna optimizes we</span>
-        <span class="c1"># need to add it manually. We add a high value since it will be used</span>
-        <span class="c1"># with early_stopping_rounds</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;n_estimators&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1000</span>  <span class="c1"># type: ignore</span>
+<span class="sd">    Args:</span>
+<span class="sd">        objective (str): objective of the model</span>
+<span class="sd">        n_class (int): number of classes in the dataset</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Optuna does not support original lighgbm implemenattion and with a workaroud it</span>
+    <span class="c1"># is possible to get the binarry classfier with focal_loss(any custom loss)</span>
+    <span class="c1"># running but the multiclass requires changes to Optuna code, eg. this post:</span>
+    <span class="c1"># https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm  # noqa</span>
+    <span class="nb">super</span><span class="p">(</span><span class="n">LGBOptunaOptimizer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">n_class</span><span class="p">)</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_params</span>
 </code></pre></div></td></tr></table></div>
-            </details>
+          </details>
 
   
 
@@ -1703,171 +1250,39 @@ <h2 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer" class="doc doc-
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h3 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.__init__" class="doc doc-heading">
-          <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">n_class</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+<h4 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">optimize</span>
 
-<a href="#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.__init__" class="headerlink" title="Permanent link">&para;</a></h3>
 
+<a href="#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">optimize</span><span class="p">(</span><span class="n">dtrain</span><span class="p">,</span> <span class="n">deval</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Fallback/backup Optuna optimizer. Development is focused on Raytune.
-Kepping this code as backup.</p>
-
-
+      <p>Optimize LGBM model on provided datasets.</p>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>objective</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>objective of the model</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>n_class</code></td>
-          <td>
-                <code>int</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>number of classes in the dataset</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>dtrain</b>
+            (<code><span title="lightgbm.Dataset">lgbDataset</span></code>)
+        – <p>training lgb dataset</p>
+      </li>
+      <li class="field-body">
+        <b>deval</b>
+            (<code><span title="lightgbm.Dataset">lgbDataset</span></code>)
+        – <p>evaluation lgb dataset</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/optuna_optimizer.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">16</span>
-<span class="normal">17</span>
-<span class="normal">18</span>
-<span class="normal">19</span>
-<span class="normal">20</span>
-<span class="normal">21</span>
-<span class="normal">22</span>
-<span class="normal">23</span>
-<span class="normal">24</span>
-<span class="normal">25</span>
-<span class="normal">26</span>
-<span class="normal">27</span>
-<span class="normal">28</span>
-<span class="normal">29</span>
-<span class="normal">30</span>
-<span class="normal">31</span>
-<span class="normal">32</span>
-<span class="normal">33</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-    <span class="bp">self</span><span class="p">,</span>
-    <span class="n">objective</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">,</span> <span class="s2">&quot;regression&quot;</span><span class="p">],</span>
-    <span class="n">n_class</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Fallback/backup Optuna optimizer. Development is focused on Raytune.</span>
-<span class="sd">    Kepping this code as backup.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        objective (str): objective of the model</span>
-<span class="sd">        n_class (int): number of classes in the dataset</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Optuna does not support original lighgbm implemenattion and with a workaroud it</span>
-    <span class="c1"># is possible to get the binarry classfier with focal_loss(any custom loss)</span>
-    <span class="c1"># running but the multiclass requires changes to Optuna code, eg. this post:</span>
-    <span class="c1"># https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm  # noqa</span>
-    <span class="nb">super</span><span class="p">(</span><span class="n">LGBOptunaOptimizer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">objective</span><span class="p">,</span> <span class="n">n_class</span><span class="p">)</span>
-    <span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_params</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h3 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">optimize</span><span class="p">(</span><span class="n">dtrain</span><span class="p">,</span> <span class="n">deval</span><span class="p">)</span></code>
-
-<a href="#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Optimize LGBM model on provided datasets.</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>dtrain</code></td>
-          <td>
-                <code><span title="lightgbm.Dataset">Dataset</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>training lgb dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>deval</code></td>
-          <td>
-                <code><span title="lightgbm.Dataset">Dataset</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>evaluation lgb dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/optuna_optimizer.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">35</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/optuna_optimizer.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">35</span>
 <span class="normal">36</span>
 <span class="normal">37</span>
 <span class="normal">38</span>
@@ -1893,7 +1308,7 @@ <h3 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize" class=
 <span class="normal">58</span>
 <span class="normal">59</span>
 <span class="normal">60</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">optimize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dtrain</span><span class="p">:</span> <span class="n">lgbDataset</span><span class="p">,</span> <span class="n">deval</span><span class="p">:</span> <span class="n">lgbDataset</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Optimize LGBM model on provided datasets.</span>
+    <span class="sd">&quot;&quot;&quot;Optimize LGBM model on provided datasets.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        dtrain (lgbDataset): training lgb dataset</span>
@@ -1919,7 +1334,7 @@ <h3 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize" class=
     <span class="c1"># with early_stopping_rounds</span>
     <span class="bp">self</span><span class="o">.</span><span class="n">best</span><span class="p">[</span><span class="s2">&quot;n_estimators&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1000</span>  <span class="c1"># type: ignore</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
@@ -1930,7 +1345,6 @@ <h3 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize" class=
 
   </div>
 
-
 </div>
 
 
@@ -1946,337 +1360,13 @@ <h3 id="churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize" class=
 
 
 
-<a id="churn_pred.training.trainer"></a>
-  <div class="doc doc-contents first">
-
-  
-
-  <div class="doc doc-children">
-
-
-
-
-
-
-
-
-<div class="doc doc-object doc-class">
-
-
-
-<h2 id="churn_pred.training.trainer.Trainer" class="doc doc-heading">
-          <code>Trainer</code>
-
-
-<a href="#churn_pred.training.trainer.Trainer" class="headerlink" title="Permanent link">&para;</a></h2>
-
-
-  <div class="doc doc-contents ">
-          <p class="doc doc-class-bases">
-            Bases: <code><span title="churn_pred.training._base.BaseTrainer">BaseTrainer</span></code></p>
-
-
-            <details class="quote">
-              <summary>Source code in <code>churn_pred/training/trainer.py</code></summary>
-              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 13</span>
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-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">cat_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
-        <span class="n">target_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-        <span class="n">id_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
-        <span class="n">objective</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">],</span>
-        <span class="n">optimizer</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">BaseOptimizer</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">n_class</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">preprocessors</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">PreprocessData</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Objects that governs training and parameter optimization of the lgbm model.</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            cat_cols (list): list of categorical feature column names</span>
-<span class="sd">            target_col (str): column name that represents target</span>
-<span class="sd">            id_cols (list): identification column names</span>
-<span class="sd">            objective (str): type of the task/objective</span>
-<span class="sd">            optimizer (BaseOptimizer): parameter optimizer object</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">Trainer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
-            <span class="n">cat_cols</span><span class="o">=</span><span class="n">cat_cols</span><span class="p">,</span>
-            <span class="n">target_col</span><span class="o">=</span><span class="n">target_col</span><span class="p">,</span>
-            <span class="n">id_cols</span><span class="o">=</span><span class="n">id_cols</span><span class="p">,</span>
-            <span class="n">objective</span><span class="o">=</span><span class="n">objective</span><span class="p">,</span>
-            <span class="n">n_class</span><span class="o">=</span><span class="n">n_class</span><span class="p">,</span>
-            <span class="n">preprocessors</span><span class="o">=</span><span class="n">preprocessors</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">if</span> <span class="n">optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">&quot;optimize&quot;</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span>
-                    <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> optimizer must have </span><span class="si">{}</span><span class="s2"> method&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">&quot;optimize&quot;</span><span class="p">)</span>
-                <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer</span>
-
-    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">df_train</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-        <span class="n">params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-        <span class="n">df_valid</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Train the model with the parameters.</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            df_train (pd.DataFrame): training dataset</span>
-<span class="sd">            params (dict): model paramaters</span>
-<span class="sd">            df_valid (pd.DataFrame): optional validation dataset</span>
-<span class="sd">        Returns:</span>
-<span class="sd">            model (lgb.basic.Booster): trained model</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">prep</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
-                <span class="n">df_train_prep</span> <span class="o">=</span> <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_train</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]))</span>
-                <span class="n">df_valid_prep</span> <span class="o">=</span> <span class="p">(</span>
-                    <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_valid</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]))</span>
-                    <span class="k">if</span> <span class="n">df_valid</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
-                    <span class="k">else</span> <span class="kc">None</span>
-                <span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">]:</span>
-                <span class="n">df_train_prep</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_train</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">df_valid</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">df_valid_prep</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_valid</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span>
-                        <span class="nb">int</span>
-                    <span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">df_train_prep</span> <span class="o">=</span> <span class="n">df_train</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-            <span class="n">df_valid_prep</span> <span class="o">=</span> <span class="n">df_valid</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="k">if</span> <span class="n">df_valid</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span>
-
-        <span class="n">cat_cols</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span>
-        <span class="k">if</span> <span class="n">params</span><span class="p">:</span>
-            <span class="n">config</span> <span class="o">=</span> <span class="n">params</span>
-        <span class="k">elif</span> <span class="p">(</span><span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">):</span>
-            <span class="n">config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">best</span>
-            <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">&quot;best_to_drop&quot;</span><span class="p">):</span>
-                <span class="n">df_train_prep</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
-                    <span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">best_to_drop</span><span class="p">,</span>  <span class="c1"># type: ignore</span>
-                    <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                <span class="p">)</span>
-                <span class="k">if</span> <span class="n">df_valid</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">df_valid_prep</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
-                        <span class="n">columns</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">best_to_drop</span><span class="p">,</span>  <span class="c1"># type: ignore</span>
-                        <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                    <span class="p">)</span>
-                <span class="n">cat_cols</span> <span class="o">=</span> <span class="n">intsec</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">,</span> <span class="n">df_train_prep</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-
-        <span class="k">elif</span> <span class="p">(</span><span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
-            <span class="n">config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_params</span>
-
-        <span class="n">lgb_train</span><span class="p">,</span> <span class="n">lgb_valid</span> <span class="o">=</span> <span class="n">to_lgbdataset</span><span class="p">(</span>
-            <span class="n">train</span><span class="o">=</span><span class="n">df_train_prep</span><span class="p">,</span>
-            <span class="n">cat_cols</span><span class="o">=</span><span class="n">cat_cols</span><span class="p">,</span>
-            <span class="n">target_col</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">,</span>
-            <span class="n">id_cols</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">,</span>
-            <span class="n">valid</span><span class="o">=</span><span class="n">df_valid_prep</span><span class="p">,</span>
-        <span class="p">)</span>
+<h2 id="churn_pred.training.trainer" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">trainer</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">lgb_train_function</span><span class="p">(</span>
-            <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
-            <span class="n">lgbtrain</span><span class="o">=</span><span class="n">lgb_train</span><span class="p">,</span>
-            <span class="n">lgbeval</span><span class="o">=</span><span class="n">lgb_valid</span><span class="p">,</span>
-            <span class="n">feval</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">feval</span><span class="p">,</span>
-        <span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
-        <span class="bp">self</span><span class="p">,</span>
-        <span class="n">df_train</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-        <span class="n">df_valid</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-        <span class="n">df_test</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Train the model and optimize the parameters.</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            df_train (pd.DataFrame): training dataset</span>
-<span class="sd">            df_valid (pd.DataFrame): validation dataset</span>
-<span class="sd">            df_test (pd.DataFrame): testing dataset</span>
-<span class="sd">        Returns:</span>
-<span class="sd">            model (lgb.basic.Booster): trained model</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
-            <span class="c1"># this should be</span>
-            <span class="c1"># df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col]))</span>
-            <span class="k">for</span> <span class="n">prep</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
-                <span class="n">df_train_prep</span> <span class="o">=</span> <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_train</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]))</span>
-                <span class="n">df_valid_prep</span> <span class="o">=</span> <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_valid</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]))</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">]:</span>
-                <span class="n">df_train_prep</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_train</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-                <span class="n">df_valid_prep</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_valid</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">df_train_prep</span> <span class="o">=</span> <span class="n">df_train</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-            <span class="n">df_valid_prep</span> <span class="o">=</span> <span class="n">df_valid</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-        <span class="n">lgb_train</span><span class="p">,</span> <span class="n">lgb_valid</span> <span class="o">=</span> <span class="n">to_lgbdataset</span><span class="p">(</span>
-            <span class="n">train</span><span class="o">=</span><span class="n">df_train_prep</span><span class="p">,</span>
-            <span class="n">cat_cols</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">,</span>
-            <span class="n">target_col</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">,</span>
-            <span class="n">id_cols</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">,</span>
-            <span class="n">valid</span><span class="o">=</span><span class="n">df_valid_prep</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">dtrain</span><span class="o">=</span><span class="n">lgb_train</span><span class="p">,</span> <span class="n">deval</span><span class="o">=</span><span class="n">lgb_valid</span><span class="p">)</span>
+<a href="#churn_pred.training.trainer" class="headerlink" title="Permanent link">&para;</a></h2>
 
-        <span class="n">df_train_valid</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_valid</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">df_train_valid</span> <span class="o">=</span> <span class="n">df_train_valid</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">df_train</span><span class="o">=</span><span class="n">df_train_valid</span><span class="p">)</span>
-        <span class="n">metrics_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span><span class="n">df_test</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">metrics_dict</span>
-</code></pre></div></td></tr></table></div>
-            </details>
+  <div class="doc doc-contents first">
 
   
 
@@ -2289,107 +1379,62 @@ <h2 id="churn_pred.training.trainer.Trainer" class="doc doc-heading">
 
 
 
+<div class="doc doc-object doc-class">
 
 
-<div class="doc doc-object doc-function">
 
+<h3 id="churn_pred.training.trainer.Trainer" class="doc doc-heading">
+        <span class="doc doc-object-name doc-class-name">Trainer</span>
 
 
-<h3 id="churn_pred.training.trainer.Trainer.__init__" class="doc doc-heading">
-          <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">cat_cols</span><span class="p">,</span> <span class="n">target_col</span><span class="p">,</span> <span class="n">id_cols</span><span class="p">,</span> <span class="n">objective</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_class</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">preprocessors</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+<a href="#churn_pred.training.trainer.Trainer" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">Trainer</span><span class="p">(</span>
+    <span class="n">cat_cols</span><span class="p">,</span>
+    <span class="n">target_col</span><span class="p">,</span>
+    <span class="n">id_cols</span><span class="p">,</span>
+    <span class="n">objective</span><span class="p">,</span>
+    <span class="n">optimizer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">n_class</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">preprocessors</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span>
+</code></pre></div>
 
-<a href="#churn_pred.training.trainer.Trainer.__init__" class="headerlink" title="Permanent link">&para;</a></h3>
+  <div class="doc doc-contents ">
+      <p class="doc doc-class-bases">
+        Bases: <code><span title="churn_pred.training._base.BaseTrainer">BaseTrainer</span></code></p>
 
 
-  <div class="doc doc-contents ">
   
       <p>Objects that governs training and parameter optimization of the lgbm model.</p>
 
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>cat_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of categorical feature column names</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>target_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>column name that represents target</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>id_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>identification column names</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>objective</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>type of the task/objective</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>optimizer</code></td>
-          <td>
-                <code><span title="churn_pred.training._base.BaseOptimizer">BaseOptimizer</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>parameter optimizer object</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>cat_cols</b>
+            (<code>list</code>)
+        – <p>list of categorical feature column names</p>
+      </li>
+      <li class="field-body">
+        <b>target_col</b>
+            (<code>str</code>)
+        – <p>column name that represents target</p>
+      </li>
+      <li class="field-body">
+        <b>id_cols</b>
+            (<code>list</code>)
+        – <p>identification column names</p>
+      </li>
+      <li class="field-body">
+        <b>objective</b>
+            (<code>str</code>)
+        – <p>type of the task/objective</p>
+      </li>
+      <li class="field-body">
+        <b>optimizer</b>
+            (<code><span title="churn_pred.training._base.BaseOptimizer">BaseOptimizer</span></code>)
+        – <p>parameter optimizer object</p>
+      </li>
+  </ul>
 
           <details class="quote">
             <summary>Source code in <code>churn_pred/training/trainer.py</code></summary>
@@ -2435,7 +1480,7 @@ <h3 id="churn_pred.training.trainer.Trainer.__init__" class="doc doc-heading">
     <span class="n">n_class</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">preprocessors</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">PreprocessData</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Objects that governs training and parameter optimization of the lgbm model.</span>
+    <span class="sd">&quot;&quot;&quot;Objects that governs training and parameter optimization of the lgbm model.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        cat_cols (list): list of categorical feature column names</span>
@@ -2460,256 +1505,65 @@ <h3 id="churn_pred.training.trainer.Trainer.__init__" class="doc doc-heading">
     <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optimizer</span>
 </code></pre></div></td></tr></table></div>
           </details>
-  </div>
 
-</div>
+  
+
+  <div class="doc doc-children">
+
+
+
+
+
+
+
 
 
 <div class="doc doc-object doc-function">
 
 
 
-<h3 id="churn_pred.training.trainer.Trainer.fit" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">fit</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_valid</span><span class="p">,</span> <span class="n">df_test</span><span class="p">)</span></code>
+<h4 id="churn_pred.training.trainer.Trainer.train" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">train</span>
 
-<a href="#churn_pred.training.trainer.Trainer.fit" class="headerlink" title="Permanent link">&para;</a></h3>
 
+<a href="#churn_pred.training.trainer.Trainer.train" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">train</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">df_valid</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Train the model and optimize the parameters.</p>
-
+      <p>Train the model with the parameters.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df_train</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>training dataset</p>
+      </li>
+      <li class="field-body">
+        <b>params</b>
+            (<code>dict</code>)
+        – <p>model paramaters</p>
+      </li>
+      <li class="field-body">
+        <b>df_valid</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>optional validation dataset</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df_train</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>training dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>df_valid</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>validation dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>df_test</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>testing dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>model</b>(            <code>lgb.<span title="lgb.basic">basic</span>.<span title="lgb.basic.Booster">Booster</span></code>
+)        – <p>trained model</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/trainer.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">118</span>
-<span class="normal">119</span>
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-<span class="normal">133</span>
-<span class="normal">134</span>
-<span class="normal">135</span>
-<span class="normal">136</span>
-<span class="normal">137</span>
-<span class="normal">138</span>
-<span class="normal">139</span>
-<span class="normal">140</span>
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-<span class="normal">142</span>
-<span class="normal">143</span>
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-<span class="normal">146</span>
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-<span class="normal">148</span>
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-<span class="normal">151</span>
-<span class="normal">152</span>
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-<span class="normal">155</span>
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-<span class="normal">159</span>
-<span class="normal">160</span>
-<span class="normal">161</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
-    <span class="bp">self</span><span class="p">,</span>
-    <span class="n">df_train</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="n">df_valid</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="n">df_test</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Train the model and optimize the parameters.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        df_train (pd.DataFrame): training dataset</span>
-<span class="sd">        df_valid (pd.DataFrame): validation dataset</span>
-<span class="sd">        df_test (pd.DataFrame): testing dataset</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        model (lgb.basic.Booster): trained model</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
-        <span class="c1"># this should be</span>
-        <span class="c1"># df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col]))</span>
-        <span class="k">for</span> <span class="n">prep</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
-            <span class="n">df_train_prep</span> <span class="o">=</span> <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_train</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]))</span>
-            <span class="n">df_valid_prep</span> <span class="o">=</span> <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_valid</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]))</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">]:</span>
-            <span class="n">df_train_prep</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_train</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-            <span class="n">df_valid_prep</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_valid</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">df_train_prep</span> <span class="o">=</span> <span class="n">df_train</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">df_valid_prep</span> <span class="o">=</span> <span class="n">df_valid</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-
-    <span class="n">lgb_train</span><span class="p">,</span> <span class="n">lgb_valid</span> <span class="o">=</span> <span class="n">to_lgbdataset</span><span class="p">(</span>
-        <span class="n">train</span><span class="o">=</span><span class="n">df_train_prep</span><span class="p">,</span>
-        <span class="n">cat_cols</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">,</span>
-        <span class="n">target_col</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">,</span>
-        <span class="n">id_cols</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">,</span>
-        <span class="n">valid</span><span class="o">=</span><span class="n">df_valid_prep</span><span class="p">,</span>
-    <span class="p">)</span>
-    <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">dtrain</span><span class="o">=</span><span class="n">lgb_train</span><span class="p">,</span> <span class="n">deval</span><span class="o">=</span><span class="n">lgb_valid</span><span class="p">)</span>
-
-    <span class="n">df_train_valid</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_valid</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">df_train_valid</span> <span class="o">=</span> <span class="n">df_train_valid</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">df_train</span><span class="o">=</span><span class="n">df_train_valid</span><span class="p">)</span>
-    <span class="n">metrics_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span><span class="n">df_test</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">metrics_dict</span>
-</code></pre></div></td></tr></table></div>
-          </details>
-  </div>
-
-</div>
-
-
-<div class="doc doc-object doc-function">
-
-
-
-<h3 id="churn_pred.training.trainer.Trainer.train" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">train</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">df_valid</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
-
-<a href="#churn_pred.training.trainer.Trainer.train" class="headerlink" title="Permanent link">&para;</a></h3>
-
-
-  <div class="doc doc-contents ">
-  
-      <p>Train the model with the parameters.</p>
-
-
-
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>df_train</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>training dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>params</code></td>
-          <td>
-                <code>dict</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>model paramaters</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>df_valid</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>optional validation dataset</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/trainer.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 48</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/trainer.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 48</span>
 <span class="normal"> 49</span>
 <span class="normal"> 50</span>
 <span class="normal"> 51</span>
@@ -2783,7 +1637,7 @@ <h3 id="churn_pred.training.trainer.Trainer.train" class="doc doc-heading">
     <span class="n">params</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Dict</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">df_valid</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Train the model with the parameters.</span>
+    <span class="sd">&quot;&quot;&quot;Train the model with the parameters.</span>
 
 <span class="sd">    Args:</span>
 <span class="sd">        df_train (pd.DataFrame): training dataset</span>
@@ -2847,17 +1701,154 @@ <h3 id="churn_pred.training.trainer.Trainer.train" class="doc doc-heading">
         <span class="n">feval</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">feval</span><span class="p">,</span>
     <span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
+<div class="doc doc-object doc-function">
+
+
+
+<h4 id="churn_pred.training.trainer.Trainer.fit" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">fit</span>
+
+
+<a href="#churn_pred.training.trainer.Trainer.fit" class="headerlink" title="Permanent link">&para;</a></h4>
+<div class="highlight"><pre><span></span><code><span class="n">fit</span><span class="p">(</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_valid</span><span class="p">,</span> <span class="n">df_test</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Train the model and optimize the parameters.</p>
+
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>df_train</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>training dataset</p>
+      </li>
+      <li class="field-body">
+        <b>df_valid</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>validation dataset</p>
+      </li>
+      <li class="field-body">
+        <b>df_test</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>testing dataset</p>
+      </li>
+  </ul>
+
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>model</b>(            <code>lgb.<span title="lgb.basic">basic</span>.<span title="lgb.basic.Booster">Booster</span></code>
+)        – <p>trained model</p>
+      </li>
+  </ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/trainer.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">118</span>
+<span class="normal">119</span>
+<span class="normal">120</span>
+<span class="normal">121</span>
+<span class="normal">122</span>
+<span class="normal">123</span>
+<span class="normal">124</span>
+<span class="normal">125</span>
+<span class="normal">126</span>
+<span class="normal">127</span>
+<span class="normal">128</span>
+<span class="normal">129</span>
+<span class="normal">130</span>
+<span class="normal">131</span>
+<span class="normal">132</span>
+<span class="normal">133</span>
+<span class="normal">134</span>
+<span class="normal">135</span>
+<span class="normal">136</span>
+<span class="normal">137</span>
+<span class="normal">138</span>
+<span class="normal">139</span>
+<span class="normal">140</span>
+<span class="normal">141</span>
+<span class="normal">142</span>
+<span class="normal">143</span>
+<span class="normal">144</span>
+<span class="normal">145</span>
+<span class="normal">146</span>
+<span class="normal">147</span>
+<span class="normal">148</span>
+<span class="normal">149</span>
+<span class="normal">150</span>
+<span class="normal">151</span>
+<span class="normal">152</span>
+<span class="normal">153</span>
+<span class="normal">154</span>
+<span class="normal">155</span>
+<span class="normal">156</span>
+<span class="normal">157</span>
+<span class="normal">158</span>
+<span class="normal">159</span>
+<span class="normal">160</span>
+<span class="normal">161</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span>
+    <span class="bp">self</span><span class="p">,</span>
+    <span class="n">df_train</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="n">df_valid</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="n">df_test</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Train the model and optimize the parameters.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        df_train (pd.DataFrame): training dataset</span>
+<span class="sd">        df_valid (pd.DataFrame): validation dataset</span>
+<span class="sd">        df_test (pd.DataFrame): testing dataset</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        model (lgb.basic.Booster): trained model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
+        <span class="c1"># this should be</span>
+        <span class="c1"># df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col]))</span>
+        <span class="k">for</span> <span class="n">prep</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
+            <span class="n">df_train_prep</span> <span class="o">=</span> <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_train</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]))</span>
+            <span class="n">df_valid_prep</span> <span class="o">=</span> <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_valid</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">objective</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">]:</span>
+            <span class="n">df_train_prep</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_train</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+            <span class="n">df_valid_prep</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_valid</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">NotImplementedError</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">df_train_prep</span> <span class="o">=</span> <span class="n">df_train</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">df_valid_prep</span> <span class="o">=</span> <span class="n">df_valid</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 
+    <span class="n">lgb_train</span><span class="p">,</span> <span class="n">lgb_valid</span> <span class="o">=</span> <span class="n">to_lgbdataset</span><span class="p">(</span>
+        <span class="n">train</span><span class="o">=</span><span class="n">df_train_prep</span><span class="p">,</span>
+        <span class="n">cat_cols</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">,</span>
+        <span class="n">target_col</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_col</span><span class="p">,</span>
+        <span class="n">id_cols</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_cols</span><span class="p">,</span>
+        <span class="n">valid</span><span class="o">=</span><span class="n">df_valid_prep</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">dtrain</span><span class="o">=</span><span class="n">lgb_train</span><span class="p">,</span> <span class="n">deval</span><span class="o">=</span><span class="n">lgb_valid</span><span class="p">)</span>
 
+    <span class="n">df_train_valid</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_valid</span><span class="p">],</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">df_train_valid</span> <span class="o">=</span> <span class="n">df_train_valid</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">df_train</span><span class="o">=</span><span class="n">df_train_valid</span><span class="p">)</span>
+    <span class="n">metrics_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span><span class="n">df_test</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">metrics_dict</span>
+</code></pre></div></td></tr></table></div>
+      </details>
   </div>
 
+</div>
+
+
+
   </div>
 
+  </div>
 
 </div>
 
@@ -2874,7 +1865,12 @@ <h3 id="churn_pred.training.trainer.Trainer.train" class="doc doc-heading">
 
 
 
-<a id="churn_pred.training.utils"></a>
+<h2 id="churn_pred.training.utils" class="doc doc-heading">
+          <span class="doc doc-object-name doc-module-name">utils</span>
+
+
+<a href="#churn_pred.training.utils" class="headerlink" title="Permanent link">&para;</a></h2>
+
   <div class="doc doc-contents first">
 
   
@@ -2889,336 +1885,363 @@ <h3 id="churn_pred.training.trainer.Trainer.train" class="doc doc-heading">
 
 
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.utils.get_feature_importance" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">get_feature_importance</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.utils.flatten_dict" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">flatten_dict</span>
 
-<a href="#churn_pred.training.utils.get_feature_importance" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.utils.flatten_dict" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">flatten_dict</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="n">parent_key</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Extract model feature importances and return sorted dataframe.</p>
+      <p>fastest according to <a href="https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/">https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/</a></p>
 
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">11</span>
+<span class="normal">12</span>
+<span class="normal">13</span>
+<span class="normal">14</span>
+<span class="normal">15</span>
+<span class="normal">16</span>
+<span class="normal">17</span>
+<span class="normal">18</span>
+<span class="normal">19</span>
+<span class="normal">20</span>
+<span class="normal">21</span>
+<span class="normal">22</span>
+<span class="normal">23</span>
+<span class="normal">24</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">flatten_dict</span><span class="p">(</span>
+    <span class="n">d</span><span class="p">:</span> <span class="n">MutableMapping</span><span class="p">,</span> <span class="n">parent_key</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">sep</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;_&quot;</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">MutableMapping</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">items</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">new_key</span> <span class="o">=</span> <span class="n">parent_key</span> <span class="o">+</span> <span class="n">sep</span> <span class="o">+</span> <span class="n">k</span> <span class="k">if</span> <span class="n">parent_key</span> <span class="k">else</span> <span class="n">k</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">MutableMapping</span><span class="p">):</span>
+            <span class="n">items</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">flatten_dict</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">new_key</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="n">sep</span><span class="p">)</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">items</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">new_key</span><span class="p">,</span> <span class="n">v</span><span class="p">))</span>
+    <span class="k">return</span> <span class="nb">dict</span><span class="p">(</span><span class="n">items</span><span class="p">)</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
 
+</div>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>model</code></td>
-          <td>
-                <code><span title="lightgbm.basic.Booster">Booster</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>LightGBM model</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-
-
-
-  <p><span class="doc-section-title">Returns:</span></p>
-  <table>
-    <thead>
-      <tr>
-<th>Name</th>        <th>Type</th>
-        <th>Description</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-<td><code>feature_imp</code></td>          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>sorted dataframe with features and their
-importances</p>
-            </div>
-          </td>
-        </tr>
-    </tbody>
-  </table>
+<div class="doc doc-object doc-function">
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">143</span>
-<span class="normal">144</span>
-<span class="normal">145</span>
-<span class="normal">146</span>
-<span class="normal">147</span>
-<span class="normal">148</span>
-<span class="normal">149</span>
-<span class="normal">150</span>
-<span class="normal">151</span>
-<span class="normal">152</span>
-<span class="normal">153</span>
-<span class="normal">154</span>
-<span class="normal">155</span>
-<span class="normal">156</span>
-<span class="normal">157</span>
-<span class="normal">158</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_feature_importance</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">lgb</span><span class="o">.</span><span class="n">basic</span><span class="o">.</span><span class="n">Booster</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Extract model feature importances and return sorted dataframe.</span>
 
-<span class="sd">    Args:</span>
-<span class="sd">        model (lgb.basic.Booster): LightGBM model</span>
+
+<h3 id="churn_pred.training.utils.get_or_create_experiment" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">get_or_create_experiment</span>
+
+
+<a href="#churn_pred.training.utils.get_or_create_experiment" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">get_or_create_experiment</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">)</span>
+</code></pre></div>
+
+  <div class="doc doc-contents ">
+  
+      <p>Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist.</p>
+<p>This function checks if an experiment with the given name exists within MLflow.
+If it does, the function returns its ID. If not, it creates a new experiment
+with the provided name and returns its ID.</p>
+      <ul>
+<li>experiment_name (str): Name of the MLflow experiment.</li>
+</ul>
+      <ul>
+<li>str: ID of the existing or newly created MLflow experiment.</li>
+</ul>
+
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">27</span>
+<span class="normal">28</span>
+<span class="normal">29</span>
+<span class="normal">30</span>
+<span class="normal">31</span>
+<span class="normal">32</span>
+<span class="normal">33</span>
+<span class="normal">34</span>
+<span class="normal">35</span>
+<span class="normal">36</span>
+<span class="normal">37</span>
+<span class="normal">38</span>
+<span class="normal">39</span>
+<span class="normal">40</span>
+<span class="normal">41</span>
+<span class="normal">42</span>
+<span class="normal">43</span>
+<span class="normal">44</span>
+<span class="normal">45</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_or_create_experiment</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Retrieve the ID of an existing MLflow experiment or create a new one if it doesn&#39;t exist.</span>
+
+<span class="sd">    This function checks if an experiment with the given name exists within MLflow.</span>
+<span class="sd">    If it does, the function returns its ID. If not, it creates a new experiment</span>
+<span class="sd">    with the provided name and returns its ID.</span>
+
+<span class="sd">    Parameters:</span>
+<span class="sd">    - experiment_name (str): Name of the MLflow experiment.</span>
 
 <span class="sd">    Returns:</span>
-<span class="sd">        feature_imp (pd.DataFrame): sorted dataframe with features and their</span>
-<span class="sd">            importances</span>
+<span class="sd">    - str: ID of the existing or newly created MLflow experiment.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">feature_imp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-        <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">feature_importance</span><span class="p">(),</span> <span class="s2">&quot;feature&quot;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">feature_name</span><span class="p">()}</span>
-    <span class="p">)</span>
-    <span class="n">feature_imp</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;value&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">feature_imp</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">feature_imp</span>
+
+    <span class="k">if</span> <span class="n">experiment</span> <span class="o">:=</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">get_experiment_by_name</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">experiment</span><span class="o">.</span><span class="n">experiment_id</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">create_experiment</span><span class="p">(</span><span class="n">experiment_name</span><span class="p">)</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.utils.predict_cls_lgbm_from_raw" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">predict_cls_lgbm_from_raw</span><span class="p">(</span><span class="n">preds_raw</span><span class="p">,</span> <span class="n">task</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.utils.to_lgbdataset" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">to_lgbdataset</span>
 
-<a href="#churn_pred.training.utils.predict_cls_lgbm_from_raw" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.utils.to_lgbdataset" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">to_lgbdataset</span><span class="p">(</span>
+    <span class="n">train</span><span class="p">,</span> <span class="n">cat_cols</span><span class="p">,</span> <span class="n">target_col</span><span class="p">,</span> <span class="n">id_cols</span><span class="o">=</span><span class="p">[],</span> <span class="n">valid</span><span class="o">=</span><span class="kc">None</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Helper function to convert raw margin predictions through a
-sigmoid to represent a probability.</p>
-
+      <p>Transform pandas dataframe to lgbm dataset, or datasets(eval).</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>train</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>training dataset</p>
+      </li>
+      <li class="field-body">
+        <b>cat_cols</b>
+            (<code>list</code>)
+        – <p>list of categorical columns</p>
+      </li>
+      <li class="field-body">
+        <b>target_col</b>
+            (<code>str</code>)
+        – <p>target column in the dataset</p>
+      </li>
+      <li class="field-body">
+        <b>id_cols</b>
+            (<code>list</code>)
+        – <p>list of identifier columns</p>
+      </li>
+      <li class="field-body">
+        <b>valid</b>
+            (<code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>)
+        – <p>validation dataset</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>preds_raw</code></td>
-          <td>
-                <code>ndarray</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>predictions</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>task</code></td>
-          <td>
-                <code><span title="typing.Literal">Literal</span>[&#39;binary&#39;, &#39;multiclass&#39;]</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>task type</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>lgb_train</b>(            <code><span title="lightgbm.Dataset">lgbDataset</span></code>
+)        – <p>lgbm training dataset</p>
+      </li>
+      <li class="field-body">
+<b>lgb_valid</b>(            <code><span title="lightgbm.Dataset">lgbDataset</span></code>
+)        – <p>lgbm valid dataset</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">119</span>
-<span class="normal">120</span>
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-<span class="normal">140</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">predict_cls_lgbm_from_raw</span><span class="p">(</span>
-    <span class="n">preds_raw</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">task</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">]</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Helper function to convert raw margin predictions through a</span>
-<span class="sd">    sigmoid to represent a probability.</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">48</span>
+<span class="normal">49</span>
+<span class="normal">50</span>
+<span class="normal">51</span>
+<span class="normal">52</span>
+<span class="normal">53</span>
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+<span class="normal">86</span>
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+<span class="normal">88</span>
+<span class="normal">89</span>
+<span class="normal">90</span>
+<span class="normal">91</span>
+<span class="normal">92</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">to_lgbdataset</span><span class="p">(</span>
+    <span class="n">train</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
+    <span class="n">cat_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
+    <span class="n">target_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">id_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[],</span>
+    <span class="n">valid</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">lgbDataset</span><span class="p">,</span> <span class="n">Optional</span><span class="p">[</span><span class="n">lgbDataset</span><span class="p">]]:</span>
+    <span class="sd">&quot;&quot;&quot;Transform pandas dataframe to lgbm dataset, or datasets(eval).</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        preds_raw (ndarray):</span>
-<span class="sd">            predictions</span>
-<span class="sd">        task (Literal[&quot;binary&quot;, &quot;multiclass&quot;]):</span>
-<span class="sd">            task type</span>
+<span class="sd">        train (pd.DataFrame):</span>
+<span class="sd">            training dataset</span>
+<span class="sd">        cat_cols (list):</span>
+<span class="sd">            list of categorical columns</span>
+<span class="sd">        target_col (str):</span>
+<span class="sd">            target column in the dataset</span>
+<span class="sd">        id_cols (list):</span>
+<span class="sd">            list of identifier columns</span>
+<span class="sd">        valid (pd.DataFrame):</span>
+<span class="sd">            validation dataset</span>
 <span class="sd">    Returns:</span>
-<span class="sd">        (y_true, preds):</span>
-<span class="sd">            tuple containg labels and predictions for further evaluation</span>
+<span class="sd">        lgb_train (lgbDataset):</span>
+<span class="sd">            lgbm training dataset</span>
+<span class="sd">        lgb_valid (lgbDataset):</span>
+<span class="sd">            lgbm valid dataset</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">predicted_probs</span> <span class="o">=</span> <span class="n">predict_proba_lgbm_from_raw</span><span class="p">(</span><span class="n">preds_raw</span><span class="o">=</span><span class="n">preds_raw</span><span class="p">,</span> <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">task</span> <span class="o">==</span> <span class="s2">&quot;binary&quot;</span><span class="p">:</span>
-        <span class="n">pred_cls</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">p</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">predicted_probs</span><span class="p">])</span>
-    <span class="k">elif</span> <span class="n">task</span> <span class="o">==</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">:</span>
-        <span class="n">pred_cls</span> <span class="o">=</span> <span class="n">predicted_probs</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
-    <span class="k">return</span> <span class="n">pred_cls</span>
+    <span class="n">lgb_train</span> <span class="o">=</span> <span class="n">lgbDataset</span><span class="p">(</span>
+        <span class="n">train</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">id_cols</span><span class="p">),</span>
+        <span class="n">train</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span>
+        <span class="n">categorical_feature</span><span class="o">=</span><span class="n">cat_cols</span><span class="p">,</span>
+        <span class="n">free_raw_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">valid</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">lgb_valid</span> <span class="o">=</span> <span class="n">lgbDataset</span><span class="p">(</span>
+            <span class="n">valid</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">id_cols</span><span class="p">),</span>
+            <span class="n">valid</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span>
+            <span class="n">reference</span><span class="o">=</span><span class="n">lgb_train</span><span class="p">,</span>
+            <span class="n">free_raw_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">lgb_valid</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">return</span> <span class="n">lgb_train</span><span class="p">,</span> <span class="n">lgb_valid</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.utils.predict_proba_lgbm_from_raw" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">predict_proba_lgbm_from_raw</span><span class="p">(</span><span class="n">preds_raw</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="n">binary2d</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.utils.predict_proba_lgbm_from_raw" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">predict_proba_lgbm_from_raw</span>
 
-<a href="#churn_pred.training.utils.predict_proba_lgbm_from_raw" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.utils.predict_proba_lgbm_from_raw" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">predict_proba_lgbm_from_raw</span><span class="p">(</span>
+    <span class="n">preds_raw</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="n">binary2d</span><span class="o">=</span><span class="kc">False</span>
+<span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
       <p>Apply softmax to array of arrays that is an output of lightgbm.predct().
 This replaces predict_proba().</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>preds_raw</b>
+            (<code>ndarray</code>)
+        – <p>1D numpy array of arrays</p>
+      </li>
+      <li class="field-body">
+        <b>task</b>
+            (<code>str</code>)
+        – <p>type of task/objective</p>
+      </li>
+      <li class="field-body">
+        <b>binary2d</b>
+            (<code>boolean</code>)
+        – <p>wether the output of binary classification should be 1 or 2d vector</p>
+      </li>
+  </ul>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>predicted_probs</b>(            <code>ndarray</code>
+)        – <p>array with predicted probabilities</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>preds_raw</code></td>
-          <td>
-                <code>ndarray</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>1D numpy array of arrays</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>task</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>type of task/objective</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>binary2d</code></td>
-          <td>
-                <code>boolean</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>wether the output of binary classification should be 1 or 2d vector</p>
-            </div>
-          </td>
-          <td>
-                <code>False</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
-
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 90</span>
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+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">129</span>
+<span class="normal">130</span>
+<span class="normal">131</span>
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+<span class="normal">155</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">predict_proba_lgbm_from_raw</span><span class="p">(</span>
     <span class="n">preds_raw</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
     <span class="n">task</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">],</span>
     <span class="n">binary2d</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Apply softmax to array of arrays that is an output of lightgbm.predct().</span>
+    <span class="sd">&quot;&quot;&quot;Apply softmax to array of arrays that is an output of lightgbm.predct().</span>
 <span class="sd">    This replaces predict_proba().</span>
 
 <span class="sd">    Args:</span>
@@ -3241,263 +2264,228 @@ <h2 id="churn_pred.training.utils.predict_proba_lgbm_from_raw" class="doc doc-he
         <span class="n">predicted_probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">apply_along_axis</span><span class="p">(</span><span class="n">_softmax</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">preds_raw</span><span class="p">)))</span>
     <span class="k">return</span> <span class="n">predicted_probs</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
-
 <div class="doc doc-object doc-function">
 
 
 
-<h2 id="churn_pred.training.utils.to_lgbdataset" class="doc doc-heading">
-          <code class="highlight language-python"><span class="n">to_lgbdataset</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">cat_cols</span><span class="p">,</span> <span class="n">target_col</span><span class="p">,</span> <span class="n">id_cols</span><span class="o">=</span><span class="p">[],</span> <span class="n">valid</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
+<h3 id="churn_pred.training.utils.predict_cls_lgbm_from_raw" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">predict_cls_lgbm_from_raw</span>
 
-<a href="#churn_pred.training.utils.to_lgbdataset" class="headerlink" title="Permanent link">&para;</a></h2>
 
+<a href="#churn_pred.training.utils.predict_cls_lgbm_from_raw" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">predict_cls_lgbm_from_raw</span><span class="p">(</span><span class="n">preds_raw</span><span class="p">,</span> <span class="n">task</span><span class="p">)</span>
+</code></pre></div>
 
   <div class="doc doc-contents ">
   
-      <p>Transform pandas dataframe to lgbm dataset, or datasets(eval).</p>
-
+      <p>Helper function to convert raw margin predictions through a
+sigmoid to represent a probability.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>preds_raw</b>
+            (<code>ndarray</code>)
+        – <p>predictions</p>
+      </li>
+      <li class="field-body">
+        <b>task</b>
+            (<code><span title="typing.Literal">Literal</span>[&#39;binary&#39;, &#39;multiclass&#39;]</code>)
+        – <p>task type</p>
+      </li>
+  </ul>
 
-  <p><span class="doc-section-title">Parameters:</span></p>
-  <table>
-    <thead>
-      <tr>
-        <th>Name</th>
-        <th>Type</th>
-        <th>Description</th>
-        <th>Default</th>
-      </tr>
-    </thead>
-    <tbody>
-        <tr class="doc-section-item">
-          <td><code>train</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>training dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>cat_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of categorical columns</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>target_col</code></td>
-          <td>
-                <code>str</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>target column in the dataset</p>
-            </div>
-          </td>
-          <td>
-              <em>required</em>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>id_cols</code></td>
-          <td>
-                <code>list</code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>list of identifier columns</p>
-            </div>
-          </td>
-          <td>
-                <code>[]</code>
-          </td>
-        </tr>
-        <tr class="doc-section-item">
-          <td><code>valid</code></td>
-          <td>
-                <code><span title="pandas.DataFrame">DataFrame</span></code>
-          </td>
-          <td>
-            <div class="doc-md-description">
-              <p>validation dataset</p>
-            </div>
-          </td>
-          <td>
-                <code>None</code>
-          </td>
-        </tr>
-    </tbody>
-  </table>
-      
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+            <code>y_true, preds</code>
+        – <p>tuple containg labels and predictions for further evaluation</p>
+      </li>
+  </ul>
 
-          <details class="quote">
-            <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
-            <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"> 9</span>
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-<span class="normal">53</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">to_lgbdataset</span><span class="p">(</span>
-    <span class="n">train</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-    <span class="n">cat_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
-    <span class="n">target_col</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
-    <span class="n">id_cols</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[],</span>
-    <span class="n">valid</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
-<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">lgbDataset</span><span class="p">,</span> <span class="n">Optional</span><span class="p">[</span><span class="n">lgbDataset</span><span class="p">]]:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Transform pandas dataframe to lgbm dataset, or datasets(eval).</span>
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">158</span>
+<span class="normal">159</span>
+<span class="normal">160</span>
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+<span class="normal">175</span>
+<span class="normal">176</span>
+<span class="normal">177</span>
+<span class="normal">178</span>
+<span class="normal">179</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">predict_cls_lgbm_from_raw</span><span class="p">(</span>
+    <span class="n">preds_raw</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">task</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">]</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Helper function to convert raw margin predictions through a</span>
+<span class="sd">    sigmoid to represent a probability.</span>
 
 <span class="sd">    Args:</span>
-<span class="sd">        train (pd.DataFrame):</span>
-<span class="sd">            training dataset</span>
-<span class="sd">        cat_cols (list):</span>
-<span class="sd">            list of categorical columns</span>
-<span class="sd">        target_col (str):</span>
-<span class="sd">            target column in the dataset</span>
-<span class="sd">        id_cols (list):</span>
-<span class="sd">            list of identifier columns</span>
-<span class="sd">        valid (pd.DataFrame):</span>
-<span class="sd">            validation dataset</span>
+<span class="sd">        preds_raw (ndarray):</span>
+<span class="sd">            predictions</span>
+<span class="sd">        task (Literal[&quot;binary&quot;, &quot;multiclass&quot;]):</span>
+<span class="sd">            task type</span>
 <span class="sd">    Returns:</span>
-<span class="sd">        lgb_train (lgbDataset):</span>
-<span class="sd">            lgbm training dataset</span>
-<span class="sd">        lgb_valid (lgbDataset):</span>
-<span class="sd">            lgbm valid dataset</span>
+<span class="sd">        (y_true, preds):</span>
+<span class="sd">            tuple containg labels and predictions for further evaluation</span>
 <span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">predicted_probs</span> <span class="o">=</span> <span class="n">predict_proba_lgbm_from_raw</span><span class="p">(</span><span class="n">preds_raw</span><span class="o">=</span><span class="n">preds_raw</span><span class="p">,</span> <span class="n">task</span><span class="o">=</span><span class="n">task</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">task</span> <span class="o">==</span> <span class="s2">&quot;binary&quot;</span><span class="p">:</span>
+        <span class="n">pred_cls</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">p</span> <span class="o">&gt;</span> <span class="mf">0.5</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">predicted_probs</span><span class="p">])</span>
+    <span class="k">elif</span> <span class="n">task</span> <span class="o">==</span> <span class="s2">&quot;multiclass&quot;</span><span class="p">:</span>
+        <span class="n">pred_cls</span> <span class="o">=</span> <span class="n">predicted_probs</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 
-    <span class="n">lgb_train</span> <span class="o">=</span> <span class="n">lgbDataset</span><span class="p">(</span>
-        <span class="n">train</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">id_cols</span><span class="p">),</span>
-        <span class="n">train</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span>
-        <span class="n">categorical_feature</span><span class="o">=</span><span class="n">cat_cols</span><span class="p">,</span>
-        <span class="n">free_raw_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">valid</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">lgb_valid</span> <span class="o">=</span> <span class="n">lgbDataset</span><span class="p">(</span>
-            <span class="n">valid</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">id_cols</span><span class="p">),</span>
-            <span class="n">valid</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span>
-            <span class="n">reference</span><span class="o">=</span><span class="n">lgb_train</span><span class="p">,</span>
-            <span class="n">free_raw_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-        <span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">lgb_valid</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">return</span> <span class="n">lgb_train</span><span class="p">,</span> <span class="n">lgb_valid</span>
+    <span class="k">return</span> <span class="n">pred_cls</span>
 </code></pre></div></td></tr></table></div>
-          </details>
+      </details>
   </div>
 
 </div>
 
+<div class="doc doc-object doc-function">
 
 
-  </div>
 
-  </div>
+<h3 id="churn_pred.training.utils.get_feature_importance" class="doc doc-heading">
+        <span class="doc doc-object-name doc-function-name">get_feature_importance</span>
 
-</div>
 
+<a href="#churn_pred.training.utils.get_feature_importance" class="headerlink" title="Permanent link">&para;</a></h3>
+<div class="highlight"><pre><span></span><code><span class="n">get_feature_importance</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</code></pre></div>
 
+  <div class="doc doc-contents ">
+  
+      <p>Extract model feature importances and return sorted dataframe.</p>
 
+  <p>Parameters:</p>
+  <ul>
+      <li class="field-body">
+        <b>model</b>
+            (<code><span title="lightgbm">lgb</span>.<span title="lightgbm.basic">basic</span>.<span title="lgb.basic.Booster">Booster</span></code>)
+        – <p>LightGBM model</p>
+      </li>
+  </ul>
 
+  <p>Returns:</p>
+  <ul>
+      <li class="field-body">
+<b>feature_imp</b>(            <code><span title="pandas">pd</span>.<span title="pandas.DataFrame">DataFrame</span></code>
+)        – <p>sorted dataframe with features and their
+importances</p>
+      </li>
+  </ul>
 
+      <details class="quote">
+        <summary>Source code in <code>churn_pred/training/utils.py</code></summary>
+        <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">182</span>
+<span class="normal">183</span>
+<span class="normal">184</span>
+<span class="normal">185</span>
+<span class="normal">186</span>
+<span class="normal">187</span>
+<span class="normal">188</span>
+<span class="normal">189</span>
+<span class="normal">190</span>
+<span class="normal">191</span>
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+<span class="normal">193</span>
+<span class="normal">194</span>
+<span class="normal">195</span>
+<span class="normal">196</span>
+<span class="normal">197</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">get_feature_importance</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">lgb</span><span class="o">.</span><span class="n">basic</span><span class="o">.</span><span class="n">Booster</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;Extract model feature importances and return sorted dataframe.</span>
 
+<span class="sd">    Args:</span>
+<span class="sd">        model (lgb.basic.Booster): LightGBM model</span>
 
+<span class="sd">    Returns:</span>
+<span class="sd">        feature_imp (pd.DataFrame): sorted dataframe with features and their</span>
+<span class="sd">            importances</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">feature_imp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+        <span class="p">{</span><span class="s2">&quot;value&quot;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">feature_importance</span><span class="p">(),</span> <span class="s2">&quot;feature&quot;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">feature_name</span><span class="p">()}</span>
+    <span class="p">)</span>
+    <span class="n">feature_imp</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="s2">&quot;value&quot;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">feature_imp</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">feature_imp</span>
+</code></pre></div></td></tr></table></div>
+      </details>
+  </div>
 
-  <aside class="md-source-file">
-    
-    
-    
-      
-  
-  <span class="md-source-file__fact">
-    <span class="md-icon" title="Contributors">
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-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 4a4 4 0 0 1 4 4 4 4 0 0 1-4 4 4 4 0 0 1-4-4 4 4 0 0 1 4-4m0 10c4.42 0 8 1.79 8 4v2H4v-2c0-2.21 3.58-4 8-4Z"/></svg>
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-    <nav>
-      
-        <a href="mailto:mulinka.pavol@gmail.com">Pavol Mulinka</a>
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+</div>
 
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+  </div>
 
+  </div>
 
+</div>
 
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-          
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-<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
+              
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         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
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+        <a href="preprocessing.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: preprocessing" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
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+              preprocessing
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+        <a href="../notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-footer__link md-footer__link--next" aria-label="Next: TBD_00_auxiliary_features_surname_origin_country_classification.ipynb" rel="next">
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+              TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
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+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
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+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
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@@ -3522,12 +2510,10 @@ <h2 id="churn_pred.training.utils.to_lgbdataset" class="doc doc-heading">
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+    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b97dbffb.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
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-    
-    
-      <script src="../assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="../assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="../stylesheets/extra.js"></script>
       
diff --git a/docs/site/cicd.html b/docs/site/cicd.html
index c1f6a93..288312f 100644
--- a/docs/site/cicd.html
+++ b/docs/site/cicd.html
@@ -8,33 +8,23 @@
       
       
       
-      
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-      
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-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>cicd - ecovadis_assignment</title>
+        <title>cicd - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
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-    <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
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+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
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+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
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@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
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+    <a href="index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="assets/images/logo.png" alt="logo">
 
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+            churnpred
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       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
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           <div class="md-search-result__meta">
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+          <ol class="md-search-result__list"></ol>
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@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
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+    churnpred
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@@ -204,97 +184,60 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
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-  
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-      <a href="index.html" class="md-tabs__link">
-        
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+    <a href="index.html" class="md-tabs__link">
+      home
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-  
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-      <a href="installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
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+    <a href="installation.html" class="md-tabs__link">
+      installation
+    </a>
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-  
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-        <a href="churn_pred/code_profiling.html" class="md-tabs__link">
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-  churn_pred
 
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-          
-  
-    
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-  notebooks
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-      </li>
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+    <li class="md-tabs__item">
+      <a href="churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
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-    <li class="md-tabs__item md-tabs__item--active">
-      <a href="cicd.html" class="md-tabs__link">
-        
   
-    
   
-  cicd
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+    <li class="md-tabs__item">
+      <a href="notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
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@@ -303,35 +246,39 @@
         
   
   
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+
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+    <a href="cicd.html" class="md-tabs__link md-tabs__link--active">
+      cicd
+    </a>
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-      <a href="contributing.html" class="md-tabs__link">
+
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+  <li class="md-tabs__item">
+    <a href="code_profiling.html" class="md-tabs__link">
+      code profiling
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+
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-    
   
-  contributing
 
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+
+  <li class="md-tabs__item">
+    <a href="contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
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@@ -356,7 +303,6 @@
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@@ -364,22 +310,22 @@
 
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+    <a href="index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
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+        <ul class="md-nav__list" data-md-scrollfix>
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+              
                 
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+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/cicd.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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 <li>create and share dashboards within organization: <a href="https://learn.microsoft.com/en-us/azure/databricks/sql/get-started/sample-dashboards">https://learn.microsoft.com/en-us/azure/databricks/sql/get-started/sample-dashboards</a></li>
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+        <a href="notebooks/05_model_analysis.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: 05_model_analysis.ipynb" rel="prev">
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+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
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+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
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-        <a href="mailto:mulinka.pavol@gmail.com">Pavol Mulinka</a>
-    </nav>
-  </span>
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-    
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-
-
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-        </div>
         
-      </main>
+        <a href="code_profiling.html" class="md-footer__link md-footer__link--next" aria-label="Next: code profiling" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
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+              code profiling
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
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-        <footer class="md-footer">
+    </nav>
   
   <div class="md-footer-meta md-typeset">
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diff --git a/docs/site/code_profiling.html b/docs/site/code_profiling.html
index 8175c90..b70aa4f 100644
--- a/docs/site/code_profiling.html
+++ b/docs/site/code_profiling.html
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+        <link rel="stylesheet" href="assets/stylesheets/palette.cbb835fc.min.css">
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+    <a href="index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
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+  <img src="assets/images/logo.png" alt="logo">
 
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+          
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+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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       <label class="md-header__button md-icon" for="__search">
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-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
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+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
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+          <ol class="md-search-result__list"></ol>
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-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
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+    churnpred
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 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
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-      <a href="index.html" class="md-tabs__link">
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+    <a href="index.html" class="md-tabs__link">
+      home
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+
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+    <a href="installation.html" class="md-tabs__link">
+      installation
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-  churn_pred
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+    <li class="md-tabs__item">
+      <a href="churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
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+      <a href="notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
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+    <a href="cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
+    
   
-    <li class="md-tabs__item">
-      <a href="contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="code_profiling.html" class="md-tabs__link md-tabs__link--active">
+      code profiling
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+
+      
         
   
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-  contributing
 
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+
+  <li class="md-tabs__item">
+    <a href="contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
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@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
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@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
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-    <a href="index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="assets/images/logo.png" alt="logo">
 
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-    ecovadis_assignment
+    churnpred
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-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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+    churnpred
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     <li class="md-nav__item">
       <a href="index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
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-  
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+        home
       </a>
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@@ -408,19 +349,14 @@
     
       
       
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-    installation
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+        installation
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-            churn_pred
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-    code_profiling
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+        code_profiling
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+          
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+      </nav>
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+          notebooks
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-        
-  
-  <span class="md-ellipsis">
-    00_auxiliary_features
-  </span>
-  
-
+        00_auxiliary_features
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="notebooks/01_feature_analysis.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    01_feature_analysis
-  </span>
-  
-
+        01_feature_analysis
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
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-        
-  
-  <span class="md-ellipsis">
-    02_target_analysis
-  </span>
-  
-
+        02_target_analysis
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     </li>
   
 
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+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="notebooks/03_models.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    03_models
-  </span>
-  
-
+        03_models
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+            
+          
             
               
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-  
   
   
   
     <li class="md-nav__item">
       <a href="notebooks/04_model_training_pipeline.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    04_model_training_pipeline
-  </span>
-  
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+        04_model_training_pipeline
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-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="notebooks/05_model_analysis.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    05_model_analysis.ipynb
-  </span>
-  
-
+        05_model_analysis.ipynb
       </a>
     </li>
   
 
-              
             
-          </ul>
-        </nav>
-      
+          
+        </ul>
+      </nav>
     </li>
   
 
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="cicd.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    cicd
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-  
-
+        cicd
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@@ -799,39 +627,28 @@
     
       
       
+      
+
   
   
     
   
   
-  
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-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          
-  
-  <span class="md-ellipsis">
-    code profiling
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-
+          code profiling
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-  
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-    code profiling
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-  
-
+        code profiling
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@@ -851,18 +668,14 @@
       
         <li class="md-nav__item">
   <a href="#carbon-emission-monitoring" class="md-nav__link">
-    <span class="md-ellipsis">
-      Carbon emission monitoring:
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+    Carbon emission monitoring:
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   <a href="#ram-usage-profiling-and-code-execution-timing" class="md-nav__link">
-    <span class="md-ellipsis">
-      RAM usage profiling and Code execution timing
-    </span>
+    RAM usage profiling and Code execution timing
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-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="contributing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    contributing
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-  
-
+        contributing
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@@ -903,14 +711,14 @@
             
             
           
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-              <article class="md-content__inner md-typeset">
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
                 
-                  
+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/code_profiling.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  
-  
 
 
 <h1 id="code-profiling">Code profiling<a class="headerlink" href="#code-profiling" title="Permanent link">&para;</a></h1>
@@ -975,50 +783,51 @@ <h3 id="code-execution-timing">Code Execution timing<a class="headerlink" href="
 </code></pre></div>
 </details>
 
-
-
-
-
-
-
-
-  <aside class="md-source-file">
-    
-    
-    
+              
+            </article>
+            
+          </div>
+        </div>
+        
+      </main>
       
+        <footer class="md-footer">
   
-  <span class="md-source-file__fact">
-    <span class="md-icon" title="Contributors">
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
       
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 4a4 4 0 0 1 4 4 4 4 0 0 1-4 4 4 4 0 0 1-4-4 4 4 0 0 1 4-4m0 10c4.42 0 8 1.79 8 4v2H4v-2c0-2.21 3.58-4 8-4Z"/></svg>
+        
+        <a href="cicd.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: cicd" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              cicd
+            </div>
+          </div>
+        </a>
       
-    </span>
-    <nav>
       
-        <a href="mailto:mulinka.pavol@gmail.com">Pavol Mulinka</a>
-    </nav>
-  </span>
-
-    
-    
-  </aside>
-
-
-
-
-
-                
-              </article>
-            </div>
-          
-          
-<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
-        </div>
         
-      </main>
+        <a href="contributing.html" class="md-footer__link md-footer__link--next" aria-label="Next: contributing" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              contributing
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
       
-        <footer class="md-footer">
+    </nav>
   
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
@@ -1044,12 +853,10 @@ <h3 id="code-execution-timing">Code Execution timing<a class="headerlink" href="
     <div class="md-dialog" data-md-component="dialog">
       <div class="md-dialog__inner md-typeset"></div>
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+    <script id="__config" type="application/json">{"base": ".", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "assets/javascripts/workers/search.b97dbffb.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-    <script id="__config" type="application/json">{"base": ".", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
-    
-    
-      <script src="assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="stylesheets/extra.js"></script>
       
diff --git a/docs/site/contributing.html b/docs/site/contributing.html
index 6c7789b..8a5726c 100644
--- a/docs/site/contributing.html
+++ b/docs/site/contributing.html
@@ -8,31 +8,23 @@
       
       
       
-      
-        <link rel="prev" href="code_profiling.html">
-      
-      
-      
       <link rel="icon" href="assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>contributing - ecovadis_assignment</title>
+        <title>contributing - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
-
-
-    
     
-      
     
     
       
@@ -48,13 +40,12 @@
     
       <link rel="stylesheet" href="stylesheets/extra.css">
     
-    <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL(".",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
-    
   </head>
   
   
@@ -65,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
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     <label class="md-overlay" for="__drawer"></label>
@@ -88,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
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         <div class="md-header__topic">
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-            ecovadis_assignment
+            churnpred
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@@ -116,39 +109,31 @@
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+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
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+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -157,15 +142,12 @@
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       <label class="md-search__icon md-icon" for="__search">
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       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
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@@ -177,7 +159,7 @@
           <div class="md-search-result__meta">
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           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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       </div>
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@@ -186,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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+    <a href="index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
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+    <a href="installation.html" class="md-tabs__link">
+      installation
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-          
   
-    
   
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+    <li class="md-tabs__item">
+      <a href="churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
         
   
   
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+        notebooks
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+      cicd
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+    <a href="code_profiling.html" class="md-tabs__link">
+      code profiling
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+  </li>
 
       
         
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+    <a href="contributing.html" class="md-tabs__link md-tabs__link--active">
+      contributing
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+  </li>
 
       
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-    <a href="index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="assets/images/logo.png" alt="logo">
 
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+    churnpred
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-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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+    churnpred
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-            churn_pred
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+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
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+          
         
           
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+      
+      
+        <label class="md-nav__link" for="__nav_4">
+          notebooks
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="notebooks" data-md-level="1">
+        <label class="md-nav__title" for="__nav_4">
+          <span class="md-nav__icon md-icon"></span>
+          notebooks
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
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+        05_model_analysis.ipynb
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-          </ul>
-        </nav>
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+          
+        </ul>
+      </nav>
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-    code profiling
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+        code profiling
       </a>
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-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
       
       
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-        
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+        contributing
       </a>
       
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-          
-            <div class="md-content" data-md-component="content">
-              <article class="md-content__inner md-typeset">
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
                 
-                  
+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/contributing.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  
-  
 
 
 <h1 id="code">Code<a class="headerlink" href="#code" title="Permanent link">&para;</a></h1>
 <p>Before making a pushing a code and making a pull request please run codestyle checks and tests</p>
 <div class="highlight"><pre><span></span><code>./code_style.sh
-pytest<span class="w"> </span>--doctest-modules<span class="w"> </span>churn_pred<span class="w"> </span>--cov-report<span class="w"> </span>xml<span class="w"> </span>--cov-report<span class="w"> </span>term<span class="w"> </span>--disable-pytest-warnings<span class="w"> </span>--cov<span class="o">=</span>eda<span class="w"> </span>tests/
+pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov<span class="o">=</span>eda tests/
 </code></pre></div>
 <h1 id="documentation">Documentation<a class="headerlink" href="#documentation" title="Permanent link">&para;</a></h1>
 <p>Documentation is created using github pages and mkdocs, <a href="https://www.mkdocs.org/user-guide/deploying-your-docs/">see</a></p>
 <div class="highlight"><pre><span></span><code><span class="c1"># to build locally</span>
-<span class="nb">cd</span><span class="w"> </span>docs
-pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt
-mkdocs<span class="w"> </span>build
+<span class="nb">cd</span> docs
+pip install -r requirements.txt
+mkdocs build
 <span class="c1"># to push to github pages</span>
-mkdocs<span class="w"> </span>gh-deploy
+mkdocs gh-deploy
 <span class="c1"># if you want to run webserver locally</span>
-mkdocs<span class="w"> </span>serve
+mkdocs serve
 </code></pre></div>
 
-
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+        <a href="code_profiling.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: code profiling" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              code profiling
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+          </div>
+        </a>
       
-    </span>
-    <nav>
       
-        <a href="mailto:mulinka.pavol@gmail.com">Pavol Mulinka</a>
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-  </span>
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-<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
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-      
-        <footer class="md-footer">
   
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
@@ -949,12 +755,10 @@ <h1 id="documentation">Documentation<a class="headerlink" href="#documentation"
     <div class="md-dialog" data-md-component="dialog">
       <div class="md-dialog__inner md-typeset"></div>
     </div>
+    <script id="__config" type="application/json">{"base": ".", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "assets/javascripts/workers/search.b97dbffb.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-    <script id="__config" type="application/json">{"base": ".", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
-    
-    
-      <script src="assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="stylesheets/extra.js"></script>
       
diff --git a/docs/site/index.html b/docs/site/index.html
index b03c87e..7effcb1 100644
--- a/docs/site/index.html
+++ b/docs/site/index.html
@@ -8,31 +8,23 @@
       
       
       
-      
-      
-        <link rel="next" href="installation.html">
-      
-      
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-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>ecovadis_assignment</title>
+        <title>churnpred</title>
       
     
     
-      <link rel="stylesheet" href="assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
-
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-      
     
     
       
@@ -48,13 +40,12 @@
     
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-    <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL(".",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
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@@ -65,16 +56,19 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
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     <label class="md-overlay" for="__drawer"></label>
     <div data-md-component="skip">
       
         
-        <a href="#churn-prediction-assignment-for-ecovadis" class="md-skip">
+        <a href="#churn-predictor" class="md-skip">
           Skip to content
         </a>
       
@@ -88,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
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         <div class="md-header__topic">
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-            ecovadis_assignment
+            churnpred
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+      <form class="md-header__option" data-md-component="palette">
+        
+          
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+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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@@ -157,15 +142,12 @@
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-        
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-        
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       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -177,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -186,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -202,7 +184,7 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
@@ -210,89 +192,54 @@
   
     
   
-  
-    <li class="md-tabs__item md-tabs__item--active">
-      <a href="index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="index.html" class="md-tabs__link md-tabs__link--active">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
         
   
   
-  
-    
-    
-      <li class="md-tabs__item">
-        <a href="notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
       </a>
     </li>
   
@@ -301,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -354,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -362,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
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-    <a href="index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="assets/images/logo.png" alt="logo">
 
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-    ecovadis_assignment
+    churnpred
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     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
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+              
                 
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+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/index.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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-  
-  
 
 
-<h1 id="churn-prediction-assignment-for-ecovadis">Churn prediction assignment for EcoVadis<a class="headerlink" href="#churn-prediction-assignment-for-ecovadis" title="Permanent link">&para;</a></h1>
-<p>Main purpose of this library is to provide solution for an asisgnemnt in the interview process of EcoVadis.</p>
+<h1 id="churn-predictor">Churn predictor<a class="headerlink" href="#churn-predictor" title="Permanent link">&para;</a></h1>
+<p>Main purpose of this library is to provide solution for an asisgnemnt below:</p>
 <div class="highlight"><pre><span></span><code>In this assignment you&#39;re tasked with developing a machine learning solution for churn prediction to identify which customers are likely to leave a service (column &quot;Exited&quot; in the attached dataset). This assignment is meant to assess
 
 - analytical skills and reasoning
@@ -912,8 +724,6 @@ <h1 id="churn-prediction-assignment-for-ecovadis">Churn prediction assignment fo
 
 Please note that multiple solutions may exist and we do not expect a production ready solution, though any reflections on how you may wish to productionalise your solution are welcome. You are free to choose the medium (e.g., notebooks, python scripts). 
 
-Please return your solution by May 2 (9am CET). Once returned, our colleagues with the HR department will schedule the final interview where you&#39;ll be asked to walk us through your solution and reflect on any decisions made.
-
 Additional explanation of independent variables:
 
 NumberOfProducts - the number of accounts and bank-affiliated products 
@@ -928,50 +738,36 @@ <h3 id="additional-work-note-mentioning">Additional work note mentioning<a class
 <p>The solution was tested in a virtual machine, spawned from <code>jupyter/datascience-notebook:python-3.10</code> image in Zero-to-JupyterHub solution. As the bare metal server with GPU was down in the kubernetes, I had to do additional troubleshooting and fixing.</p>
 <p>The code is easily extendable to <code>multiclass</code>, <code>regression</code> and <code>quantile_regression</code> tasks.</p>
 
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+        <a href="installation.html" class="md-footer__link md-footer__link--next" aria-label="Next: installation" rel="next">
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+      <script src="assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
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diff --git a/docs/site/index.md b/docs/site/index.md
index db5ae09..e3f72a1 100644
--- a/docs/site/index.md
+++ b/docs/site/index.md
@@ -1,6 +1,6 @@
-# Churn prediction assignment for EcoVadis
+# Churn predictor
 
-Main purpose of this library is to provide solution for an asisgnemnt in the interview process of EcoVadis.
+Main purpose of this library is to provide solution for an asisgnemnt below:
 
 ```
 In this assignment you're tasked with developing a machine learning solution for churn prediction to identify which customers are likely to leave a service (column "Exited" in the attached dataset). This assignment is meant to assess
@@ -11,8 +11,6 @@ In this assignment you're tasked with developing a machine learning solution for
 
 Please note that multiple solutions may exist and we do not expect a production ready solution, though any reflections on how you may wish to productionalise your solution are welcome. You are free to choose the medium (e.g., notebooks, python scripts). 
 
-Please return your solution by May 2 (9am CET). Once returned, our colleagues with the HR department will schedule the final interview where you'll be asked to walk us through your solution and reflect on any decisions made.
-
 Additional explanation of independent variables:
 
 NumberOfProducts - the number of accounts and bank-affiliated products 
diff --git a/docs/site/installation.html b/docs/site/installation.html
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-        <title>installation - ecovadis_assignment</title>
+        <title>installation - churnpred</title>
       
     
     
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+      <link rel="stylesheet" href="assets/stylesheets/main.1d29e8d0.min.css">
       
         
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+    <script>__md_scope=new URL(".",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
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+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -118,39 +109,31 @@
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     </div>
     
-      
-        <form class="md-header__option" data-md-component="palette">
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
-    
-      <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
-    
-      <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,24 +184,19 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
@@ -229,72 +204,42 @@
   
     
   
-  
-    <li class="md-tabs__item md-tabs__item--active">
-      <a href="installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="installation.html" class="md-tabs__link md-tabs__link--active">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
         
   
   
-  
-    
-    
-      <li class="md-tabs__item">
-        <a href="notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="notebooks/00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
     </div>
@@ -388,19 +334,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,39 +349,28 @@
     
       
       
+      
+
   
   
     
   
   
-  
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+          installation
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="installation.html" class="md-nav__link md-nav__link--active">
-        
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+        installation
       </a>
       
         
@@ -460,27 +390,21 @@
       
         <li class="md-nav__item">
   <a href="#install-using-pip-directly-from-github" class="md-nav__link">
-    <span class="md-ellipsis">
-      Install using pip directly from github:
-    </span>
+    Install using pip directly from github:
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#locally" class="md-nav__link">
-    <span class="md-ellipsis">
-      Locally
-    </span>
+    Locally
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#additional-reqs-on-a-newly-spawned-machine" class="md-nav__link">
-    <span class="md-ellipsis">
-      Additional reqs on a newly spawned machine
-    </span>
+    Additional reqs on a newly spawned machine
   </a>
   
 </li>
@@ -495,370 +419,262 @@
     
       
       
-  
+      
+
   
   
   
     
-    
-      
-        
-      
-        
-      
-        
+    <li class="md-nav__item md-nav__item--nested">
       
-        
       
-    
-    
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
       
       
-    
-    
-    <li class="md-nav__item md-nav__item--nested">
       
         
+          
         
           
         
-        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" type="checkbox" id="__nav_3" >
+          
         
           
-          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-            
-  
-  <span class="md-ellipsis">
-    churn_pred
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_3">
-            <span class="md-nav__icon md-icon"></span>
-            churn_pred
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="churn_pred/code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+        code_profiling
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="churn_pred/eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
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+      
+      
+        <label class="md-nav__link" for="__nav_4">
+          notebooks
+          <span class="md-nav__icon md-icon"></span>
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+      <nav class="md-nav" aria-label="notebooks" data-md-level="1">
+        <label class="md-nav__title" for="__nav_4">
+          <span class="md-nav__icon md-icon"></span>
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+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
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-    TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
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+        TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
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+        00_auxiliary_features
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-    01_feature_analysis
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+        01_feature_analysis
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-    02_target_analysis
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+        02_target_analysis
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-    03_models
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+        03_models
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       <a href="notebooks/04_model_training_pipeline.html" class="md-nav__link">
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-    04_model_training_pipeline
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+        04_model_training_pipeline
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-    05_model_analysis.ipynb
-  </span>
-  
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+        05_model_analysis.ipynb
       </a>
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-          </ul>
-        </nav>
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+          
+        </ul>
+      </nav>
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       <a href="cicd.html" class="md-nav__link">
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-    cicd
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+        cicd
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+
   
   
   
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-    code profiling
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+        code profiling
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+        contributing
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@@ -912,75 +718,76 @@
             
             
           
-          
-            <div class="md-content" data-md-component="content">
-              <article class="md-content__inner md-typeset">
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
                 
-                  
+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/installation.md" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  
-  
 
 
 <h1 id="installation">Installation<a class="headerlink" href="#installation" title="Permanent link">&para;</a></h1>
 <h2 id="install-using-pip-directly-from-github">Install using pip directly from github:<a class="headerlink" href="#install-using-pip-directly-from-github" title="Permanent link">&para;</a></h2>
-<div class="highlight"><pre><span></span><code>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/5uperpalo/churn_pred.git
+<div class="highlight"><pre><span></span><code>pip install git+https://github.com/5uperpalo/churn_pred.git
 </code></pre></div>
 <h2 id="locally">Locally<a class="headerlink" href="#locally" title="Permanent link">&para;</a></h2>
-<div class="highlight"><pre><span></span><code>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/5uperpalo/churn_pred.git
-<span class="nb">cd</span><span class="w"> </span>churn_pred
-pip<span class="w"> </span>install<span class="w"> </span>.
+<div class="highlight"><pre><span></span><code>git clone https://github.com/5uperpalo/churn_pred.git
+<span class="nb">cd</span> churn_pred
+pip install .
 </code></pre></div>
 <h2 id="additional-reqs-on-a-newly-spawned-machine">Additional reqs on a newly spawned machine<a class="headerlink" href="#additional-reqs-on-a-newly-spawned-machine" title="Permanent link">&para;</a></h2>
 <p>H20 AutoML requires JRE and language models are faster on GPU.</p>
-<div class="highlight"><pre><span></span><code>sudo<span class="w"> </span>apt<span class="w"> </span>install<span class="w"> </span>nvidia-utils-535
-sudo<span class="w"> </span>apt<span class="w"> </span>install<span class="w"> </span>openjdk-17-jdk
+<div class="highlight"><pre><span></span><code>sudo apt install nvidia-utils-535
+sudo apt install openjdk-17-jdk
 </code></pre></div>
 
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+        </div>
+        
+      </main>
       
+        <footer class="md-footer">
   
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+        <a href="index.html" class="md-footer__link md-footer__link--prev" aria-label="Previous: home" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
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+            <div class="md-ellipsis">
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-<script>var target=document.getElementById(location.hash.slice(1));target&&target.name&&(target.checked=target.name.startsWith("__tabbed_"))</script>
-        </div>
         
-      </main>
+        <a href="churn_pred/code_profiling.html" class="md-footer__link md-footer__link--next" aria-label="Next: code_profiling" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              code_profiling
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
       
-        <footer class="md-footer">
+    </nav>
   
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
@@ -1006,12 +813,10 @@ <h2 id="additional-reqs-on-a-newly-spawned-machine">Additional reqs on a newly s
     <div class="md-dialog" data-md-component="dialog">
       <div class="md-dialog__inner md-typeset"></div>
     </div>
+    <script id="__config" type="application/json">{"base": ".", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "assets/javascripts/workers/search.b97dbffb.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-    <script id="__config" type="application/json">{"base": ".", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
-    
-    
-      <script src="assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
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diff --git a/docs/site/notebooks/00_auxiliary_features.html b/docs/site/notebooks/00_auxiliary_features.html
index 64c71a1..28c97c3 100644
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+++ b/docs/site/notebooks/00_auxiliary_features.html
@@ -8,34 +8,24 @@
       
       
       
-      
-        <link rel="prev" href="00_auxiliary_features_surname_origin_country_classification.html">
-      
-      
-        <link rel="next" href="01_feature_analysis.html">
-      
-      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>00_auxiliary_features - ecovadis_assignment</title>
+        <title>00_auxiliary_features - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
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@@ -50,13 +40,12 @@
     
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-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
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+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
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     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
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+            churnpred
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         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,60 +184,46 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
@@ -266,35 +232,14 @@
   
     
   
-  
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link md-tabs__link--active">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
     </div>
@@ -388,19 +334,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,19 +349,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../installation.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+        installation
       </a>
     </li>
   
@@ -428,260 +364,186 @@
     
       
       
+      
+
   
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--nested">
       
-        
-      
-        
-      
-        
-      
-        
       
-    
-    
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
       
       
-    
-    
-    <li class="md-nav__item md-nav__item--nested">
       
         
+          
         
           
         
-        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" type="checkbox" id="__nav_3" >
+          
         
           
-          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-            
-  
-  <span class="md-ellipsis">
-    churn_pred
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_3">
-            <span class="md-nav__icon md-icon"></span>
-            churn_pred
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+        code_profiling
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/preprocessing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    preprocessing
-  </span>
-  
-
+        preprocessing
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/training.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    training
-  </span>
-  
-
+        training
       </a>
     </li>
   
 
-              
             
-          </ul>
-        </nav>
-      
+          
+        </ul>
+      </nav>
     </li>
   
 
     
       
       
+      
+
   
   
     
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-        
-      
-        
       
-        
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" checked>
       
-        
       
-        
       
         
-      
+          
         
-      
-    
-    
-      
+          
         
+          
         
-      
-      
-    
-    
-    <li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
-      
+          
         
+          
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
+          
         
           
-          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="">
-            
-  
-  <span class="md-ellipsis">
-    notebooks
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_4">
-            <span class="md-nav__icon md-icon"></span>
-            notebooks
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_4">
+          notebooks
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="notebooks" data-md-level="1">
+        <label class="md-nav__title" for="__nav_4">
+          <span class="md-nav__icon md-icon"></span>
+          notebooks
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
-  </span>
-  
-
+        TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
   
   
     
   
   
-  
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
-          
-  
-  <span class="md-ellipsis">
-    00_auxiliary_features
-  </span>
-  
-
+          00_auxiliary_features
           <span class="md-nav__icon md-icon"></span>
         </label>
       
       <a href="00_auxiliary_features.html" class="md-nav__link md-nav__link--active">
-        
-  
-  <span class="md-ellipsis">
-    00_auxiliary_features
-  </span>
-  
-
+        00_auxiliary_features
       </a>
       
         
@@ -701,9 +563,7 @@
       
         <li class="md-nav__item">
   <a href="#sentiment-analysis" class="md-nav__link">
-    <span class="md-ellipsis">
-      Sentiment analysis
-    </span>
+    Sentiment analysis
   </a>
   
     <nav class="md-nav" aria-label="Sentiment analysis">
@@ -711,18 +571,14 @@
         
           <li class="md-nav__item">
   <a href="#cleaning-and-langauge-detection" class="md-nav__link">
-    <span class="md-ellipsis">
-      Cleaning and langauge detection
-    </span>
+    Cleaning and langauge detection
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#sentiment-analysis-prediction" class="md-nav__link">
-    <span class="md-ellipsis">
-      Sentiment analysis prediction
-    </span>
+    Sentiment analysis prediction
   </a>
   
 </li>
@@ -734,18 +590,14 @@
       
         <li class="md-nav__item">
   <a href="#surname-origin-country" class="md-nav__link">
-    <span class="md-ellipsis">
-      Surname origin country
-    </span>
+    Surname origin country
   </a>
   
 </li>
       
         <li class="md-nav__item">
   <a href="#additional-country-info" class="md-nav__link">
-    <span class="md-ellipsis">
-      Additional country info
-    </span>
+    Additional country info
   </a>
   
     <nav class="md-nav" aria-label="Additional country info">
@@ -753,36 +605,28 @@
         
           <li class="md-nav__item">
   <a href="#country-region-and-subregion" class="md-nav__link">
-    <span class="md-ellipsis">
-      Country region and subregion
-    </span>
+    Country region and subregion
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#native-or-not" class="md-nav__link">
-    <span class="md-ellipsis">
-      Native or not
-    </span>
+    Native or not
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#country-hemisphere" class="md-nav__link">
-    <span class="md-ellipsis">
-      Country hemisphere
-    </span>
+    Country hemisphere
   </a>
   
 </li>
         
           <li class="md-nav__item">
   <a href="#country-gdpp" class="md-nav__link">
-    <span class="md-ellipsis">
-      Country gdpp
-    </span>
+    Country gdpp
   </a>
   
 </li>
@@ -794,9 +638,7 @@
       
         <li class="md-nav__item">
   <a href="#additional-age-info" class="md-nav__link">
-    <span class="md-ellipsis">
-      Additional age info
-    </span>
+    Additional age info
   </a>
   
 </li>
@@ -808,135 +650,94 @@
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="01_feature_analysis.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    01_feature_analysis
-  </span>
-  
-
+        01_feature_analysis
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="02_target_analysis.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    02_target_analysis
-  </span>
-  
-
+        02_target_analysis
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="03_models.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    03_models
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var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color),.5) transparent}.jupyter-wrapper .jp-scrollbar-tiny{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color),.5) transparent;scrollbar-width:thin}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid transparent;background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid var(--jp-scrollbar-background-color);border-right:var(--jp-scrollbar-endpad) solid var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:vertical{border-top:var(--jp-scrollbar-endpad) solid var(--jp-scrollbar-background-color);border-bottom:var(--jp-scrollbar-endpad) solid var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar-corner,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar-corner{background-color:transparent}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar-thumb,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar-thumb{background:rgba(var(--jp-scrollbar-thumb-color),.5);border:var(--jp-scrollbar-thumb-margin) solid transparent;background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid transparent;border-right:var(--jp-scrollbar-endpad) solid transparent}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar-track:vertical{border-top:var(--jp-scrollbar-endpad) solid transparent;border-bottom:var(--jp-scrollbar-endpad) solid transparent}.jupyter-wrapper .jp-scrollbar-tiny::-webkit-scrollbar,.jupyter-wrapper .jp-scrollbar-tiny::-webkit-scrollbar-corner{background-color:transparent;height:4px;width:4px}.jupyter-wrapper 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.83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
-</style>
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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-<h1 id="add-auxiliary-features-to-the-dataset">Add auxiliary features to the dataset<a class="anchor-link" href="#add-auxiliary-features-to-the-dataset">¶</a></h1><p><code>sudo apt install nvidia-utils-535</code> to get GPU running</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="add-auxiliary-features-to-the-dataset">Add auxiliary features to the dataset<a class="anchor-link" href="#add-auxiliary-features-to-the-dataset">&#182;</a></h1><p><code>sudo apt install nvidia-utils-535</code> to get GPU running</p>
+
 </div>
 </div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 
 <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
-<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">"../.."</span><span class="p">)</span>
+<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;../..&quot;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">from</span> <span class="nn">churn_pred.preprocessing</span> <span class="kn">import</span> <span class="n">auxiliary_data</span><span class="p">,</span> <span class="n">preprocess_text</span>
 <span class="kn">import</span> <span class="nn">spacy_fastlang</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-1">import os
+<div id="cell-1" class="clipboard-copy-txt">import os
 import sys
 
 sys.path.append(os.getcwd())
@@ -1483,48 +1872,64 @@ <h1 id="add-auxiliary-features-to-the-dataset">Add auxiliary features to the dat
 import pandas as pd
 from churn_pred.preprocessing import auxiliary_data, preprocess_text
 import spacy_fastlang</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-2">
-<div>
-<span class="notice" hidden="">Copied!</span>
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"data/dataset.csv"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/dataset.csv&quot;</span><span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-2">df_pd = pd.read_csv("data/dataset.csv")
+<div id="cell-2" class="clipboard-copy-txt">df_pd = pd.read_csv("data/dataset.csv")
 df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[2]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
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@@ -1538,229 +1943,261 @@ <h1 id="add-auxiliary-features-to-the-dataset">Add auxiliary features to the dat
     }
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-<td>Male</td>
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-<td>10</td>
-<td>Last week, I reached out to my bank's support ...</td>
-<td>151762.74</td>
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-<td>1679</td>
-<td>15569178</td>
-<td>Kharlamov</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>NaN</td>
-<td>82767.42</td>
-<td>1</td>
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-<td>2022</td>
-<td>15795519</td>
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-<td>Germany</td>
-<td>Female</td>
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-<td>128743.80</td>
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-<td>Yo, I gotta give some love to this bank's cust...</td>
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-<td>1</td>
-<td>1</td>
-<td>46941.41</td>
-<td>0</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>RowNumber</th>
+      <th>CustomerId</th>
+      <th>Surname</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>CustomerFeedback</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>747</td>
+      <td>15787619</td>
+      <td>Hsieh</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>I can not link my card to any third-party plat...</td>
+      <td>160980.03</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1620</td>
+      <td>15770309</td>
+      <td>McDonald</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>Last week, I reached out to my bank's support ...</td>
+      <td>151762.74</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1679</td>
+      <td>15569178</td>
+      <td>Kharlamov</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
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+      <td>82767.42</td>
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+      <td>1</td>
+      <td>0</td>
+      <td>71811.90</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>2022</td>
+      <td>15795519</td>
+      <td>Vasiliev</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>NaN</td>
+      <td>128743.80</td>
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+      <td>0</td>
+      <td>197322.13</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>2137</td>
+      <td>15621893</td>
+      <td>Bellucci</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>Yo, I gotta give some love to this bank's cust...</td>
+      <td>133550.67</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1</td>
+      <td>46941.41</td>
+      <td>0</td>
+    </tr>
+  </tbody>
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+
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+
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+
 </div>
+
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 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-<h2 id="sentiment-analysis">Sentiment analysis<a class="anchor-link" href="#sentiment-analysis">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="sentiment-analysis">Sentiment analysis<a class="anchor-link" href="#sentiment-analysis">&#182;</a></h2>
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="cleaning-and-langauge-detection">Cleaning and langauge detection<a class="anchor-link" href="#cleaning-and-langauge-detection">¶</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="cleaning-and-langauge-detection">Cleaning and langauge detection<a class="anchor-link" href="#cleaning-and-langauge-detection">&#182;</a></h3>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">preprocess_text</span><span class="o">.</span><span class="n">text_cleaning</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">text_col</span><span class="o">=</span><span class="s2">"CustomerFeedback"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">preprocess_text</span><span class="o">.</span><span class="n">text_cleaning</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">text_col</span><span class="o">=</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">)</span>
 <span class="n">df_pd</span> <span class="o">=</span> <span class="n">preprocess_text</span><span class="o">.</span><span class="n">language_detection</span><span class="p">(</span>
-    <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">text_col</span><span class="o">=</span><span class="s2">"CustomerFeedback"</span><span class="p">,</span> <span class="n">model_type</span><span class="o">=</span><span class="s2">"fasttext"</span>
+    <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">text_col</span><span class="o">=</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">,</span> <span class="n">model_type</span><span class="o">=</span><span class="s2">&quot;fasttext&quot;</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-3">df_pd = preprocess_text.text_cleaning(df=df_pd, text_col="CustomerFeedback")
+<div id="cell-3" class="clipboard-copy-txt">df_pd = preprocess_text.text_cleaning(df=df_pd, text_col="CustomerFeedback")
 df_pd = preprocess_text.language_detection(
     df=df_pd, text_col="CustomerFeedback", model_type="fasttext"
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+
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+
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
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+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>Cleaning Progress: 100%|██████████| 10000/10000 [00:34&lt;00:00, 287.95it/s]
 Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-4">
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-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-4">df_pd.head()</div>
-</div>
-</div>
+<div id="cell-4" class="clipboard-copy-txt">df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
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 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+
+
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-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -1774,221 +2211,256 @@ <h3 id="cleaning-and-langauge-detection">Cleaning and langauge detection<a class
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>RowNumber</th>
-<th>CustomerId</th>
-<th>Surname</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>CustomerFeedback</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_language</th>
-<th>CustomerFeedback_language_score</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>747</td>
-<td>15787619</td>
-<td>Hsieh</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>link card _IS_PUNCT_ party platform _IS_PUNCT_</td>
-<td>160980.03</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>145936.28</td>
-<td>0</td>
-<td>en</td>
-<td>0.786177</td>
-</tr>
-<tr>
-<th>1</th>
-<td>1620</td>
-<td>15770309</td>
-<td>McDonald</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>week _IS_PUNCT_ reach bank support team confus...</td>
-<td>151762.74</td>
-<td>1</td>
-<td>0</td>
-<td>1</td>
-<td>127014.32</td>
-<td>0</td>
-<td>en</td>
-<td>0.911405</td>
-</tr>
-<tr>
-<th>2</th>
-<td>1679</td>
-<td>15569178</td>
-<td>Kharlamov</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>nan</td>
-<td>82767.42</td>
-<td>1</td>
-<td>1</td>
-<td>0</td>
-<td>71811.90</td>
-<td>0</td>
-<td>ht</td>
-<td>0.974604</td>
-</tr>
-<tr>
-<th>3</th>
-<td>2022</td>
-<td>15795519</td>
-<td>Vasiliev</td>
-<td>716</td>
-<td>Germany</td>
-<td>Female</td>
-<td>18</td>
-<td>3</td>
-<td>nan</td>
-<td>128743.80</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>197322.13</td>
-<td>0</td>
-<td>ht</td>
-<td>0.974604</td>
-</tr>
-<tr>
-<th>4</th>
-<td>2137</td>
-<td>15621893</td>
-<td>Bellucci</td>
-<td>727</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>4</td>
-<td>Yo _IS_PUNCT_ get to love bank customer servic...</td>
-<td>133550.67</td>
-<td>1</td>
-<td>1</td>
-<td>1</td>
-<td>46941.41</td>
-<td>0</td>
-<td>en</td>
-<td>0.758962</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>RowNumber</th>
+      <th>CustomerId</th>
+      <th>Surname</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>CustomerFeedback</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_language</th>
+      <th>CustomerFeedback_language_score</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>747</td>
+      <td>15787619</td>
+      <td>Hsieh</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>link card _IS_PUNCT_ party platform _IS_PUNCT_</td>
+      <td>160980.03</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.786177</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1620</td>
+      <td>15770309</td>
+      <td>McDonald</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>week _IS_PUNCT_ reach bank support team confus...</td>
+      <td>151762.74</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.911405</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1679</td>
+      <td>15569178</td>
+      <td>Kharlamov</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
+      <td>nan</td>
+      <td>82767.42</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0</td>
+      <td>71811.90</td>
+      <td>0</td>
+      <td>ht</td>
+      <td>0.974604</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>2022</td>
+      <td>15795519</td>
+      <td>Vasiliev</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>nan</td>
+      <td>128743.80</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>197322.13</td>
+      <td>0</td>
+      <td>ht</td>
+      <td>0.974604</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>2137</td>
+      <td>15621893</td>
+      <td>Bellucci</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>Yo _IS_PUNCT_ get to love bank customer servic...</td>
+      <td>133550.67</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1</td>
+      <td>46941.41</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.758962</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-5">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"CustomerFeedback"</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">"nan"</span><span class="p">][</span>
-    <span class="s2">"CustomerFeedback_language"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">&quot;nan&quot;</span><span class="p">][</span>
+    <span class="s2">&quot;CustomerFeedback_language&quot;</span>
 <span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-5">df_pd[df_pd["CustomerFeedback"].astype(str) != "nan"][
+<div id="cell-5" class="clipboard-copy-txt">df_pd[df_pd["CustomerFeedback"].astype(str) != "nan"][
     "CustomerFeedback_language"
 ].unique()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>array(['en'], dtype=object)</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([&#39;en&#39;], dtype=object)</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-6">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span>
-    <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"CustomerFeedback"</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">"nan"</span><span class="p">)</span>
-    <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"CustomerFeedback_language_score"</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.5</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">&quot;nan&quot;</span><span class="p">)</span>
+    <span class="o">&amp;</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;CustomerFeedback_language_score&quot;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.5</span><span class="p">)</span>
 <span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-6">df_pd[
+<div id="cell-6" class="clipboard-copy-txt">df_pd[
     (df_pd["CustomerFeedback"].astype(str) != "nan")
-    &amp; (df_pd["CustomerFeedback_language_score"] &lt; 0.5)
+    & (df_pd["CustomerFeedback_language_score"] < 0.5)
 ]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -2002,218 +2474,274 @@ <h3 id="cleaning-and-langauge-detection">Cleaning and langauge detection<a class
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>RowNumber</th>
-<th>CustomerId</th>
-<th>Surname</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>CustomerFeedback</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_language</th>
-<th>CustomerFeedback_language_score</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>105</th>
-<td>2866</td>
-<td>15590228</td>
-<td>Greenwalt</td>
-<td>715</td>
-<td>France</td>
-<td>Male</td>
-<td>21</td>
-<td>6</td>
-<td>hsi</td>
-<td>76467.16</td>
-<td>1</td>
-<td>1</td>
-<td>1</td>
-<td>173511.72</td>
-<td>0</td>
-<td>en</td>
-<td>0.124504</td>
-</tr>
-<tr>
-<th>6270</th>
-<td>7107</td>
-<td>15723989</td>
-<td>Carroll</td>
-<td>646</td>
-<td>France</td>
-<td>Male</td>
-<td>40</td>
-<td>5</td>
-<td>fund accessible anticipate date _IS_PUNCT_ _IS...</td>
-<td>93680.43</td>
-<td>2</td>
-<td>1</td>
-<td>1</td>
-<td>179473.26</td>
-<td>0</td>
-<td>en</td>
-<td>0.468742</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>RowNumber</th>
+      <th>CustomerId</th>
+      <th>Surname</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>CustomerFeedback</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_language</th>
+      <th>CustomerFeedback_language_score</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>105</th>
+      <td>2866</td>
+      <td>15590228</td>
+      <td>Greenwalt</td>
+      <td>715</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>21</td>
+      <td>6</td>
+      <td>hsi</td>
+      <td>76467.16</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1</td>
+      <td>173511.72</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.124504</td>
+    </tr>
+    <tr>
+      <th>6270</th>
+      <td>7107</td>
+      <td>15723989</td>
+      <td>Carroll</td>
+      <td>646</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>40</td>
+      <td>5</td>
+      <td>fund accessible anticipate date _IS_PUNCT_ _IS...</td>
+      <td>93680.43</td>
+      <td>2</td>
+      <td>1</td>
+      <td>1</td>
+      <td>179473.26</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.468742</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-7">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">105</span><span class="p">,</span> <span class="p">[</span><span class="s2">"CustomerFeedback"</span><span class="p">]]</span> <span class="o">=</span> <span class="s2">"nan"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">105</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="s2">&quot;nan&quot;</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-7">df_pd.loc[105, ["CustomerFeedback"]] = "nan"</div>
-</div>
-</div>
+<div id="cell-7" class="clipboard-copy-txt">df_pd.loc[105, ["CustomerFeedback"]] = "nan"</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-8">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"CustomerFeedback"</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">"nan"</span><span class="p">][</span>
-    <span class="s2">"CustomerFeedback_language_score"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span> <span class="o">!=</span> <span class="s2">&quot;nan&quot;</span><span class="p">][</span>
+    <span class="s2">&quot;CustomerFeedback_language_score&quot;</span>
 <span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-8">df_pd[df_pd["CustomerFeedback"].astype(str) != "nan"][
+<div id="cell-8" class="clipboard-copy-txt">df_pd[df_pd["CustomerFeedback"].astype(str) != "nan"][
     "CustomerFeedback_language_score"
 ].plot.hist(bins=50)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;Axes: ylabel='Frequency'&gt;</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&lt;Axes: ylabel=&#39;Frequency&#39;&gt;</pre>
 </div>
+
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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"
+class="
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-<h3 id="sentiment-analysis-prediction">Sentiment analysis prediction<a class="anchor-link" href="#sentiment-analysis-prediction">¶</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="sentiment-analysis-prediction">Sentiment analysis prediction<a class="anchor-link" href="#sentiment-analysis-prediction">&#182;</a></h3>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[[</span><span class="s2">"CustomerFeedback_sentiment3"</span><span class="p">,</span> <span class="s2">"CustomerFeedback_sentiment3_score"</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[[</span><span class="s2">&quot;CustomerFeedback_sentiment3&quot;</span><span class="p">,</span> <span class="s2">&quot;CustomerFeedback_sentiment3_score&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
     <span class="n">preprocess_text</span><span class="o">.</span><span class="n">sentiment_analysis</span><span class="p">(</span>
-        <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">text_col</span><span class="o">=</span><span class="s2">"CustomerFeedback"</span><span class="p">,</span> <span class="n">sentiment_depth</span><span class="o">=</span><span class="mi">3</span>
-    <span class="p">)[[</span><span class="s2">"CustomerFeedback_sentiment"</span><span class="p">,</span> <span class="s2">"CustomerFeedback_sentiment_score"</span><span class="p">]]</span>
+        <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">text_col</span><span class="o">=</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">,</span> <span class="n">sentiment_depth</span><span class="o">=</span><span class="mi">3</span>
+    <span class="p">)[[</span><span class="s2">&quot;CustomerFeedback_sentiment&quot;</span><span class="p">,</span> <span class="s2">&quot;CustomerFeedback_sentiment_score&quot;</span><span class="p">]]</span>
 <span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-9">df_pd[["CustomerFeedback_sentiment3", "CustomerFeedback_sentiment3_score"]] = (
+<div id="cell-9" class="clipboard-copy-txt">df_pd[["CustomerFeedback_sentiment3", "CustomerFeedback_sentiment3_score"]] = (
     preprocess_text.sentiment_analysis(
         df=df_pd, text_col="CustomerFeedback", sentiment_depth=3
     )[["CustomerFeedback_sentiment", "CustomerFeedback_sentiment_score"]]
 )
 df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: [&#39;roberta.pooler.dense.bias&#39;, &#39;roberta.pooler.dense.weight&#39;]
 - This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
 - This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
 </pre>
 </div>
 </div>
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -2227,194 +2755,211 @@ <h3 id="sentiment-analysis-prediction">Sentiment analysis prediction<a class="an
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>RowNumber</th>
-<th>CustomerId</th>
-<th>Surname</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>CustomerFeedback</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_language</th>
-<th>CustomerFeedback_language_score</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment3_score</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>747</td>
-<td>15787619</td>
-<td>Hsieh</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>link card _IS_PUNCT_ party platform _IS_PUNCT_</td>
-<td>160980.03</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>145936.28</td>
-<td>0</td>
-<td>en</td>
-<td>0.786177</td>
-<td>neutral</td>
-<td>0.827256</td>
-</tr>
-<tr>
-<th>1</th>
-<td>1620</td>
-<td>15770309</td>
-<td>McDonald</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>week _IS_PUNCT_ reach bank support team confus...</td>
-<td>151762.74</td>
-<td>1</td>
-<td>0</td>
-<td>1</td>
-<td>127014.32</td>
-<td>0</td>
-<td>en</td>
-<td>0.911405</td>
-<td>neutral</td>
-<td>0.671348</td>
-</tr>
-<tr>
-<th>2</th>
-<td>1679</td>
-<td>15569178</td>
-<td>Kharlamov</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>nan</td>
-<td>82767.42</td>
-<td>1</td>
-<td>1</td>
-<td>0</td>
-<td>71811.90</td>
-<td>0</td>
-<td>ht</td>
-<td>0.974604</td>
-<td>neutral</td>
-<td>0.680865</td>
-</tr>
-<tr>
-<th>3</th>
-<td>2022</td>
-<td>15795519</td>
-<td>Vasiliev</td>
-<td>716</td>
-<td>Germany</td>
-<td>Female</td>
-<td>18</td>
-<td>3</td>
-<td>nan</td>
-<td>128743.80</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>197322.13</td>
-<td>0</td>
-<td>ht</td>
-<td>0.974604</td>
-<td>neutral</td>
-<td>0.680865</td>
-</tr>
-<tr>
-<th>4</th>
-<td>2137</td>
-<td>15621893</td>
-<td>Bellucci</td>
-<td>727</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>4</td>
-<td>Yo _IS_PUNCT_ get to love bank customer servic...</td>
-<td>133550.67</td>
-<td>1</td>
-<td>1</td>
-<td>1</td>
-<td>46941.41</td>
-<td>0</td>
-<td>en</td>
-<td>0.758962</td>
-<td>positive</td>
-<td>0.878306</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>RowNumber</th>
+      <th>CustomerId</th>
+      <th>Surname</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>CustomerFeedback</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_language</th>
+      <th>CustomerFeedback_language_score</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment3_score</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>747</td>
+      <td>15787619</td>
+      <td>Hsieh</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>link card _IS_PUNCT_ party platform _IS_PUNCT_</td>
+      <td>160980.03</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.786177</td>
+      <td>neutral</td>
+      <td>0.827256</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1620</td>
+      <td>15770309</td>
+      <td>McDonald</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>week _IS_PUNCT_ reach bank support team confus...</td>
+      <td>151762.74</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.911405</td>
+      <td>neutral</td>
+      <td>0.671348</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1679</td>
+      <td>15569178</td>
+      <td>Kharlamov</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
+      <td>nan</td>
+      <td>82767.42</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0</td>
+      <td>71811.90</td>
+      <td>0</td>
+      <td>ht</td>
+      <td>0.974604</td>
+      <td>neutral</td>
+      <td>0.680865</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>2022</td>
+      <td>15795519</td>
+      <td>Vasiliev</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>nan</td>
+      <td>128743.80</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>197322.13</td>
+      <td>0</td>
+      <td>ht</td>
+      <td>0.974604</td>
+      <td>neutral</td>
+      <td>0.680865</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>2137</td>
+      <td>15621893</td>
+      <td>Bellucci</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>Yo _IS_PUNCT_ get to love bank customer servic...</td>
+      <td>133550.67</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1</td>
+      <td>46941.41</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.758962</td>
+      <td>positive</td>
+      <td>0.878306</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-10">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[[</span><span class="s2">"CustomerFeedback_sentiment5"</span><span class="p">,</span> <span class="s2">"CustomerFeedback_sentiment5_score"</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[[</span><span class="s2">&quot;CustomerFeedback_sentiment5&quot;</span><span class="p">,</span> <span class="s2">&quot;CustomerFeedback_sentiment5_score&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
     <span class="n">preprocess_text</span><span class="o">.</span><span class="n">sentiment_analysis</span><span class="p">(</span>
-        <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">text_col</span><span class="o">=</span><span class="s2">"CustomerFeedback"</span><span class="p">,</span> <span class="n">sentiment_depth</span><span class="o">=</span><span class="mi">5</span>
-    <span class="p">)[[</span><span class="s2">"CustomerFeedback_sentiment"</span><span class="p">,</span> <span class="s2">"CustomerFeedback_sentiment_score"</span><span class="p">]]</span>
+        <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">text_col</span><span class="o">=</span><span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">,</span> <span class="n">sentiment_depth</span><span class="o">=</span><span class="mi">5</span>
+    <span class="p">)[[</span><span class="s2">&quot;CustomerFeedback_sentiment&quot;</span><span class="p">,</span> <span class="s2">&quot;CustomerFeedback_sentiment_score&quot;</span><span class="p">]]</span>
 <span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-10">df_pd[["CustomerFeedback_sentiment5", "CustomerFeedback_sentiment5_score"]] = (
+<div id="cell-10" class="clipboard-copy-txt">df_pd[["CustomerFeedback_sentiment5", "CustomerFeedback_sentiment5_score"]] = (
     preprocess_text.sentiment_analysis(
         df=df_pd, text_col="CustomerFeedback", sentiment_depth=5
     )[["CustomerFeedback_sentiment", "CustomerFeedback_sentiment_score"]]
 )
 df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -2428,270 +2973,297 @@ <h3 id="sentiment-analysis-prediction">Sentiment analysis prediction<a class="an
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>RowNumber</th>
-<th>CustomerId</th>
-<th>Surname</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>CustomerFeedback</th>
-<th>Balance (EUR)</th>
-<th>...</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_language</th>
-<th>CustomerFeedback_language_score</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment3_score</th>
-<th>CustomerFeedback_sentiment5</th>
-<th>CustomerFeedback_sentiment5_score</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>747</td>
-<td>15787619</td>
-<td>Hsieh</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>link card _IS_PUNCT_ party platform _IS_PUNCT_</td>
-<td>160980.03</td>
-<td>...</td>
-<td>0</td>
-<td>0</td>
-<td>145936.28</td>
-<td>0</td>
-<td>en</td>
-<td>0.786177</td>
-<td>neutral</td>
-<td>0.827256</td>
-<td>4 stars</td>
-<td>0.259499</td>
-</tr>
-<tr>
-<th>1</th>
-<td>1620</td>
-<td>15770309</td>
-<td>McDonald</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>week _IS_PUNCT_ reach bank support team confus...</td>
-<td>151762.74</td>
-<td>...</td>
-<td>0</td>
-<td>1</td>
-<td>127014.32</td>
-<td>0</td>
-<td>en</td>
-<td>0.911405</td>
-<td>neutral</td>
-<td>0.671348</td>
-<td>1 star</td>
-<td>0.359483</td>
-</tr>
-<tr>
-<th>2</th>
-<td>1679</td>
-<td>15569178</td>
-<td>Kharlamov</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>nan</td>
-<td>82767.42</td>
-<td>...</td>
-<td>1</td>
-<td>0</td>
-<td>71811.90</td>
-<td>0</td>
-<td>ht</td>
-<td>0.974604</td>
-<td>neutral</td>
-<td>0.680865</td>
-<td>2 stars</td>
-<td>0.260568</td>
-</tr>
-<tr>
-<th>3</th>
-<td>2022</td>
-<td>15795519</td>
-<td>Vasiliev</td>
-<td>716</td>
-<td>Germany</td>
-<td>Female</td>
-<td>18</td>
-<td>3</td>
-<td>nan</td>
-<td>128743.80</td>
-<td>...</td>
-<td>0</td>
-<td>0</td>
-<td>197322.13</td>
-<td>0</td>
-<td>ht</td>
-<td>0.974604</td>
-<td>neutral</td>
-<td>0.680865</td>
-<td>2 stars</td>
-<td>0.260568</td>
-</tr>
-<tr>
-<th>4</th>
-<td>2137</td>
-<td>15621893</td>
-<td>Bellucci</td>
-<td>727</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>4</td>
-<td>Yo _IS_PUNCT_ get to love bank customer servic...</td>
-<td>133550.67</td>
-<td>...</td>
-<td>1</td>
-<td>1</td>
-<td>46941.41</td>
-<td>0</td>
-<td>en</td>
-<td>0.758962</td>
-<td>positive</td>
-<td>0.878306</td>
-<td>1 star</td>
-<td>0.291449</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>RowNumber</th>
+      <th>CustomerId</th>
+      <th>Surname</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>CustomerFeedback</th>
+      <th>Balance (EUR)</th>
+      <th>...</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_language</th>
+      <th>CustomerFeedback_language_score</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment3_score</th>
+      <th>CustomerFeedback_sentiment5</th>
+      <th>CustomerFeedback_sentiment5_score</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>747</td>
+      <td>15787619</td>
+      <td>Hsieh</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>link card _IS_PUNCT_ party platform _IS_PUNCT_</td>
+      <td>160980.03</td>
+      <td>...</td>
+      <td>0</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.786177</td>
+      <td>neutral</td>
+      <td>0.827256</td>
+      <td>4 stars</td>
+      <td>0.259499</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1620</td>
+      <td>15770309</td>
+      <td>McDonald</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>week _IS_PUNCT_ reach bank support team confus...</td>
+      <td>151762.74</td>
+      <td>...</td>
+      <td>0</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.911405</td>
+      <td>neutral</td>
+      <td>0.671348</td>
+      <td>1 star</td>
+      <td>0.359483</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1679</td>
+      <td>15569178</td>
+      <td>Kharlamov</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
+      <td>nan</td>
+      <td>82767.42</td>
+      <td>...</td>
+      <td>1</td>
+      <td>0</td>
+      <td>71811.90</td>
+      <td>0</td>
+      <td>ht</td>
+      <td>0.974604</td>
+      <td>neutral</td>
+      <td>0.680865</td>
+      <td>2 stars</td>
+      <td>0.260568</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>2022</td>
+      <td>15795519</td>
+      <td>Vasiliev</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>nan</td>
+      <td>128743.80</td>
+      <td>...</td>
+      <td>0</td>
+      <td>0</td>
+      <td>197322.13</td>
+      <td>0</td>
+      <td>ht</td>
+      <td>0.974604</td>
+      <td>neutral</td>
+      <td>0.680865</td>
+      <td>2 stars</td>
+      <td>0.260568</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>2137</td>
+      <td>15621893</td>
+      <td>Bellucci</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>Yo _IS_PUNCT_ get to love bank customer servic...</td>
+      <td>133550.67</td>
+      <td>...</td>
+      <td>1</td>
+      <td>1</td>
+      <td>46941.41</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.758962</td>
+      <td>positive</td>
+      <td>0.878306</td>
+      <td>1 star</td>
+      <td>0.291449</td>
+    </tr>
+  </tbody>
 </table>
 <p>5 rows × 21 columns</p>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># save intermediate results</span>
-<span class="n">df_pd</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span><span class="s2">"data/dataset_sentiment.parquet"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># save intermediate results</span>
+<span class="n">df_pd</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span><span class="s2">&quot;data/dataset_sentiment.parquet&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-11"># save intermediate results
+<div id="cell-11" class="clipboard-copy-txt"># save intermediate results
 df_pd.to_parquet("data/dataset_sentiment.parquet")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="surname-origin-country">Surname origin country<a class="anchor-link" href="#surname-origin-country">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="surname-origin-country">Surname origin country<a class="anchor-link" href="#surname-origin-country">&#182;</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-12">
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-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># df_pd = pd.read_parquet("data/dataset_sentiment.parquet")</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># df_pd = pd.read_parquet(&quot;data/dataset_sentiment.parquet&quot;)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-12"># df_pd = pd.read_parquet("data/dataset_sentiment.parquet")</div>
-</div>
-</div>
+<div id="cell-12" class="clipboard-copy-txt"># df_pd = pd.read_parquet("data/dataset_sentiment.parquet")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname"</span> <span class="o">+</span> <span class="s2">"_Country"</span><span class="p">]</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">surname_origin</span><span class="p">(</span>
-    <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">surname_col</span><span class="o">=</span><span class="s2">"Surname"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname&quot;</span> <span class="o">+</span> <span class="s2">&quot;_Country&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">surname_origin</span><span class="p">(</span>
+    <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">surname_col</span><span class="o">=</span><span class="s2">&quot;Surname&quot;</span>
 <span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-13">df_pd["Surname" + "_Country"] = auxiliary_data.surname_origin(
+<div id="cell-13" class="clipboard-copy-txt">df_pd["Surname" + "_Country"] = auxiliary_data.surname_origin(
     df=df_pd, surname_col="Surname"
 )
 df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -2705,289 +3277,320 @@ <h2 id="surname-origin-country">Surname origin country<a class="anchor-link" hre
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>RowNumber</th>
-<th>CustomerId</th>
-<th>Surname</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>CustomerFeedback</th>
-<th>Balance (EUR)</th>
-<th>...</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_language</th>
-<th>CustomerFeedback_language_score</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment3_score</th>
-<th>CustomerFeedback_sentiment5</th>
-<th>CustomerFeedback_sentiment5_score</th>
-<th>Surname_Country</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>747</td>
-<td>15787619</td>
-<td>Hsieh</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>link card _IS_PUNCT_ party platform _IS_PUNCT_</td>
-<td>160980.03</td>
-<td>...</td>
-<td>0</td>
-<td>145936.28</td>
-<td>0</td>
-<td>en</td>
-<td>0.786177</td>
-<td>neutral</td>
-<td>0.827256</td>
-<td>4 stars</td>
-<td>0.259499</td>
-<td>Taiwan, Province of China</td>
-</tr>
-<tr>
-<th>1</th>
-<td>1620</td>
-<td>15770309</td>
-<td>McDonald</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>week _IS_PUNCT_ reach bank support team confus...</td>
-<td>151762.74</td>
-<td>...</td>
-<td>1</td>
-<td>127014.32</td>
-<td>0</td>
-<td>en</td>
-<td>0.911405</td>
-<td>neutral</td>
-<td>0.671348</td>
-<td>1 star</td>
-<td>0.359483</td>
-<td>United States</td>
-</tr>
-<tr>
-<th>2</th>
-<td>1679</td>
-<td>15569178</td>
-<td>Kharlamov</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>nan</td>
-<td>82767.42</td>
-<td>...</td>
-<td>0</td>
-<td>71811.90</td>
-<td>0</td>
-<td>ht</td>
-<td>0.974604</td>
-<td>neutral</td>
-<td>0.680865</td>
-<td>2 stars</td>
-<td>0.260568</td>
-<td>Russian Federation</td>
-</tr>
-<tr>
-<th>3</th>
-<td>2022</td>
-<td>15795519</td>
-<td>Vasiliev</td>
-<td>716</td>
-<td>Germany</td>
-<td>Female</td>
-<td>18</td>
-<td>3</td>
-<td>nan</td>
-<td>128743.80</td>
-<td>...</td>
-<td>0</td>
-<td>197322.13</td>
-<td>0</td>
-<td>ht</td>
-<td>0.974604</td>
-<td>neutral</td>
-<td>0.680865</td>
-<td>2 stars</td>
-<td>0.260568</td>
-<td>Russian Federation</td>
-</tr>
-<tr>
-<th>4</th>
-<td>2137</td>
-<td>15621893</td>
-<td>Bellucci</td>
-<td>727</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>4</td>
-<td>Yo _IS_PUNCT_ get to love bank customer servic...</td>
-<td>133550.67</td>
-<td>...</td>
-<td>1</td>
-<td>46941.41</td>
-<td>0</td>
-<td>en</td>
-<td>0.758962</td>
-<td>positive</td>
-<td>0.878306</td>
-<td>1 star</td>
-<td>0.291449</td>
-<td>Italy</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>RowNumber</th>
+      <th>CustomerId</th>
+      <th>Surname</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>CustomerFeedback</th>
+      <th>Balance (EUR)</th>
+      <th>...</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_language</th>
+      <th>CustomerFeedback_language_score</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment3_score</th>
+      <th>CustomerFeedback_sentiment5</th>
+      <th>CustomerFeedback_sentiment5_score</th>
+      <th>Surname_Country</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>747</td>
+      <td>15787619</td>
+      <td>Hsieh</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>link card _IS_PUNCT_ party platform _IS_PUNCT_</td>
+      <td>160980.03</td>
+      <td>...</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.786177</td>
+      <td>neutral</td>
+      <td>0.827256</td>
+      <td>4 stars</td>
+      <td>0.259499</td>
+      <td>Taiwan, Province of China</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>1620</td>
+      <td>15770309</td>
+      <td>McDonald</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>week _IS_PUNCT_ reach bank support team confus...</td>
+      <td>151762.74</td>
+      <td>...</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.911405</td>
+      <td>neutral</td>
+      <td>0.671348</td>
+      <td>1 star</td>
+      <td>0.359483</td>
+      <td>United States</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>1679</td>
+      <td>15569178</td>
+      <td>Kharlamov</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
+      <td>nan</td>
+      <td>82767.42</td>
+      <td>...</td>
+      <td>0</td>
+      <td>71811.90</td>
+      <td>0</td>
+      <td>ht</td>
+      <td>0.974604</td>
+      <td>neutral</td>
+      <td>0.680865</td>
+      <td>2 stars</td>
+      <td>0.260568</td>
+      <td>Russian Federation</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>2022</td>
+      <td>15795519</td>
+      <td>Vasiliev</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>nan</td>
+      <td>128743.80</td>
+      <td>...</td>
+      <td>0</td>
+      <td>197322.13</td>
+      <td>0</td>
+      <td>ht</td>
+      <td>0.974604</td>
+      <td>neutral</td>
+      <td>0.680865</td>
+      <td>2 stars</td>
+      <td>0.260568</td>
+      <td>Russian Federation</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>2137</td>
+      <td>15621893</td>
+      <td>Bellucci</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>Yo _IS_PUNCT_ get to love bank customer servic...</td>
+      <td>133550.67</td>
+      <td>...</td>
+      <td>1</td>
+      <td>46941.41</td>
+      <td>0</td>
+      <td>en</td>
+      <td>0.758962</td>
+      <td>positive</td>
+      <td>0.878306</td>
+      <td>1 star</td>
+      <td>0.291449</td>
+      <td>Italy</td>
+    </tr>
+  </tbody>
 </table>
 <p>5 rows × 22 columns</p>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">)][</span><span class="s2">"Surname"</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">)][</span><span class="s2">&quot;Surname&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-14">df_pd[(df_pd["Surname_Country"] == "NA")]["Surname"].nunique()</div>
-</div>
-</div>
+<div id="cell-14" class="clipboard-copy-txt">df_pd[(df_pd["Surname_Country"] == "NA")]["Surname"].nunique()</div>
+    
+        </div>
+    </div>
+
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 </div>
+
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-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[13]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>496</pre>
 </div>
+
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+
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+
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+
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 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>less than 500 so I can use Namsor free trial to get data,
 I will use their <a href="https://namsor.app/csv-excel-tool/">online csv tool</a> and not API function<code>auxiliary_data.surname_origin_namsor</code> to avoid wasting another email address for free trail</p>
+
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 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">namsor_csv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
-    <span class="p">{</span><span class="s2">"Surname"</span><span class="p">:</span> <span class="n">df_pd</span><span class="p">[(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">)][</span><span class="s2">"Surname"</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()}</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">namsor_csv</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
+    <span class="p">{</span><span class="s2">&quot;Surname&quot;</span><span class="p">:</span> <span class="n">df_pd</span><span class="p">[(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">)][</span><span class="s2">&quot;Surname&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()}</span>
 <span class="p">)</span>
-<span class="n">namsor_csv</span><span class="p">[</span><span class="s2">"first_name"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"unset"</span>
-<span class="n">namsor_csv</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"data/surnames/surnames_for_namsor.csv"</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">namsor_csv</span><span class="p">[</span><span class="s2">&quot;first_name&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;unset&quot;</span>
+<span class="n">namsor_csv</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">&quot;data/surnames/surnames_for_namsor.csv&quot;</span><span class="p">,</span> <span class="n">index</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-15">namsor_csv = pd.DataFrame(
+<div id="cell-15" class="clipboard-copy-txt">namsor_csv = pd.DataFrame(
     {"Surname": df_pd[(df_pd["Surname_Country"] == "NA")]["Surname"].unique()}
 )
 namsor_csv["first_name"] = "unset"
 namsor_csv.to_csv("data/surnames/surnames_for_namsor.csv", index=False)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>after namsor processing</p>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># keep_default_na=False as NA-Namibia is intrepreted as NaN</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># keep_default_na=False as NA-Namibia is intrepreted as NaN</span>
 <span class="n">namsor_surnames</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
-    <span class="s2">"data/surnames/namsor_name-origin_surnames_for_namsor.csv"</span><span class="p">,</span>
+    <span class="s2">&quot;data/surnames/namsor_name-origin_surnames_for_namsor.csv&quot;</span><span class="p">,</span>
     <span class="n">keep_default_na</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-    <span class="n">na_values</span><span class="o">=</span><span class="p">[</span><span class="s2">"_"</span><span class="p">],</span>
+    <span class="n">na_values</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;_&quot;</span><span class="p">],</span>
 <span class="p">)</span>
-<span class="n">namsor_surnames</span> <span class="o">=</span> <span class="n">namsor_surnames</span><span class="p">[[</span><span class="s2">"lastName"</span><span class="p">,</span> <span class="s2">"countryOrigin"</span><span class="p">]]</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
-    <span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">"lastName"</span><span class="p">:</span> <span class="s2">"Surname"</span><span class="p">,</span> <span class="s2">"countryOrigin"</span><span class="p">:</span> <span class="s2">"Surname_Country_namsor"</span><span class="p">}</span>
+<span class="n">namsor_surnames</span> <span class="o">=</span> <span class="n">namsor_surnames</span><span class="p">[[</span><span class="s2">&quot;lastName&quot;</span><span class="p">,</span> <span class="s2">&quot;countryOrigin&quot;</span><span class="p">]]</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;lastName&quot;</span><span class="p">:</span> <span class="s2">&quot;Surname&quot;</span><span class="p">,</span> <span class="s2">&quot;countryOrigin&quot;</span><span class="p">:</span> <span class="s2">&quot;Surname_Country_namsor&quot;</span><span class="p">}</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-16"># keep_default_na=False as NA-Namibia is intrepreted as NaN
+<div id="cell-16" class="clipboard-copy-txt"># keep_default_na=False as NA-Namibia is intrepreted as NaN
 namsor_surnames = pd.read_csv(
     "data/surnames/namsor_name-origin_surnames_for_namsor.csv",
     keep_default_na=False,
@@ -2996,52 +3599,68 @@ <h2 id="surname-origin-country">Surname origin country<a class="anchor-link" hre
 namsor_surnames = namsor_surnames[["lastName", "countryOrigin"]].rename(
     columns={"lastName": "Surname", "countryOrigin": "Surname_Country_namsor"}
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">namsor_surnames</span><span class="p">[</span><span class="s2">"Surname_Country_namsor"</span><span class="p">]</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">get_country_name</span><span class="p">(</span>
-    <span class="n">df</span><span class="o">=</span><span class="n">namsor_surnames</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">"Surname_Country_namsor"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-17">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">namsor_surnames</span><span class="p">[</span><span class="s2">&quot;Surname_Country_namsor&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">get_country_name</span><span class="p">(</span>
+    <span class="n">df</span><span class="o">=</span><span class="n">namsor_surnames</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">&quot;Surname_Country_namsor&quot;</span>
 <span class="p">)</span>
 <span class="n">namsor_surnames</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-17">namsor_surnames["Surname_Country_namsor"] = auxiliary_data.get_country_name(
+<div id="cell-17" class="clipboard-copy-txt">namsor_surnames["Surname_Country_namsor"] = auxiliary_data.get_country_name(
     df=namsor_surnames, country_name_col="Surname_Country_namsor"
 )
 namsor_surnames.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3055,225 +3674,264 @@ <h2 id="surname-origin-country">Surname origin country<a class="anchor-link" hre
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>Surname</th>
-<th>Surname_Country_namsor</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>Vaguine</td>
-<td>France</td>
-</tr>
-<tr>
-<th>1</th>
-<td>Onyekaozulu</td>
-<td>Nigeria</td>
-</tr>
-<tr>
-<th>2</th>
-<td>Chiazagomekpele</td>
-<td>Nigeria</td>
-</tr>
-<tr>
-<th>3</th>
-<td>Fleetwood-Smith</td>
-<td>United Kingdom</td>
-</tr>
-<tr>
-<th>4</th>
-<td>Anayolisa</td>
-<td>Nigeria</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Surname</th>
+      <th>Surname_Country_namsor</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>Vaguine</td>
+      <td>France</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Onyekaozulu</td>
+      <td>Nigeria</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Chiazagomekpele</td>
+      <td>Nigeria</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Fleetwood-Smith</td>
+      <td>United Kingdom</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Anayolisa</td>
+      <td>Nigeria</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">namsor_surnames</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">"Surname"</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">"left"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">namsor_surnames</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s2">&quot;Surname&quot;</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s2">&quot;left&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-18">df_pd = df_pd.merge(namsor_surnames, on="Surname", how="left")</div>
-</div>
-</div>
+<div id="cell-18" class="clipboard-copy-txt">df_pd = df_pd.merge(namsor_surnames, on="Surname", how="left")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">),</span> <span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-    <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">),</span> <span class="s2">"Surname_Country_namsor"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">),</span> <span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">),</span> <span class="s2">&quot;Surname_Country_namsor&quot;</span>
 <span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-19">df_pd.loc[(df_pd["Surname_Country"] == "NA"), "Surname_Country"] = df_pd.loc[
+<div id="cell-19" class="clipboard-copy-txt">df_pd.loc[(df_pd["Surname_Country"] == "NA"), "Surname_Country"] = df_pd.loc[
     (df_pd["Surname_Country"] == "NA"), "Surname_Country_namsor"
 ]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"Surname_Country_namsor"</span><span class="p">])</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Surname_Country_namsor&quot;</span><span class="p">])</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-20">df_pd = df_pd.drop(columns=["Surname_Country_namsor"])</div>
-</div>
-</div>
+<div id="cell-20" class="clipboard-copy-txt">df_pd = df_pd.drop(columns=["Surname_Country_namsor"])</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-21">df_pd["Surname_Country"].unique()</div>
-</div>
-</div>
+<div id="cell-21" class="clipboard-copy-txt">df_pd["Surname_Country"].unique()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+
+
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-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>array(['Taiwan, Province of China', 'United States', 'Russian Federation',
-       'Italy', 'Macao', 'Ireland', 'United Kingdom', 'Nigeria', 'France',
-       'Algeria', 'Germany', 'Malaysia', 'Mexico', 'Hong Kong', 'China',
-       'Israel', 'Kazakhstan', 'Colombia', 'Lesotho', 'Saudi Arabia',
-       'Uruguay', 'Togo', 'Ethiopia', 'Spain', 'Egypt', 'Ukraine',
-       'Lebanon', 'Zimbabwe', 'India', 'Thailand', 'Brazil', 'Panama',
-       'Türkiye', "Côte d'Ivoire", 'South Africa', 'Azerbaijan', 'Congo',
-       'Czechia', 'Burkina Faso', 'Uzbekistan', 'Morocco', 'Ghana',
-       'Portugal', 'Austria', 'Sri Lanka', 'Bulgaria', 'Norway', 'Greece',
-       'Estonia', 'Bangladesh', 'Viet Nam', 'Netherlands', 'Cameroon',
-       'Sweden', 'Costa Rica', 'Japan', 'Liberia', 'Myanmar',
-       'Brunei Darussalam', 'Canada', 'Moldova, Republic of', 'Denmark',
-       'Benin', 'Niger', 'Latvia', 'Finland', 'Chile', 'Poland',
-       'Switzerland', 'Mozambique', 'Indonesia', 'Rwanda', 'Argentina',
-       'Namibia', 'Belgium', 'Botswana', 'Romania', 'Mali', 'Belarus',
-       'Cyprus', 'Palestine, State of', 'Hungary', 'Mauritius',
-       'Pakistan', 'Senegal', 'Slovakia', 'Iran, Islamic Republic of',
-       'Malawi', 'Jamaica'], dtype=object)</pre>
-</div>
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-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([&#39;Taiwan, Province of China&#39;, &#39;United States&#39;, &#39;Russian Federation&#39;,
+       &#39;Italy&#39;, &#39;Macao&#39;, &#39;Ireland&#39;, &#39;United Kingdom&#39;, &#39;Nigeria&#39;, &#39;France&#39;,
+       &#39;Algeria&#39;, &#39;Germany&#39;, &#39;Malaysia&#39;, &#39;Mexico&#39;, &#39;Hong Kong&#39;, &#39;China&#39;,
+       &#39;Israel&#39;, &#39;Kazakhstan&#39;, &#39;Colombia&#39;, &#39;Lesotho&#39;, &#39;Saudi Arabia&#39;,
+       &#39;Uruguay&#39;, &#39;Togo&#39;, &#39;Ethiopia&#39;, &#39;Spain&#39;, &#39;Egypt&#39;, &#39;Ukraine&#39;,
+       &#39;Lebanon&#39;, &#39;Zimbabwe&#39;, &#39;India&#39;, &#39;Thailand&#39;, &#39;Brazil&#39;, &#39;Panama&#39;,
+       &#39;Türkiye&#39;, &#34;Côte d&#39;Ivoire&#34;, &#39;South Africa&#39;, &#39;Azerbaijan&#39;, &#39;Congo&#39;,
+       &#39;Czechia&#39;, &#39;Burkina Faso&#39;, &#39;Uzbekistan&#39;, &#39;Morocco&#39;, &#39;Ghana&#39;,
+       &#39;Portugal&#39;, &#39;Austria&#39;, &#39;Sri Lanka&#39;, &#39;Bulgaria&#39;, &#39;Norway&#39;, &#39;Greece&#39;,
+       &#39;Estonia&#39;, &#39;Bangladesh&#39;, &#39;Viet Nam&#39;, &#39;Netherlands&#39;, &#39;Cameroon&#39;,
+       &#39;Sweden&#39;, &#39;Costa Rica&#39;, &#39;Japan&#39;, &#39;Liberia&#39;, &#39;Myanmar&#39;,
+       &#39;Brunei Darussalam&#39;, &#39;Canada&#39;, &#39;Moldova, Republic of&#39;, &#39;Denmark&#39;,
+       &#39;Benin&#39;, &#39;Niger&#39;, &#39;Latvia&#39;, &#39;Finland&#39;, &#39;Chile&#39;, &#39;Poland&#39;,
+       &#39;Switzerland&#39;, &#39;Mozambique&#39;, &#39;Indonesia&#39;, &#39;Rwanda&#39;, &#39;Argentina&#39;,
+       &#39;Namibia&#39;, &#39;Belgium&#39;, &#39;Botswana&#39;, &#39;Romania&#39;, &#39;Mali&#39;, &#39;Belarus&#39;,
+       &#39;Cyprus&#39;, &#39;Palestine, State of&#39;, &#39;Hungary&#39;, &#39;Mauritius&#39;,
+       &#39;Pakistan&#39;, &#39;Senegal&#39;, &#39;Slovakia&#39;, &#39;Iran, Islamic Republic of&#39;,
+       &#39;Malawi&#39;, &#39;Jamaica&#39;], dtype=object)</pre>
+</div>
+
+</div>
+
+</div>
+
+</div>
+
+</div>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [20]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">country_info_problematic_vals</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">"Iran, Islamic Republic of"</span><span class="p">:</span> <span class="s2">"Iran"</span><span class="p">,</span>
-    <span class="s2">"Moldova, Republic of"</span><span class="p">:</span> <span class="s2">"Moldova"</span><span class="p">,</span>
-    <span class="s2">"Taiwan, Province of China"</span><span class="p">:</span> <span class="s2">"Taiwan"</span><span class="p">,</span>
-    <span class="s2">"Czechia"</span><span class="p">:</span> <span class="s2">"Czech republic"</span><span class="p">,</span>
-    <span class="s2">"Côte d'Ivoire"</span><span class="p">:</span> <span class="s2">"Ivory Coast"</span><span class="p">,</span>
-    <span class="s2">"Brunei Darussalam"</span><span class="p">:</span> <span class="s2">"Brunei"</span><span class="p">,</span>
-    <span class="s2">"Viet Nam"</span><span class="p">:</span> <span class="s2">"Vietnam"</span><span class="p">,</span>
-    <span class="s2">"Macao"</span><span class="p">:</span> <span class="s2">"Macau"</span><span class="p">,</span>
-    <span class="s2">"Türkiye"</span><span class="p">:</span> <span class="s2">"Turkey"</span><span class="p">,</span>
-    <span class="s2">"Congo"</span><span class="p">:</span> <span class="s2">"Democratic Republic of the Congo"</span><span class="p">,</span>
-    <span class="s2">"Palestine, State of"</span><span class="p">:</span> <span class="s2">"Palestine"</span><span class="p">,</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">country_info_problematic_vals</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;Iran, Islamic Republic of&quot;</span><span class="p">:</span> <span class="s2">&quot;Iran&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Moldova, Republic of&quot;</span><span class="p">:</span> <span class="s2">&quot;Moldova&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Taiwan, Province of China&quot;</span><span class="p">:</span> <span class="s2">&quot;Taiwan&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Czechia&quot;</span><span class="p">:</span> <span class="s2">&quot;Czech republic&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Côte d&#39;Ivoire&quot;</span><span class="p">:</span> <span class="s2">&quot;Ivory Coast&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Brunei Darussalam&quot;</span><span class="p">:</span> <span class="s2">&quot;Brunei&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Viet Nam&quot;</span><span class="p">:</span> <span class="s2">&quot;Vietnam&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Macao&quot;</span><span class="p">:</span> <span class="s2">&quot;Macau&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Türkiye&quot;</span><span class="p">:</span> <span class="s2">&quot;Turkey&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Congo&quot;</span><span class="p">:</span> <span class="s2">&quot;Democratic Republic of the Congo&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Palestine, State of&quot;</span><span class="p">:</span> <span class="s2">&quot;Palestine&quot;</span><span class="p">,</span>
 <span class="p">}</span>
-<span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span>
+<span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span>
     <span class="n">country_info_problematic_vals</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-22">country_info_problematic_vals = {
+<div id="cell-22" class="clipboard-copy-txt">country_info_problematic_vals = {
     "Iran, Islamic Republic of": "Iran",
     "Moldova, Republic of": "Moldova",
     "Taiwan, Province of China": "Taiwan",
@@ -3289,109 +3947,130 @@ <h2 id="surname-origin-country">Surname origin country<a class="anchor-link" hre
 df_pd["Surname_Country"] = df_pd["Surname_Country"].replace(
     country_info_problematic_vals
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="additional-country-info">Additional country info<a class="anchor-link" href="#additional-country-info">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="additional-country-info">Additional country info<a class="anchor-link" href="#additional-country-info">&#182;</a></h2>
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="country-region-and-subregion">Country region and subregion<a class="anchor-link" href="#country-region-and-subregion">¶</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="country-region-and-subregion">Country region and subregion<a class="anchor-link" href="#country-region-and-subregion">&#182;</a></h3>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[[</span><span class="s2">"Surname_Country_region"</span><span class="p">,</span> <span class="s2">"Surname_Country_subregion"</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-23">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[[</span><span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
     <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">get_country_region_subregion</span><span class="p">(</span>
-        <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">"Surname_Country"</span>
+        <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">&quot;Surname_Country&quot;</span>
     <span class="p">)</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-23">df_pd[["Surname_Country_region", "Surname_Country_subregion"]] = (
+<div id="cell-23" class="clipboard-copy-txt">df_pd[["Surname_Country_region", "Surname_Country_subregion"]] = (
     auxiliary_data.get_country_region_subregion(
         df=df_pd, country_name_col="Surname_Country"
     )
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-24">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span>
-    <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country_region"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">)</span>
-    <span class="o">|</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country_subregion"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">)</span>
-<span class="p">][[</span><span class="s2">"Surname_Country"</span><span class="p">,</span> <span class="s2">"Surname_Country_region"</span><span class="p">,</span> <span class="s2">"Surname_Country_subregion"</span><span class="p">]]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-24">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span>
+    <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">)</span>
+    <span class="o">|</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">)</span>
+<span class="p">][[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">]]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-24">df_pd[
+<div id="cell-24" class="clipboard-copy-txt">df_pd[
     (df_pd["Surname_Country_region"] == "NA")
     | (df_pd["Surname_Country_subregion"] == "NA")
 ][["Surname_Country", "Surname_Country_region", "Surname_Country_subregion"]]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3405,143 +4084,149 @@ <h3 id="country-region-and-subregion">Country region and subregion<a class="anch
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>Surname_Country</th>
-<th>Surname_Country_region</th>
-<th>Surname_Country_subregion</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>2024</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>3322</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>4237</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>4626</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>4892</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>5348</th>
-<td>Palestine</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>6190</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>7064</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>7098</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>7693</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>7895</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>8299</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>8789</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-<tr>
-<th>9600</th>
-<td>Myanmar</td>
-<td>NA</td>
-<td>NA</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Surname_Country</th>
+      <th>Surname_Country_region</th>
+      <th>Surname_Country_subregion</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>2024</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>3322</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>4237</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>4626</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>4892</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>5348</th>
+      <td>Palestine</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>6190</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>7064</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>7098</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>7693</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>7895</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>8299</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>8789</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+    <tr>
+      <th>9600</th>
+      <td>Myanmar</td>
+      <td>NA</td>
+      <td>NA</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-25">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># https://en.wikipedia.org/wiki/Palestine_(region) - &#39;Western Asia&#39; to follow countryinfo naming</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-    <span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"Palestine"</span><span class="p">,</span>
-    <span class="p">[</span><span class="s2">"Surname_Country_region"</span><span class="p">,</span> <span class="s2">"Surname_Country_subregion"</span><span class="p">],</span>
-<span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"Asia"</span><span class="p">,</span> <span class="s2">"Western Asia"</span><span class="p">)</span>
-<span class="c1"># https://en.wikipedia.org/wiki/Myanmar - 'South-Eastern Asia' to follow countryinfo naming</span>
+    <span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Palestine&quot;</span><span class="p">,</span>
+    <span class="p">[</span><span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">],</span>
+<span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;Asia&quot;</span><span class="p">,</span> <span class="s2">&quot;Western Asia&quot;</span><span class="p">)</span>
+<span class="c1"># https://en.wikipedia.org/wiki/Myanmar - &#39;South-Eastern Asia&#39; to follow countryinfo naming</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-    <span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"Myanmar"</span><span class="p">,</span>
-    <span class="p">[</span><span class="s2">"Surname_Country_region"</span><span class="p">,</span> <span class="s2">"Surname_Country_subregion"</span><span class="p">],</span>
-<span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">"Asia"</span><span class="p">,</span> <span class="s2">"South-Eastern Asia"</span><span class="p">)</span>
+    <span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Myanmar&quot;</span><span class="p">,</span>
+    <span class="p">[</span><span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">],</span>
+<span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;Asia&quot;</span><span class="p">,</span> <span class="s2">&quot;South-Eastern Asia&quot;</span><span class="p">)</span>
 
 <span class="n">df_pd</span><span class="p">[</span>
-    <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country_region"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">)</span>
-    <span class="o">|</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country_subregion"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">)</span>
+    <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">)</span>
+    <span class="o">|</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">)</span>
 <span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-25"># https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming
+<div id="cell-25" class="clipboard-copy-txt"># https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming
 df_pd.loc[
     df_pd["Surname_Country"] == "Palestine",
     ["Surname_Country_region", "Surname_Country_subregion"],
@@ -3556,19 +4241,30 @@ <h3 id="country-region-and-subregion">Country region and subregion<a class="anch
     (df_pd["Surname_Country_region"] == "NA")
     | (df_pd["Surname_Country_subregion"] == "NA")
 ]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3582,112 +4278,134 @@ <h3 id="country-region-and-subregion">Country region and subregion<a class="anch
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>RowNumber</th>
-<th>CustomerId</th>
-<th>Surname</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>CustomerFeedback</th>
-<th>Balance (EUR)</th>
-<th>...</th>
-<th>Exited</th>
-<th>CustomerFeedback_language</th>
-<th>CustomerFeedback_language_score</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment3_score</th>
-<th>CustomerFeedback_sentiment5</th>
-<th>CustomerFeedback_sentiment5_score</th>
-<th>Surname_Country</th>
-<th>Surname_Country_region</th>
-<th>Surname_Country_subregion</th>
-</tr>
-</thead>
-<tbody>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>RowNumber</th>
+      <th>CustomerId</th>
+      <th>Surname</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>CustomerFeedback</th>
+      <th>Balance (EUR)</th>
+      <th>...</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_language</th>
+      <th>CustomerFeedback_language_score</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment3_score</th>
+      <th>CustomerFeedback_sentiment5</th>
+      <th>CustomerFeedback_sentiment5_score</th>
+      <th>Surname_Country</th>
+      <th>Surname_Country_region</th>
+      <th>Surname_Country_subregion</th>
+    </tr>
+  </thead>
+  <tbody>
+  </tbody>
 </table>
 <p>0 rows × 24 columns</p>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[[</span><span class="s2">"Country_region"</span><span class="p">,</span> <span class="s2">"Country_subregion"</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
-    <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">get_country_region_subregion</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">"Country"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-26">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[[</span><span class="s2">&quot;Country_region&quot;</span><span class="p">,</span> <span class="s2">&quot;Country_subregion&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">get_country_region_subregion</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">&quot;Country&quot;</span><span class="p">)</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-26">df_pd[["Country_region", "Country_subregion"]] = (
+<div id="cell-26" class="clipboard-copy-txt">df_pd[["Country_region", "Country_subregion"]] = (
     auxiliary_data.get_country_region_subregion(df=df_pd, country_name_col="Country")
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [25]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-27">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Country_region"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Country_subregion"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"NA"</span><span class="p">)][</span>
-    <span class="p">[</span><span class="s2">"Country"</span><span class="p">,</span> <span class="s2">"Country_region"</span><span class="p">,</span> <span class="s2">"Country_subregion"</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[25]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Country_region&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Country_subregion&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">)][</span>
+    <span class="p">[</span><span class="s2">&quot;Country&quot;</span><span class="p">,</span> <span class="s2">&quot;Country_region&quot;</span><span class="p">,</span> <span class="s2">&quot;Country_subregion&quot;</span><span class="p">]</span>
 <span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-27">df_pd[(df_pd["Country_region"] == "NA") | (df_pd["Country_subregion"] == "NA")][
+<div id="cell-27" class="clipboard-copy-txt">df_pd[(df_pd["Country_region"] == "NA") | (df_pd["Country_subregion"] == "NA")][
     ["Country", "Country_region", "Country_subregion"]
 ]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[25]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[25]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3701,155 +4419,174 @@ <h3 id="country-region-and-subregion">Country region and subregion<a class="anch
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>Country</th>
-<th>Country_region</th>
-<th>Country_subregion</th>
-</tr>
-</thead>
-<tbody>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Country</th>
+      <th>Country_region</th>
+      <th>Country_subregion</th>
+    </tr>
+  </thead>
+  <tbody>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="native-or-not">Native or not<a class="anchor-link" href="#native-or-not">¶</a></h3><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="native-or-not">Native or not<a class="anchor-link" href="#native-or-not">&#182;</a></h3><ul>
 <li>if the client is possibly an immigrant(from immigrant family?) or not</li>
 </ul>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [26]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"is_native"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="n">df_pd</span><span class="p">[</span><span class="s2">"Country"</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[26]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;is_native&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Country&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-28">df_pd["is_native"] = (df_pd["Surname_Country"] == df_pd["Country"]).astype(int)</div>
-</div>
-</div>
+<div id="cell-28" class="clipboard-copy-txt">df_pd["is_native"] = (df_pd["Surname_Country"] == df_pd["Country"]).astype(int)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="country-hemisphere">Country hemisphere<a class="anchor-link" href="#country-hemisphere">¶</a></h3><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="country-hemisphere">Country hemisphere<a class="anchor-link" href="#country-hemisphere">&#182;</a></h3><ul>
 <li>could be usefull with some timestamp combination to know the season(oposite season in hemispeheres) etc.; anything that might affect mood/behaviour</li>
 </ul>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [27]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># no need to know the hemisphere of the surname origin country...</span>
-<span class="n">df_pd</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">hemisphere</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">cc_col</span><span class="o">=</span><span class="s2">"Country"</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
-    <span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">"hemisphere"</span><span class="p">:</span> <span class="s2">"Country_hemisphere"</span><span class="p">}</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[27]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># no need to know the hemisphere of the surname origin country...</span>
+<span class="n">df_pd</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">hemisphere</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">cc_col</span><span class="o">=</span><span class="s2">&quot;Country&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
+    <span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;hemisphere&quot;</span><span class="p">:</span> <span class="s2">&quot;Country_hemisphere&quot;</span><span class="p">}</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-29"># no need to know the hemisphere of the surname origin country...
+<div id="cell-29" class="clipboard-copy-txt"># no need to know the hemisphere of the surname origin country...
 df_pd = auxiliary_data.hemisphere(df=df_pd, cc_col="Country").rename(
     columns={"hemisphere": "Country_hemisphere"}
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="country-gdpp">Country gdpp<a class="anchor-link" href="#country-gdpp">¶</a></h3><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="country-gdpp">Country gdpp<a class="anchor-link" href="#country-gdpp">&#182;</a></h3><ul>
 <li>check 'wealthiness' of the current and origin country</li>
 </ul>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">gdppc</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">"Country"</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">gdppc</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">&quot;Country&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
     <span class="n">columns</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">"gdp_per_capita"</span><span class="p">:</span> <span class="s2">"Country_gdp_per_capita"</span><span class="p">,</span>
-        <span class="s2">"IncomeGroup"</span><span class="p">:</span> <span class="s2">"Country_IncomeGroup"</span><span class="p">,</span>
+        <span class="s2">&quot;gdp_per_capita&quot;</span><span class="p">:</span> <span class="s2">&quot;Country_gdp_per_capita&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;IncomeGroup&quot;</span><span class="p">:</span> <span class="s2">&quot;Country_IncomeGroup&quot;</span><span class="p">,</span>
     <span class="p">}</span>
 <span class="p">)</span>
-<span class="n">df_pd</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">gdppc</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">"Surname_Country"</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
+<span class="n">df_pd</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">gdppc</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">country_name_col</span><span class="o">=</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span>
     <span class="n">columns</span><span class="o">=</span><span class="p">{</span>
-        <span class="s2">"gdp_per_capita"</span><span class="p">:</span> <span class="s2">"Surname_Country_gdp_per_capita"</span><span class="p">,</span>
-        <span class="s2">"IncomeGroup"</span><span class="p">:</span> <span class="s2">"Surname_Country_IncomeGroup"</span><span class="p">,</span>
+        <span class="s2">&quot;gdp_per_capita&quot;</span><span class="p">:</span> <span class="s2">&quot;Surname_Country_gdp_per_capita&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;IncomeGroup&quot;</span><span class="p">:</span> <span class="s2">&quot;Surname_Country_IncomeGroup&quot;</span><span class="p">,</span>
     <span class="p">}</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-30">df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col="Country").rename(
+<div id="cell-30" class="clipboard-copy-txt">df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col="Country").rename(
     columns={
         "gdp_per_capita": "Country_gdp_per_capita",
         "IncomeGroup": "Country_IncomeGroup",
@@ -3861,176 +4598,219 @@ <h3 id="country-gdpp">Country gdpp<a class="anchor-link" href="#country-gdpp">¶
         "IncomeGroup": "Surname_Country_IncomeGroup",
     }
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
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+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>/home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:283: SettingWithCopyWarning: 
 A value is trying to be set on a copy of a slice from a DataFrame
 
 See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-  gdp_per_capita.rename(columns={"Country Code": "iso_a3"}, inplace=True)
+  gdp_per_capita.rename(columns={&#34;Country Code&#34;: &#34;iso_a3&#34;}, inplace=True)
 /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:284: SettingWithCopyWarning: 
 A value is trying to be set on a copy of a slice from a DataFrame.
 Try using .loc[row_indexer,col_indexer] = value instead
 
 See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-  gdp_per_capita["gdp_per_capita"] = gdp_per_capita_raw.drop(columns=["Country Name", "Country Code", "Indicator Name", "Indicator Code"]).ffill(axis=1).iloc[:, -1]
+  gdp_per_capita[&#34;gdp_per_capita&#34;] = gdp_per_capita_raw.drop(columns=[&#34;Country Name&#34;, &#34;Country Code&#34;, &#34;Indicator Name&#34;, &#34;Indicator Code&#34;]).ffill(axis=1).iloc[:, -1]
 /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:283: SettingWithCopyWarning: 
 A value is trying to be set on a copy of a slice from a DataFrame
 
 See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-  gdp_per_capita.rename(columns={"Country Code": "iso_a3"}, inplace=True)
+  gdp_per_capita.rename(columns={&#34;Country Code&#34;: &#34;iso_a3&#34;}, inplace=True)
 /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:284: SettingWithCopyWarning: 
 A value is trying to be set on a copy of a slice from a DataFrame.
 Try using .loc[row_indexer,col_indexer] = value instead
 
 See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
-  gdp_per_capita["gdp_per_capita"] = gdp_per_capita_raw.drop(columns=["Country Name", "Country Code", "Indicator Name", "Indicator Code"]).ffill(axis=1).iloc[:, -1]
+  gdp_per_capita[&#34;gdp_per_capita&#34;] = gdp_per_capita_raw.drop(columns=[&#34;Country Name&#34;, &#34;Country Code&#34;, &#34;Indicator Name&#34;, &#34;Indicator Code&#34;]).ffill(axis=1).iloc[:, -1]
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>SubdivisionHierarchy(code='MX-MEX', country_code='MX', name='México', parent_code=None, type='State')
-SubdivisionHierarchy(code='PA-8', country_code='PA', name='Panamá', parent_code=None, type='Province')
-SubdivisionHierarchy(code='NG-NI', country_code='NG', name='Niger', parent_code=None, type='State')
-SubdivisionHierarchy(code='GN-ML', country_code='GN', name='Mali', parent='L', parent_code='GN-L', type='Prefecture')
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>SubdivisionHierarchy(code=&#39;MX-MEX&#39;, country_code=&#39;MX&#39;, name=&#39;México&#39;, parent_code=None, type=&#39;State&#39;)
+SubdivisionHierarchy(code=&#39;PA-8&#39;, country_code=&#39;PA&#39;, name=&#39;Panamá&#39;, parent_code=None, type=&#39;Province&#39;)
+SubdivisionHierarchy(code=&#39;NG-NI&#39;, country_code=&#39;NG&#39;, name=&#39;Niger&#39;, parent_code=None, type=&#39;State&#39;)
+SubdivisionHierarchy(code=&#39;GN-ML&#39;, country_code=&#39;GN&#39;, name=&#39;Mali&#39;, parent=&#39;L&#39;, parent_code=&#39;GN-L&#39;, type=&#39;Prefecture&#39;)
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="additional-age-info">Additional age info<a class="anchor-link" href="#additional-age-info">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="additional-age-info">Additional age info<a class="anchor-link" href="#additional-age-info">&#182;</a></h2>
 </div>
 </div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [29]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">age_categories</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">age_col</span><span class="o">=</span><span class="s2">"Age"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[29]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-31">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">auxiliary_data</span><span class="o">.</span><span class="n">age_categories</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">age_col</span><span class="o">=</span><span class="s2">&quot;Age&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-31">df_pd = auxiliary_data.age_categories(df=df_pd, age_col="Age")</div>
-</div>
-</div>
+<div id="cell-31" class="clipboard-copy-txt">df_pd = auxiliary_data.age_categories(df=df_pd, age_col="Age")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [30]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country_gdp_per_capita"</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()][</span><span class="s2">"Surname_Country"</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[30]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country_gdp_per_capita&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()][</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-32">df_pd[df_pd["Surname_Country_gdp_per_capita"].isna()]["Surname_Country"].unique()</div>
-</div>
-</div>
+<div id="cell-32" class="clipboard-copy-txt">df_pd[df_pd["Surname_Country_gdp_per_capita"].isna()]["Surname_Country"].unique()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[30]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>array(['Taiwan', 'Macau', 'Turkey', 'Ivory Coast',
-       'Democratic Republic of the Congo'], dtype=object)</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[30]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>array([&#39;Taiwan&#39;, &#39;Macau&#39;, &#39;Turkey&#39;, &#39;Ivory Coast&#39;,
+       &#39;Democratic Republic of the Congo&#39;], dtype=object)</pre>
 </div>
+
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+
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+
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+
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita</span>
-<span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"Taiwan"</span><span class="p">,</span> <span class="p">[</span><span class="s2">"Surname_Country_gdp_per_capita"</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[31]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-33">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita</span>
+<span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Taiwan&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Surname_Country_gdp_per_capita&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
     <span class="mf">32756.0</span>
 <span class="p">)</span>
 <span class="c1"># remaining countries have some naming issues - ToBeResolved; e.g. Macau -&gt; Macao, Turkey -&gt; Turkiye, etc</span>
-<span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"Macau"</span><span class="p">,</span> <span class="p">[</span><span class="s2">"Surname_Country_gdp_per_capita"</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
+<span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Macau&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Surname_Country_gdp_per_capita&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
     <span class="mf">61230.96247</span>
 <span class="p">)</span>
-<span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"Turkey"</span><span class="p">,</span> <span class="p">[</span><span class="s2">"Surname_Country_gdp_per_capita"</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
+<span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Turkey&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Surname_Country_gdp_per_capita&quot;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
     <span class="mf">38355.15397</span>
 <span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-    <span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"Ivory Coast"</span><span class="p">,</span> <span class="p">[</span><span class="s2">"Surname_Country_gdp_per_capita"</span><span class="p">]</span>
+    <span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Ivory Coast&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;Surname_Country_gdp_per_capita&quot;</span><span class="p">]</span>
 <span class="p">]</span> <span class="o">=</span> <span class="mf">6540.462355</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span>
-    <span class="n">df_pd</span><span class="p">[</span><span class="s2">"Surname_Country"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"Democratic Republic of the Congo"</span><span class="p">,</span>
-    <span class="p">[</span><span class="s2">"Surname_Country_gdp_per_capita"</span><span class="p">],</span>
+    <span class="n">df_pd</span><span class="p">[</span><span class="s2">&quot;Surname_Country&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;Democratic Republic of the Congo&quot;</span><span class="p">,</span>
+    <span class="p">[</span><span class="s2">&quot;Surname_Country_gdp_per_capita&quot;</span><span class="p">],</span>
 <span class="p">]</span> <span class="o">=</span> <span class="mf">1337.834149</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-33"># Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita
+<div id="cell-33" class="clipboard-copy-txt"># Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita
 df_pd.loc[df_pd["Surname_Country"] == "Taiwan", ["Surname_Country_gdp_per_capita"]] = (
     32756.0
 )
-# remaining countries have some naming issues - ToBeResolved; e.g. Macau -&gt; Macao, Turkey -&gt; Turkiye, etc
+# remaining countries have some naming issues - ToBeResolved; e.g. Macau -> Macao, Turkey -> Turkiye, etc
 df_pd.loc[df_pd["Surname_Country"] == "Macau", ["Surname_Country_gdp_per_capita"]] = (
     61230.96247
 )
@@ -4044,121 +4824,149 @@ <h2 id="additional-age-info">Additional age info<a class="anchor-link" href="#ad
     df_pd["Surname_Country"] == "Democratic Republic of the Congo",
     ["Surname_Country_gdp_per_capita"],
 ] = 1337.834149</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span><span class="s2">"data/dataset_auxiliary_features_raw.parquet"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span><span class="s2">&quot;data/dataset_auxiliary_features_raw.parquet&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-34">df_pd.to_parquet("data/dataset_auxiliary_features_raw.parquet")</div>
-</div>
-</div>
+<div id="cell-34" class="clipboard-copy-txt">df_pd.to_parquet("data/dataset_auxiliary_features_raw.parquet")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [33]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">columns</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[33]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">columns</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-35">df_pd.columns</div>
-</div>
-</div>
+<div id="cell-35" class="clipboard-copy-txt">df_pd.columns</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[33]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Country',
-       'Gender', 'Age', 'Tenure', 'CustomerFeedback', 'Balance (EUR)',
-       'NumberOfProducts', 'HasCreditCard', 'IsActiveMember',
-       'EstimatedSalary', 'Exited', 'CustomerFeedback_language',
-       'CustomerFeedback_language_score', 'CustomerFeedback_sentiment3',
-       'CustomerFeedback_sentiment3_score', 'CustomerFeedback_sentiment5',
-       'CustomerFeedback_sentiment5_score', 'Surname_Country',
-       'Surname_Country_region', 'Surname_Country_subregion', 'Country_region',
-       'Country_subregion', 'is_native', 'Country_hemisphere',
-       'Country_gdp_per_capita', 'Country_IncomeGroup',
-       'Surname_Country_gdp_per_capita', 'Surname_Country_IncomeGroup',
-       'working_class', 'stage_of_life', 'generation'],
-      dtype='object')</pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[33]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>Index([&#39;RowNumber&#39;, &#39;CustomerId&#39;, &#39;Surname&#39;, &#39;CreditScore&#39;, &#39;Country&#39;,
+       &#39;Gender&#39;, &#39;Age&#39;, &#39;Tenure&#39;, &#39;CustomerFeedback&#39;, &#39;Balance (EUR)&#39;,
+       &#39;NumberOfProducts&#39;, &#39;HasCreditCard&#39;, &#39;IsActiveMember&#39;,
+       &#39;EstimatedSalary&#39;, &#39;Exited&#39;, &#39;CustomerFeedback_language&#39;,
+       &#39;CustomerFeedback_language_score&#39;, &#39;CustomerFeedback_sentiment3&#39;,
+       &#39;CustomerFeedback_sentiment3_score&#39;, &#39;CustomerFeedback_sentiment5&#39;,
+       &#39;CustomerFeedback_sentiment5_score&#39;, &#39;Surname_Country&#39;,
+       &#39;Surname_Country_region&#39;, &#39;Surname_Country_subregion&#39;, &#39;Country_region&#39;,
+       &#39;Country_subregion&#39;, &#39;is_native&#39;, &#39;Country_hemisphere&#39;,
+       &#39;Country_gdp_per_capita&#39;, &#39;Country_IncomeGroup&#39;,
+       &#39;Surname_Country_gdp_per_capita&#39;, &#39;Surname_Country_IncomeGroup&#39;,
+       &#39;working_class&#39;, &#39;stage_of_life&#39;, &#39;generation&#39;],
+      dtype=&#39;object&#39;)</pre>
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [34]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[34]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+                <span class="notice" hidden>Copied!</span>
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
     <span class="n">columns</span><span class="o">=</span><span class="p">[</span>
-        <span class="s2">"RowNumber"</span><span class="p">,</span>
-        <span class="s2">"Surname"</span><span class="p">,</span>
-        <span class="s2">"CustomerFeedback"</span><span class="p">,</span>
-        <span class="s2">"CustomerFeedback_language"</span><span class="p">,</span>
-        <span class="s2">"CustomerFeedback_language_score"</span><span class="p">,</span>
-        <span class="s2">"CustomerFeedback_sentiment3_score"</span><span class="p">,</span>
-        <span class="s2">"CustomerFeedback_sentiment5_score"</span><span class="p">,</span>
+        <span class="s2">&quot;RowNumber&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;Surname&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;CustomerFeedback&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;CustomerFeedback_language&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;CustomerFeedback_language_score&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;CustomerFeedback_sentiment3_score&quot;</span><span class="p">,</span>
+        <span class="s2">&quot;CustomerFeedback_sentiment5_score&quot;</span><span class="p">,</span>
     <span class="p">]</span>
-<span class="p">)</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span><span class="s2">"data/dataset_auxiliary_features_cleaned.parquet"</span><span class="p">)</span>
+<span class="p">)</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span><span class="s2">&quot;data/dataset_auxiliary_features_cleaned.parquet&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-36">df_pd.drop(
+<div id="cell-36" class="clipboard-copy-txt">df_pd.drop(
     columns=[
         "RowNumber",
         "Surname",
@@ -4169,78 +4977,79 @@ <h2 id="additional-age-info">Additional age info<a class="anchor-link" href="#ad
         "CustomerFeedback_sentiment5_score",
     ]
 ).to_parquet("data/dataset_auxiliary_features_cleaned.parquet")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="appendix-tbd">Appendix [TBD]<a class="anchor-link" href="#appendix-tbd">¶</a></h1><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="appendix-tbd">Appendix [TBD]<a class="anchor-link" href="#appendix-tbd">&#182;</a></h1><ul>
 <li>analyze score and probabilities of the sentiment and country detection methods</li>
-<li>make an object or procedure to do this automatically</li>
+<li>make an object or procedure to do this automatically </li>
 </ul>
+
+</div>
 </div>
 </div>
 </div>
 </div>
-</div> <!-- jp-Notebook -->
-</div> <!-- jupyter-wrapper -->
-
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+        <a href="01_feature_analysis.html" class="md-footer__link md-footer__link--next" aria-label="Next: 01_feature_analysis" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
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+                Next
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+              01_feature_analysis
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-    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
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-    
-      <script src="../assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="../assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="../stylesheets/extra.js"></script>
       
diff --git a/docs/site/notebooks/00_auxiliary_features_surname_origin_country_classification.html b/docs/site/notebooks/00_auxiliary_features_surname_origin_country_classification.html
index 5ba4429..c8d5aac 100644
--- a/docs/site/notebooks/00_auxiliary_features_surname_origin_country_classification.html
+++ b/docs/site/notebooks/00_auxiliary_features_surname_origin_country_classification.html
@@ -8,34 +8,24 @@
       
       
       
-      
-        <link rel="prev" href="../churn_pred/training.html">
-      
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-      
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       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>TBD_00_auxiliary_features_surname_origin_country_classification.ipynb - ecovadis_assignment</title>
+        <title>TBD_00_auxiliary_features_surname_origin_country_classification.ipynb - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
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@@ -50,13 +40,12 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
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@@ -67,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
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@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
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+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
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-            ecovadis_assignment
+            churnpred
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@@ -118,39 +109,31 @@
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+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
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       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
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@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
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@@ -204,60 +184,46 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
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-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
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-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
@@ -266,35 +232,14 @@
   
     
   
-  
-    
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-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
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-    
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-      
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     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link md-tabs__link--active">
+        notebooks
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@@ -303,35 +248,37 @@
         
   
   
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-  
-    
-  
-  code profiling
 
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-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
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-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
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-    ecovadis_assignment
+    churnpred
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     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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-    ecovadis_assignment
+    churnpred
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 </a>
     </div>
@@ -388,19 +334,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,19 +349,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../installation.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+        installation
       </a>
     </li>
   
@@ -428,239 +364,172 @@
     
       
       
+      
+
   
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--nested">
       
-        
-      
-        
-      
-        
-      
-        
       
-    
-    
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
       
       
-    
-    
-    <li class="md-nav__item md-nav__item--nested">
       
         
+          
         
           
         
-        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" type="checkbox" id="__nav_3" >
+          
         
           
-          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
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-  <span class="md-ellipsis">
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-            churn_pred
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
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+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
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+        code_profiling
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
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-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/preprocessing.html" class="md-nav__link">
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-  
-  <span class="md-ellipsis">
-    preprocessing
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-  
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+        preprocessing
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/training.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    training
-  </span>
-  
-
+        training
       </a>
     </li>
   
 
-              
             
-          </ul>
-        </nav>
-      
+          
+        </ul>
+      </nav>
     </li>
   
 
     
       
       
+      
+
   
   
     
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
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-      
-        
       
-        
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-        
       
-        
       
         
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-  <span class="md-ellipsis">
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-            notebooks
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+          notebooks
+          <span class="md-nav__icon md-icon"></span>
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+      
+      <nav class="md-nav" aria-label="notebooks" data-md-level="1">
+        <label class="md-nav__title" for="__nav_4">
+          <span class="md-nav__icon md-icon"></span>
+          notebooks
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
   
   
     
   
   
-  
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
       
       
         <label class="md-nav__link md-nav__link--active" for="__toc">
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@@ -680,9 +549,7 @@
       
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   <a href="#surname-origin-country-classification" class="md-nav__link">
-    <span class="md-ellipsis">
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+    Surname origin country classification
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 </li>
@@ -694,156 +561,108 @@
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-              
             
-              
-                
-  
+          
+            
+              
   
   
   
     <li class="md-nav__item">
       <a href="00_auxiliary_features.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    00_auxiliary_features
-  </span>
-  
-
+        00_auxiliary_features
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="01_feature_analysis.html" class="md-nav__link">
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-  
-  <span class="md-ellipsis">
-    01_feature_analysis
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+        01_feature_analysis
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="02_target_analysis.html" class="md-nav__link">
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-    02_target_analysis
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+        02_target_analysis
       </a>
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-              
+            
+          
             
               
-                
-  
   
   
   
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       <a href="03_models.html" class="md-nav__link">
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-  
-  <span class="md-ellipsis">
-    03_models
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-  
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+        03_models
       </a>
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-              
+            
+          
             
               
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-    04_model_training_pipeline
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-              
             
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-        </nav>
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+          
+        </ul>
+      </nav>
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-  
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-    cicd
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-
+        cicd
       </a>
     </li>
   
@@ -851,19 +670,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code profiling
-  </span>
-  
-
+        code profiling
       </a>
     </li>
   
@@ -871,19 +685,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../contributing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    contributing
-  </span>
-  
-
+        contributing
       </a>
     </li>
   
@@ -897,14 +706,14 @@
             
             
           
-          
-            <div class="md-content" data-md-component="content">
-              <article class="md-content__inner md-typeset">
+          <div class="md-content" data-md-component="content">
+            <article class="md-content__inner md-typeset">
+              
                 
-                  
+  <a href="https://github.com/5uperpalo/churnpred/edit/main/docs/sources/notebooks/00_auxiliary_features_surname_origin_country_classification.ipynb" title="Edit this page" class="md-content__button md-icon">
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  
-  
 
 
 <script>
@@ -1068,7 +877,12 @@
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+
+
+
+
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+    
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 td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
 span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
@@ -1096,7 +910,6 @@
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 .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
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 .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
@@ -1118,16 +931,791 @@
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 .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
   </style>
+
+
+
+
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#00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper [data-jp-theme-scrollbars=true]{scrollbar-color:rgb(var(--jp-scrollbar-thumb-color)) var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color),.5) transparent}.jupyter-wrapper .jp-scrollbar-tiny{scrollbar-color:rgba(var(--jp-scrollbar-thumb-color),.5) transparent;scrollbar-width:thin}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-corner{background:var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-thumb{background:rgb(var(--jp-scrollbar-thumb-color));border:var(--jp-scrollbar-thumb-margin) solid transparent;background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid var(--jp-scrollbar-background-color);border-right:var(--jp-scrollbar-endpad) solid var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] ::-webkit-scrollbar-track:vertical{border-top:var(--jp-scrollbar-endpad) solid var(--jp-scrollbar-background-color);border-bottom:var(--jp-scrollbar-endpad) solid var(--jp-scrollbar-background-color)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar-corner,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar-corner{background-color:transparent}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar-thumb,.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar-thumb{background:rgba(var(--jp-scrollbar-thumb-color),.5);border:var(--jp-scrollbar-thumb-margin) solid transparent;background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid transparent;border-right:var(--jp-scrollbar-endpad) solid transparent}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar-track:vertical{border-top:var(--jp-scrollbar-endpad) solid transparent;border-bottom:var(--jp-scrollbar-endpad) solid transparent}.jupyter-wrapper .jp-scrollbar-tiny::-webkit-scrollbar,.jupyter-wrapper .jp-scrollbar-tiny::-webkit-scrollbar-corner{background-color:transparent;height:4px;width:4px}.jupyter-wrapper .jp-scrollbar-tiny::-webkit-scrollbar-thumb{background:rgba(var(--jp-scrollbar-thumb-color),.5)}.jupyter-wrapper .jp-scrollbar-tiny::-webkit-scrollbar-track:horizontal{border-left:0px solid transparent;border-right:0px solid transparent}.jupyter-wrapper .jp-scrollbar-tiny::-webkit-scrollbar-track:vertical{border-top:0px solid transparent;border-bottom:0px solid transparent}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal]{min-height:16px;max-height:16px;min-width:45px;border-top:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=vertical]{min-width:16px;max-width:16px;min-height:45px;border-left:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar-button{background-color:#f0f0f0;background-position:center center;min-height:15px;max-height:15px;min-width:15px;max-width:15px}.jupyter-wrapper .lm-ScrollBar-button:hover{background-color:#dadada}.jupyter-wrapper .lm-ScrollBar-button.lm-mod-active{background-color:#cdcdcd}.jupyter-wrapper .lm-ScrollBar-track{background:#f0f0f0}.jupyter-wrapper .lm-ScrollBar-thumb{background:#cdcdcd}.jupyter-wrapper .lm-ScrollBar-thumb:hover{background:#bababa}.jupyter-wrapper .lm-ScrollBar-thumb.lm-mod-active{background:#a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal] .lm-ScrollBar-thumb{height:100%;min-width:15px;border-left:1px solid #a0a0a0;border-right:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=vertical] .lm-ScrollBar-thumb{width:100%;min-height:15px;border-top:1px solid #a0a0a0;border-bottom:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal] .lm-ScrollBar-button[data-action=decrement]{background-image:var(--jp-icon-caret-left);background-size:17px}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal] .lm-ScrollBar-button[data-action=increment]{background-image:var(--jp-icon-caret-right);background-size:17px}.jupyter-wrapper .lm-ScrollBar[data-orientation=vertical] 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.83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
-</style>
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="this-is-a-work-in-progress">THIS IS A WORK IN PROGRESS<a class="anchor-link" href="#this-is-a-work-in-progress">¶</a></h1>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="this-is-a-work-in-progress">THIS IS A WORK IN PROGRESS<a class="anchor-link" href="#this-is-a-work-in-progress">&#182;</a></h1>
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="surname-origin-country-classification">Surname origin country classification<a class="anchor-link" href="#surname-origin-country-classification">¶</a></h2><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="surname-origin-country-classification">Surname origin country classification<a class="anchor-link" href="#surname-origin-country-classification">&#182;</a></h2><ul>
 <li>approach shamelessly copied from <a href="https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert">https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert</a></li>
-<li>NOTE from author:</li>
-</ul>
-<pre><code>Based on the discussion [here](https://www.kaggle.com/competitions/playground-series-s4e1/discussion/465517), I created a Bert based Surname classifier.
-</code></pre>
-<ul>
+<li>NOTE from author:
+<pre><code>Based on the discussion [here](https://www.kaggle.com/competitions/playground-series-s4e1/discussion/465517), I created a Bert based Surname classifier.</code></pre>
+</li>
 <li>possible improvement of the base dataset by scraping wiki: <a href="https://github.com/greenelab/wiki-nationality-estimate">https://github.com/greenelab/wiki-nationality-estimate</a></li>
 </ul>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 
 <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
-<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">"../../..."</span><span class="p">)</span>
+<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;../../...&quot;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">torch</span>
@@ -1409,7 +1814,7 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
 <span class="p">)</span>
 <span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">BertForSequenceClassification</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-1">import os
+<div id="cell-1" class="clipboard-copy-txt">import os
 import sys
 
 sys.path.append(os.getcwd())
@@ -1427,75 +1832,96 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
     BertTokenizer,
 )
 from transformers import BertForSequenceClassification</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-2">
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-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">surname_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"data/surnames/surname-nationality.csv"</span><span class="p">)</span>
-<span class="n">splitted_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"data/surnames/surnames_with_splits.csv"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">surname_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/surnames/surname-nationality.csv&quot;</span><span class="p">)</span>
+<span class="n">splitted_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/surnames/surnames_with_splits.csv&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-2">surname_data = pd.read_csv("data/surnames/surname-nationality.csv")
+<div id="cell-2" class="clipboard-copy-txt">surname_data = pd.read_csv("data/surnames/surname-nationality.csv")
 splitted_data = pd.read_csv("data/surnames/surnames_with_splits.csv")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-3">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">splitted_data</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">splitted_data</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-3">splitted_data</div>
-</div>
-</div>
+<div id="cell-3" class="clipboard-copy-txt">splitted_data</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -1509,314 +1935,371 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>nationality</th>
-<th>nationality_index</th>
-<th>split</th>
-<th>surname</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>Arabic</td>
-<td>15</td>
-<td>train</td>
-<td>Totah</td>
-</tr>
-<tr>
-<th>1</th>
-<td>Arabic</td>
-<td>15</td>
-<td>train</td>
-<td>Abboud</td>
-</tr>
-<tr>
-<th>2</th>
-<td>Arabic</td>
-<td>15</td>
-<td>train</td>
-<td>Fakhoury</td>
-</tr>
-<tr>
-<th>3</th>
-<td>Arabic</td>
-<td>15</td>
-<td>train</td>
-<td>Srour</td>
-</tr>
-<tr>
-<th>4</th>
-<td>Arabic</td>
-<td>15</td>
-<td>train</td>
-<td>Sayegh</td>
-</tr>
-<tr>
-<th>...</th>
-<td>...</td>
-<td>...</td>
-<td>...</td>
-<td>...</td>
-</tr>
-<tr>
-<th>10975</th>
-<td>Vietnamese</td>
-<td>11</td>
-<td>test</td>
-<td>Dinh</td>
-</tr>
-<tr>
-<th>10976</th>
-<td>Vietnamese</td>
-<td>11</td>
-<td>test</td>
-<td>Phung</td>
-</tr>
-<tr>
-<th>10977</th>
-<td>Vietnamese</td>
-<td>11</td>
-<td>test</td>
-<td>Quang</td>
-</tr>
-<tr>
-<th>10978</th>
-<td>Vietnamese</td>
-<td>11</td>
-<td>test</td>
-<td>Vu</td>
-</tr>
-<tr>
-<th>10979</th>
-<td>Vietnamese</td>
-<td>11</td>
-<td>test</td>
-<td>Ha</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>nationality</th>
+      <th>nationality_index</th>
+      <th>split</th>
+      <th>surname</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>Arabic</td>
+      <td>15</td>
+      <td>train</td>
+      <td>Totah</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>Arabic</td>
+      <td>15</td>
+      <td>train</td>
+      <td>Abboud</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>Arabic</td>
+      <td>15</td>
+      <td>train</td>
+      <td>Fakhoury</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>Arabic</td>
+      <td>15</td>
+      <td>train</td>
+      <td>Srour</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>Arabic</td>
+      <td>15</td>
+      <td>train</td>
+      <td>Sayegh</td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+      <td>...</td>
+    </tr>
+    <tr>
+      <th>10975</th>
+      <td>Vietnamese</td>
+      <td>11</td>
+      <td>test</td>
+      <td>Dinh</td>
+    </tr>
+    <tr>
+      <th>10976</th>
+      <td>Vietnamese</td>
+      <td>11</td>
+      <td>test</td>
+      <td>Phung</td>
+    </tr>
+    <tr>
+      <th>10977</th>
+      <td>Vietnamese</td>
+      <td>11</td>
+      <td>test</td>
+      <td>Quang</td>
+    </tr>
+    <tr>
+      <th>10978</th>
+      <td>Vietnamese</td>
+      <td>11</td>
+      <td>test</td>
+      <td>Vu</td>
+    </tr>
+    <tr>
+      <th>10979</th>
+      <td>Vietnamese</td>
+      <td>11</td>
+      <td>test</td>
+      <td>Ha</td>
+    </tr>
+  </tbody>
 </table>
 <p>10980 rows × 4 columns</p>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">surname_data</span><span class="o">.</span><span class="n">surname</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">surname_data</span><span class="o">.</span><span class="n">surname</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-4">surname_data.surname.nunique()</div>
-</div>
-</div>
+<div id="cell-4" class="clipboard-copy-txt">surname_data.surname.nunique()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>30923</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">splitted_data</span><span class="p">[</span><span class="s2">"nationality_index"</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">splitted_data</span><span class="p">[</span><span class="s2">&quot;nationality_index&quot;</span><span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-5">splitted_data["nationality_index"]</div>
-</div>
-</div>
+<div id="cell-5" class="clipboard-copy-txt">splitted_data["nationality_index"]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>0        15
-1        15
-2        15
-3        15
-4        15
-         ..
-10975    11
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>0        15
+1        15
+2        15
+3        15
+4        15
+         ..
+10975    11
 10976    11
 10977    11
 10978    11
 10979    11
 Name: nationality_index, Length: 10980, dtype: int64</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">labeld_decoder</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
-    <span class="nb">zip</span><span class="p">(</span><span class="n">splitted_data</span><span class="p">[</span><span class="s2">"nationality"</span><span class="p">],</span> <span class="n">splitted_data</span><span class="p">[</span><span class="s2">"nationality_index"</span><span class="p">])</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">labeld_decoder</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span>
+    <span class="nb">zip</span><span class="p">(</span><span class="n">splitted_data</span><span class="p">[</span><span class="s2">&quot;nationality&quot;</span><span class="p">],</span> <span class="n">splitted_data</span><span class="p">[</span><span class="s2">&quot;nationality_index&quot;</span><span class="p">])</span>
 <span class="p">)</span>
 <span class="n">inverse_label_dict</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">labeld_decoder</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-6">labeld_decoder = dict(
+<div id="cell-6" class="clipboard-copy-txt">labeld_decoder = dict(
     zip(splitted_data["nationality"], splitted_data["nationality_index"])
 )
 inverse_label_dict = {v: k for k, v in labeld_decoder.items()}</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Split data into train, validation, and test sets</span>
-<span class="n">train_df</span> <span class="o">=</span> <span class="n">splitted_data</span><span class="p">[</span><span class="n">splitted_data</span><span class="p">[</span><span class="s2">"split"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"train"</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Split data into train, validation, and test sets</span>
+<span class="n">train_df</span> <span class="o">=</span> <span class="n">splitted_data</span><span class="p">[</span><span class="n">splitted_data</span><span class="p">[</span><span class="s2">&quot;split&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;train&quot;</span><span class="p">]</span>
 <span class="n">val_df</span> <span class="o">=</span> <span class="n">splitted_data</span><span class="p">[</span>
-    <span class="n">splitted_data</span><span class="p">[</span><span class="s2">"split"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"val"</span>
-<span class="p">]</span>  <span class="c1"># Assuming "val" is used for validation set</span>
-<span class="n">test_df</span> <span class="o">=</span> <span class="n">splitted_data</span><span class="p">[</span><span class="n">splitted_data</span><span class="p">[</span><span class="s2">"split"</span><span class="p">]</span> <span class="o">==</span> <span class="s2">"test"</span><span class="p">]</span>
+    <span class="n">splitted_data</span><span class="p">[</span><span class="s2">&quot;split&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;val&quot;</span>
+<span class="p">]</span>  <span class="c1"># Assuming &quot;val&quot; is used for validation set</span>
+<span class="n">test_df</span> <span class="o">=</span> <span class="n">splitted_data</span><span class="p">[</span><span class="n">splitted_data</span><span class="p">[</span><span class="s2">&quot;split&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;test&quot;</span><span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-7"># Split data into train, validation, and test sets
+<div id="cell-7" class="clipboard-copy-txt"># Split data into train, validation, and test sets
 train_df = splitted_data[splitted_data["split"] == "train"]
 val_df = splitted_data[
     splitted_data["split"] == "val"
 ]  # Assuming "val" is used for validation set
 test_df = splitted_data[splitted_data["split"] == "test"]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
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 </div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Tokenize each dataset</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Tokenize each dataset</span>
 <span class="n">train_encodings</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">tokenize_data</span><span class="p">(</span><span class="n">train_df</span><span class="p">)</span>
 <span class="n">val_encodings</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">tokenize_data</span><span class="p">(</span><span class="n">val_df</span><span class="p">)</span>
 <span class="n">test_encodings</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">tokenize_data</span><span class="p">(</span><span class="n">test_df</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-8"># Tokenize each dataset
+<div id="cell-8" class="clipboard-copy-txt"># Tokenize each dataset
 train_encodings = surname_classification.tokenize_data(train_df)
 val_encodings = surname_classification.tokenize_data(val_df)
 test_encodings = surname_classification.tokenize_data(test_df)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
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 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
 
 <span class="n">train_dataset</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">train_encodings</span><span class="p">,</span> <span class="n">train_df</span><span class="p">)</span>
 <span class="n">val_dataset</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">create_dataset</span><span class="p">(</span><span class="n">val_encodings</span><span class="p">,</span> <span class="n">val_df</span><span class="p">)</span>
@@ -1826,7 +2309,7 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
 <span class="n">val_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">val_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
 <span class="n">test_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-9">batch_size = 32
+<div id="cell-9" class="clipboard-copy-txt">batch_size = 32
 
 train_dataset = surname_classification.create_dataset(train_encodings, train_df)
 val_dataset = surname_classification.create_dataset(val_encodings, val_df)
@@ -1835,34 +2318,39 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
 train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
 val_dataloader = DataLoader(val_dataset, batch_size=batch_size)
 test_dataloader = DataLoader(test_dataset, batch_size=batch_size)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">num_labels</span> <span class="o">=</span> <span class="n">splitted_data</span><span class="p">[</span><span class="s2">"nationality_index"</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">num_labels</span> <span class="o">=</span> <span class="n">splitted_data</span><span class="p">[</span><span class="s2">&quot;nationality_index&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span>
 <span class="n">num_epochs</span> <span class="o">=</span> <span class="mi">10</span>
 <span class="n">model</span> <span class="o">=</span> <span class="n">BertForSequenceClassification</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
-    <span class="s2">"bert-base-cased"</span><span class="p">,</span> <span class="n">num_labels</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span> <span class="n">hidden_dropout_prob</span><span class="o">=</span><span class="mf">0.4</span>
+    <span class="s2">&quot;bert-base-cased&quot;</span><span class="p">,</span> <span class="n">num_labels</span><span class="o">=</span><span class="n">num_labels</span><span class="p">,</span> <span class="n">hidden_dropout_prob</span><span class="o">=</span><span class="mf">0.4</span>
 <span class="p">)</span>
 <span class="c1"># freeze layers</span>
 <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
@@ -1877,10 +2365,10 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
     <span class="n">num_training_steps</span><span class="o">=</span><span class="n">total_steps</span><span class="p">,</span>
 <span class="p">)</span>
 
-<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">"cpu"</span><span class="p">)</span>
+<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
 <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-10">num_labels = splitted_data["nationality_index"].nunique()
+<div id="cell-10" class="clipboard-copy-txt">num_labels = splitted_data["nationality_index"].nunique()
 num_epochs = 10
 model = BertForSequenceClassification.from_pretrained(
     "bert-base-cased", num_labels=num_labels, hidden_dropout_prob=0.4
@@ -1900,27 +2388,44 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: [&#39;classifier.bias&#39;, &#39;classifier.weight&#39;]
 You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
 /mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
   warnings.warn(
 </pre>
 </div>
 </div>
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>BertForSequenceClassification(
   (bert): BertModel(
     (embeddings): BertEmbeddings(
@@ -1967,30 +2472,36 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
   (classifier): Linear(in_features=768, out_features=18, bias=True)
 )</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">trained_model</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">train_model</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">trained_model</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">train_model</span><span class="p">(</span>
     <span class="n">model</span><span class="p">,</span>
     <span class="n">train_dataloader</span><span class="p">,</span>
     <span class="n">val_dataloader</span><span class="p">,</span>
@@ -2000,7 +2511,7 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
     <span class="n">num_epochs</span><span class="o">=</span><span class="n">num_epochs</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-11">trained_model = surname_classification.train_model(
+<div id="cell-11" class="clipboard-copy-txt">trained_model = surname_classification.train_model(
     model,
     train_dataloader,
     val_dataloader,
@@ -2009,19 +2520,23 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
     device,
     num_epochs=num_epochs,
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p><strong>log from running a script on a server</strong></p>
+
 <pre><code>(ecovadis) pmulinka@supercom-wssonata:~/assignment/ecovadis_assignment$ python notebooks/eda/surname_classification_with_bert.py
 /home/pmulinka/assignment/ecovadis_assignment
 Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
@@ -2071,418 +2586,496 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
 Training complete
 Evaluation on test dataset yield
 Loss: 0.7684005849206677
-Accuracy: 0.7960164835164836
-</code></pre>
+Accuracy: 0.7960164835164836</code></pre>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">loss</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">evaluate_model</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">loss</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">evaluate_model</span><span class="p">(</span>
     <span class="n">trained_model</span><span class="p">,</span> <span class="n">test_dataloader</span><span class="p">,</span> <span class="n">device</span>
 <span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Evaluation on test dataset yield</span><span class="se">\n</span><span class="s2">Loss: </span><span class="si">{</span><span class="n">loss</span><span class="si">}</span><span class="se">\n</span><span class="s2">Accuracy: </span><span class="si">{</span><span class="n">accuracy</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Evaluation on test dataset yield</span><span class="se">\n</span><span class="s2">Loss: </span><span class="si">{</span><span class="n">loss</span><span class="si">}</span><span class="se">\n</span><span class="s2">Accuracy: </span><span class="si">{</span><span class="n">accuracy</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-12">loss, accuracy = surname_classification.evaluate_model(
+<div id="cell-12" class="clipboard-copy-txt">loss, accuracy = surname_classification.evaluate_model(
     trained_model, test_dataloader, device
 )
 print(f"Evaluation on test dataset yield\nLoss: {loss}\nAccuracy: {accuracy}")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">inverse_label_dict</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">inverse_label_dict</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-13">inverse_label_dict</div>
-</div>
-</div>
+<div id="cell-13" class="clipboard-copy-txt">inverse_label_dict</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>{15: 'Arabic',
- 3: 'Chinese',
- 5: 'Czech',
- 2: 'Dutch',
- 12: 'English',
- 8: 'French',
- 9: 'German',
- 4: 'Greek',
- 1: 'Irish',
- 17: 'Italian',
- 7: 'Japanese',
- 16: 'Korean',
- 14: 'Polish',
- 0: 'Portuguese',
- 13: 'Russian',
- 10: 'Scottish',
- 6: 'Spanish',
- 11: 'Vietnamese'}</pre>
-</div>
-</div>
-</div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
-<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>{15: &#39;Arabic&#39;,
+ 3: &#39;Chinese&#39;,
+ 5: &#39;Czech&#39;,
+ 2: &#39;Dutch&#39;,
+ 12: &#39;English&#39;,
+ 8: &#39;French&#39;,
+ 9: &#39;German&#39;,
+ 4: &#39;Greek&#39;,
+ 1: &#39;Irish&#39;,
+ 17: &#39;Italian&#39;,
+ 7: &#39;Japanese&#39;,
+ 16: &#39;Korean&#39;,
+ 14: &#39;Polish&#39;,
+ 0: &#39;Portuguese&#39;,
+ 13: &#39;Russian&#39;,
+ 10: &#39;Scottish&#39;,
+ 6: &#39;Spanish&#39;,
+ 11: &#39;Vietnamese&#39;}</pre>
 </div>
-<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-14">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
+
+</div>
+
 </div>
-</clipboard-copy>
+
+</div>
+
 </div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">utils</span><span class="o">.</span><span class="n">dill_dump</span><span class="p">(</span>
-    <span class="n">file_loc</span><span class="o">=</span><span class="s2">"data/surnames/inverse_label_dict.dill"</span><span class="p">,</span> <span class="n">content</span><span class="o">=</span><span class="n">inverse_label_dict</span>
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
+</div>
+<div class="jp-InputArea jp-Cell-inputArea">
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">utils</span><span class="o">.</span><span class="n">dill_dump</span><span class="p">(</span>
+    <span class="n">file_loc</span><span class="o">=</span><span class="s2">&quot;data/surnames/inverse_label_dict.dill&quot;</span><span class="p">,</span> <span class="n">content</span><span class="o">=</span><span class="n">inverse_label_dict</span>
 <span class="p">)</span>
-<span class="n">inverse_label_dict</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">dill_load</span><span class="p">(</span><span class="n">file_loc</span><span class="o">=</span><span class="s2">"data/surnames/inverse_label_dict.dill"</span><span class="p">)</span>
+<span class="n">inverse_label_dict</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">dill_load</span><span class="p">(</span><span class="n">file_loc</span><span class="o">=</span><span class="s2">&quot;data/surnames/inverse_label_dict.dill&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-14">utils.dill_dump(
+<div id="cell-14" class="clipboard-copy-txt">utils.dill_dump(
     file_loc="data/surnames/inverse_label_dict.dill", content=inverse_label_dict
 )
 inverse_label_dict = utils.dill_load(file_loc="data/surnames/inverse_label_dict.dill")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
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-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Save the model</span>
-<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trained_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s2">"surname_model_state_dict.pth"</span><span class="p">)</span>
-<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trained_model</span><span class="p">,</span> <span class="s2">"data/surnames/surname_model.pth"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Save the model</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trained_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s2">&quot;surname_model_state_dict.pth&quot;</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trained_model</span><span class="p">,</span> <span class="s2">&quot;data/surnames/surname_model.pth&quot;</span><span class="p">)</span>
 <span class="n">utils</span><span class="o">.</span><span class="n">json_dump</span><span class="p">(</span>
-    <span class="n">file_loc</span><span class="o">=</span><span class="s2">"data/surnames/inverse_label_dict.json"</span><span class="p">,</span> <span class="n">content</span><span class="o">=</span><span class="n">inverse_label_dict</span>
+    <span class="n">file_loc</span><span class="o">=</span><span class="s2">&quot;data/surnames/inverse_label_dict.json&quot;</span><span class="p">,</span> <span class="n">content</span><span class="o">=</span><span class="n">inverse_label_dict</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-15"># Save the model
+<div id="cell-15" class="clipboard-copy-txt"># Save the model
 torch.save(trained_model.state_dict(), "surname_model_state_dict.pth")
 torch.save(trained_model, "data/surnames/surname_model.pth")
 utils.json_dump(
     file_loc="data/surnames/inverse_label_dict.json", content=inverse_label_dict
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Load the model</span>
-<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">"cuda"</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">"cpu"</span><span class="p">)</span>
-<span class="n">trained_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">"data/surnames/surname_model.pth"</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-<span class="n">inverse_label_dict</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">dill_load</span><span class="p">(</span><span class="n">file_loc</span><span class="o">=</span><span class="s2">"data/surnames/inverse_label_dict.dill"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-16">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># Load the model</span>
+<span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+<span class="n">trained_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;data/surnames/surname_model.pth&quot;</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
+<span class="n">inverse_label_dict</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">dill_load</span><span class="p">(</span><span class="n">file_loc</span><span class="o">=</span><span class="s2">&quot;data/surnames/inverse_label_dict.dill&quot;</span><span class="p">)</span>
 <span class="c1"># model.eval()</span>
-<span class="c1"># trained_model.load_state_dict(torch.load("surname_model.pth"))</span>
+<span class="c1"># trained_model.load_state_dict(torch.load(&quot;surname_model.pth&quot;))</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-16"># Load the model
+<div id="cell-16" class="clipboard-copy-txt"># Load the model
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 trained_model = torch.load("data/surnames/surname_model.pth", map_location=device)
 inverse_label_dict = utils.dill_load(file_loc="data/surnames/inverse_label_dict.dill")
 # model.eval()
 # trained_model.load_state_dict(torch.load("surname_model.pth"))</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">predicted_surnames</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-17">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">predicted_surnames</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-17">predicted_surnames</div>
-</div>
-</div>
+<div id="cell-17" class="clipboard-copy-txt">predicted_surnames</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>[('Hsieh', 'English'),
- ('McDonald', 'Spanish'),
- ('Kharlamov', 'Arabic'),
- ('Vasiliev', 'English'),
- ('Bellucci', 'English'),
- ('Wallace', 'English'),
- ('Chao', 'Chinese'),
- ('Boylan', 'English'),
- ('Burgess', 'Chinese'),
- ('Cattaneo', 'English')]</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[15]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>[(&#39;Hsieh&#39;, &#39;English&#39;),
+ (&#39;McDonald&#39;, &#39;Spanish&#39;),
+ (&#39;Kharlamov&#39;, &#39;Arabic&#39;),
+ (&#39;Vasiliev&#39;, &#39;English&#39;),
+ (&#39;Bellucci&#39;, &#39;English&#39;),
+ (&#39;Wallace&#39;, &#39;English&#39;),
+ (&#39;Chao&#39;, &#39;Chinese&#39;),
+ (&#39;Boylan&#39;, &#39;English&#39;),
+ (&#39;Burgess&#39;, &#39;Chinese&#39;),
+ (&#39;Cattaneo&#39;, &#39;English&#39;)]</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">"data/dataset.csv"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/dataset.csv&quot;</span><span class="p">)</span>
 <span class="n">uq_surnames</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">Surname</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
-<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">BertTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">"bert-base-uncased"</span><span class="p">)</span>
+<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">BertTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;bert-base-uncased&quot;</span><span class="p">)</span>
 <span class="n">predicted_surnames</span> <span class="o">=</span> <span class="n">surname_classification</span><span class="o">.</span><span class="n">predict_nationality</span><span class="p">(</span>
     <span class="n">trained_model</span><span class="p">,</span> <span class="n">uq_surnames</span><span class="p">[:</span><span class="mi">10</span><span class="p">],</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">inverse_label_dict</span><span class="p">,</span> <span class="n">device</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-18">df_pd = pd.read_csv("data/dataset.csv")
+<div id="cell-18" class="clipboard-copy-txt">df_pd = pd.read_csv("data/dataset.csv")
 uq_surnames = df_pd.Surname.unique()
 tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
 predicted_surnames = surname_classification.predict_nationality(
     trained_model, uq_surnames[:10], tokenizer, inverse_label_dict, device
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
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+
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>Predicting Nationalities:   0%|          | 0/10 [00:00&lt;?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
-/mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2674: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Predicting Nationalities:   0%|          | 0/10 [00:00&lt;?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to &#39;longest_first&#39; truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.
+/mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2674: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding=&#39;longest&#39;` to pad to the longest sequence in the batch, or use `padding=&#39;max_length&#39;` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).
   warnings.warn(
                                                                          
</pre>
 </div>
 </div>
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">predicted_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">predicted_surnames</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"Surname"</span><span class="p">,</span> <span class="s2">"Nationality"</span><span class="p">])</span>
-<span class="n">predicted_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"predicted_surnames_dataset.csv"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[&nbsp;]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">predicted_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">predicted_surnames</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Surname&quot;</span><span class="p">,</span> <span class="s2">&quot;Nationality&quot;</span><span class="p">])</span>
+<span class="n">predicted_df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">&quot;predicted_surnames_dataset.csv&quot;</span><span class="p">)</span>
 <span class="n">predicted_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-19">predicted_df = pd.DataFrame(predicted_surnames, columns=["Surname", "Nationality"])
+<div id="cell-19" class="clipboard-copy-txt">predicted_df = pd.DataFrame(predicted_surnames, columns=["Surname", "Nationality"])
 predicted_df.to_csv("predicted_surnames_dataset.csv")
 predicted_df.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
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+            <div class="highlight-ipynb hl-python"><pre><span></span>
 </pre></div>
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+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
       
-        <footer class="md-footer">
+    </nav>
   
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
@@ -2508,12 +3101,10 @@ <h2 id="surname-origin-country-classification">Surname origin country classifica
     <div class="md-dialog" data-md-component="dialog">
       <div class="md-dialog__inner md-typeset"></div>
     </div>
+    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b97dbffb.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
-    
-    
-      <script src="../assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="../assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="../stylesheets/extra.js"></script>
       
diff --git a/docs/site/notebooks/01_feature_analysis.html b/docs/site/notebooks/01_feature_analysis.html
index 40e8273..b7e14cc 100644
--- a/docs/site/notebooks/01_feature_analysis.html
+++ b/docs/site/notebooks/01_feature_analysis.html
@@ -8,34 +8,24 @@
       
       
       
-      
-        <link rel="prev" href="00_auxiliary_features.html">
-      
-      
-        <link rel="next" href="02_target_analysis.html">
-      
-      
       <link rel="icon" href="../assets/images/favicon.png">
-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>01_feature_analysis - ecovadis_assignment</title>
+        <title>01_feature_analysis - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
-
-
     
     
-      
-    
     
       
         
@@ -50,13 +40,12 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
-    
   </head>
   
   
@@ -67,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -118,39 +109,31 @@
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+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
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       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,60 +184,46 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
@@ -266,35 +232,14 @@
   
     
   
-  
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link md-tabs__link--active">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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#ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{--jp-shadow-base-lightness: 0;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-400);--jp-border-color1: var(--md-grey-400);--jp-border-color2: var(--md-grey-300);--jp-border-color3: var(--md-grey-200);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: 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.83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
-</style>
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
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+<div class="jp-Cell-inputWrapper">
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 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="feature-analysis">Feature analysis<a class="anchor-link" href="#feature-analysis">¶</a></h1><p><strong>Purpose</strong>:</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="feature-analysis">Feature analysis<a class="anchor-link" href="#feature-analysis">&#182;</a></h1><p><strong>Purpose</strong>:</p>
 <ul>
 <li>analyse dataset features without inclusion of target value<ul>
 <li>if it looks healthy<ul>
@@ -1405,52 +1801,55 @@ <h1 id="feature-analysis">Feature analysis<a class="anchor-link" href="#feature-
 <li>number of unique values in categorical features is too high - most probably they are unable to identify any group of targets</li>
 </ul>
 </li>
-<li>drop highly correlated values<ul>
-<li>to improve performance of the model training</li>
-<li>to decrease dataset size for further exploration</li>
+<li>drop highly correlated values <ul>
+<li>to improve performance of the model training </li>
+<li>to decrease dataset size for further exploration </li>
 </ul>
 </li>
 </ul>
 </li>
 </ul>
+
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="imports">Imports<a class="anchor-link" href="#imports">&#182;</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-1">
-<div>
-<span class="notice" hidden="">Copied!</span>
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 
 <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
-<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">"../.."</span><span class="p">)</span>
+<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;../..&quot;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
@@ -1465,11 +1864,11 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
     <span class="n">nunique</span><span class="p">,</span>
 <span class="p">)</span>
 
-<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_columns"</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_rows"</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_columns&quot;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
 <span class="kn">from</span> <span class="nn">churn_pred.eda.features.plotting</span> <span class="kn">import</span> <span class="n">cross_correlation</span><span class="p">,</span> <span class="n">distributions</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-1">import os
+<div id="cell-1" class="clipboard-copy-txt">import os
 import sys
 
 sys.path.append(os.getcwd())
@@ -1491,59 +1890,75 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
 pd.set_option("display.max_columns", 100)
 pd.set_option("display.max_rows", 300)
 from churn_pred.eda.features.plotting import cross_correlation, distributions</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="dataset">Dataset<a class="anchor-link" href="#dataset">&#182;</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-2">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">"data/dataset_auxiliary_features_cleaned.parquet"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">&quot;data/dataset_auxiliary_features_cleaned.parquet&quot;</span><span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-2">df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
+<div id="cell-2" class="clipboard-copy-txt">df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
 df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[2]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[2]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -1557,249 +1972,255 @@ <h2 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h2>
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>CustomerId</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment5</th>
-<th>Surname_Country</th>
-<th>Surname_Country_region</th>
-<th>Surname_Country_subregion</th>
-<th>Country_region</th>
-<th>Country_subregion</th>
-<th>is_native</th>
-<th>Country_hemisphere</th>
-<th>Country_gdp_per_capita</th>
-<th>Country_IncomeGroup</th>
-<th>Surname_Country_gdp_per_capita</th>
-<th>Surname_Country_IncomeGroup</th>
-<th>working_class</th>
-<th>stage_of_life</th>
-<th>generation</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>15787619</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>160980.03</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>145936.28</td>
-<td>0</td>
-<td>neutral</td>
-<td>4 stars</td>
-<td>Taiwan</td>
-<td>Asia</td>
-<td>Eastern Asia</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>32756.00000</td>
-<td>None</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>1</th>
-<td>15770309</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>151762.74</td>
-<td>1</td>
-<td>0</td>
-<td>1</td>
-<td>127014.32</td>
-<td>0</td>
-<td>neutral</td>
-<td>1 star</td>
-<td>United States</td>
-<td>Americas</td>
-<td>Northern America</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>76329.58227</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>2</th>
-<td>15569178</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>82767.42</td>
-<td>1</td>
-<td>1</td>
-<td>0</td>
-<td>71811.90</td>
-<td>0</td>
-<td>neutral</td>
-<td>2 stars</td>
-<td>Russian Federation</td>
-<td>Europe</td>
-<td>Eastern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>3</th>
-<td>15795519</td>
-<td>716</td>
-<td>Germany</td>
-<td>Female</td>
-<td>18</td>
-<td>3</td>
-<td>128743.80</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>197322.13</td>
-<td>0</td>
-<td>neutral</td>
-<td>2 stars</td>
-<td>Russian Federation</td>
-<td>Europe</td>
-<td>Eastern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>66616.02225</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>4</th>
-<td>15621893</td>
-<td>727</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>4</td>
-<td>133550.67</td>
-<td>1</td>
-<td>1</td>
-<td>1</td>
-<td>46941.41</td>
-<td>0</td>
-<td>positive</td>
-<td>1 star</td>
-<td>Italy</td>
-<td>Europe</td>
-<td>Southern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>55442.07843</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>CustomerId</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment5</th>
+      <th>Surname_Country</th>
+      <th>Surname_Country_region</th>
+      <th>Surname_Country_subregion</th>
+      <th>Country_region</th>
+      <th>Country_subregion</th>
+      <th>is_native</th>
+      <th>Country_hemisphere</th>
+      <th>Country_gdp_per_capita</th>
+      <th>Country_IncomeGroup</th>
+      <th>Surname_Country_gdp_per_capita</th>
+      <th>Surname_Country_IncomeGroup</th>
+      <th>working_class</th>
+      <th>stage_of_life</th>
+      <th>generation</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>15787619</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>160980.03</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>4 stars</td>
+      <td>Taiwan</td>
+      <td>Asia</td>
+      <td>Eastern Asia</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>32756.00000</td>
+      <td>None</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>15770309</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>151762.74</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>1 star</td>
+      <td>United States</td>
+      <td>Americas</td>
+      <td>Northern America</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>76329.58227</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>15569178</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
+      <td>82767.42</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0</td>
+      <td>71811.90</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>2 stars</td>
+      <td>Russian Federation</td>
+      <td>Europe</td>
+      <td>Eastern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>15795519</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>128743.80</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>197322.13</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>2 stars</td>
+      <td>Russian Federation</td>
+      <td>Europe</td>
+      <td>Eastern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>66616.02225</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>15621893</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>133550.67</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1</td>
+      <td>46941.41</td>
+      <td>0</td>
+      <td>positive</td>
+      <td>1 star</td>
+      <td>Italy</td>
+      <td>Europe</td>
+      <td>Southern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>55442.07843</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">target_col</span> <span class="o">=</span> <span class="s2">"Exited"</span>
-<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"CustomerId"</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">target_col</span> <span class="o">=</span> <span class="s2">&quot;Exited&quot;</span>
+<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CustomerId&quot;</span><span class="p">]</span>
 <span class="n">cat_cols</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">"Country"</span><span class="p">,</span>
-    <span class="s2">"Gender"</span><span class="p">,</span>
-    <span class="s2">"HasCreditCard"</span><span class="p">,</span>
-    <span class="s2">"IsActiveMember"</span><span class="p">,</span>
-    <span class="s2">"CustomerFeedback_sentiment3"</span><span class="p">,</span>
-    <span class="s2">"CustomerFeedback_sentiment5"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_region"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_subregion"</span><span class="p">,</span>
-    <span class="s2">"Country_region"</span><span class="p">,</span>
-    <span class="s2">"Country_subregion"</span><span class="p">,</span>
-    <span class="s2">"is_native"</span><span class="p">,</span>
-    <span class="s2">"Country_hemisphere"</span><span class="p">,</span>
-    <span class="s2">"Country_IncomeGroup"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_IncomeGroup"</span><span class="p">,</span>
-    <span class="s2">"working_class"</span><span class="p">,</span>
-    <span class="s2">"stage_of_life"</span><span class="p">,</span>
-    <span class="s2">"generation"</span><span class="p">,</span>
+    <span class="s2">&quot;Country&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Gender&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;HasCreditCard&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;IsActiveMember&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CustomerFeedback_sentiment3&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CustomerFeedback_sentiment5&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_region&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_subregion&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;is_native&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_hemisphere&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_IncomeGroup&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_IncomeGroup&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;working_class&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;stage_of_life&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;generation&quot;</span><span class="p">,</span>
 <span class="p">]</span>
 <span class="n">cont_cols</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
     <span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span> <span class="o">+</span> <span class="n">cat_cols</span> <span class="o">+</span> <span class="p">[</span><span class="n">target_col</span><span class="p">]</span>
 <span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-3">target_col = "Exited"
+<div id="cell-3" class="clipboard-copy-txt">target_col = "Exited"
 id_cols = ["CustomerId"]
 cat_cols = [
     "Country",
@@ -1824,80 +2245,90 @@ <h2 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h2>
 cont_cols = df_pd.drop(
     columns=id_cols + cat_cols + [target_col]
 ).columns.values.tolist()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-4">df_pd[cat_cols] = df_pd[cat_cols].astype(str)</div>
-</div>
-</div>
+<div id="cell-4" class="clipboard-copy-txt">df_pd[cat_cols] = df_pd[cat_cols].astype(str)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="features">Features<a class="anchor-link" href="#features">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="features">Features<a class="anchor-link" href="#features">&#182;</a></h2>
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">&#182;</a></h3>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-5">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">duplicated_ids</span><span class="p">,</span> <span class="n">cont_cols_desc</span><span class="p">,</span> <span class="n">cat_cols_desc</span> <span class="o">=</span> <span class="n">init_check</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">duplicated_ids</span><span class="p">,</span> <span class="n">cont_cols_desc</span><span class="p">,</span> <span class="n">cat_cols_desc</span> <span class="o">=</span> <span class="n">init_check</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">,</span>
     <span class="n">identifier</span><span class="o">=</span><span class="n">id_cols</span><span class="p">,</span>
     <span class="n">cat_cols</span><span class="o">=</span><span class="n">cat_cols</span><span class="p">,</span>
@@ -1907,7 +2338,7 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 
 <span class="c1"># duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-5">duplicated_ids, cont_cols_desc, cat_cols_desc = init_check(
+<div id="cell-5" class="clipboard-copy-txt">duplicated_ids, cont_cols_desc, cat_cols_desc = init_check(
     df=df_pd,
     identifier=id_cols,
     cat_cols=cat_cols,
@@ -1916,17 +2347,27 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 )
 
 # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>[CHECK] Number of duplicated ids: 0
 [CHECK] Numerical columns
                                   count       mean       std       min  \
@@ -1971,54 +2412,71 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-6">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">missing_val_frac</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">missing</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">missing_val_frac</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">missing</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span><span class="p">),</span>
-    <span class="n">scale</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span>
+    <span class="n">scale</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="n">missing_val_frac</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-6">missing_val_frac, fig = missing(
+<div id="cell-6" class="clipboard-copy-txt">missing_val_frac, fig = missing(
     df=df_pd.drop(columns=id_cols),
     scale="linear",
     plot=True,
 )
 
 missing_val_frac.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>CreditScore                    0.0
 Surname_Country_region         0.0
 stage_of_life                  0.0
@@ -2026,61 +2484,90 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 Surname_Country_IncomeGroup    0.0
 dtype: float64</pre>
 </div>
+
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">zero_val_frac</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">zero</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span><span class="p">),</span>
-    <span class="n">scale</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span>
+    <span class="n">scale</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="n">zero_val_frac</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-7">zero_val_frac, fig = zero(
+<div id="cell-7" class="clipboard-copy-txt">zero_val_frac, fig = zero(
     df=df_pd.drop(columns=id_cols),
     scale="linear",
     plot=True,
 )
 
 zero_val_frac.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>Exited                       0.7963
 Balance (EUR)                0.3617
 Tenure                       0.0413
@@ -2088,61 +2575,90 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 Surname_Country_subregion    0.0000
 dtype: float64</pre>
 </div>
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">data_nunique</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">nunique</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">data_nunique</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">nunique</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span><span class="p">),</span>
-    <span class="n">scale</span><span class="o">=</span><span class="s2">"log"</span><span class="p">,</span>
+    <span class="n">scale</span><span class="o">=</span><span class="s2">&quot;log&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="n">data_nunique</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-8">data_nunique, fig = nunique(
+<div id="cell-8" class="clipboard-copy-txt">data_nunique, fig = nunique(
     df=df_pd.drop(columns=id_cols),
     scale="log",
     plot=True,
 )
 
 data_nunique.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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+
+
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-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>Country_IncomeGroup    1
 Country_hemisphere     1
 Country_region         1
@@ -2150,61 +2666,90 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 is_native              2
 dtype: int64</pre>
 </div>
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">data_std</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">std</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-9">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">data_std</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">std</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">cont_cols</span><span class="p">],</span>
-    <span class="n">scale</span><span class="o">=</span><span class="s2">"log"</span><span class="p">,</span>
+    <span class="n">scale</span><span class="o">=</span><span class="s2">&quot;log&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="n">data_std</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-9">data_std, fig = std(
+<div id="cell-9" class="clipboard-copy-txt">data_std, fig = std(
     df=df_pd[cont_cols],
     scale="log",
     plot=True,
 )
 
 data_std.head()</div>
+    
+        </div>
+    </div>
+
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-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
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+
+<div class="jp-OutputArea-child">
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[9]:</div>
+
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+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>Balance (EUR)                     62397.405202
 EstimatedSalary                   57510.492818
 Surname_Country_gdp_per_capita    25976.502611
@@ -2212,61 +2757,90 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 CreditScore                          96.653299
 dtype: float64</pre>
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+
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545c0ZRUVHavn27tm3bpm3btlnDVkdGRurq1as3Xnwueuyxx9Kdd+jQIW3YsEHStZGiMnLLLbdYQ/KGh4db0ytUqGDdz2ywhJwoV66cOnbsKEnpdsafNWuWdb9nz56ZrjMuLk779+9XZGSktQ2NMdb8zZs3Z/j4jN7TnEhMTNThw4e1c+dOq54dO3ZY13zKrJ6ePXum2ZFckho2bGjd37NnT4p5y5YtU0xMjCRp8ODByp8/f5Zrvnr1qtXZunv37uk+vyT5+/urbt26klJ+drLqxIkT1v2SJUtm+/GS9NJLL6lYsWKSZA37nB2RkZHWdyWt0d3uvPNOlStXTlLKC4XmhhvZ51SpUkUtW7ZMd7679pPXW7x4saRr2+/+++9Pd7knnngiy+vM6Hu4YcMGnT17VtK1wQPS+2wXL17c2vdFRkbqyJEj6a6zX79+6c574IEHrG2S9FrTUrdu3RTfyeslvf74+HhrQBEpd75v8fHx1iA4d955pypWrJjm4/Ply6c+ffqku/7rlSpVyrqf/LsK5CZCDhxvxIgRMtfOWqZ5Sz6qzvr16yVdG4Xm+hHNrr89++yzkq6NwpPWSFRbt27VE088oQoVKqhUqVKqUaOG6tSpo7p166pu3brW8yYmJurMmTNufx+yI6MDl6T3SJJ69OiR6fuUNKLT0aNHrcdVq1bNOoj6z3/+o1tvvVXDhw/X0qVLrQPoG5V0MHPkyBEtXbo01fykoXtvv/12BQcHp7mOS5cuafTo0brttttUpEgRBQQE6NZbb7W2Yf369a1lMxu5KrODwawwxmjGjBlq27atihYtqkqVKqlWrVpWPXXr1tWmTZuyVE+tWrXSnZf8AOTChQsp5v3555/W/aQD2KyKjIy0tu+rr76a6Wcn6bOW/LOTVcm/kzkNOaVLl9aQIUMkSatXr9Yvv/ySrcdPmzZNkuTj46NHH3001XwfHx9rON0FCxbo3LlzOaozSW7tczL7rObmfjKz1yNJ9evXl49P+tcur1evngoWLJildWb02rZt22bdb9KkSYbrST4/+eOSK1iwYIbPV6BAAWsfkt46JKlRo0YZ1tK4ceM0a8mN71tUVJS1juzUkZnk38lTp05l+XFAdhBygGSyO1xxkusPzCdPnqwGDRroyy+/zNIBWkbD7Noho4PC3HqPZs+erWbNmkm69s/4nXfeUfv27eXv76/WrVtrwoQJio2NzdFzSVLXrl1VpEgRSUp1LZJNmzYpMjJSUvq/7EZHR6tu3bp67bXXtGXLlkx/hc5sG+b0QDtJbGysunTposcff1zLly/P9Pkym1+4cOF05+XL93//Gq5/3cnDU/IzclmRW5+drPDz87Pu38j3a+jQodav7cOHD8/y45JfA+fOO+9U2bJl01wu6QxPbGysNeR1TuTmPiezz6qntmNSEEs625UeHx+fFME8Ixm9tuQh7KabbspwPeXLl0/zccmVKlUqw3CW/HkyCoCZvf7ktSZfT25sp+RhODt1ZCb5569QoULZqA7Iuoy/fYCXSTqgu+OOOzRhwoQsPy75Kfy//vpLAwcOVHx8vMqVK6eXXnpJ7dq1U2BgoIoVK6YCBQpIkqZMmaInn3xSklI0e8oLMmqClPygd+bMmVk+Q3H9wUWlSpX0xx9/aMmSJfr222+1YsUKqxnNypUrtXLlSn300UdauHChgoKCsv0aihQpovvvv1+zZs3St99+q88++8w68E1qqpYvXz7rl/TrPf7449q7d69cLpf69eunRx99VLfccovKli1rXSwyMTHReq8y24bZadaVlnfffddqetK6dWuFhoaqQYMGKl++vAoVKmQFk1atWun333/Pc58pKeVn58MPP8zytWuSwmp2JA8V2T2DkJy/v7+GDh2q4cOHa926dfrxxx91zz33ZPq4JUuW6NChQ5KuXeMlo6ZCSaZPn66nn3462zXm9j4ns89qbuwnsyMr711WP+9Z/R5m9pxZeb7cqjsr60lLbnzfkteXG+9JkuTfyfR+AABuFCEHSKZ06dI6duyYTpw4oTp16uRoHVOnTlV8fLzy58+v5cuX65Zbbklzudxqopb8V/fMrh596dKlG36+0qVLW/ddLleO36ck7du3V/v27SVda7awePFiff7551q6dKmioqL0yCOPpGgilR29evXSrFmzdP78ef3444/q3r27jDGaPXu29dxpnY3466+/rIvUvfrqq3r33XfTXL+nmhkaYzRp0iRJUosWLbR06dIU292TNSX1sZKuNQWsVq1alh+b/LNz9erVG/7sZCT5gdONvidDhgzRxx9/rFOnTmn48OHq0qVLpo9JaqqWHatXr9aePXt08803Z+txnt7n5MZ+MitKliypo0eP6tixYxkuFx8fnyuvLfnZoKNHj2b440rymtI7i3Tq1CklJCRkGKySzrZkdCYqs9efXi258X1Lvr7M6sjOmaPk2yv5PgXITTRXA5JJah+9a9cu7du3L0fr2L59uyTptttuS/dgQ0rZtyUtWf31LqljtJTxQcypU6cy7aeRFcn7ofz66683vL7kSpcurUceeURLlizRfffdJ+la07Ldu3fnaH0dO3a0DnaTzt6sXLnSukJ4ek3VkrahpDT7UiTJbBvmltOnT1tNkB5++OF0A87Fixe1c+dOt9bSoEED6/7KlSuz9dhbb73V6juR25+d6yV1opaufZ9vRLFixfTSSy9JutYn6fvvv89w+YsXL+q7776TdC1Iz549O8NbUoA1xuRoAILc2udkVW7sJ7MiaRtu2rRJ8fHx6S63efNmXbly5YafL3kIWLt2bYbLrlu3Ls3HJXflypUMBwCJj4+3+tBlFEAiIiIyrCX5/OTryY3vW/Xq1a2mrdmpIzNJ38mgoCDrzDiQ2wg5QDJJB9aS9MEHH+RoHUn/jDNqf3706FFrhKL0JDWtiouLy3C5kiVLWn0GMjqISTp7caNq1Kih2rVrS5LmzJmj/fv358p6r5d0dkfKvBN9epJ37F64cKHOnj1r9ZPw8/PTAw88kObjkh9QZbQds9NU50ZktZ7Jkye7faS+tm3bWs1ZPvnkk2yNmFW4cGFruy5fvjzFgWJuq1ixonVGJDsHX+l59tlnrT4JSYOZpGfu3LnWdho0aJAeffTRDG9PPvmkNXpWTkJObu1zsio39pNZ0aFDB0nXQv4PP/yQ7nJTpkzJledr2LChtS+dNm1aup/tCxcuWP2nateunWHftIzO6H333XfWD1NJrzUtW7duzfBsdtLrz58/v9q0aWNNz43vm4+Pj1q3bi3pWlBKbyS5xMTEbJ29TPpfldEofsCNIuQAyXTr1s36JfSzzz7T5MmTM1x+27Ztqf751qxZU9K1X6rWrFmT6jExMTHq2bNnpp2hk/5x7tmzJ9O2zkmjXM2fP19RUVGp5u/YsSNbnaYz88Ybb0i61ln6wQcfzHAI0Li4OI0fPz7FIAKbNm2yfsFMizHGGlLV5XIpMDAwx7Umna2Ji4vTrFmzNHfuXEnXDtSKFy+e5mOStqGU/kHKZ599lukv+rmlbNmy1sHXnDlz0vzVOiIiwtou7uTv768BAwZIujbk7pAhQ9L9fF69ejVVE5bXX3/dOkv56KOPpvl5TZKQkKBZs2ZZZ96yK+kAKjfCVJEiRfTyyy9LunbQuXDhwnSXnT59uqRrB5l33313ltbfvXt3SddGs1q9enW2asutfU5W5cZ+Miv69OljdUofOnRoms2lVqxYoc8//zzb606Lr6+vnnrqKUnXzo6NHDky1TLGGD377LPWDy9Jo8el57PPPrOaviZ39OhR/etf/5J07XOS2fDL/fv3T7O58axZs6zPYteuXVMFrtz4vg0aNEjStX3ogAED0gx/o0ePtkbDy8yePXus94+QA7fy1AV5AE9KfnG/5Bf7zIrkVwaXZDp16mSmTZtm1qxZYzZs2GB+/vln895775nmzZsbSebFF19M8fh169ZZjy1ZsqQZPXq0WbFihVm7dq0ZP368qVmzpnVxvKTl9u7dm6qOL774wpo/ZMgQs379erN7926ze/duEx0dnWLZ5BdvrFy5spk0aZLZsGGDWbFihXnzzTdN8eLFTY0aNUzZsmWzdMG8tOq5XvIL3pUpU8a8/vrr5tdffzV//vmnWbVqlZk2bZp56qmnTKlSpYykFFfrTnquRo0ambffftv8+OOPZv369SY8PNzMmjXLdOzY0Vp3165ds7TdMlKjRg0jybrQn9K4yGVyiYmJKS5s16NHD/Pjjz+aDRs2mO+//95079491TZM63OW/GKgWZHRxUBDQ0OtdTVu3NjMnj3bREREmMWLF5uhQ4caPz8/U6ZMGRMUFGSUzkVkk38vli1blmEtGb2uS5cumbp166a4sOjnn39uwsPDzYYNG8z8+fPNSy+9ZCpVqpTmRQGTvy9FixY1zz//vPnpp5/Mxo0bTXh4uJk9e7YZPHiwqVixopFktm7dmqX373rffPON9Ty7d+9Od7n0LgZ6vZiYGFOhQoUUF2m8ftvu27fPuFwuI8l069Yty7Xu2rXLWl///v2z/Dhjcm+fk52L0d7ofjKrPvroI+s5KlWqZD799FOzbt06s3LlSvPKK68YX19fExAQkGv7tvPnz5ubb77ZWv6BBx4wP/zwg9mwYYOZO3euadOmjTWvWbNmaV4wNGnfWLZsWRMQEGD8/PzMq6++an7//Xezbt068+mnn1qfbUlm7NixadaSNP/22283kkytWrXMl19+adavX2+WLFliBg0aZPLly2ckmWLFiqX72nLj+3bvvfda62jSpImZM2eOtZ0feeQRa3+ele/R559/biSZ/PnzmyNHjmS4PYAbQciBI91IyDHGmM2bN1sHBpndRo4cmerxI0eOzPAxL774Yqb/eC9cuJDin23yW0BAQKrlBw8enO7zValSxWzfvj3Dg5jshpz4+HgzbNgwkz9//kzfoyJFipiYmJg0nyujW4sWLcypU6cyrSUzw4cPT7HeUqVKmbi4uAwf8+eff5qSJUumW1vdunXN4cOHPRZyzp49a+rVq5duPaVKlTIrVqwwrVu3dnvIMcaYEydOmFatWmW6DdM72PnPf/5jXWE9o1vBggUzDCgZuXz5shVs0/qeJslqyDHGmE8++SRVjcmNGjXKmj579uxs1RsSEmKka2E8NjY2W4/NjX1OdkKOMTe+n8yqjPZtZcqUMREREbm6b9u7d6+pVatWhq/njjvuSHfflBRyAgICTEREhClTpky66xk8eHC6dST/Dibfl1x/K168uFm+fHmGr+lGv2/nz59PEZKvvzVo0MBs3LgxS9+jpKDYqVOnDGsGbhQhB450oyHHGGOuXr1qpk2bZrp27WqqVKli/Pz8TMGCBU2FChVMmzZtzBtvvGE2bNiQ7uN/+uknc+edd5qSJUuaggULmsqVK5sHH3zQ/Prrr8aYrP3jPXr0qHn++efNLbfcYgoXLmwtn1bIMcaYWbNmmVatWpnixYubQoUKmeDgYPPKK6+YkydPGmMyPojJ7oFAkp07d5oXX3zR1K9f35QsWdLkz5/fFCtWzNx6663mscceM9OmTTPnz59P8Zi4uDizbNky89prr5mWLVuaatWqmcKFC1vv03333WdmzZplEhISslxHZjUm/4ec1V/K9+3bZwYOHGgCAgJMgQIFTKlSpUzjxo3NRx99ZC5fvmyMyTgM5GbIMebaGZR33nnH1K1b1/j5+ZmiRYuaW265xfzrX/8yBw4cMMYYj4WcJN9++63p3r27qVy5svH19TUlS5Y0derUMY899piZP39+hmHy4MGD5s033zRNmzY1ZcqUMT4+PqZIkSImKCjIdOvWzUyYMMGcOHEiw+fPzNChQ40kExQUlO4y2Qk5sbGxpkqVKumGnODgYCPJ+Pr6pvrcZyZ5UPnmm2+y9Vhjbnyfk92QY8yN7yez89o6depkSpUqZfz8/EyNGjXM4MGDrc99bu/b4uLizKeffmpat25tSpcubQoUKGBuuukmc9ddd5mvvvoqw31T8pBjjDH79+83gwcPNtWrVzd+fn6mdOnS5q677jILFy7MsIbrv4OLFi0yXbp0MTfddJMpWLCgCQwMNM8884z1HmTmRr9vV69eNZ988olp1KiRKVq0qClWrJipV6+eGT16tLl8+bLZu3dvpt+jgwcPWmefMjqbDuQGlzF58GIKAADkgv3796tmzZq6cuWKfv/9d7Vo0cLukuBwffv21bRp0xQQEKDo6OgcryepL82IESP01ltv5U5xNhs1apTefPNNBQcHKzIyMt2RIoHcwKcLAOBYVatWtS6A+c4779hcDeC9Ll68qHHjxkm6FtwIOHA3PmEAAEcbMWKEihcvrl9//TXN0ccAuF9YWJhOnTqlRo0aZXj9MSC3+NhdAAAA7nTTTTdp5syZWr9+fa5cEBdA9hUrVkwjRozQgw8+mOWLXQM3gpADAHC8e+65R/fcc4/dZQBe65lnnrG7BHgZmqsBAAAAcBRGVwMAAADgKB5prhYdHa27775brVu3Vvny5bM1FGJiYqIOHz6sYsWK0YYTAAAA8GLGGF24cEEVK1bMcJS+bIecwYMHa8GCBdq3b5+2bt2qOnXqWPN2796tPn366OTJk/L399fUqVNVu3ZtSVKJEiUUGxuratWqZev5Dh8+rCpVqmS3TAAAAAAOdeDAAVWuXDnd+dlurrZy5UrdfPPNatGihX788ccUIaddu3bq3bu3+vbtq7lz52rs2LEKDw+XMUYul0vGGHXv3l1jx45VYGBglp7v3Llz8vf314EDB1S8ePHslAoAAADAQc6fP68qVaro7NmzKlGiRLrLZftMTqtWrdKcfvz4cW3cuFG//vqrJKlbt2569tlnFR0dbQUal8ulm266SRcuXEh3/XFxcYqLi7P+Tlq2ePHihBwAAAAAmXZjybU+OQcOHFDFihXl4+NjPXHVqlW1f/9+7d27V9OnT5ck+fv7q27duumuZ/To0Ro5cmRulQUAAADAy+TqwAPXJ6qklnBt27ZV27Zts7SOV199VUOHDrX+TjolBQAAAABZkWshp0qVKjp48KDi4+Pl4+MjY4wOHDigqlWrZms9vr6+8vX1za2yAAAAAHiZXLsYaLly5VS/fn3NmDFDkjRv3jwFBgZmeYCB64WFhal27dpq1KhRbpUIAAAAwAtke3S10NBQzZ8/X0ePHlWZMmVUtGhR/f3335KknTt3qm/fvjp16pSKFy+uadOm6dZbb72hAs+fP68SJUro3LlzDDwAAAAAeLGsZoNshxxPI+QAAAAAkLKeDXKtuRoAAAAA5AV5NuTQJwcAAABATtBcDQAAAMA/As3VAAAAAHglQg4AAAAAR8mzIYc+OQAAAABygj45AAAAAP4R6JMDAAAAwCv52F1AXhD4yk92l3BDot/vYncJAAAAQJ6RZ8/k0CcHAAAAQE7k2ZATGhqqyMhIRURE2F0KAAAAgH8QmqshT6DJIAAAAHJLnj2TAwAAAAA5QcgBAAAA4CiEHAAAAACOkmdDDqOrAQAAAMiJPBtyGF0NAAAAQE7k2ZADAAAAADlByAEAAADgKIQcAAAAAI5CyAEAAADgKIQcAAAAAI6SZ0MOQ0gDAAAAyIk8G3IYQhoAAABATuTZkAMAAAAAOUHIAQAAAOAohBwAAAAAjkLIAQAAAOAohBwAAAAAjkLIAQAAAOAoeTbkcJ0cAAAAADmRZ0MO18kBAAAAkBN5NuQAAAAAQE4QcgAAAAA4CiEHAAAAgKP42F0AAPsFvvKT3SXcsOj3u9hdAgAAyCM4kwMAAADAUQg5AAAAAByFkAMAAADAUQg5AAAAAByFkAMAAADAUQg5AAAAABwlz4acsLAw1a5dW40aNbK7FAAAAAD/IHk25ISGhioyMlIRERF2lwIAAADgHyTPhhwAAAAAyAlCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABH8WjIeeONN9ShQwdPPiUAAAAAL5PtkDN48GAFBgbK5XJp27ZtKebt3r1bzZs3V1BQkBo3bqzIyEhr3v/+9z81aNDgxisGAAAAgAxkO+R0795dq1atUkBAQKp5AwYMUP/+/bVr1y4NGzZMTz75pKRr4WfDhg168MEHM11/XFyczp8/n+IGAAAAAFmV7ZDTqlUrVa5cOdX048ePa+PGjerVq5ckqVu3btq7d6+io6O1cOFCnTt3TkOGDNGuXbu0YMGCdNc/evRolShRwrpVqVIluyUCAAAA8GI+ubWiAwcOqGLFivLxubZKl8ulqlWrav/+/Xr++eet5bZt26b77rsv3fW8+uqrGjp0qPX3+fPnCToAAAAAsizXQo50LdgkZ4xJtczixYszXIevr698fX1zsywAAAAAXiTXRlerUqWKDh48qPj4eEnXAs6BAwdUtWrV3HoKAAAAAMhUroWccuXKqX79+poxY4Ykad68eQoMDFRgYGCO1hcWFqbatWurUaNGuVUiAAAAAC+Q7ZATGhqqypUr6+DBg+rQoYNq1KhhzZs4caImTpyooKAgvf/++5o8eXKOCwsNDVVkZKQiIiJyvA4AAAAA3ifbfXLCwsIUFhaW5rzg4GCFh4ffcFEAAAAAkFO51lwtt9FcDQAAAEBO5NmQQ3M1AAAAADmRZ0MOAAAAAOQEIQcAAACAo+TZkEOfHAAAAAA5kWdDDn1yAAAAAOREng05AAAAAJAT2b5ODgAg9wW+8pPdJdyw6Pe72F0CAACSOJMDAAAAwGHybMhh4AEAAAAAOZFnQw4DDwAAAADIiTwbcgAAAAAgJwg5AAAAAByFkAMAAADAUfJsyGHgAQAAAAA5kWdDDgMPAAAAAMiJPBtyAAAAACAnCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBR8mzIYQhpAAAAADmRZ0MOQ0gDAAAAyIk8G3IAAAAAICcIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFHybMjhYqAAAAAAciLPhhwuBgoAAAAgJ/JsyAEAAACAnCDkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHCUPBtywsLCVLt2bTVq1MjuUgAAAAD8g+TZkBMaGqrIyEhFRETYXQoAAACAf5A8G3IAAAAAICcIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAcxccTT7Jt2zZ9+umnunjxovLnz6+pU6fK5XJ54qkBAAAAeJlsn8kZPHiwAgMD5XK5tG3bthTzdu/erebNmysoKEiNGzdWZGSkJKlOnTqaMGGCZsyYoZiYGF26dCl3qgcAAACA62Q75HTv3l2rVq1SQEBAqnkDBgxQ//79tWvXLg0bNkxPPvmkNe+3335Tz549VaZMGRUuXDjd9cfFxen8+fMpbgAAAACQVdkOOa1atVLlypVTTT9+/Lg2btyoXr16SZK6deumvXv3Kjo6WpLUsWNHzZo1S/ny5dOmTZvSXf/o0aNVokQJ61alSpXslggAAADAi+Van5wDBw6oYsWK8vG5tkqXy6WqVatq//792r17t77//nvFx8dLkurWrZvuel599VUNHTrU+vv8+fMEHQAAAABZlqsDD1w/mIAxRtK1szgdO3bM0jp8fX3l6+ubm2UBAAAA8CK5NoR0lSpVdPDgQetsjTFGBw4cUNWqVXO0vrCwMNWuXVuNGjXKrRIBAAAAeIFcCznlypVT/fr1NWPGDEnSvHnzFBgYqMDAwBytLzQ0VJGRkYqIiMitEgEAAAB4gWyHnNDQUFWuXFkHDx5Uhw4dVKNGDWvexIkTNXHiRAUFBen999/X5MmTc7VYAAAAAMhMtvvkhIWFKSwsLM15wcHBCg8Pv+GiAAAAACCncq25Wm6jTw4AAACAnMizIYc+OQAAAAByIs+GHAAAAADICUIOAAAAAEfJsyGHPjkAAAAAciLPhhz65AAAAADIiTwbcgAAAAAgJwg5AAAAABwlz4Yc+uQAAAAAyIk8G3LokwMAAAAgJ/JsyAEAAACAnCDkAAAAAHAUQg4AAAAARyHkAAAAAHCUPBtyGF0NAAAAQE7k2ZDD6GoAAAAAciLPhhwAAAAAyAlCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcJQ8G3IYQhoAAABATuTZkMMQ0gAAAAByIs+GHAAAAADICUIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEfJsyGH6+QAAAAAyIk8G3K4Tg4AAACAnMizIQcAAAAAcoKQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHCXPhpywsDDVrl1bjRo1srsUAAAAAP8geTbkhIaGKjIyUhEREXaXAgAAAOAfJM+GHAAAAADICUIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFB9PPMmKFSs0ffp0xcbGqmTJkvr000898bQAAAAAvFC2z+QMHjxYgYGBcrlc2rZtW4p5u3fvVvPmzRUUFKTGjRsrMjJSktS6dWtNnjxZM2fO1P79+3XhwoXcqR4AAAAArpPtkNO9e3etWrVKAQEBqeYNGDBA/fv3165duzRs2DA9+eSTKeYvXLhQt9xyi4oVK5bzigEAAAAgA9lurtaqVas0px8/flwbN27Ur7/+Kknq1q2bnn32WUVHRyswMFBffPGFDh06pDFjxmS4/ri4OMXFxVl/nz9/PrslAgAAAPBiuTbwwIEDB1SxYkX5+FzLTS6XS1WrVtX+/fs1b948vf322zp69KgGDhyoEydOpLue0aNHq0SJEtatSpUquVUiAAAAAC+QqwMPuFyuFH8bYyRdO6vTrVu3LK3j1Vdf1dChQ62/z58/T9ABALhd4Cs/2V3CDYt+v4vdJQBAnpBrIadKlSo6ePCg4uPj5ePjI2OMDhw4oKpVq2ZrPb6+vvL19c2tsgAAAAB4mVxrrlauXDnVr19fM2bMkCTNmzdPgYGBCgwMzNH6wsLCVLt2bTVq1Ci3SgQAAADgBbIdckJDQ1W5cmUdPHhQHTp0UI0aNax5EydO1MSJExUUFKT3339fkydPznFhoaGhioyMVERERI7XAQAAAMD7ZLu5WlhYmMLCwtKcFxwcrPDw8BsuCgAAAAByKteaq+U2mqsBAAAAyIk8G3JorgYAAAAgJ/JsyAEAAACAnCDkAAAAAHCUPBty6JMDAAAAICfybMihTw4AAACAnMizIQcAAAAAcoKQAwAAAMBRCDkAAAAAHCXPhhwGHgAAAACQE3k25DDwAAAAAICcyLMhBwAAAABygpADAAAAwFEIOQAAAAAchZADAAAAwFHybMhhdDUAAAAAOZFnQw6jqwEAAADIiTwbcgAAAAAgJwg5AAAAAByFkAMAAADAUQg5AAAAABwlz4YcRlcDAAAAkBN5NuQwuhoAAACAnMizIQcAAAAAcoKQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHCXPhhwuBgoAAAAgJ/JsyOFioAAAAAByIs+GHAAAAADICUIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEfJsyEnLCxMtWvXVqNGjewuBQAAAMA/SJ4NOaGhoYqMjFRERITdpQAAAAD4B8mzIQcAAAAAcoKQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRPBJyzpw5oyeffFJVqlTxxNMBAAAA8GLZDjmDBw9WYGCgXC6Xtm3blmLe7t271bx5cwUFBalx48aKjIyUJJUsWVKTJ09WcHBw7lQNAAAAAOnwye4DunfvrmHDhqlFixap5g0YMED9+/dX3759NXfuXD355JMKDw/P1vrj4uIUFxdn/X3+/PnslggAAP6BAl/5ye4Sblj0+13sLgGAcnAmp1WrVqpcuXKq6cePH9fGjRvVq1cvSVK3bt20d+9eRUdHZ2v9o0ePVokSJawbTdwAAAAAZEeu9ck5cOCAKlasKB+fayeHXC6Xqlatqv3790uSBg4cqL/++ksDBw5UVFRUuut59dVXde7cOet24MCB3CoRAAAAgBfIdnO1jLhcrhR/G2Os+xMmTMjSOnx9feXr65ubZQEAAADwIrl2JqdKlSo6ePCg4uPjJV0LOAcOHFDVqlVztL6wsDDVrl1bjRo1yq0SAQAAAHiBXAs55cqVU/369TVjxgxJ0rx58xQYGKjAwMAcrS80NFSRkZGKiIjIrRIBAAAAeIFsh5zQ0FBVrlxZBw8eVIcOHVSjRg1r3sSJEzVx4kQFBQXp/fff1+TJk3O1WAAAAADITLb75ISFhSksLCzNecHBwdkeMhoAAAAAclOuNVfLbfTJAQAAAJATeTbk0CcHAAAAQE7k2ZADAAAAADlByAEAAADgKHk25NAnBwAAAEBO5NmQQ58cAAAAADmRZ0MOAAAAAOQEIQcAAACAoxByAAAAADhKng05DDwAAAAAICfybMhh4AEAAAAAOZFnQw4AAAAA5AQhBwAAAICjEHIAAAAAOEqeDTkMPAAAAAAgJ/JsyGHgAQAAAAA5kWdDDgAAAADkBCEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4Sp4NOQwhDQAAACAn8mzIYQhpAAAAADmRZ0MOAAAAAOQEIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADhKng05XAwUAAAAQE742F1AekJDQxUaGqrz58+rRIkSdpcDAADgeIGv/GR3CTck+v0udpeAPCLPnskBAAAAgJwg5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwlDwbcsLCwlS7dm01atTI7lIAAAAA/IPk2ZATGhqqyMhIRURE2F0KAAAAgH+QPBtyAAAAACAnCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRfDzxJHFxcRowYICKFy+uhIQEhYWFeeJpAQAAAHihbJ/JGTx4sAIDA+VyubRt27YU83bv3q3mzZsrKChIjRs3VmRkpCTp22+/VevWrfXf//5XJUuWVHh4eO5UDwAAAADXyXbI6d69u1atWqWAgIBU8wYMGKD+/ftr165dGjZsmJ588klJ0r59+xQYGChJuvnmm7Vv374bqxoAAAAA0pHtkNOqVStVrlw51fTjx49r48aN6tWrlySpW7du2rt3r6Kjo1WlShUr2ERHR6tq1arprj8uLk7nz59PcQMAAACArMq1gQcOHDigihUrysfnWjcfl8ulqlWrav/+/erWrZuWL1+uoUOH6sSJE2revHm66xk9erRKlChh3apUqZJbJQIAAADwArk68IDL5UrxtzFGkuTn56epU6dmaR2vvvqqhg4dav19/vx5gg4AAACALMu1kFOlShUdPHhQ8fHx8vHxkTFGBw4cyLBpWlp8fX3l6+ubW2UBAAAA8DK51lytXLlyql+/vmbMmCFJmjdvngIDA60BB7IrLCxMtWvXVqNGjXKrRAAAAABeINshJzQ0VJUrV9bBgwfVoUMH1ahRw5o3ceJETZw4UUFBQXr//fc1efLkHBcWGhqqyMhIRURE5HgdAAAAALxPtpurhYWFpXsxz+DgYK6BAwAAAMBWudZcLbfRXA0AAABATuTZkENzNQAAAAA5kWdDDgAAAADkBCEHAAAAgKMQcgAAAAA4Sp4NOQw8AAAAACAn8mzIYeABAAAAADmRZ0MOAAAAAOQEIQcAAACAo+TZkEOfHAAAAAA54WN3AekJDQ1VaGiozp8/rxIlSthdDgAAAOB2ga/8ZHcJNyT6/S52lyApD5/JAQAAAICcIOQAAAAAcBRCDgAAAABHIeQAAAAAcJQ8G3IYXQ0AAABATuTZkBMaGqrIyEhFRETYXQoAAACAf5A8G3IAAAAAICcIOQAAAAAchZADAAAAwFEIOQAAAAAcJc+GHEZXAwAAAJATeTbkMLoaAAAAgJzIsyEHAAAAAHKCkAMAAADAUQg5AAAAAByFkAMAAADAUXzsLiAzxhhJ0vnz5932HIlxMW5btye4873xFLaBvf7p77/ENsgL2Ab2YxvYj21gr3/6+y+xDbK6/qSMkB6XyWwJmx08eFBVqlSxuwwAAAAAecSBAwdUuXLldOfn+ZCTmJiow4cPq1ixYnK5XHaXk23nz59XlSpVdODAARUvXtzucrwS28B+bAP7sQ3sxftvP7aB/dgG9nPCNjDG6MKFC6pYsaLy5Uu/502eb66WL1++DFPaP0Xx4sX/sR8mp2Ab2I9tYD+2gb14/+3HNrAf28B+//RtUKJEiUyXYeABAAAAAI5CyAEAAADgKIQcN/P19dWIESPk6+trdylei21gP7aB/dgG9uL9tx/bwH5sA/t50zbI8wMPAAAAAEB2cCYHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAbpGQkKCPP/5Yzz77rCQpKipKS5cutbkqeAMfuwtwoqioKC1ZskQHDx5UoUKFdNttt6ldu3by8/OzuzTA4xISErRv3z7FxsZa02rXrm1jRYBnJSQk6MCBAwoMDLS7FMDjnnvuOV29elWrVq2SJJUuXVqPPvqoIiIibK4MTseZnFy0Zs0atW3bVvfee6/Wrl2rhIQEnTx5Up9++qlq1aqlESNG6PLly3aX6TUmTpyomJgYu8vwaj///LOqVq2q2rVrq2XLlqpbt666dOlid1le59tvv9Xw4cM1bNgw6wbP+P333xUQEKBWrVpJkiIiIvT444/bXJX3OHTokLp27aqGDRtKkjZt2qRx48bZW5SX+eOPP/TFF19YP/T6+/vrypUrNlcFb0DIyUVjx47Vv//9b0VGRmry5Ml69913NXbsWC1cuFDbt29XxYoVNXPmTLvL9BorVqxQtWrV9MILL+jvv/+2uxyv9Prrr+uPP/7QLbfcolOnTmnatGl6+OGH7S7LqwwZMkRffvmlJk2apISEBM2ZM0enTp2yuyyvMWzYMK1YsUKlS5eWJDVq1EgbN260uSrvMWDAAHXv3l3x8fGSpDp16mjy5Mk2V+Vdrm/FkpCQoMTERJuq8V7Dhw/X2bNnZYxRly5dVKZMGc2bN8/ustyKkJOL/ve//6l+/fppzitSpIgGDBigp556ysNVea9Zs2Zp8+bN8vf3V9u2bdW5c2ctXLjQ7rK8isvlUkBAgHWA0atXL4WHh9tclXdZsmSJ5s+fr7Jly2rs2LGKiIjQ8ePH7S7La8THx6t69eopphUsWNCmarzP0aNH1atXL+XLd+1wx8fHRz4+tNT3pJCQEM2cOVPGGEVHR+uZZ56xzmzCc+bPny9/f38tXrxYPj4+Wr16td599127y3IrQo4HHDt2TM8//7zdZXil8uXLa8SIEZo5c6a2bdumXr16qVatWlqyZIndpXmFAgUKSJIqV66sH374QVu2bNHBgwdtrsq7+Pn5KV++fHK5XLp69apuuukmHTp0yO6yvIafn58uXrwol8slSdq+fTv9Mz3Ix8dHxhjr7zNnznAWwcP+/e9/a+XKlTpy5IiaNGmixMREffDBB3aX5XWSgv6KFSv00EMPKTg42OaK3I+fM3LRiRMnNHLkSO3bt0+PPvqoHnroIQ0fPlxhYWE00bFBbGysZs2apbCwMPn5+enDDz9U9+7dtWHDBj388MOKjo62u0THe/7553XmzBmNGjVKjz76qM6ePUt7eA8rVqyYYmJi1KJFC/Xp00fly5e3wifc780331SnTp10+PBh9e3bV4sWLdKMGTPsLstrPPTQQxo4cKAuXLigqVOnavz48XryySftLstrJCQk6J133tHEiRM1ceJEu8vxakWKFNH777+vOXPmaPXq1UpMTHR83yiXSf4TB25I165dVahQIbVu3Vo//vijzpw5o7i4OE2aNEn16tWzuzyvU758eXXs2FGDBw9Wo0aNUsx7+umn9cUXX9hUmfc4efKkypQpk+k0uM+xY8dUsmRJJSQk6N///rfOnDmj559/XlWqVLG7NMczxujo0aOKjY3VokWLZIzRnXfeqRo1athdmleZPXu2vv/+exljdN9996lXr152l+RV2rZtq2XLltldhtf7+++/9emnn6pVq1Z68MEH9ffff+t///ufXn31VbtLcxtCTi669dZbtX37dklSXFycypYtq/3798vf39/ewrxQQkKCZs6cqd69e9tdildr0KBBqk7WaU2D+0yZMkVPPPFEptOQ+4wxql+/vjZt2mR3KYBt/v3vf+vq1avq16+fihYtak0vXLiwjVV5H2/8X0DIyUXXH7xxMGevO+64Q6tXr7a7DK8UHx+vK1euqHnz5goPD7faxJ87d05t27bVX3/9ZXOF3oOgaa9u3brp888/t0ZXg2c99NBDVn+oJCVKlFCzZs3Ut29fq58C3Cf5e+xyuWSMkcvlUkJCgo1VeR9v/F9An5xcdOjQoRTXn7j+bzraedbtt9+u8PBwNWvWzO5SvM67776rkSNHyuVyqUiRItb04sWL68UXX7SxMu+xfv16rV27VidPntT48eOt6efOnXN8O+y8pEiRIqpfv77uueeeFL9i8//AM8qVK6eNGzeqR48ekqSvv/5a5cuX1zfffKNNmzbpv//9r80VOh8DPdjLm/8XEHJy0TPPPJPh3/CslStXKiwsTEFBQSkOLtatW2djVd5hxIgRGjFihAYNGqTPPvvM7nK80qFDh7R+/XpdunQpxZXFixcvrqlTp9pXmJepXr16qiGk4TlbtmzR8uXL5evrK0nq37+/7r33Xv3000/0lYVX8Ob/BTRXg2OtWLEizemtW7f2cCWAfX7++WfdfffddpcB2CI4OFh//fWX1WQtISFBderU0Y4dO1S/fn39+eefNlfofElD2F+P5mqe5Y3/CziTk4uSnwaUrrU9LVeunO644w6VL1/epqq8F2HGPu3bt9eSJUtUtmzZFP/cktpiczFK91u9erXuuOMOGWPSvAhu586dbajK+1y4cEGvvfaaFi9eLJfLpY4dO2rUqFEqVqyY3aV5hdatW6tLly56/PHH5XK59NVXX6lFixa6ePGidXYH7nXhwgXr/uXLlzV9+nTHN5PKi9asWaPGjRtb/QNPnjypsLAwjRgxwubK3IczObmoX79+qaadPHlSERERmjFjhjp06GBDVd7r5MmTGjlypDZv3qzY2FhrOs3V3O/IkSOqUKGC9u3bl+b8gIAAD1fkfZKGSW/btm2qeS6XS0uXLrWhKu/z+OOPq3Dhwho0aJAk6fPPP9eFCxf01Vdf2VyZd7h69aomTJig5cuXS5LatGmjChUqqHv37vYW5uXatGljbRN4Rr169VKN9Oj0s5mEHA/466+/1K9fP4WHh9tdile57777dMcdd2jy5MkaO3asJk6cqPr16+udd96xuzSvcvnyZW3evFkul0shISEqVKiQ3SUBHnPbbbdp8+bNmU6De/3111+aMmWKpk+frkqVKmnDhg12l+S1du/erbvuuktRUVF2l+JV0trv1KlTR9u2bbOpIvejuZoH1KpVS3FxcXaX4XX279+vBQsWaObMmbr33nvVqVMnr2uPardly5apZ8+eqlChgowxOnbsmGbPnk1TQg+bO3duiuZSDz74oN0leY2EhARduHDBap526dIlRpvykJiYGH3zzTeaPHmyoqKidPnyZa1atUq33nqr3aV5leTNlhMSEhQfH8+odjYICgrSv//9b73wwgsyxug///mPatWqZXdZbkXI8YDY2Fg62NmgYMGCkiRfX1+dPn1a/v7+OnjwoM1VeZfBgwfr+++/V5MmTSRdayr45JNPauvWrTZX5j2GDRumZcuWWVd5HzNmjNatW6f333/f5sq8Q+/evdW0aVM99thjcrlcmjNnjvr06WN3WY7Xv39/zZs3Ty1bttRLL72kzp07q2bNmgQcG6xfv9667+Pjo/Llyyt//vw2VuSdPv74Y/Xq1UuvvfaaXC6XWrRooenTp9tdllsRcnJRWp17T548qalTp6pbt242VOTdgoODdfr0afXq1UtNmzZViRIlVL9+fbvL8ipFihSxAo4kNW7cOMV1c+B+8+fP16ZNm6xmgv3791e9evUIOR4ybNgwhYSEaPHixTLGaMyYMbrrrrvsLsvxZs+erYYNG2rAgAG666675HK50hzhC+5HH8y8oWLFilq6dKkuXbokSV7xv5g+Obno+g6+LpdLZcuWVadOndSvXz92sDZatWqVzp49q7vvvptfkDzopZde0m233WadRZg5c6b+/vtvR4/mkte0bdtWS5Yssa46npCQoA4dOmjZsmU2V+YdYmNj5evra+3/ExMTdeXKFfn5+dlcmbNdvHhRc+bM0eTJk3Xw4EH17t1b06dP14EDB+wuzets3LhRr732mvbs2aP4+HhrlM09e/bYXZpXWblyZZrTW7Vq5eFKPIeQA8BtypYtq1OnTllDtcbFxVnDVzKUtGe88MIL2rFjh9VE6quvvlKDBg3UvHlzSQwl7W7NmzfXzz//rBIlSki6dpXxLl26aNWqVTZX5j22b9+uKVOmaMaMGapevbp69erFxbo9qG7dunr22WfVrFmzFD8y0nTQsxo1amTdj42N1c6dO1WnTh1t3LjRxqrci5CTi2bMmGH9Yr1mzRo1bdrUmvfxxx/r+eeft6s0r3L9tVmScI0Wz0tvCOkkNGNwv7SGkE7CUNLu543DtuZV8fHx+v777zVlypQ0m5fDPdL6DsB+69at07Rp0xQWFmZ3KW5DyMlFDRo0sBJx8vtp/Q334cAaQF4REhKi8PBwq/37hQsX1KxZM0cP2wokFxoaqqeeeoo+sXlQs2bNHH15EwYeyEXJ8+L12ZEs6TmEmLwjKipKQ4YMSXVBVs6medaxY8e0ffv2FNuAZmqe8dhjj+nOO++0Lgb62WefMboavMoff/yhSZMmKTg4OEVfNC7M7VmRkZHW/YSEBK1du1YXLlywsSL3I+TkouRNpK5vLsWgA57HAbb9nnrqKQ0cOFB79uzRTz/9pE8++USBgYF2l+VVpk6dqpEjR+rUqVOqWbOmNm/erKZNmxJyPOTll19W+fLltWDBAknSoEGDrGbNgDcYN26c3SVAUpcuXaz7Pj4+qlGjhqZNm2ZjRe5Hc7VcVKVKFb366quSpNGjR1v3Jen999/X/v377SrNK7Vt21YDBw7U22+/rTlz5lgH2K+99prdpXmNpGaadevW1datW2WMUbt27RjZy4NCQkK0YsUKtWvXTn/++adWrlyp6dOna9KkSXaXBsBLJCQk6MCBA/zIBY/KZ3cBTtKhQwdFREQoIiIixf2IiAi1b9/e7vK8zrlz5/TII48oX758qlu3riZOnKjffvvN7rK8SoECBSRJxYoV0759+xQXF5dpnynkrgIFCqhkyZKKj4+XdG240OTNFuAec+bMUVRUlPX3c889J39/fzVs2FA7duywsTLAs37//XcFBARYQxVHRETo8ccft7kq7xEZGZnhzclorpaLvvzyS7tLQDLXH2DfdNNNHGB7WOvWrXX69Gk9++yzuv322+Xr66vu3bvbXZZX8fX1lTFGQUFB+uSTTxQQEKCTJ0/aXZbjvffee1aH3p9++knff/+9fvnlF61fv15Dhw7Vzz//bHOFgGcMGzZMK1assPb9jRo1YiAmD+rSpYtcLpeMMdq/f7+KFy8u6doPwQEBAdq7d6/NFboPIScXHThwQFWqVMlwmcOHD6tixYoeqsi7pXWA/dBDD9ldllf54IMPJEk9e/ZUy5Ytde7cOdWpU8fmqrzLqFGjdP78eX3wwQcaOHCgzp49q/Hjx9tdluO5XC5rRLVFixapX79+atKkiZo0aaIvvvjC5uoAz4mPj1f16tVTTCtYsKBN1XifpBDz3HPPqVWrVtZx0Ny5c7V+/Xo7S3M7mqvloh49euipp57SsmXLUnR0j46O1vjx49WkSROtWbPGxgq9ywcffKBSpUqpZ8+e2rhxoxYtWqT//Oc/dpflVX744QedPXtW0rU+a5UqVdKPP/5ob1Fepl27dipRooSqV6+u3377zWpOC/dKTEy07q9du9a6+Or18wCn8/Pz08WLF60BmLZv355ilDV4RkRERIofert3767ly5fbV5AHEHJy0apVq9SpUye9++67KlWqlEqWLKlChQqpXbt22rt3r/73v//pwQcftLtMr7Nnzx7NmzfP0adk86o333xT/v7+1t/+/v5688037SvICz311FM6deqU9ffJkyc1YMAAGyvyDvXq1dNLL72kcePGac+ePWrdurUkWaEfcLoePXpIkt544w116tRJhw8fVt++fdW+fXu98847NlfnfWJiYvT7779bf69atUoxMTE2VuR+NFfLZQ899JAeeughxcfH6+TJkypcuLDV/hGe0bFjR3344YeqV6+eDh8+rNtvv11NmjTR3r17FRkZqZdfftnuEr2Wy+XiV2wP27Bhg0qXLm39XaZMGUVERNhYkXcICwvTG2+8oeXLl2vu3LkqVKiQpGu/pvbt29fe4gAP+OuvvyRJnTp1UlBQkBYtWiRjjN544w3VqFHD5uq8T1hYmHr06KEiRYrIGKPY2FjNnj3b7rLcipDjJj4+PipfvrzdZXilQ4cOqV69epKkWbNmqXXr1vruu+905swZtW7dmpDjQcWLF9fatWvVpEkTSdKaNWtUrFgxm6vyLgkJCSn+NsYoLi7Opmq8R/HixfXf//5XW7ZsUUhIiDW9Y8eOuummm2ysDPCM5NcHrFatmnVBXNijZcuW2rNnj3bu3CljjGrVquX4vlGEHDhO8ra+f/zxh3XRw5IlS8rHh4+8J40ZM0Zdu3bVrbfeKknasWOHvvvuO5ur8i5NmjTR888/r2HDhskYow8//FDNmjWzuyyv0bdv31QjSaU1DXCarVu3qly5cqmmG2Pkcrm4MLcNtmzZoiVLlsjlcunq1atq2LCh3SW5FUd8cJx8+fLp4MGDKlmypFasWKH333/fmuf09qd5TbNmzRQZGWkNpdu8efMUfXT27t2ratWq2VSddxg7dqyGDBmi+vXry+Vy6b777mMADg84efKkjh8/rtjYWO3YsUNJ190+d+6cLl26ZHN1gPsFBQVp4cKFdpeB/++LL77QO++8Y/UNf/DBB/Xmm2/qqaeesrky93GZpD0v4BDffvutBg0apAIFCqhp06aaO3eupGtndd5++20tWrTI5gqRpEGDBvyibbOPP/5Yzz//vN1lOM7HH3+scePGpbpsQIkSJfTcc8/pySeftLE6wP3Yv+ctISEhWrJkicqWLStJOnHihNq3b68tW7bYXJn7EHLc4Pz58xo+fLj27t2r+fPnKzIyUps3b7ZGGoH7HTt2TEeOHNFtt91mtQs+fPiw4uPjVbVqVZurQ5L69evrzz//tLsMr8aBiHu98847jCgIr8T+PW8JCQlJFWhuu+02bd682aaK3I8hpN1g4MCBKlOmjKKioiRd63A3ZswYm6vyHjExMSpWrJiCgoJ0+fJlxcTEKCYmRv7+/ipTpozd5SGZ5B1TYQ9+53KPpMEdXnzxRWsflPwGOB0BJ2+pUaOGXn/9dR0+fFhHjhzRyJEjU12k1WkIOW7w119/6Y033lCBAgUkSYUKFeJAwoOKFi2qYsWKpXsD8H8Imu6RNLhD0v6oaNGi1o39EABPmzBhgqKiohQSEqKQkBD99ddfmjBhgt1luRUDD7jB9UPyXb58mZDjQUnXYRk1apR8fX3Vv39/GWM0adIkRlfLY/hewKmSmgByXSgAdktISNDYsWM1Z84cu0vxKM7kuEHbtm313nvvKS4uTsuXL9cjjzyirl272l2W1/n555/10ksvqUSJEvL399e//vUvaxACuF9CQoI+/vjjDJdJGuUF9iFoukdaTdRorgbADvnz59e6devsLsPjCDlu8M4778jlcqlYsWIaNmyYGjdurOHDh9tdltc5ffq0/v77b+vvv//+WydPnrSxIu+SP39+zZs3L8Nl6JDtXgkJCerYsWOGy0ydOtUzxXgZms0CyEvuvfdejRkzRsePH/eaH1wYXQ2O9e2336p///7Wxa7+/PNPff7555xV86B33nlHQUFBeuSRR+wuxWt16NBBv/zyi/Lnz293KV4pvWazQ4YMsbs0AF4kX77/O6/hcrmsi7ImJCTYWJV7EXLc4KmnntKYMWNUunRpSdcuCvf6669r4sSJNlfmfU6cOKE1a9bIGKNmzZpZ48PDM8qWLatTp06pUKFCKlKkCFe6tsEbb7yhzZs36/HHH1fRokWt6Z07d7axKu9xxx13aPXq1SmmtWjRQqtWrbKpIgDwDvTCdoMNGzZYAUeSypQpo4iICBsr8l6HDh3ShQsX1LNnT509e1ZHjhxRhQoV7C7La6xfv97uErxe0gH2Z599Zk1zuVyEHA9JajZbo0YNSTSbBeBZBw8e1JAhQ7Rr1y7dfvvt+uijj1SqVCm7y/IIzuS4wfUXXDLGqE6dOtq+fbuNVXmfCRMm6LPPPtPFixcVFRWlqKgoPf3001q6dKndpXmV48ePa+fOnWrZsqXi4+OVmJiYagRCwKloNgvATl26dFFwcLA6duyo//3vf8qXL58mTZpkd1keQchxg6efflqFCxfWsGHDZIzRhx9+qEuXLnnNhyqvqF+/vv744w81b97cuihZnTp1tG3bNpsr8x7ffvuthg4dKkmKjo7W5s2b9eqrr2rhwoU2V+Y9EhIS9Omnn+rvv//WJ598oqioKO3bt0/t2rWzuzSvQbNZAHapW7eutm7dKuna/4NGjRpZQ9w7HaOrucHYsWN14cIF1a9fXw0bNlRMTIz+85//2F2W1ylYsKAKFSqUYhrXyfGs9957Txs2bFDJkiUlSbfddpv27dtnc1Xe5bnnntO2bdu0ePFiSVLp0qX18ssv21yVd0lqNnvfffepQIECOnLkiN0lAfASSReml+R1A9BwxOcGxYsX15QpU+wuw+uVLVtWu3btsq7o/tVXX6lKlSo2V+Vd8uXLl6J/mpT6Yrlwrz/++EObNm1S/fr1JUn+/v66cuWKzVV5j+TNZnv27KlTp07RbBaAx+zdu1cPP/xwun9/8803dpTlEYQcN1m7dq2ioqIUHx9vTevdu7eNFXmfcePGqWfPntq5c6cCAwNVuHBh/fDDD3aX5VWKFSumY8eOWUFz2bJl1lkdeIafn1+KvxMSEpSYmGhTNd5n4sSJWrNmjZo3by5Jql69OqMLAvCYcePGpfi7S5cu9hRiA0KOGwwaNEi//PKL6tWrZ50adLlchBwPq1GjhtasWaOdO3fKGKPg4GCvO1VrtzFjxqhz587au3ev2rRpo927dxM0PSwkJEQzZ86UMUbR0dEaPXq0WrVqZXdZXoNmswDstH79en3yySeaMWOGevXqZXc5HsWe1g0WL16syMjIVL+gwrO6du2q77//XrfcckuqafCM22+/XUuXLtUff/whY4yaN28uf39/u8vyKv/+97/14osv6siRI2rcuLHuv/9+ffDBB3aX5TVoNgvATr///ruka/8LCDm4YRUqVCDg5AH79+9PNS0qKsqGSrzbxYsXde7cOblcLsXExBByPKxo0aKaOHEiFyO2Cc1mAdipcuXKqlu3rvbu3avGjRunmr9u3TobqvIMQo4bNG/eXA8//LAeffTRFGGHi+95xhdffKHPP/9cu3btSvGFPnfunIKDg22szPvMmTNHzz33nFq2bCljjJ577jl9+umnKTo9wr2uXr2qjz/+WIsXL5bL5VLHjh317LPPMgCEByQkJGjz5s00mwVgm++++04bN25Ur1699OGHH9pdjkdxnRw3aNu2bappLpeL0XQ8ZN++fdq7d68GDRqkCRMmWNOLFy+ukJAQDjA8qFatWvr5559VrVo1SdeulXPXXXfpr7/+srky7/HEE0/o1KlTeuKJJyRJX375pUqXLq3JkyfbXJl3uOOOO7R69Wq7ywDg5Xbs2JGi+b434EyOGyxbtszuErxaQECAAgICtGPHDrtL8XplypSxAo4kBQYGqkyZMjZW5H3WrFmj7du3W31C7rnnHtWtW9fmqrzH7bffrvDwcDVr1szuUgB4qW3btunDDz+0LgLaoEEDvfTSS6pTp47NlbkXIcdNjh07pu3btys2NtaaRnM1zzp58qRGjhypzZs3p9gOTm5/mtd07NhRo0aN0lNPPSVjjKZMmaKuXbsqJiZGklS4cGGbK3S+ihUrKjY21hrh68qVK6pcubLNVXmPlStXKiwsTEFBQSpatKg1nf0QAE8IDw/XfffdpyeeeELvvPOOjDEKDw9X27ZttWDBAkf/AENzNTeYOnWqRo4cqVOnTqlmzZravHmzmjZtqlWrVtldmle57777dMcdd2jy5MkaO3asJk6cqPr16+udd96xuzSvkS9fvnTnuVwuJSQkeLAa79S/f3+tW7dOjzzyiCTpf//7n1q1aqWgoCBJ0jPPPGNneY51/vx5nT59Wvv27UsxPSoqSqVLl9b9999vU2UAvEnbtm315ptvql27dimmL126VCNHjtSKFStsqsz9CDluEBISohUrVqhdu3b6888/tXLlSk2fPl2TJk2yuzSvUq9ePW3atEkhISHasmWLrly5orvvvltLliyxuzT8f3v37k3RnA25r1+/funOc7lcmjJliger8R4DBw5Ux44d1a1btxTTv/rqK4WHh2v8+PE2VQbAmwQHB2vnzp3ZnucENFdzgwIFCqhkyZKKj4+XJLVq1UqvvPKKzVV5n6TRo3x9fXX69Gn5+/vr4MGDNleF5Lp162a1EYZ7fPnllxnO//jjj/X88897qBrvsXLlyhQDnyR5/PHHNWbMGBsqAuCNko5F03L16lUPVuJ56bclQY75+vrKGKOgoCB98sknWrBggU6ePGl3WV4nODhYp0+fVq9evdS0aVM1adJE9evXt7ssJMOJZPtNmzbN7hIcKaNRHJMGgQAAd2vcuLE++uijVNM//PBDNWrUyIaKPIczOW4watQonT9/Xh988IEGDhyos2fP0jTBBl999ZUk6fnnn9ftt9+uM2fO6O6777a5KiTHwZ79CJruER8fr/Pnz6t48eIppp87d87xv54CyDvGjh2rNm3aaO7cuWrWrJlcLpf++OMPnTp1SsuXL7e7PLeiTw4c78qVKylO1zKiV97RoEEDmqvZjG3gHm+//bY2bNigqVOnqmTJkpKkM2fO6Mknn1RISIjeeustewsE4DViYmI0e/bsFENI9+jRw/HHQ5zJyUWZna1hFCPP+vrrrzV06FAdPXpU0rVfrBnRK2/hNxY41euvv64nnnhCVapUUc2aNSVJu3fvVrdu3fTmm2/aXB0Ab+Lr66vly5dbLVy8BSEnF0VERKQ7j2Y5nvfKK6/o+++/V8OGDTMcyhj2efDBB+0uwesRNN0jf/78mjZtmoYPH57i19Pq1avbXBkAb5M/f34dOnTI7jI8juZqcKw77rhDq1evtrsMr3bu3Dm99tprWrJkiVwulzp06KBRo0apRIkSdpfmVS5fvqzNmzfL5XIpJCTEujCoJG3evFm33XabjdUBANztvffe04EDB9SvX78UFyauXbu2jVW5FyHHTTZu3KhNmzYpNjbWmkZzNc/6+uuvtX37dnXt2lV+fn7WdCd/ofOarl27qlKlSurfv78kafLkydq/f7++//57ewvzIsuWLVPPnj1VoUIFGWN07NgxzZ49W61bt7a7NACAh6R1TTqXy6U9e/bYUI1nEHLcYMyYMfr666+1f/9+tW7dWr/99pvat2+v7777zu7SvMpHH32k4cOHq0yZMtZwrk7/Quc1devW1datWzOdBvepW7euJk2apCZNmkiS1q1bpyeffJJtAABwNDoquMFXX32lP/74Q5UrV9a8efMUERFhXZgSnvPJJ58oKipK+/fv1969e7V3714CjodVrVo1xTWiTp48meavSXCfIkWKWAFHunbNhCJFithYEQDADvPnz7cuRnz48GHH/9hFyHEDPz8/+fn5KTExUcYYBQcHKzo62u6yvE5gYKAqVKhgdxlerWjRorrttts0YMAADRgwQPXq1VPJkiU1bNgwDRs2zO7yvELLli01Y8YM6++ZM2dyvSgA8DJvvfWWJkyYoMmTJ0u61rJl4MCBNlflXoyu5gaFCxfW1atXVa9ePb388suqXLmyYmJi7C7L6zRp0kQ9evTQQw89lKJPTufOnW2syrvUrl07RR+op59+2sZqvNPUqVN16tQp672Pi4tT6dKl9emnn8rlcun48eM2VwgAcLfvv/9eGzZs0O233y5JqlChgi5cuGBzVe5Fnxw32LZtm6pVq6aYmBi99tprOnPmjN544w3Vq1fP7tK8Stu2bVNNc7lcWrp0qQ3VAPbYt29fhvMDAgI8VAkAwC5NmzbVmjVrVL9+ff3555+SpJCQEG3ZssXmytyHMzluUKdOHUnX2sJ/8cUXNlfjvZYtW2Z3CV4rs6ZoH3zwgYcqASEGABAQEKBVq1bJ5XIpMTFR7733nurWrWt3WW5FyMlFly9f1tSpU1WyZEk9/PDDGjZsmH755RfVqlVL48aNU6VKlewu0Svs3btX1apVU2RkZJrzGULa/ZI6tv/9999auXKlddHP7777TnfeeaedpXmNsmXLZngRYpqpAYD3+O9//6s+ffpo27ZtKly4sFq2bKmZM2faXZZb0VwtF/Xq1Uvnzp3TpUuXlC9fPgUGBqpbt25aunSp/vrrL/3www92l+gV7rnnHv34449eOSZ8XnP33XdrxowZKl26tCTp9OnTevzxx/XTTz/ZXJnzJTVTmzRpkk6fPq3+/fvLGKMpU6aoUqVKevnll22uEADgaTExMUpMTExxQVCnIuTkotq1aysyMlKxsbEqX768Tp8+rXz5rg1gV6dOHW3bts3mCgHPSutzz3fBs1q3bq0VK1akmNaqVSutXLnSpooAAHaYO3euFi9eLJfLpY4dO1qtLJyKIaRzka+vr6RrQ0hXq1bNCjiSuE6ODbp27ZqlaXCfW265RU899ZTCw8MVHh6u/v37q1atWnaX5VUOHz6c6lpFR44csbEiAICnDRs2TGPGjNEtt9yiWrVqacyYMXrllVfsLsut6JOTi+Li4rRjxw4ZY1Lcl6TY2Fibq/M++/fvTzUtKirKhkq81+TJkzVy5Eg9++yzMsaoffv2+uijj+wuy6sMGTJE9erVU5cuXSRJCxcu1GuvvWZzVQAAT5o/f742bdqkQoUKSZL69++vevXq6f3337e5MvehuVouCgwMTLejL31BPOeLL77Q559/rh07dqQYZODcuXMKDg7WggULbKwO8LwtW7ZoxYoVMsaobdu2jh9RBwCQUtu2bbVkyRKrlVFCQoI6dOjg6JFoCTlwnH379mnv3r0aNGiQJkyYYE0vXry4QkJClD9/fhur8y5pDSVdokQJNWvWTO3atbOhIgAAvMfChQslSb/99pt27NihPn36SJK++uorNWjQQKNGjbKzPLci5MArnDt3TgcOHLCuYQTP6N27t1atWmX1hZo/f75atGihTZs26eGHH9brr79ub4FeIK2hpJOC5gcffKDy5cvbVBkAwN3SujB6EqdfIJ2QA8e66667NGfOHPn4+Fjhpnfv3nr77bdtrsx7dOjQQXPnzpW/v78k6ezZs+rVq5dmz56txo0ba8eOHfYW6AWGDx+uCxcuqF+/fjLGaPr06SpWrJhcLpfWr1/P0PYAAEdi4AE41rFjx+Tv769vvvlG999/vz766CM1bNiQkONBhw8ftgKOJPn7+ys6OlrFihWTn5+ffYV5kUWLFmndunXW32PHjrWGlb711lttrAwA4El///239uzZo/j4eGta586dbazIvQg5cKyrV69KklauXKm77rpLBQoUyPAK8Mh9tWvXVv/+/dWvXz+5XC5NnTpVwcHBiouLo2+Uh5w9e1anTp2yLsh66tQpawhphrYHAO/w4osvasaMGQoODrb+/7pcLkIO8E9Up04dderUSTt37tQHH3ygmJgYQo6HTZkyRW+//bY1hHTbtm01ZswY5c+fXz///LPd5XmFwYMHq169eurcubNcLpcWLlyoYcOG6eLFi7rjjjvsLg8A4AHz58/X3r17VbhwYbtL8Rj65MBx/vrrL9WqVUuxsbFasGCBGjVqpGrVqunQoUOaOnUqnd3zkJdfflljxoyxuwzH27p1q5YvXy5jjNq0aaOQkBBrXlxcnHUhYwCAM7Vv316//PKLfHy85/wGIQeO06BBA23cuDHV/bT+hr3YHvZjGwCA823atEmvvvqqOnbsmKJP7DPPPGNjVe7lPXEOXiN5br8+w5Pp8xa2h/3YBgDgfKNHj9aRI0e0adOmFH1ynIyQA8dJ/qW9/gvs9C/0Pw3bw35sAwBwvo0bN2rXrl1etc8n5MBxYmNjtWPHDhljUtxPmgcAAOBNatWqpUuXLqlo0aJ2l+IxhBw4TkxMTIohEZPf96ZfMP4JaCplP7YBADhfsWLF1LBhQ3Xq1ClFn5wPPvjAxqrci5ADx4mOjra7BPx/EydO1OOPP57ukJW//vqrhyvC9Zo2bWp3CQAANwsODlZwcLDdZXgUo6sBcJuePXtqyZIl6tmzp0JDQ1WjRg27S/I6TZs21eDBg/XQQw+pQIECdpcDAIBHEHIAuNXRo0c1ceJETZo0SXXr1tWzzz7r6Css5zW//PKLwsLCtGHDBj3xxBMaOHCgKlWqZHdZAAAPevvtt9OcPnz4cA9X4jn57C4AgLOVL19eI0aM0MyZM7Vt2zb16tVLtWrV0pIlS+wuzSt06tRJCxYs0OrVqxUXF6eGDRvqoYce0urVq+0uDQDgIRcuXLBuJ06c0LRp07Rz5067y3Ir+uQAcJvY2FjNmjVLYWFh8vPz04cffqju3btrw4YNevjhh+k/5UEXLlzQuXPnVLBgQVWoUEGhoaFq0aKFPv30U7tLAwC42Ycffpji77feektPP/20TdV4BiEHgNsEBgaqY8eOmjBhgho1amRNb9y4sTp27GhjZd5jzpw5CgsL09mzZzV48GCNGzdOhQoVUkJCgqpXr07IAQAvVLp0aUVFRdldhlvRJweAWyQkJGjmzJnq3bu33aV4tXvuuUdDhgxRhw4dUs374YcfdO+999pQFQDAk8aPH2/dT0hI0Nq1a3XkyBFHNx0n5ABwmzvuuIO+HzZKSEhQ37599dVXX9ldCgDARv369bPu+/j4qHr16howYIBKlixpY1XuRXM1AG5z++23Kzw8XM2aNbO7FK+UP39+HTp0yO4yAAA2WblypaSUISfJ1q1b1apVK0+X5DGcyQHgNvXr19fWrVsVFBSkokWLWtPXrVtnY1Xe5b333tOBAwfUr1+/FNugdu3aNlYFAPCE5P1hk7hcLh0+fFhHjhxRQkKCDVV5BiEHgNusWLEizemtW7f2cCXeq1q1aqmmuVwu7dmzx4ZqAAB2On36tEaNGqUZM2Zo8ODBeuONN+wuyW1orgbAbaKiovTEE0+kmDZlyhRCjgft3bvX7hIAADaLjY3Vf/7zH40bN049evRQZGSkypQpY3dZbsXFQAG4TVrDEzNksWd17do1S9MAAM6TmJiozz//XDVr1tT27du1Zs0ajRs3zvEBR+JMDgA3WL9+vdauXauTJ0+mGLby3LlzunLlio2VeZ/9+/enmub0ayMAAK6pU6eO4uLiNHr0aDVo0ECXL19WZGSkNd/J/TMJOQBy3aFDh7R+/XpdunRJERER1vTixYtr6tSp9hXmRb744gt9/vnn2rVrlxo3bmxNP3funIKDg22sDADgKTExMXK5XHrzzTflcrmUvCu+0/tnMvAAALf5+eefdffdd9tdhlfat2+f9u7dq0GDBmnChAnW9OLFiyskJET58+e3sToAANyLkAPArdauXauoqCjFx8db03r37m1jRQAAwOkIOQDc5plnntGiRYtUr14968yBy+XSN998Y3Nl3iM6OlpjxoxJFTSXLl1qY1UAALgXIQeA29SsWVNbt26Vn5+f3aV4rcaNG6t9+/Zq1qxZiiZqXbp0sbEqAADci4EHALhNhQoVCDg2i42N1ejRo+0uAwAAjyLkAHCb5s2b6+GHH9ajjz6aIux07tzZxqq8S506dXTw4EFVrlzZ7lIAAPAYmqsBcJu2bdummuZyuegP4kF33XWX1q1bpxYtWqQImvSLAgA4GSEHABxs2rRpaU7v06ePhysBAMBzCDkA3GblypVpTm/VqpWHKwEAAN6EkAPAbRo1amTdj42N1c6dO1WnTh1t3LjRxqq8yxNPPJHm9ClTpni4EgAAPIeBBwC4TURERIq/161bl27zKbhHw4YNrfuxsbGaN2+e6tevb2NFAAC4H2dyAHhUs2bNFB4ebncZXismJkbdu3fXwoUL7S4FAAC34UwOALeJjIy07ickJGjt2rW6cOGCjRWhUKFCio6OtrsMAADcipADwG26dOli3ffx8VGNGjVoruZhw4YNs+4nJCRo/fr1ql27to0VAQDgfoQcAG6zd+9eu0vwekWKFLHu+/j4aNCgQerWrZuNFQEA4H70yQHgVuvXr9eSJUvkcrnUvn37FB3hAQAA3CGf3QUAcK4vvvhCDz74oI4cOaLDhw/rwQcf1KRJk+wuy6tcuHBBzz33nG655RbVrl1bgwcPpl8UAMDxOJMDwG1CQkK0ZMkSlS1bVpJ04sQJtW/fXlu2bLG5Mu/x+OOPq3Dhwho0aJAk6fPPP9eFCxf01Vdf2VwZAADuQ58cAG6VFHCS7rtcLhur8T5btmzR5s2brb/Hjx+v2267zcaKAABwP5qrAXCbGjVq6PXXX9fhw4d15MgRjRw5UtWrV7e7LK+SkJCQonnaxYsXlZiYaGNFAAC4H2dyALjNhAkTNHjwYIWEhMjlcqlDhw6aMGGC3WV5ld69e6tp06Z67LHH5HK5NGfOHPXp08fusgAAcCv65ACAA50/f16nT59WYGCgfv75Zy1ZskTGGFWvXl29evVS8eLF7S4RAAC3IeQAyHVTpkxRfHy8+vfvn2L6xx9/rBIlSqhv3772FOZFBg4cqI4dO6a6Js5XX32l8PBwjR8/3qbKAABwP0IOgFx3++23a/HixfL3908x/fTp0+rUqZMiIiLsKcyL1K5dW5GRkWnOq1OnjrZt2+bhigAA8BwGHgCQ665evZoq4EhSqVKldPXqVc8X5IXy58+f7jxGuAMAOB0hB0Cuu3jxYrrzuBClZ8THx+v8+fOppp87d46gCQBwPEIOgFzXsGFDTZkyJdX0adOmqX79+jZU5H169Oihxx9/XGfOnLGmnTlzRv369dOjjz5qY2UAALgffXIA5Lo9e/aoRYsWatWqlZo1ayZJ+uOPP7Ry5UqtWrWKa+V4QEJCgp544gnNmzdPNWvWlCTt3r1b3bp105QpUzJszgYAwD8dIQeAWxw7dkxhYWHasGGDpGtnd5555hmVL1/e5sq8S1RUlDZu3ChJatCgAQETAOAVCDkAbPP444/rq6++srsMAADgMPTJAWCb7du3210CAABwIEIOAAAAAEch5AAAAABwFEIOAAAAAEch5ACwDcMYAwAAdyDkAHCr+fPna8yYMZKkQ4cOaevWrda8iIgIu8oCAAAOxhDSANzmrbfe0tq1axUVFaVdu3bpyJEj6t69u1avXm13aQAAwME4kwPAbb7//nv9+OOPKlKkiCSpQoUKunDhgs1VAQAApyPkAHAbPz8/+t0AAACP87G7AADOFRAQoFWrVsnlcikxMVHvvfee6tata3dZAADA4eiTA8Btjh07pj59+mjp0qXKly+fWrZsqZkzZ6pcuXJ2lwYAAByMkAPA7WJiYpSYmKiiRYvaXQoAAPACNFcD4FZ///239uzZo/j4eGta586dbawIAAA4HWdyALjNiy++qBkzZig4ONgagMDlcmnp0qU2VwYAAJyMkAPAbWrUqKEtW7aocOHCdpcCAAC8CENIA3CbgIAAFSxY0O4yAACAl+FMDgC32bRpk1599VV17NhRfn5+1vRnnnnGxqoAAIDTMfAAALcZPXq0jhw5ok2bNqXokwMAAOBOnMkB4DY1a9bUrl27CDYAAMCj6JMDwG1q1aqlS5cu2V0GAADwMjRXA+A2xYoVU8OGDdWpU6cUfXI++OADG6sCAABOR8gB4DbBwcEKDg62uwwAAOBl6JMDAAAAwFE4kwPArb799ltt2rRJsbGx1jSaqwEAAHdi4AEAbjNkyBB9+eWXmjRpkhISEjRnzhydOnXK7rIAAIDDEXIAuM2SJUs0f/58lS1bVmPHjlVERISOHz9ud1kAAMDhCDkA3MbPz0/58uWTy+XS1atXddNNN+nQoUN2lwUAAByOPjkA3KZYsWKKiYlRixYt1KdPH5UvX14FChSwuywAAOBwjK4GwG2OHTumkiVLKiEhQf/+97915swZDR48WFWrVrW7NAAA4GCEHAAAAACOQnM1AG4THR2tMWPGKCoqSvHx8db0pUuX2lgVAABwOs7kAHCbxo0bq3379mrWrJny589vTe/SpYuNVQEAAKcj5ABwm5CQEG3ZssXuMgAAgJdhCGkAblOnTh0dPHjQ7jIAAICXoU8OALc5ffq0QkJC1KJFC/n5+VnTv/nmGxurAgAATkfIAeA2PXr0UI8ePewuAwAAeBn65ABwi4SEBN1111367bff7C4FAAB4GfrkAHCL/PnzyxijxMREu0sBAABehuZqANymadOmuv/++/X444+raNGi1vTOnTvbWBUAAHA6mqsBcJu2bdummuZyubgYKAAAcCtCDgAAAABHobkaALdZuXJlmtNbtWrl4UoAAIA34UwOALdp1KiRdT82NlY7d+5UnTp1tHHjRhurAgAATseZHABuExERkeLvdevWadq0aTZVAwAAvAVDSAPwmMaNG3MWBwAAuB1ncgC4TWRkpHU/ISFBa9as0YULF2ysCAAAeANCDgC36dKli3W/QIECql69Os3VAACA2zHwAAC3i4qK0oIFC1SjRg3de++9dpcDAAAcjj45AHJdx44dtWnTJknS4cOH1ahRI/36668aNmyYxowZY29xAADA8Qg5AHLdoUOHVK9ePUnSrFmz1Lp1a/3888/6448/NHPmTHuLAwAAjkfIAZDr/Pz8rPt//PGHOnfuLEkqWbKkfHzoCggAANyLkAMg1+XLl08HDx7UpUuXtGLFCrVu3dqaFxMTY2NlAADAG/CTKoBc99prr6lhw4YqUKCA2rZtq6CgIEnXzuoEBgbaWxwAAHA8RlcD4BbHjh3TkSNHdNttt8nlckm6NghBfHy8qlatanN1AADAyQg5AAAAAByFPjkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAgFRmzZqlcePGuWXdgYGB6tu3b6bLRUdHq0uXLipVqpRcLpeGDBnilnrc+VoBAPbgOjkAgFRmzZqlbdu2uS1YZMULL7ygtWvXasqUKSpfvrwqVKjglufJC68VAJC7CDkAAEtMTIwKFy5sdxmSpG3btqlx48bq2rWr3aXkSF56LwHA29BcDQD+QU6cOKH+/furSpUq8vX1VdmyZXXHHXdo8eLFKZabMmWKbrvtNvn5+alUqVJ64IEHtGPHjhTL9O3bV0WLFtXWrVt15513qlixYmrfvr3atGmjn376Sfv27ZPL5bJuSa5cuaJRo0apVq1aVg39+vXTiRMnUqz/6tWrGjZsmMqXL6/ChQurRYsWWrduXaavcfny5XK5XPr777/1888/W88fHR0tSTp//rz+9a9/qVq1aipYsKAqVaqkIUOG6NKlSynWExYWplatWqlcuXIqUqSI6tatqw8++EBXr161lsnotSbVsXz58hTrjY6Olsvl0tSpUzN9L7Pzfi1dulRt2rRR6dKlVahQIVWtWlXdunVTTExMpu8ZACAlzuQAwD/I448/ro0bN+rdd99VUFCQzp49q40bN+rUqVPWMqNHj9Zrr72mHj16aPTo0Tp16pTeeustNWvWTBEREapZs6a17JUrV3TfffdpwIABeuWVVxQfH6/KlSurf//+ioqK0nfffZfi+RMTE3X//ffr999/17Bhw9S8eXPt27dPI0aMUJs2bbR+/XoVKlRIkvT0009r+vTp+te//qWOHTtq27ZtevDBB3XhwoUMX2ODBg0UHh6uBx54QNWrV9dHH30kSapQoYJiYmLUunVrHTx4UK+99ppCQkK0fft2DR8+XFu3btXixYutkBIVFaWePXtaYWjz5s1699139ddff2nKlCmSpPHjx6f7WrMrrfcyq+9XUv+jli1basqUKfL399ehQ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545c0ZRUVHavn27tm3bpm3btlnDVkdGRurq1as3Xnwueuyxx9Kdd+jQIW3YsEHStZGiMnLLLbdYQ/KGh4db0ytUqGDdz2ywhJwoV66cOnbsKEnpdsafNWuWdb9nz56ZrjMuLk779+9XZGSktQ2NMdb8zZs3Z/j4jN7TnEhMTNThw4e1c+dOq54dO3ZY13zKrJ6ePXum2ZFckho2bGjd37NnT4p5y5YtU0xMjCRp8ODByp8/f5Zrvnr1qtXZunv37uk+vyT5+/urbt26klJ+drLqxIkT1v2SJUtm+/GS9NJLL6lYsWKSZA37nB2RkZHWdyWt0d3uvPNOlStXTlLKC4XmhhvZ51SpUkUtW7ZMd7679pPXW7x4saRr2+/+++9Pd7knnngiy+vM6Hu4YcMGnT17VtK1wQPS+2wXL17c2vdFRkbqyJEj6a6zX79+6c574IEHrG2S9FrTUrdu3RTfyeslvf74+HhrQBEpd75v8fHx1iA4d955pypWrJjm4/Ply6c+ffqku/7rlSpVyrqf/LsK5CZCDhxvxIgRMtfOWqZ5Sz6qzvr16yVdG4Xm+hHNrr89++yzkq6NwpPWSFRbt27VE088oQoVKqhUqVKqUaOG6tSpo7p166pu3brW8yYmJurMmTNufx+yI6MDl6T3SJJ69OiR6fuUNKLT0aNHrcdVq1bNOoj6z3/+o1tvvVXDhw/X0qVLrQPoG5V0MHPkyBEtXbo01fykoXtvv/12BQcHp7mOS5cuafTo0brttttUpEgRBQQE6NZbb7W2Yf369a1lMxu5KrODwawwxmjGjBlq27atihYtqkqVKqlWrVpWPXXr1tWmTZuyVE+tWrXSnZf8AOTChQsp5v3555/W/aQD2KyKjIy0tu+rr76a6Wcn6bOW/LOTVcm/kzkNOaVLl9aQIUMkSatXr9Yvv/ySrcdPmzZNkuTj46NHH3001XwfHx9rON0FCxbo3LlzOaozSW7tczL7rObmfjKz1yNJ9evXl49P+tcur1evngoWLJildWb02rZt22bdb9KkSYbrST4/+eOSK1iwYIbPV6BAAWsfkt46JKlRo0YZ1tK4ceM0a8mN71tUVJS1juzUkZnk38lTp05l+XFAdhBygGSyO1xxkusPzCdPnqwGDRroyy+/zNIBWkbD7Noho4PC3HqPZs+erWbNmkm69s/4nXfeUfv27eXv76/WrVtrwoQJio2NzdFzSVLXrl1VpEgRSUp1LZJNmzYpMjJSUvq/7EZHR6tu3bp67bXXtGXLlkx/hc5sG+b0QDtJbGysunTposcff1zLly/P9Pkym1+4cOF05+XL93//Gq5/3cnDU/IzclmRW5+drPDz87Pu38j3a+jQodav7cOHD8/y45JfA+fOO+9U2bJl01wu6QxPbGysNeR1TuTmPiezz6qntmNSEEs625UeHx+fFME8Ixm9tuQh7KabbspwPeXLl0/zccmVKlUqw3CW/HkyCoCZvf7ktSZfT25sp+RhODt1ZCb5569QoULZqA7Iuoy/fYCXSTqgu+OOOzRhwoQsPy75Kfy//vpLAwcOVHx8vMqVK6eXXnpJ7dq1U2BgoIoVK6YCBQpIkqZMmaInn3xSklI0e8oLMmqClPygd+bMmVk+Q3H9wUWlSpX0xx9/aMmSJfr222+1YsUKqxnNypUrtXLlSn300UdauHChgoKCsv0aihQpovvvv1+zZs3St99+q88++8w68E1qqpYvXz7rl/TrPf7449q7d69cLpf69eunRx99VLfccovKli1rXSwyMTHReq8y24bZadaVlnfffddqetK6dWuFhoaqQYMGKl++vAoVKmQFk1atWun333/Pc58pKeVn58MPP8zytWuSwmp2JA8V2T2DkJy/v7+GDh2q4cOHa926dfrxxx91zz33ZPq4JUuW6NChQ5KuXeMlo6ZCSaZPn66nn3462zXm9j4ns89qbuwnsyMr711WP+9Z/R5m9pxZeb7cqjsr60lLbnzfkteXG+9JkuTfyfR+AABuFCEHSKZ06dI6duyYTpw4oTp16uRoHVOnTlV8fLzy58+v5cuX65Zbbklzudxqopb8V/fMrh596dKlG36+0qVLW/ddLleO36ck7du3V/v27SVda7awePFiff7551q6dKmioqL0yCOPpGgilR29evXSrFmzdP78ef3444/q3r27jDGaPXu29dxpnY3466+/rIvUvfrqq3r33XfTXL+nmhkaYzRp0iRJUosWLbR06dIU292TNSX1sZKuNQWsVq1alh+b/LNz9erVG/7sZCT5gdONvidDhgzRxx9/rFOnTmn48OHq0qVLpo9JaqqWHatXr9aePXt08803Z+txnt7n5MZ+MitKliypo0eP6tixYxkuFx8fnyuvLfnZoKNHj2b440rymtI7i3Tq1CklJCRkGKySzrZkdCYqs9efXi258X1Lvr7M6sjOmaPk2yv5PgXITTRXA5JJah+9a9cu7du3L0fr2L59uyTptttuS/dgQ0rZtyUtWf31LqljtJTxQcypU6cy7aeRFcn7ofz66683vL7kSpcurUceeURLlizRfffdJ+la07Ldu3fnaH0dO3a0DnaTzt6sXLnSukJ4ek3VkrahpDT7UiTJbBvmltOnT1tNkB5++OF0A87Fixe1c+dOt9bSoEED6/7KlSuz9dhbb73V6juR25+d6yV1opaufZ9vRLFixfTSSy9JutYn6fvvv89w+YsXL+q7776TdC1Iz549O8NbUoA1xuRoAILc2udkVW7sJ7MiaRtu2rRJ8fHx6S63efNmXbly5YafL3kIWLt2bYbLrlu3Ls3HJXflypUMBwCJj4+3+tBlFEAiIiIyrCX5/OTryY3vW/Xq1a2mrdmpIzNJ38mgoCDrzDiQ2wg5QDJJB9aS9MEHH+RoHUn/jDNqf3706FFrhKL0JDWtiouLy3C5kiVLWn0GMjqISTp7caNq1Kih2rVrS5LmzJmj/fv358p6r5d0dkfKvBN9epJ37F64cKHOnj1r9ZPw8/PTAw88kObjkh9QZbQds9NU50ZktZ7Jkye7faS+tm3bWs1ZPvnkk2yNmFW4cGFruy5fvjzFgWJuq1ixonVGJDsHX+l59tlnrT4JSYOZpGfu3LnWdho0aJAeffTRDG9PPvmkNXpWTkJObu1zsio39pNZ0aFDB0nXQv4PP/yQ7nJTpkzJledr2LChtS+dNm1aup/tCxcuWP2nateunWHftIzO6H333XfWD1NJrzUtW7duzfBsdtLrz58/v9q0aWNNz43vm4+Pj1q3bi3pWlBKbyS5xMTEbJ29TPpfldEofsCNIuQAyXTr1s36JfSzzz7T5MmTM1x+27Ztqf751qxZU9K1X6rWrFmT6jExMTHq2bNnpp2hk/5x7tmzJ9O2zkmjXM2fP19RUVGp5u/YsSNbnaYz88Ybb0i61ln6wQcfzHAI0Li4OI0fPz7FIAKbNm2yfsFMizHGGlLV5XIpMDAwx7Umna2Ji4vTrFmzNHfuXEnXDtSKFy+e5mOStqGU/kHKZ599lukv+rmlbNmy1sHXnDlz0vzVOiIiwtou7uTv768BAwZIujbk7pAhQ9L9fF69ejVVE5bXX3/dOkv56KOPpvl5TZKQkKBZs2ZZZ96yK+kAKjfCVJEiRfTyyy9LunbQuXDhwnSXnT59uqRrB5l33313ltbfvXt3SddGs1q9enW2asutfU5W5cZ+Miv69OljdUofOnRoms2lVqxYoc8//zzb606Lr6+vnnrqKUnXzo6NHDky1TLGGD377LPWDy9Jo8el57PPPrOaviZ39OhR/etf/5J07XOS2fDL/fv3T7O58axZs6zPYteuXVMFrtz4vg0aNEjStX3ogAED0gx/o0ePtkbDy8yePXus94+QA7fy1AV5AE9KfnG/5Bf7zIrkVwaXZDp16mSmTZtm1qxZYzZs2GB+/vln895775nmzZsbSebFF19M8fh169ZZjy1ZsqQZPXq0WbFihVm7dq0ZP368qVmzpnVxvKTl9u7dm6qOL774wpo/ZMgQs379erN7926ze/duEx0dnWLZ5BdvrFy5spk0aZLZsGGDWbFihXnzzTdN8eLFTY0aNUzZsmWzdMG8tOq5XvIL3pUpU8a8/vrr5tdffzV//vmnWbVqlZk2bZp56qmnTKlSpYykFFfrTnquRo0ambffftv8+OOPZv369SY8PNzMmjXLdOzY0Vp3165ds7TdMlKjRg0jybrQn9K4yGVyiYmJKS5s16NHD/Pjjz+aDRs2mO+//95079491TZM63OW/GKgWZHRxUBDQ0OtdTVu3NjMnj3bREREmMWLF5uhQ4caPz8/U6ZMGRMUFGSUzkVkk38vli1blmEtGb2uS5cumbp166a4sOjnn39uwsPDzYYNG8z8+fPNSy+9ZCpVqpTmRQGTvy9FixY1zz//vPnpp5/Mxo0bTXh4uJk9e7YZPHiwqVixopFktm7dmqX373rffPON9Ty7d+9Od7n0LgZ6vZiYGFOhQoUUF2m8ftvu27fPuFwuI8l069Yty7Xu2rXLWl///v2z/Dhjcm+fk52L0d7ofjKrPvroI+s5KlWqZD799FOzbt06s3LlSvPKK68YX19fExAQkGv7tvPnz5ubb77ZWv6BBx4wP/zwg9mwYYOZO3euadOmjTWvWbNmaV4wNGnfWLZsWRMQEGD8/PzMq6++an7//Xezbt068+mnn1qfbUlm7NixadaSNP/22283kkytWrXMl19+adavX2+WLFliBg0aZPLly2ckmWLFiqX72nLj+3bvvfda62jSpImZM2eOtZ0feeQRa3+ele/R559/biSZ/PnzmyNHjmS4PYAbQciBI91IyDHGmM2bN1sHBpndRo4cmerxI0eOzPAxL774Yqb/eC9cuJDin23yW0BAQKrlBw8enO7zValSxWzfvj3Dg5jshpz4+HgzbNgwkz9//kzfoyJFipiYmJg0nyujW4sWLcypU6cyrSUzw4cPT7HeUqVKmbi4uAwf8+eff5qSJUumW1vdunXN4cOHPRZyzp49a+rVq5duPaVKlTIrVqwwrVu3dnvIMcaYEydOmFatWmW6DdM72PnPf/5jXWE9o1vBggUzDCgZuXz5shVs0/qeJslqyDHGmE8++SRVjcmNGjXKmj579uxs1RsSEmKka2E8NjY2W4/NjX1OdkKOMTe+n8yqjPZtZcqUMREREbm6b9u7d6+pVatWhq/njjvuSHfflBRyAgICTEREhClTpky66xk8eHC6dST/Dibfl1x/K168uFm+fHmGr+lGv2/nz59PEZKvvzVo0MBs3LgxS9+jpKDYqVOnDGsGbhQhB450oyHHGGOuXr1qpk2bZrp27WqqVKli/Pz8TMGCBU2FChVMmzZtzBtvvGE2bNiQ7uN/+uknc+edd5qSJUuaggULmsqVK5sHH3zQ/Prrr8aYrP3jPXr0qHn++efNLbfcYgoXLmwtn1bIMcaYWbNmmVatWpnixYubQoUKmeDgYPPKK6+YkydPGmMyPojJ7oFAkp07d5oXX3zR1K9f35QsWdLkz5/fFCtWzNx6663mscceM9OmTTPnz59P8Zi4uDizbNky89prr5mWLVuaatWqmcKFC1vv03333WdmzZplEhISslxHZjUm/4ec1V/K9+3bZwYOHGgCAgJMgQIFTKlSpUzjxo3NRx99ZC5fvmyMyTgM5GbIMebaGZR33nnH1K1b1/j5+ZmiRYuaW265xfzrX/8yBw4cMMYYj4WcJN9++63p3r27qVy5svH19TUlS5Y0derUMY899piZP39+hmHy4MGD5s033zRNmzY1ZcqUMT4+PqZIkSImKCjIdOvWzUyYMMGcOHEiw+fPzNChQ40kExQUlO4y2Qk5sbGxpkqVKumGnODgYCPJ+Pr6pvrcZyZ5UPnmm2+y9Vhjbnyfk92QY8yN7yez89o6depkSpUqZfz8/EyNGjXM4MGDrc99bu/b4uLizKeffmpat25tSpcubQoUKGBuuukmc9ddd5mvvvoqw31T8pBjjDH79+83gwcPNtWrVzd+fn6mdOnS5q677jILFy7MsIbrv4OLFi0yXbp0MTfddJMpWLCgCQwMNM8884z1HmTmRr9vV69eNZ988olp1KiRKVq0qClWrJipV6+eGT16tLl8+bLZu3dvpt+jgwcPWmefMjqbDuQGlzF58GIKAADkgv3796tmzZq6cuWKfv/9d7Vo0cLukuBwffv21bRp0xQQEKDo6OgcryepL82IESP01ltv5U5xNhs1apTefPNNBQcHKzIyMt2RIoHcwKcLAOBYVatWtS6A+c4779hcDeC9Ll68qHHjxkm6FtwIOHA3PmEAAEcbMWKEihcvrl9//TXN0ccAuF9YWJhOnTqlRo0aZXj9MSC3+NhdAAAA7nTTTTdp5syZWr9+fa5cEBdA9hUrVkwjRozQgw8+mOWLXQM3gpADAHC8e+65R/fcc4/dZQBe65lnnrG7BHgZmqsBAAAAcBRGVwMAAADgKB5prhYdHa27775brVu3Vvny5bM1FGJiYqIOHz6sYsWK0YYTAAAA8GLGGF24cEEVK1bMcJS+bIecwYMHa8GCBdq3b5+2bt2qOnXqWPN2796tPn366OTJk/L399fUqVNVu3ZtSVKJEiUUGxuratWqZev5Dh8+rCpVqmS3TAAAAAAOdeDAAVWuXDnd+dlurrZy5UrdfPPNatGihX788ccUIaddu3bq3bu3+vbtq7lz52rs2LEKDw+XMUYul0vGGHXv3l1jx45VYGBglp7v3Llz8vf314EDB1S8ePHslAoAAADAQc6fP68qVaro7NmzKlGiRLrLZftMTqtWrdKcfvz4cW3cuFG//vqrJKlbt2569tlnFR0dbQUal8ulm266SRcuXEh3/XFxcYqLi7P+Tlq2ePHihBwAAAAAmXZjybU+OQcOHFDFihXl4+NjPXHVqlW1f/9+7d27V9OnT5ck+fv7q27duumuZ/To0Ro5cmRulQUAAADAy+TqwAPXJ6qklnBt27ZV27Zts7SOV199VUOHDrX+TjolBQAAAABZkWshp0qVKjp48KDi4+Pl4+MjY4wOHDigqlWrZms9vr6+8vX1za2yAAAAAHiZXLsYaLly5VS/fn3NmDFDkjRv3jwFBgZmeYCB64WFhal27dpq1KhRbpUIAAAAwAtke3S10NBQzZ8/X0ePHlWZMmVUtGhR/f3335KknTt3qm/fvjp16pSKFy+uadOm6dZbb72hAs+fP68SJUro3LlzDDwAAAAAeLGsZoNshxxPI+QAAAAAkLKeDXKtuRoAAAAA5AV5NuTQJwcAAABATtBcDQAAAMA/As3VAAAAAHglQg4AAAAAR8mzIYc+OQAAAABygj45AAAAAP4R6JMDAAAAwCv52F1AXhD4yk92l3BDot/vYncJAAAAQJ6RZ8/k0CcHAAAAQE7k2ZATGhqqyMhIRURE2F0KAAAAgH8QmqshT6DJIAAAAHJLnj2TAwAAAAA5QcgBAAAA4CiEHAAAAACOkmdDDqOrAQAAAMiJPBtyGF0NAAAAQE7k2ZADAAAAADlByAEAAADgKIQcAAAAAI5CyAEAAADgKIQcAAAAAI6SZ0MOQ0gDAAAAyIk8G3IYQhoAAABATuTZkAMAAAAAOUHIAQAAAOAohBwAAAAAjkLIAQAAAOAohBwAAAAAjkLIAQAAAOAoeTbkcJ0cAAAAADmRZ0MO18kBAAAAkBN5NuQAAAAAQE4QcgAAAAA4CiEHAAAAgKP42F0AAPsFvvKT3SXcsOj3u9hdAgAAyCM4kwMAAADAUQg5AAAAAByFkAMAAADAUQg5AAAAAByFkAMAAADAUQg5AAAAABwlz4acsLAw1a5dW40aNbK7FAAAAAD/IHk25ISGhioyMlIRERF2lwIAAADgHyTPhhwAAAAAyAlCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcBRCDgAAAABH8WjIeeONN9ShQwdPPiUAAAAAL5PtkDN48GAFBgbK5XJp27ZtKebt3r1bzZs3V1BQkBo3bqzIyEhr3v/+9z81aNDgxisGAAAAgAxkO+R0795dq1atUkBAQKp5AwYMUP/+/bVr1y4NGzZMTz75pKRr4WfDhg168MEHM11/XFyczp8/n+IGAAAAAFmV7ZDTqlUrVa5cOdX048ePa+PGjerVq5ckqVu3btq7d6+io6O1cOFCnTt3TkOGDNGuXbu0YMGCdNc/evRolShRwrpVqVIluyUCAAAA8GI+ubWiAwcOqGLFivLxubZKl8ulqlWrav/+/Xr++eet5bZt26b77rsv3fW8+uqrGjp0qPX3+fPnCToAAAAAsizXQo50LdgkZ4xJtczixYszXIevr698fX1zsywAAAAAXiTXRlerUqWKDh48qPj4eEnXAs6BAwdUtWrV3HoKAAAAAMhUroWccuXKqX79+poxY4Ykad68eQoMDFRgYGCO1hcWFqbatWurUaNGuVUiAAAAAC+Q7ZATGhqqypUr6+DBg+rQoYNq1KhhzZs4caImTpyooKAgvf/++5o8eXKOCwsNDVVkZKQiIiJyvA4AAAAA3ifbfXLCwsIUFhaW5rzg4GCFh4ffcFEAAAAAkFO51lwtt9FcDQAAAEBO5NmQQ3M1AAAAADmRZ0MOAAAAAOQEIQcAAACAo+TZkEOfHAAAAAA5kWdDDn1yAAAAAOREng05AAAAAJAT2b5ODgAg9wW+8pPdJdyw6Pe72F0CAACSOJMDAAAAwGHybMhh4AEAAAAAOZFnQw4DDwAAAADIiTwbcgAAAAAgJwg5AAAAAByFkAMAAADAUfJsyGHgAQAAAAA5kWdDDgMPAAAAAMiJPBtyAAAAACAnCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBR8mzIYQhpAAAAADmRZ0MOQ0gDAAAAyIk8G3IAAAAAICcIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFHybMjhYqAAAAAAciLPhhwuBgoAAAAgJ/JsyAEAAACAnCDkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHAUQg4AAAAARyHkAAAAAHCUPBtywsLCVLt2bTVq1MjuUgAAAAD8g+TZkBMaGqrIyEhFRETYXQoAAACAf5A8G3IAAAAAICcIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAchZADAAAAwFEIOQAAAAAcxccTT7Jt2zZ9+umnunjxovLnz6+pU6fK5XJ54qkBAAAAeJlsn8kZPHiwAgMD5XK5tG3bthTzdu/erebNmysoKEiNGzdWZGSkJKlOnTqaMGGCZsyYoZiYGF26dCl3qgcAAACA62Q75HTv3l2rVq1SQEBAqnkDBgxQ//79tWvXLg0bNkxPPvmkNe+3335Tz549VaZMGRUuXDjd9cfFxen8+fMpbgAAAACQVdkOOa1atVLlypVTTT9+/Lg2btyoXr16SZK6deumvXv3Kjo6WpLUsWNHzZo1S/ny5dOmTZvSXf/o0aNVokQJ61alSpXslggAAADAi+Van5wDBw6oYsWK8vG5tkqXy6WqVatq//792r17t77//nvFx8dLkurWrZvuel599VUNHTrU+vv8+fMEHQAAAABZlqsDD1w/mIAxRtK1szgdO3bM0jp8fX3l6+ubm2UBAAAA8CK5NoR0lSpVdPDgQetsjTFGBw4cUNWqVXO0vrCwMNWuXVuNGjXKrRIBAAAAeIFcCznlypVT/fr1NWPGDEnSvHnzFBgYqMDAwBytLzQ0VJGRkYqIiMitEgEAAAB4gWyHnNDQUFWuXFkHDx5Uhw4dVKNGDWvexIkTNXHiRAUFBen999/X5MmTc7VYAAAAAMhMtvvkhIWFKSwsLM15wcHBCg8Pv+GiAAAAACCncq25Wm6jTw4AAACAnMizIYc+OQAAAAByIs+GHAAAAADICUIOAAAAAEfJsyGHPjkAAAAAciLPhhz65AAAAADIiTwbcgAAAAAgJwg5AAAAABwlz4Yc+uQAAAAAyIk8G3LokwMAAAAgJ/JsyAEAAACAnCDkAAAAAHAUQg4AAAAARyHkAAAAAHCUPBtyGF0NAAAAQE7k2ZDD6GoAAAAAciLPhhwAAAAAyAlCDgAAAABHIeQAAAAAcBRCDgAAAABHIeQAAAAAcJQ8G3IYQhoAAABATuTZkMMQ0gAAAAByIs+GHAAAAADICUIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEfJsyGH6+QAAAAAyIk8G3K4Tg4AAACAnMizIQcAAAAAcoKQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHCXPhpywsDDVrl1bjRo1srsUAAAAAP8geTbkhIaGKjIyUhEREXaXAgAAAOAfJM+GHAAAAADICUIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFB9PPMmKFSs0ffp0xcbGqmTJkvr000898bQAAAAAvFC2z+QMHjxYgYGBcrlc2rZtW4p5u3fvVvPmzRUUFKTGjRsrMjJSktS6dWtNnjxZM2fO1P79+3XhwoXcqR4AAAAArpPtkNO9e3etWrVKAQEBqeYNGDBA/fv3165duzRs2DA9+eSTKeYvXLhQt9xyi4oVK5bzigEAAAAgA9lurtaqVas0px8/flwbN27Ur7/+Kknq1q2bnn32WUVHRyswMFBffPGFDh06pDFjxmS4/ri4OMXFxVl/nz9/PrslAgAAAPBiuTbwwIEDB1SxYkX5+FzLTS6XS1WrVtX+/fs1b948vf322zp69KgGDhyoEydOpLue0aNHq0SJEtatSpUquVUiAAAAAC+QqwMPuFyuFH8bYyRdO6vTrVu3LK3j1Vdf1dChQ62/z58/T9ABALhd4Cs/2V3CDYt+v4vdJQBAnpBrIadKlSo6ePCg4uPj5ePjI2OMDhw4oKpVq2ZrPb6+vvL19c2tsgAAAAB4mVxrrlauXDnVr19fM2bMkCTNmzdPgYGBCgwMzNH6wsLCVLt2bTVq1Ci3SgQAAADgBbIdckJDQ1W5cmUdPHhQHTp0UI0aNax5EydO1MSJExUUFKT3339fkydPznFhoaGhioyMVERERI7XAQAAAMD7ZLu5WlhYmMLCwtKcFxwcrPDw8BsuCgAAAAByKteaq+U2mqsBAAAAyIk8G3JorgYAAAAgJ/JsyAEAAACAnCDkAAAAAHCUPBty6JMDAAAAICfybMihTw4AAACAnMizIQcAAAAAcoKQAwAAAMBRCDkAAAAAHCXPhhwGHgAAAACQE3k25DDwAAAAAICcyLMhBwAAAABygpADAAAAwFEIOQAAAAAchZADAAAAwFHybMhhdDUAAAAAOZFnQw6jqwEAAADIiTwbcgAAAAAgJwg5AAAAAByFkAMAAADAUQg5AAAAABwlz4YcRlcDAAAAkBN5NuQwuhoAAACAnMizIQcAAAAAcoKQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHCXPhhwuBgoAAAAgJ/JsyOFioAAAAAByIs+GHAAAAADICUIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEfJsyEnLCxMtWvXVqNGjewuBQAAAMA/SJ4NOaGhoYqMjFRERITdpQAAAAD4B8mzIQcAAAAAcoKQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRPBJyzpw5oyeffFJVqlTxxNMBAAAA8GLZDjmDBw9WYGCgXC6Xtm3blmLe7t271bx5cwUFBalx48aKjIyUJJUsWVKTJ09WcHBw7lQNAAAAAOnwye4DunfvrmHDhqlFixap5g0YMED9+/dX3759NXfuXD355JMKDw/P1vrj4uIUFxdn/X3+/PnslggAAP6BAl/5ye4Sblj0+13sLgGAcnAmp1WrVqpcuXKq6cePH9fGjRvVq1cvSVK3bt20d+9eRUdHZ2v9o0ePVokSJawbTdwAAAAAZEeu9ck5cOCAKlasKB+fayeHXC6Xqlatqv3790uSBg4cqL/++ksDBw5UVFRUuut59dVXde7cOet24MCB3CoRAAAAgBfIdnO1jLhcrhR/G2Os+xMmTMjSOnx9feXr65ubZQEAAADwIrl2JqdKlSo6ePCg4uPjJV0LOAcOHFDVqlVztL6wsDDVrl1bjRo1yq0SAQAAAHiBXAs55cqVU/369TVjxgxJ0rx58xQYGKjAwMAcrS80NFSRkZGKiIjIrRIBAAAAeIFsh5zQ0FBVrlxZBw8eVIcOHVSjRg1r3sSJEzVx4kQFBQXp/fff1+TJk3O1WAAAAADITLb75ISFhSksLCzNecHBwdkeMhoAAAAAclOuNVfLbfTJAQAAAJATeTbk0CcHAAAAQE7k2ZADAAAAADlByAEAAADgKHk25NAnBwAAAEBO5NmQQ58cAAAAADmRZ0MOAAAAAOQEIQcAAACAoxByAAAAADhKng05DDwAAAAAICfybMhh4AEAAAAAOZFnQw4AAAAA5AQhBwAAAICjEHIAAAAAOEqeDTkMPAAAAAAgJ/JsyGHgAQAAAAA5kWdDDgAAAADkBCEHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4Sp4NOQwhDQAAACAn8mzIYQhpAAAAADmRZ0MOAAAAAOQEIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADgKIQcAAACAoxByAAAAADhKng05XAwUAAAAQE742F1AekJDQxUaGqrz58+rRIkSdpcDAADgeIGv/GR3CTck+v0udpeAPCLPnskBAAAAgJwg5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwFEIOAAAAAEch5AAAAABwlDwbcsLCwlS7dm01atTI7lIAAAAA/IPk2ZATGhqqyMhIRURE2F0KAAAAgH+QPBtyAAAAACAnCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRCDkAAAAAHIWQAwAAAMBRfDzxJHFxcRowYICKFy+uhIQEhYWFeeJpAQAAAHihbJ/JGTx4sAIDA+VyubRt27YU83bv3q3mzZsrKChIjRs3VmRkpCTp22+/VevWrfXf//5XJUuWVHh4eO5UDwAAAADXyXbI6d69u1atWqWAgIBU8wYMGKD+/ftr165dGjZsmJ588klJ0r59+xQYGChJuvnmm7Vv374bqxoAAAAA0pHtkNOqVStVrlw51fTjx49r48aN6tWrlySpW7du2rt3r6Kjo1WlShUr2ERHR6tq1arprj8uLk7nz59PcQMAAACArMq1gQcOHDigihUrysfnWjcfl8ulqlWrav/+/erWrZuWL1+uoUOH6sSJE2revHm66xk9erRKlChh3apUqZJbJQIAAADwArk68IDL5UrxtzFGkuTn56epU6dmaR2vvvqqhg4dav19/vx5gg4AAACALMu1kFOlShUdPHhQ8fHx8vHxkTFGBw4cyLBpWlp8fX3l6+ubW2UBAAAA8DK51lytXLlyql+/vmbMmCFJmjdvngIDA60BB7IrLCxMtWvXVqNGjXKrRAAAAABeINshJzQ0VJUrV9bBgwfVoUMH1ahRw5o3ceJETZw4UUFBQXr//fc1efLkHBcWGhqqyMhIRURE5HgdAAAAALxPtpurhYWFpXsxz+DgYK6BAwAAAMBWudZcLbfRXA0AAABATuTZkENzNQAAAAA5kWdDDgAAAADkBCEHAAAAgKMQcgAAAAA4Sp4NOQw8AAAAACAn8mzIYeABAAAAADmRZ0MOAAAAAOQEIQcAAACAo+TZkEOfHAAAAAA54WN3AekJDQ1VaGiozp8/rxIlSthdDgAAAOB2ga/8ZHcJNyT6/S52lyApD5/JAQAAAICcIOQAAAAAcBRCDgAAAABHIeQAAAAAcJQ8G3IYXQ0AAABATuTZkBMaGqrIyEhFRETYXQoAAACAf5A8G3IAAAAAICcIOQAAAAAchZADAAAAwFEIOQAAAAAcJc+GHEZXAwAAAJATeTbkMLoaAAAAgJzIsyEHAAAAAHKCkAMAAADAUQg5AAAAAByFkAMAAADAUXzsLiAzxhhJ0vnz5932HIlxMW5btye4873xFLaBvf7p77/ENsgL2Ab2YxvYj21gr3/6+y+xDbK6/qSMkB6XyWwJmx08eFBVqlSxuwwAAAAAecSBAwdUuXLldOfn+ZCTmJiow4cPq1ixYnK5XHaXk23nz59XlSpVdODAARUvXtzucrwS28B+bAP7sQ3sxftvP7aB/dgG9nPCNjDG6MKFC6pYsaLy5Uu/502eb66WL1++DFPaP0Xx4sX/sR8mp2Ab2I9tYD+2gb14/+3HNrAf28B+//RtUKJEiUyXYeABAAAAAI5CyAEAAADgKIQcN/P19dWIESPk6+trdylei21gP7aB/dgG9uL9tx/bwH5sA/t50zbI8wMPAAAAAEB2cCYHAAAAgKMQcgAAAAA4CiEHAAAAgKMQcgAAAAA4CiEHAAAAbpGQkKCPP/5Yzz77rCQpKipKS5cutbkqeAMfuwtwoqioKC1ZskQHDx5UoUKFdNttt6ldu3by8/OzuzTA4xISErRv3z7FxsZa02rXrm1jRYBnJSQk6MCBAwoMDLS7FMDjnnvuOV29elWrVq2SJJUuXVqPPvqoIiIibK4MTseZnFy0Zs0atW3bVvfee6/Wrl2rhIQEnTx5Up9++qlq1aqlESNG6PLly3aX6TUmTpyomJgYu8vwaj///LOqVq2q2rVrq2XLlqpbt666dOlid1le59tvv9Xw4cM1bNgw6wbP+P333xUQEKBWrVpJkiIiIvT444/bXJX3OHTokLp27aqGDRtKkjZt2qRx48bZW5SX+eOPP/TFF19YP/T6+/vrypUrNlcFb0DIyUVjx47Vv//9b0VGRmry5Ml69913NXbsWC1cuFDbt29XxYoVNXPmTLvL9BorVqxQtWrV9MILL+jvv/+2uxyv9Prrr+uPP/7QLbfcolOnTmnatGl6+OGH7S7LqwwZMkRffvmlJk2apISEBM2ZM0enTp2yuyyvMWzYMK1YsUKlS5eWJDVq1EgbN260uSrvMWDAAHXv3l3x8fGSpDp16mjy5Mk2V+Vdrm/FkpCQoMTERJuq8V7Dhw/X2bNnZYxRly5dVKZMGc2bN8/ustyKkJOL/ve//6l+/fppzitSpIgGDBigp556ysNVea9Zs2Zp8+bN8vf3V9u2bdW5c2ctXLjQ7rK8isvlUkBAgHWA0atXL4WHh9tclXdZsmSJ5s+fr7Jly2rs2LGKiIjQ8ePH7S7La8THx6t69eopphUsWNCmarzP0aNH1atXL+XLd+1wx8fHRz4+tNT3pJCQEM2cOVPGGEVHR+uZZ56xzmzCc+bPny9/f38tXrxYPj4+Wr16td599127y3IrQo4HHDt2TM8//7zdZXil8uXLa8SIEZo5c6a2bdumXr16qVatWlqyZIndpXmFAgUKSJIqV66sH374QVu2bNHBgwdtrsq7+Pn5KV++fHK5XLp69apuuukmHTp0yO6yvIafn58uXrwol8slSdq+fTv9Mz3Ix8dHxhjr7zNnznAWwcP+/e9/a+XKlTpy5IiaNGmixMREffDBB3aX5XWSgv6KFSv00EMPKTg42OaK3I+fM3LRiRMnNHLkSO3bt0+PPvqoHnroIQ0fPlxhYWE00bFBbGysZs2apbCwMPn5+enDDz9U9+7dtWHDBj388MOKjo62u0THe/7553XmzBmNGjVKjz76qM6ePUt7eA8rVqyYYmJi1KJFC/Xp00fly5e3wifc780331SnTp10+PBh9e3bV4sWLdKMGTPsLstrPPTQQxo4cKAuXLigqVOnavz48XryySftLstrJCQk6J133tHEiRM1ceJEu8vxakWKFNH777+vOXPmaPXq1UpMTHR83yiXSf4TB25I165dVahQIbVu3Vo//vijzpw5o7i4OE2aNEn16tWzuzyvU758eXXs2FGDBw9Wo0aNUsx7+umn9cUXX9hUmfc4efKkypQpk+k0uM+xY8dUsmRJJSQk6N///rfOnDmj559/XlWqVLG7NMczxujo0aOKjY3VokWLZIzRnXfeqRo1athdmleZPXu2vv/+exljdN9996lXr152l+RV2rZtq2XLltldhtf7+++/9emnn6pVq1Z68MEH9ffff+t///ufXn31VbtLcxtCTi669dZbtX37dklSXFycypYtq/3798vf39/ewrxQQkKCZs6cqd69e9tdildr0KBBqk7WaU2D+0yZMkVPPPFEptOQ+4wxql+/vjZt2mR3KYBt/v3vf+vq1avq16+fihYtak0vXLiwjVV5H2/8X0DIyUXXH7xxMGevO+64Q6tXr7a7DK8UHx+vK1euqHnz5goPD7faxJ87d05t27bVX3/9ZXOF3oOgaa9u3brp888/t0ZXg2c99NBDVn+oJCVKlFCzZs3Ut29fq58C3Cf5e+xyuWSMkcvlUkJCgo1VeR9v/F9An5xcdOjQoRTXn7j+bzraedbtt9+u8PBwNWvWzO5SvM67776rkSNHyuVyqUiRItb04sWL68UXX7SxMu+xfv16rV27VidPntT48eOt6efOnXN8O+y8pEiRIqpfv77uueeeFL9i8//AM8qVK6eNGzeqR48ekqSvv/5a5cuX1zfffKNNmzbpv//9r80VOh8DPdjLm/8XEHJy0TPPPJPh3/CslStXKiwsTEFBQSkOLtatW2djVd5hxIgRGjFihAYNGqTPPvvM7nK80qFDh7R+/XpdunQpxZXFixcvrqlTp9pXmJepXr16qiGk4TlbtmzR8uXL5evrK0nq37+/7r33Xv3000/0lYVX8Ob/BTRXg2OtWLEizemtW7f2cCWAfX7++WfdfffddpcB2CI4OFh//fWX1WQtISFBderU0Y4dO1S/fn39+eefNlfofElD2F+P5mqe5Y3/CziTk4uSnwaUrrU9LVeunO644w6VL1/epqq8F2HGPu3bt9eSJUtUtmzZFP/cktpiczFK91u9erXuuOMOGWPSvAhu586dbajK+1y4cEGvvfaaFi9eLJfLpY4dO2rUqFEqVqyY3aV5hdatW6tLly56/PHH5XK59NVXX6lFixa6ePGidXYH7nXhwgXr/uXLlzV9+nTHN5PKi9asWaPGjRtb/QNPnjypsLAwjRgxwubK3IczObmoX79+qaadPHlSERERmjFjhjp06GBDVd7r5MmTGjlypDZv3qzY2FhrOs3V3O/IkSOqUKGC9u3bl+b8gIAAD1fkfZKGSW/btm2qeS6XS0uXLrWhKu/z+OOPq3Dhwho0aJAk6fPPP9eFCxf01Vdf2VyZd7h69aomTJig5cuXS5LatGmjChUqqHv37vYW5uXatGljbRN4Rr169VKN9Oj0s5mEHA/466+/1K9fP4WHh9tdile57777dMcdd2jy5MkaO3asJk6cqPr16+udd96xuzSvcvnyZW3evFkul0shISEqVKiQ3SUBHnPbbbdp8+bNmU6De/3111+aMmWKpk+frkqVKmnDhg12l+S1du/erbvuuktRUVF2l+JV0trv1KlTR9u2bbOpIvejuZoH1KpVS3FxcXaX4XX279+vBQsWaObMmbr33nvVqVMnr2uPardly5apZ8+eqlChgowxOnbsmGbPnk1TQg+bO3duiuZSDz74oN0leY2EhARduHDBap526dIlRpvykJiYGH3zzTeaPHmyoqKidPnyZa1atUq33nqr3aV5leTNlhMSEhQfH8+odjYICgrSv//9b73wwgsyxug///mPatWqZXdZbkXI8YDY2Fg62NmgYMGCkiRfX1+dPn1a/v7+OnjwoM1VeZfBgwfr+++/V5MmTSRdayr45JNPauvWrTZX5j2GDRumZcuWWVd5HzNmjNatW6f333/f5sq8Q+/evdW0aVM99thjcrlcmjNnjvr06WN3WY7Xv39/zZs3Ty1bttRLL72kzp07q2bNmgQcG6xfv9667+Pjo/Llyyt//vw2VuSdPv74Y/Xq1UuvvfaaXC6XWrRooenTp9tdllsRcnJRWp17T548qalTp6pbt242VOTdgoODdfr0afXq1UtNmzZViRIlVL9+fbvL8ipFihSxAo4kNW7cOMV1c+B+8+fP16ZNm6xmgv3791e9evUIOR4ybNgwhYSEaPHixTLGaMyYMbrrrrvsLsvxZs+erYYNG2rAgAG666675HK50hzhC+5HH8y8oWLFilq6dKkuXbokSV7xv5g+Obno+g6+LpdLZcuWVadOndSvXz92sDZatWqVzp49q7vvvptfkDzopZde0m233WadRZg5c6b+/vtvR4/mkte0bdtWS5Yssa46npCQoA4dOmjZsmU2V+YdYmNj5evra+3/ExMTdeXKFfn5+dlcmbNdvHhRc+bM0eTJk3Xw4EH17t1b06dP14EDB+wuzets3LhRr732mvbs2aP4+HhrlM09e/bYXZpXWblyZZrTW7Vq5eFKPIeQA8BtypYtq1OnTllDtcbFxVnDVzKUtGe88MIL2rFjh9VE6quvvlKDBg3UvHlzSQwl7W7NmzfXzz//rBIlSki6dpXxLl26aNWqVTZX5j22b9+uKVOmaMaMGapevbp69erFxbo9qG7dunr22WfVrFmzFD8y0nTQsxo1amTdj42N1c6dO1WnTh1t3LjRxqrci5CTi2bMmGH9Yr1mzRo1bdrUmvfxxx/r+eeft6s0r3L9tVmScI0Wz0tvCOkkNGNwv7SGkE7CUNLu543DtuZV8fHx+v777zVlypQ0m5fDPdL6DsB+69at07Rp0xQWFmZ3KW5DyMlFDRo0sBJx8vtp/Q334cAaQF4REhKi8PBwq/37hQsX1KxZM0cP2wokFxoaqqeeeoo+sXlQs2bNHH15EwYeyEXJ8+L12ZEs6TmEmLwjKipKQ4YMSXVBVs6medaxY8e0ffv2FNuAZmqe8dhjj+nOO++0Lgb62WefMboavMoff/yhSZMmKTg4OEVfNC7M7VmRkZHW/YSEBK1du1YXLlywsSL3I+TkouRNpK5vLsWgA57HAbb9nnrqKQ0cOFB79uzRTz/9pE8++USBgYF2l+VVpk6dqpEjR+rUqVOqWbOmNm/erKZNmxJyPOTll19W+fLltWDBAknSoEGDrGbNgDcYN26c3SVAUpcuXaz7Pj4+qlGjhqZNm2ZjRe5Hc7VcVKVKFb366quSpNGjR1v3Jen999/X/v377SrNK7Vt21YDBw7U22+/rTlz5lgH2K+99prdpXmNpGaadevW1datW2WMUbt27RjZy4NCQkK0YsUKtWvXTn/++adWrlyp6dOna9KkSXaXBsBLJCQk6MCBA/zIBY/KZ3cBTtKhQwdFREQoIiIixf2IiAi1b9/e7vK8zrlz5/TII48oX758qlu3riZOnKjffvvN7rK8SoECBSRJxYoV0759+xQXF5dpnynkrgIFCqhkyZKKj4+XdG240OTNFuAec+bMUVRUlPX3c889J39/fzVs2FA7duywsTLAs37//XcFBARYQxVHRETo8ccft7kq7xEZGZnhzclorpaLvvzyS7tLQDLXH2DfdNNNHGB7WOvWrXX69Gk9++yzuv322+Xr66vu3bvbXZZX8fX1lTFGQUFB+uSTTxQQEKCTJ0/aXZbjvffee1aH3p9++knff/+9fvnlF61fv15Dhw7Vzz//bHOFgGcMGzZMK1assPb9jRo1YiAmD+rSpYtcLpeMMdq/f7+KFy8u6doPwQEBAdq7d6/NFboPIScXHThwQFWqVMlwmcOHD6tixYoeqsi7pXWA/dBDD9ldllf54IMPJEk9e/ZUy5Ytde7cOdWpU8fmqrzLqFGjdP78eX3wwQcaOHCgzp49q/Hjx9tdluO5XC5rRLVFixapX79+atKkiZo0aaIvvvjC5uoAz4mPj1f16tVTTCtYsKBN1XifpBDz3HPPqVWrVtZx0Ny5c7V+/Xo7S3M7mqvloh49euipp57SsmXLUnR0j46O1vjx49WkSROtWbPGxgq9ywcffKBSpUqpZ8+e2rhxoxYtWqT//Oc/dpflVX744QedPXtW0rU+a5UqVdKPP/5ob1Fepl27dipRooSqV6+u3377zWpOC/dKTEy07q9du9a6+Or18wCn8/Pz08WLF60BmLZv355ilDV4RkRERIofert3767ly5fbV5AHEHJy0apVq9SpUye9++67KlWqlEqWLKlChQqpXbt22rt3r/73v//pwQcftLtMr7Nnzx7NmzfP0adk86o333xT/v7+1t/+/v5688037SvICz311FM6deqU9ffJkyc1YMAAGyvyDvXq1dNLL72kcePGac+ePWrdurUkWaEfcLoePXpIkt544w116tRJhw8fVt++fdW+fXu98847NlfnfWJiYvT7779bf69atUoxMTE2VuR+NFfLZQ899JAeeughxcfH6+TJkypcuLDV/hGe0bFjR3344YeqV6+eDh8+rNtvv11NmjTR3r17FRkZqZdfftnuEr2Wy+XiV2wP27Bhg0qXLm39XaZMGUVERNhYkXcICwvTG2+8oeXLl2vu3LkqVKiQpGu/pvbt29fe4gAP+OuvvyRJnTp1UlBQkBYtWiRjjN544w3VqFHD5uq8T1hYmHr06KEiRYrIGKPY2FjNnj3b7rLcipDjJj4+PipfvrzdZXilQ4cOqV69epKkWbNmqXXr1vruu+905swZtW7dmpDjQcWLF9fatWvVpEkTSdKaNWtUrFgxm6vyLgkJCSn+NsYoLi7Opmq8R/HixfXf//5XW7ZsUUhIiDW9Y8eOuummm2ysDPCM5NcHrFatmnVBXNijZcuW2rNnj3bu3CljjGrVquX4vlGEHDhO8ra+f/zxh3XRw5IlS8rHh4+8J40ZM0Zdu3bVrbfeKknasWOHvvvuO5ur8i5NmjTR888/r2HDhskYow8//FDNmjWzuyyv0bdv31QjSaU1DXCarVu3qly5cqmmG2Pkcrm4MLcNtmzZoiVLlsjlcunq1atq2LCh3SW5FUd8cJx8+fLp4MGDKlmypFasWKH333/fmuf09qd5TbNmzRQZGWkNpdu8efMUfXT27t2ratWq2VSddxg7dqyGDBmi+vXry+Vy6b777mMADg84efKkjh8/rtjYWO3YsUNJ190+d+6cLl26ZHN1gPsFBQVp4cKFdpeB/++LL77QO++8Y/UNf/DBB/Xmm2/qqaeesrky93GZpD0v4BDffvutBg0apAIFCqhp06aaO3eupGtndd5++20tWrTI5gqRpEGDBvyibbOPP/5Yzz//vN1lOM7HH3+scePGpbpsQIkSJfTcc8/pySeftLE6wP3Yv+ctISEhWrJkicqWLStJOnHihNq3b68tW7bYXJn7EHLc4Pz58xo+fLj27t2r+fPnKzIyUps3b7ZGGoH7HTt2TEeOHNFtt91mtQs+fPiw4uPjVbVqVZurQ5L69evrzz//tLsMr8aBiHu98847jCgIr8T+PW8JCQlJFWhuu+02bd682aaK3I8hpN1g4MCBKlOmjKKioiRd63A3ZswYm6vyHjExMSpWrJiCgoJ0+fJlxcTEKCYmRv7+/ipTpozd5SGZ5B1TYQ9+53KPpMEdXnzxRWsflPwGOB0BJ2+pUaOGXn/9dR0+fFhHjhzRyJEjU12k1WkIOW7w119/6Y033lCBAgUkSYUKFeJAwoOKFi2qYsWKpXsD8H8Imu6RNLhD0v6oaNGi1o39EABPmzBhgqKiohQSEqKQkBD99ddfmjBhgt1luRUDD7jB9UPyXb58mZDjQUnXYRk1apR8fX3Vv39/GWM0adIkRlfLY/hewKmSmgByXSgAdktISNDYsWM1Z84cu0vxKM7kuEHbtm313nvvKS4uTsuXL9cjjzyirl272l2W1/n555/10ksvqUSJEvL399e//vUvaxACuF9CQoI+/vjjDJdJGuUF9iFoukdaTdRorgbADvnz59e6devsLsPjCDlu8M4778jlcqlYsWIaNmyYGjdurOHDh9tdltc5ffq0/v77b+vvv//+WydPnrSxIu+SP39+zZs3L8Nl6JDtXgkJCerYsWOGy0ydOtUzxXgZms0CyEvuvfdejRkzRsePH/eaH1wYXQ2O9e2336p///7Wxa7+/PNPff7555xV86B33nlHQUFBeuSRR+wuxWt16NBBv/zyi/Lnz293KV4pvWazQ4YMsbs0AF4kX77/O6/hcrmsi7ImJCTYWJV7EXLc4KmnntKYMWNUunRpSdcuCvf6669r4sSJNlfmfU6cOKE1a9bIGKNmzZpZ48PDM8qWLatTp06pUKFCKlKkCFe6tsEbb7yhzZs36/HHH1fRokWt6Z07d7axKu9xxx13aPXq1SmmtWjRQqtWrbKpIgDwDvTCdoMNGzZYAUeSypQpo4iICBsr8l6HDh3ShQsX1LNnT509e1ZHjhxRhQoV7C7La6xfv97uErxe0gH2Z599Zk1zuVyEHA9JajZbo0YNSTSbBeBZBw8e1JAhQ7Rr1y7dfvvt+uijj1SqVCm7y/IIzuS4wfUXXDLGqE6dOtq+fbuNVXmfCRMm6LPPPtPFixcVFRWlqKgoPf3001q6dKndpXmV48ePa+fOnWrZsqXi4+OVmJiYagRCwKloNgvATl26dFFwcLA6duyo//3vf8qXL58mTZpkd1keQchxg6efflqFCxfWsGHDZIzRhx9+qEuXLnnNhyqvqF+/vv744w81b97cuihZnTp1tG3bNpsr8x7ffvuthg4dKkmKjo7W5s2b9eqrr2rhwoU2V+Y9EhIS9Omnn+rvv//WJ598oqioKO3bt0/t2rWzuzSvQbNZAHapW7eutm7dKuna/4NGjRpZQ9w7HaOrucHYsWN14cIF1a9fXw0bNlRMTIz+85//2F2W1ylYsKAKFSqUYhrXyfGs9957Txs2bFDJkiUlSbfddpv27dtnc1Xe5bnnntO2bdu0ePFiSVLp0qX18ssv21yVd0lqNnvfffepQIECOnLkiN0lAfASSReml+R1A9BwxOcGxYsX15QpU+wuw+uVLVtWu3btsq7o/tVXX6lKlSo2V+Vd8uXLl6J/mpT6Yrlwrz/++EObNm1S/fr1JUn+/v66cuWKzVV5j+TNZnv27KlTp07RbBaAx+zdu1cPP/xwun9/8803dpTlEYQcN1m7dq2ioqIUHx9vTevdu7eNFXmfcePGqWfPntq5c6cCAwNVuHBh/fDDD3aX5VWKFSumY8eOWUFz2bJl1lkdeIafn1+KvxMSEpSYmGhTNd5n4sSJWrNmjZo3by5Jql69OqMLAvCYcePGpfi7S5cu9hRiA0KOGwwaNEi//PKL6tWrZ50adLlchBwPq1GjhtasWaOdO3fKGKPg4GCvO1VrtzFjxqhz587au3ev2rRpo927dxM0PSwkJEQzZ86UMUbR0dEaPXq0WrVqZXdZXoNmswDstH79en3yySeaMWOGevXqZXc5HsWe1g0WL16syMjIVL+gwrO6du2q77//XrfcckuqafCM22+/XUuXLtUff/whY4yaN28uf39/u8vyKv/+97/14osv6siRI2rcuLHuv/9+ffDBB3aX5TVoNgvATr///ruka/8LCDm4YRUqVCDg5AH79+9PNS0qKsqGSrzbxYsXde7cOblcLsXExBByPKxo0aKaOHEiFyO2Cc1mAdipcuXKqlu3rvbu3avGjRunmr9u3TobqvIMQo4bNG/eXA8//LAeffTRFGGHi+95xhdffKHPP/9cu3btSvGFPnfunIKDg22szPvMmTNHzz33nFq2bCljjJ577jl9+umnKTo9wr2uXr2qjz/+WIsXL5bL5VLHjh317LPPMgCEByQkJGjz5s00mwVgm++++04bN25Ur1699OGHH9pdjkdxnRw3aNu2bappLpeL0XQ8ZN++fdq7d68GDRqkCRMmWNOLFy+ukJAQDjA8qFatWvr5559VrVo1SdeulXPXXXfpr7/+srky7/HEE0/o1KlTeuKJJyRJX375pUqXLq3JkyfbXJl3uOOOO7R69Wq7ywDg5Xbs2JGi+b434EyOGyxbtszuErxaQECAAgICtGPHDrtL8XplypSxAo4kBQYGqkyZMjZW5H3WrFmj7du3W31C7rnnHtWtW9fmqrzH7bffrvDwcDVr1szuUgB4qW3btunDDz+0LgLaoEEDvfTSS6pTp47NlbkXIcdNjh07pu3btys2NtaaRnM1zzp58qRGjhypzZs3p9gOTm5/mtd07NhRo0aN0lNPPSVjjKZMmaKuXbsqJiZGklS4cGGbK3S+ihUrKjY21hrh68qVK6pcubLNVXmPlStXKiwsTEFBQSpatKg1nf0QAE8IDw/XfffdpyeeeELvvPOOjDEKDw9X27ZttWDBAkf/AENzNTeYOnWqRo4cqVOnTqlmzZravHmzmjZtqlWrVtldmle57777dMcdd2jy5MkaO3asJk6cqPr16+udd96xuzSvkS9fvnTnuVwuJSQkeLAa79S/f3+tW7dOjzzyiCTpf//7n1q1aqWgoCBJ0jPPPGNneY51/vx5nT59Wvv27UsxPSoqSqVLl9b9999vU2UAvEnbtm315ptvql27dimmL126VCNHjtSKFStsqsz9CDluEBISohUrVqhdu3b6888/tXLlSk2fPl2TJk2yuzSvUq9ePW3atEkhISHasmWLrly5orvvvltLliyxuzT8f3v37k3RnA25r1+/funOc7lcmjJliger8R4DBw5Ux44d1a1btxTTv/rqK4WHh2v8+PE2VQbAmwQHB2vnzp3ZnucENFdzgwIFCqhkyZKKj4+XJLVq1UqvvPKKzVV5n6TRo3x9fXX69Gn5+/vr4MGDNleF5Lp162a1EYZ7fPnllxnO//jjj/X88897qBrvsXLlyhQDnyR5/PHHNWbMGBsqAuCNko5F03L16lUPVuJ56bclQY75+vrKGKOgoCB98sknWrBggU6ePGl3WV4nODhYp0+fVq9evdS0aVM1adJE9evXt7ssJMOJZPtNmzbN7hIcKaNRHJMGgQAAd2vcuLE++uijVNM//PBDNWrUyIaKPIczOW4watQonT9/Xh988IEGDhyos2fP0jTBBl999ZUk6fnnn9ftt9+uM2fO6O6777a5KiTHwZ79CJruER8fr/Pnz6t48eIppp87d87xv54CyDvGjh2rNm3aaO7cuWrWrJlcLpf++OMPnTp1SsuXL7e7PLeiTw4c78qVKylO1zKiV97RoEEDmqvZjG3gHm+//bY2bNigqVOnqmTJkpKkM2fO6Mknn1RISIjeeustewsE4DViYmI0e/bsFENI9+jRw/HHQ5zJyUWZna1hFCPP+vrrrzV06FAdPXpU0rVfrBnRK2/hNxY41euvv64nnnhCVapUUc2aNSVJu3fvVrdu3fTmm2/aXB0Ab+Lr66vly5dbLVy8BSEnF0VERKQ7j2Y5nvfKK6/o+++/V8OGDTMcyhj2efDBB+0uwesRNN0jf/78mjZtmoYPH57i19Pq1avbXBkAb5M/f34dOnTI7jI8juZqcKw77rhDq1evtrsMr3bu3Dm99tprWrJkiVwulzp06KBRo0apRIkSdpfmVS5fvqzNmzfL5XIpJCTEujCoJG3evFm33XabjdUBANztvffe04EDB9SvX78UFyauXbu2jVW5FyHHTTZu3KhNmzYpNjbWmkZzNc/6+uuvtX37dnXt2lV+fn7WdCd/ofOarl27qlKlSurfv78kafLkydq/f7++//57ewvzIsuWLVPPnj1VoUIFGWN07NgxzZ49W61bt7a7NACAh6R1TTqXy6U9e/bYUI1nEHLcYMyYMfr666+1f/9+tW7dWr/99pvat2+v7777zu7SvMpHH32k4cOHq0yZMtZwrk7/Quc1devW1datWzOdBvepW7euJk2apCZNmkiS1q1bpyeffJJtAABwNDoquMFXX32lP/74Q5UrV9a8efMUERFhXZgSnvPJJ58oKipK+/fv1969e7V3714CjodVrVo1xTWiTp48meavSXCfIkWKWAFHunbNhCJFithYEQDADvPnz7cuRnz48GHH/9hFyHEDPz8/+fn5KTExUcYYBQcHKzo62u6yvE5gYKAqVKhgdxlerWjRorrttts0YMAADRgwQPXq1VPJkiU1bNgwDRs2zO7yvELLli01Y8YM6++ZM2dyvSgA8DJvvfWWJkyYoMmTJ0u61rJl4MCBNlflXoyu5gaFCxfW1atXVa9ePb388suqXLmyYmJi7C7L6zRp0kQ9evTQQw89lKJPTufOnW2syrvUrl07RR+op59+2sZqvNPUqVN16tQp672Pi4tT6dKl9emnn8rlcun48eM2VwgAcLfvv/9eGzZs0O233y5JqlChgi5cuGBzVe5Fnxw32LZtm6pVq6aYmBi99tprOnPmjN544w3Vq1fP7tK8Stu2bVNNc7lcWrp0qQ3VAPbYt29fhvMDAgI8VAkAwC5NmzbVmjVrVL9+ff3555+SpJCQEG3ZssXmytyHMzluUKdOHUnX2sJ/8cUXNlfjvZYtW2Z3CV4rs6ZoH3zwgYcqASEGABAQEKBVq1bJ5XIpMTFR7733nurWrWt3WW5FyMlFly9f1tSpU1WyZEk9/PDDGjZsmH755RfVqlVL48aNU6VKlewu0Svs3btX1apVU2RkZJrzGULa/ZI6tv/9999auXKlddHP7777TnfeeaedpXmNsmXLZngRYpqpAYD3+O9//6s+ffpo27ZtKly4sFq2bKmZM2faXZZb0VwtF/Xq1Uvnzp3TpUuXlC9fPgUGBqpbt25aunSp/vrrL/3www92l+gV7rnnHv34449eOSZ8XnP33XdrxowZKl26tCTp9OnTevzxx/XTTz/ZXJnzJTVTmzRpkk6fPq3+/fvLGKMpU6aoUqVKevnll22uEADgaTExMUpMTExxQVCnIuTkotq1aysyMlKxsbEqX768Tp8+rXz5rg1gV6dOHW3bts3mCgHPSutzz3fBs1q3bq0VK1akmNaqVSutXLnSpooAAHaYO3euFi9eLJfLpY4dO1qtLJyKIaRzka+vr6RrQ0hXq1bNCjiSuE6ODbp27ZqlaXCfW265RU899ZTCw8MVHh6u/v37q1atWnaX5VUOHz6c6lpFR44csbEiAICnDRs2TGPGjNEtt9yiWrVqacyYMXrllVfsLsut6JOTi+Li4rRjxw4ZY1Lcl6TY2Fibq/M++/fvTzUtKirKhkq81+TJkzVy5Eg9++yzMsaoffv2+uijj+wuy6sMGTJE9erVU5cuXSRJCxcu1GuvvWZzVQAAT5o/f742bdqkQoUKSZL69++vevXq6f3337e5MvehuVouCgwMTLejL31BPOeLL77Q559/rh07dqQYZODcuXMKDg7WggULbKwO8LwtW7ZoxYoVMsaobdu2jh9RBwCQUtu2bbVkyRKrlVFCQoI6dOjg6JFoCTlwnH379mnv3r0aNGiQJkyYYE0vXry4QkJClD9/fhur8y5pDSVdokQJNWvWTO3atbOhIgAAvMfChQslSb/99pt27NihPn36SJK++uorNWjQQKNGjbKzPLci5MArnDt3TgcOHLCuYQTP6N27t1atWmX1hZo/f75atGihTZs26eGHH9brr79ub4FeIK2hpJOC5gcffKDy5cvbVBkAwN3SujB6EqdfIJ2QA8e66667NGfOHPn4+Fjhpnfv3nr77bdtrsx7dOjQQXPnzpW/v78k6ezZs+rVq5dmz56txo0ba8eOHfYW6AWGDx+uCxcuqF+/fjLGaPr06SpWrJhcLpfWr1/P0PYAAEdi4AE41rFjx+Tv769vvvlG999/vz766CM1bNiQkONBhw8ftgKOJPn7+ys6OlrFihWTn5+ffYV5kUWLFmndunXW32PHjrWGlb711lttrAwA4El///239uzZo/j4eGta586dbazIvQg5cKyrV69KklauXKm77rpLBQoUyPAK8Mh9tWvXVv/+/dWvXz+5XC5NnTpVwcHBiouLo2+Uh5w9e1anTp2yLsh66tQpawhphrYHAO/w4osvasaMGQoODrb+/7pcLkIO8E9Up04dderUSTt37tQHH3ygmJgYQo6HTZkyRW+//bY1hHTbtm01ZswY5c+fXz///LPd5XmFwYMHq169eurcubNcLpcWLlyoYcOG6eLFi7rjjjvsLg8A4AHz58/X3r17VbhwYbtL8Rj65MBx/vrrL9WqVUuxsbFasGCBGjVqpGrVqunQoUOaOnUqnd3zkJdfflljxoyxuwzH27p1q5YvXy5jjNq0aaOQkBBrXlxcnHUhYwCAM7Vv316//PKLfHy85/wGIQeO06BBA23cuDHV/bT+hr3YHvZjGwCA823atEmvvvqqOnbsmKJP7DPPPGNjVe7lPXEOXiN5br8+w5Pp8xa2h/3YBgDgfKNHj9aRI0e0adOmFH1ynIyQA8dJ/qW9/gvs9C/0Pw3bw35sAwBwvo0bN2rXrl1etc8n5MBxYmNjtWPHDhljUtxPmgcAAOBNatWqpUuXLqlo0aJ2l+IxhBw4TkxMTIohEZPf96ZfMP4JaCplP7YBADhfsWLF1LBhQ3Xq1ClFn5wPPvjAxqrci5ADx4mOjra7BPx/EydO1OOPP57ukJW//vqrhyvC9Zo2bWp3CQAANwsODlZwcLDdZXgUo6sBcJuePXtqyZIl6tmzp0JDQ1WjRg27S/I6TZs21eDBg/XQQw+pQIECdpcDAIBHEHIAuNXRo0c1ceJETZo0SXXr1tWzzz7r6Css5zW//PKLwsLCtGHDBj3xxBMaOHCgKlWqZHdZAAAPevvtt9OcPnz4cA9X4jn57C4AgLOVL19eI0aM0MyZM7Vt2zb16tVLtWrV0pIlS+wuzSt06tRJCxYs0OrVqxUXF6eGDRvqoYce0urVq+0uDQDgIRcuXLBuJ06c0LRp07Rz5067y3Ir+uQAcJvY2FjNmjVLYWFh8vPz04cffqju3btrw4YNevjhh+k/5UEXLlzQuXPnVLBgQVWoUEGhoaFq0aKFPv30U7tLAwC42Ycffpji77feektPP/20TdV4BiEHgNsEBgaqY8eOmjBhgho1amRNb9y4sTp27GhjZd5jzpw5CgsL09mzZzV48GCNGzdOhQoVUkJCgqpXr07IAQAvVLp0aUVFRdldhlvRJweAWyQkJGjmzJnq3bu33aV4tXvuuUdDhgxRhw4dUs374YcfdO+999pQFQDAk8aPH2/dT0hI0Nq1a3XkyBFHNx0n5ABwmzvuuIO+HzZKSEhQ37599dVXX9ldCgDARv369bPu+/j4qHr16howYIBKlixpY1XuRXM1AG5z++23Kzw8XM2aNbO7FK+UP39+HTp0yO4yAAA2WblypaSUISfJ1q1b1apVK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-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">col_entropies</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">entropy</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
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+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">col_entropies</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">entropy</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span><span class="p">),</span>
-    <span class="n">scale</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span>
+    <span class="n">scale</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="n">col_entropies</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-10">col_entropies, fig = entropy(
+<div id="cell-10" class="clipboard-copy-txt">col_entropies, fig = entropy(
     df=df_pd.drop(columns=id_cols),
     scale="linear",
     plot=True,
 )
 
 col_entropies.head()</div>
+    
+        </div>
+    </div>
+
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+<div class="jp-OutputArea-child">
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[10]:</div>
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+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>EstimatedSalary    9.210202
 Balance (EUR)      6.246510
 CreditScore        5.873574
@@ -2274,60 +2848,87 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 Surname_Country    2.481289
 dtype: float64</pre>
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-<h3 id="plotting">Plotting<a class="anchor-link" href="#plotting">¶</a></h3>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">cross_correlation</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">cont_cols</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">cross_correlation</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">cont_cols</span><span class="p">],</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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-<div class="clipboard-copy-txt" id="cell-11">fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True)</div>
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+<div id="cell-11" class="clipboard-copy-txt">fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True)</div>
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 <pre>Top 10 absolute correlations
 Balance (EUR)     Country_gdp_per_capita            0.329762
                   NumberOfProducts                  0.304180
@@ -2343,118 +2944,150 @@ <h3 id="plotting">Plotting<a class="anchor-link" href="#plotting">¶</a></h3>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">cont_cols</span><span class="p">],</span>
     <span class="n">low_per_cut</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
     <span class="n">high_per_cut</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-    <span class="nb">type</span><span class="o">=</span><span class="s2">"box"</span><span class="p">,</span>
+    <span class="nb">type</span><span class="o">=</span><span class="s2">&quot;box&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-12">fig = distributions(
+<div id="cell-12" class="clipboard-copy-txt">fig = distributions(
     df=df_pd[cont_cols],
     low_per_cut=0,
     high_per_cut=1,
     type="box",
 )</div>
+    
+        </div>
+    </div>
+
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           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -118,39 +109,31 @@
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-    
-      <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
-    
-      <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
-      </label>
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+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,60 +184,46 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
@@ -266,35 +232,14 @@
   
     
   
-  
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link md-tabs__link--active">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../code_profiling.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
+
+      
         
   
-    
   
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
-        
-  
-    
-  
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
     </div>
@@ -388,19 +334,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,19 +349,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../installation.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+        installation
       </a>
     </li>
   
@@ -428,302 +364,214 @@
     
       
       
-  
+      
+
   
   
   
     
-    
-      
-        
-      
-        
+    <li class="md-nav__item md-nav__item--nested">
       
-        
       
-        
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
       
-    
-    
       
-      
-    
-    
-    <li class="md-nav__item md-nav__item--nested">
       
         
+          
         
           
         
-        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" type="checkbox" id="__nav_3" >
+          
         
           
-          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-            
-  
-  <span class="md-ellipsis">
-    churn_pred
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_3">
-            <span class="md-nav__icon md-icon"></span>
-            churn_pred
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+        code_profiling
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/preprocessing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    preprocessing
-  </span>
-  
-
+        preprocessing
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/training.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    training
-  </span>
-  
-
+        training
       </a>
     </li>
   
 
-              
             
-          </ul>
-        </nav>
-      
+          
+        </ul>
+      </nav>
     </li>
   
 
     
       
       
+      
+
   
   
     
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-        
       
-        
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" checked>
       
-        
       
-        
       
         
-      
-        
-      
+          
         
-      
-    
-    
-      
+          
         
+          
         
-      
-      
-    
-    
-    <li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
-      
+          
         
+          
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
+          
         
           
-          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="">
-            
-  
-  <span class="md-ellipsis">
-    notebooks
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_4">
-            <span class="md-nav__icon md-icon"></span>
-            notebooks
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_4">
+          notebooks
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="notebooks" data-md-level="1">
+        <label class="md-nav__title" for="__nav_4">
+          <span class="md-nav__icon md-icon"></span>
+          notebooks
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-nav__link">
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var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper 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.83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
-</style>
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="target-analysis">Target analysis<a class="anchor-link" href="#target-analysis">¶</a></h1><p><strong>Purpose</strong>:</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="target-analysis">Target analysis<a class="anchor-link" href="#target-analysis">&#182;</a></h1><p><strong>Purpose</strong>:</p>
 <ul>
 <li>put features into context with predicted or actual target values<ul>
 <li>analyse feature distribution per group in binary classification</li>
@@ -1400,44 +1798,47 @@ <h1 id="target-analysis">Target analysis<a class="anchor-link" href="#target-ana
 <li>check how your model is perfroming</li>
 <li>check the possible connection between feature and target values (eg. if the distributions of features are different for each class)</li>
 </ul>
+
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="imports">Imports<a class="anchor-link" href="#imports">&#182;</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-1">
-<div>
-<span class="notice" hidden="">Copied!</span>
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-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 
 <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
-<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">"../.."</span><span class="p">)</span>
+<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;../..&quot;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">from</span> <span class="nn">churn_pred.eda.target.analysis</span> <span class="kn">import</span> <span class="n">correlation</span>
@@ -1445,7 +1846,7 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
     <span class="n">distributions_in_binary_cls</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-1">import os
+<div id="cell-1" class="clipboard-copy-txt">import os
 import sys
 
 sys.path.append(os.getcwd())
@@ -1456,59 +1857,75 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
 from churn_pred.eda.target.plotting import (
     distributions_in_binary_cls,
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="dataset">Dataset<a class="anchor-link" href="#dataset">&#182;</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-2">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-</svg>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">"data/dataset_auxiliary_features_cleaned.parquet"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">&quot;data/dataset_auxiliary_features_cleaned.parquet&quot;</span><span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-2">df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
+<div id="cell-2" class="clipboard-copy-txt">df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
 df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[2]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[2]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -1522,208 +1939,214 @@ <h2 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h2>
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>CustomerId</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>...</th>
-<th>Country_subregion</th>
-<th>is_native</th>
-<th>Country_hemisphere</th>
-<th>Country_gdp_per_capita</th>
-<th>Country_IncomeGroup</th>
-<th>Surname_Country_gdp_per_capita</th>
-<th>Surname_Country_IncomeGroup</th>
-<th>working_class</th>
-<th>stage_of_life</th>
-<th>generation</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>15787619</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>160980.03</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>...</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>32756.00000</td>
-<td>None</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>1</th>
-<td>15770309</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>151762.74</td>
-<td>1</td>
-<td>0</td>
-<td>1</td>
-<td>...</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>76329.58227</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>2</th>
-<td>15569178</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>82767.42</td>
-<td>1</td>
-<td>1</td>
-<td>0</td>
-<td>...</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>3</th>
-<td>15795519</td>
-<td>716</td>
-<td>Germany</td>
-<td>Female</td>
-<td>18</td>
-<td>3</td>
-<td>128743.80</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>...</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>66616.02225</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>4</th>
-<td>15621893</td>
-<td>727</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>4</td>
-<td>133550.67</td>
-<td>1</td>
-<td>1</td>
-<td>1</td>
-<td>...</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>55442.07843</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>CustomerId</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>...</th>
+      <th>Country_subregion</th>
+      <th>is_native</th>
+      <th>Country_hemisphere</th>
+      <th>Country_gdp_per_capita</th>
+      <th>Country_IncomeGroup</th>
+      <th>Surname_Country_gdp_per_capita</th>
+      <th>Surname_Country_IncomeGroup</th>
+      <th>working_class</th>
+      <th>stage_of_life</th>
+      <th>generation</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>15787619</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>160980.03</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>...</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>32756.00000</td>
+      <td>None</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>15770309</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>151762.74</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>...</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>76329.58227</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>15569178</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
+      <td>82767.42</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0</td>
+      <td>...</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>15795519</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>128743.80</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>...</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>66616.02225</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>15621893</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>133550.67</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1</td>
+      <td>...</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>55442.07843</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+  </tbody>
 </table>
 <p>5 rows × 28 columns</p>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">target_col</span> <span class="o">=</span> <span class="s2">"Exited"</span>
-<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"CustomerId"</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">target_col</span> <span class="o">=</span> <span class="s2">&quot;Exited&quot;</span>
+<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CustomerId&quot;</span><span class="p">]</span>
 <span class="n">cat_cols</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">"Country"</span><span class="p">,</span>
-    <span class="s2">"Gender"</span><span class="p">,</span>
-    <span class="s2">"HasCreditCard"</span><span class="p">,</span>
-    <span class="s2">"IsActiveMember"</span><span class="p">,</span>
-    <span class="s2">"CustomerFeedback_sentiment3"</span><span class="p">,</span>
-    <span class="s2">"CustomerFeedback_sentiment5"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_region"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_subregion"</span><span class="p">,</span>
-    <span class="s2">"Country_region"</span><span class="p">,</span>
-    <span class="s2">"Country_subregion"</span><span class="p">,</span>
-    <span class="s2">"is_native"</span><span class="p">,</span>
-    <span class="s2">"Country_hemisphere"</span><span class="p">,</span>
-    <span class="s2">"Country_IncomeGroup"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_IncomeGroup"</span><span class="p">,</span>
-    <span class="s2">"working_class"</span><span class="p">,</span>
-    <span class="s2">"stage_of_life"</span><span class="p">,</span>
-    <span class="s2">"generation"</span><span class="p">,</span>
+    <span class="s2">&quot;Country&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Gender&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;HasCreditCard&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;IsActiveMember&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CustomerFeedback_sentiment3&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CustomerFeedback_sentiment5&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_region&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_subregion&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;is_native&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_hemisphere&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_IncomeGroup&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_IncomeGroup&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;working_class&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;stage_of_life&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;generation&quot;</span><span class="p">,</span>
 <span class="p">]</span>
 <span class="n">cont_cols</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
     <span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span> <span class="o">+</span> <span class="n">cat_cols</span> <span class="o">+</span> <span class="p">[</span><span class="n">target_col</span><span class="p">]</span>
 <span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-3">target_col = "Exited"
+<div id="cell-3" class="clipboard-copy-txt">target_col = "Exited"
 id_cols = ["CustomerId"]
 cat_cols = [
     "Country",
@@ -1748,78 +2171,88 @@ <h2 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h2>
 cont_cols = df_pd.drop(
     columns=id_cols + cat_cols + [target_col]
 ).columns.values.tolist()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-4">df_pd[cat_cols] = df_pd[cat_cols].astype(str)</div>
-</div>
-</div>
+<div id="cell-4" class="clipboard-copy-txt">df_pd[cat_cols] = df_pd[cat_cols].astype(str)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">&#182;</a></h3>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<span class="notice" hidden="">Copied!</span>
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">sorted_corr_cols</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">correlation</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">sorted_corr_cols</span><span class="p">,</span> <span class="n">fig</span> <span class="o">=</span> <span class="n">correlation</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">cont_cols</span><span class="p">],</span>
     <span class="n">target</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span>
-    <span class="n">scale</span><span class="o">=</span><span class="s2">"linear"</span><span class="p">,</span>
+    <span class="n">scale</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span>
     <span class="n">plot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="n">sorted_corr_cols</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-5">sorted_corr_cols, fig = correlation(
+<div id="cell-5" class="clipboard-copy-txt">sorted_corr_cols, fig = correlation(
     df=df_pd[cont_cols],
     target=df_pd[target_col],
     scale="linear",
@@ -1827,17 +2260,29 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 )
 
 sorted_corr_cols.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>Age                               0.285323
 Country_gdp_per_capita            0.139180
 Balance (EUR)                     0.118533
@@ -1845,130 +2290,164 @@ <h3 id="analysis">Analysis<a class="anchor-link" href="#analysis">¶</a></h3>
 Surname_Country_gdp_per_capita    0.002470
 Name: Exited, dtype: float64</pre>
 </div>
+
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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+3aNaN69epmyE3tPfxP159hZDwYSTe/jLl48WK6z+12xo4day7vySefTHM7lpiYaJw4ccJl2pIlS1y2gfHx8Snu980335htnn766RTzb33tp06dmmatd9LWeYdq8uTJqba5evWqeXTQz88vxTq9XTCKiooyrly5kmYNly5dMr84S207bRgZ234mSy8YnT171tzGli5d2jh+/HiKNmFhYeaOapkyZVLsiDr/z0gy+vbtayQlJaVYTp8+fcw2YWFht637VmfOnDHv/7///c9lXlRUlBneHn/8cUOS8eKLL7q0SUpKMooUKWJIqX+JdrujBxn9DLh1ezd8+PAUbZKSkszPUk9Pz1SPbmWE8/sgJCQk1ZCcXghJdu3aNfM9lz9/fuPQoUOGYdz8wrVBgwaGdPOo4a5du25bR2oyemQmKSnJrKNmzZqp7kMZhmEsX77cPBp0a5CLj483t5W+vr6pHqHcs2eP4ePjY9ZMMLo9glEOk5iYaPTq1ctlY+V8u+eee4xnnnnGWLx4caobdMNw/bYqvW9XZ82aZbb79NNPU8xPnufh4XHbUJHRnezr16+bG4LXXnst3WUuW7bMXGbyxs8wbm4YCxcubEg3v1VN66haZGSk+Y28u4PRG2+8YX5gp9cl6saNG2Zofeedd+6qnuDgYEOS8dJLL6U638pg9NFHH6W5nL///tvw9vY2JBnjxo1Lt7YJEyYY0s3w7Lxj9sknnxjSzaMR7uC8o3DPPfekuVMYGxtrFC9e3JBuHtm41UcffWT+76S2A2cYhlGjRg3zPXw37jYYdejQIc12EydONNvdumORGevPMO4sGK1bty7dx7mdxMRE8/+tTJkyRmxs7B3d/9FHHzWfR1rr0TAM4+GHHzZ3HG/tXun82j/00EPpPt6dtK1du7YZ9tKzf/9+c5m///67y7zbBaOMWLRokbmMc+fOpZifWcFo9OjR5nLmzp2b5jKcey98//33LvOc/2dKlSqVZkg+cOCA2e527/W0BAQEGNLN3gzOkj9377//fmPGjBnm78527txpPn5qR/rcEYxq166d5j7FL7/8Yrb76aef0l1mWpzfBxm5pdedcu/evea2qF69esaNGzdclv/FF19kqI7UZDQY/fzzz2luK2+VfNS5cePGLtOd99U+//zzNO/v/N4nGN0egy/kMB4eHpo6daqWL1+uli1bysPDdRWfOXNG3333ndq1a6d69erpyJEjKZaxYsUKSTdPdO3Vq1eaj9WpUyf5+vq63Cc1jRs3vu3JsBm1detWc1CFp59+Ot22Dz74oPn7pk2bzN+3b9+uixcvSpK6d++e4jVKVrZsWT3yyCP/tOQM+emnnyRJjz/+uLy9vdNs5+npqYYNG0pyfU6pMQxDp0+f1sGDB7V3717zVrp0aUnSrl27Mqn6zPPcc8+lOe/XX3/VtWvXJGV83d+4cUPbt283p5cqVUrSzUEqkl9zd3n66aeVL1++VOcVKFDAfA779+9PMVBIr169lCtXLiUlJWn69Okp7r99+3Zz8JP0/kfdIb11VLt2bfP3o0ePuszLjPV3J8qVK6cmTZrc1X2T7dy50xxuv2/fvipQoECG75uQkGCeiN2yZUuVK1cuzbZ9+/Y175PeNWbSe+3vpO2JEyfM1/V26+K+++5TsWLFJN1+m3M7V65cUUREhPbt22duj5xH6nTnNin5M6pQoULq2LFjmu369OmT4j6peeqpp+Tl5ZXqvKpVq5rvlVv/DzIqeWCE5PdQsuT3R7Nmzcw2+/fv199//52ijYeHxz/+H8ioZ599Ns2BTdLbLljh/vvv1+jRoyXd3Kd49tln9cknn0iSHn74YQ0aNMjtNSR//lStWlWBgYHptk3eHoaGhroMxOC8r9a9e/c07588uA4yhmCUQ7Vu3Vq//fabzp07p59//lkffPCBHnvsMTPISNK2bdvUpEmTFDtle/fulXRzhJoSJUqk+Rh58uRRcHCwy31Sc7t/+juxbds28/eGDRumOkpR8s15J+b06dPm786j6d3uInH16tXLtNrTEh0drcOHD0uSJk2alO5zcjgcmj9/viTX5+Rs6dKl5rouVaqUqlatqho1api35BGKzp075/bndicKFCige++9N835zuu+VKlS6b5Gzte4cH6d2rVrp0KFCkmSnnzyST300EP68ssvtX379gyN/HMn7uS9dev/T5kyZdSqVStJN0fnMgzDZf63334r6eb/YNeuXTOj3AwLCAhIc16RIkXM32NjY13mZcb6uxOZsd3ZsWOH+bvzFy0ZcfToUXOEvvr166fb1nl+Zm1L02vrvC66dOly221O8rbibtbFuXPnNHToUFWtWlUFCxY0r0GTvD1q27atS1t3SX5dg4OD071swj333GN+kZfeukjv/0CSChcuLCnl/0FGNW3aVNLN1/zAgQPmdOdgVK5cOd17770yDMMlQCW3qVmzprm9c7e73S7cjdWrV8u42eMpzVt6owtK0ssvv6zWrVtLkn744QclJCSoSJEimj59epaEiOT/wb/++uu2/38vvfSSJOn69esuo/sl78tUrFjR/PIiNcWLF8+0L6ftgGCUwxUuXFiPPfaYPvzwQ/388886c+aMQkJCzI32qVOn9N5777ncJ/kf75577rnt8kuWLOlyn7RqyCxnz569q/s5DyGcfLRIUrrBT8rYa/BPZcZzkm4eIerTp48ee+wxLV269LYfQKkNF22l232AZ8brVLRoUS1evFhlypSRYRhavXq1Xn/9ddWpU0dFihRRx44dzWuB/VN38t5K7f8n+SjCkSNHtH79enN6fHy8OTT3E088oaJFi2ZGuRmW1lEwSS5HX28Nmpn1Ps+ozNjuOO+oJx9tzCjndXq77UjydvTW+93qTp5Tem2zal1s375dAQEBGjlypA4ePJgi4N/KndukzP5cS+//QPq//4W7/cLFeSjt5KBz8uRJHT58WA6HwwxOye2S2xiGYW4vkttkhbvdLljF4XBo0qRJLrX997//NXtUuFtm7svc7rNGypp9mZzC0+oCkLW8vLzUs2dPlS5d2vy2ZOHChfrmm29SdCnLyLcmt/ugk6RcuXLdXbGpcN6orlmzJsM7hc4bDueab/ccM/L8/inn5zRo0CD17t07Q/fLkyePy98hISGaOnWqJCkoKEiDBg1S/fr1VaZMGeXLl89cD926ddPMmTOz5Lndidu9T5Jfpzx58txR96qyZcu6/N2kSRMdPnxYCxYs0LJly7Ru3TpFRUUpJiZGCxcu1MKFC9WqVSstXLjwtjs/6fmn763HHntMJUuW1OnTp/Xtt9+aRywWLVpkfiBmdTe6fyKz1l9GZeZ2R8rY9tAd93V2J88pvbbO25zZs2dn+EjUnQSz69ev6+mnn9b58+eVO3duvfzyy3riiSfk7++vwoULm93Qjh49qkqVKknKmu1tZn2uuVvJkiXl7++vgwcPas2aNRowYIAZfqpVq6bixYtLuhmMQkJCzHl79uwxr9Fz63WK4Orrr79WUlKS+fdvv/12R91V/4nk/8HGjRtr4sSJGb6fc3BLfp/+W97T/xYEI5tq1aqVypUrp8jISF28eFHnz583N7TJh70z0m3izJkzLvdxN+cglCdPHpcuNxnlXOuZM2fk7++fZtu7/VbnTjg/p7i4uLt6TpI0efJkSVKlSpW0ceNG5c2bN9V2zkfM/gnnIO384XKrK1euZMrjJb9O169fV9GiRe/4G3xn3t7eeu6558wPwaNHj2rp0qX6+uuvdfDgQf36669655139OWXX971YyT/b6TF+b2V2v+Pp6enunfvrtGjR+uHH37QV199pQIFCpjd6LLyHLjMkJnrL6s4d085efKkqlatmuH7Oq/T221LnednxbbUeZtza9fFzLJq1SrzfJLx48ebR0BvlVnbo9spUqSITp06lS0/19LSrFkzHTx40Owm59yNLlnz5s0l/d95RsltHA5Hlp1f9G+0Zs0ajRkzRpLk4+OjmJgYzZgxQ4899pg6derk9scvWrSozpw5o7///vuu//+S35+3+6yRsmZfJqegK52NOX/z4LyTm/xPGhERke4/040bN8w++P/0gzWj36gmn9MkKcUVqTPK+UrhoaGh6ba93fzMULx4cZUpU0bSzZMp7/abnX379km62b0qrVBkGIbCwsLurtBbFCxY0Pw9vZ2bzLrydmas+7Tce++9evnllxUaGmoeofj+++//0TLv5L2V1v9Pnz595HA4dOXKFf3www+KiorS77//Lin9gUMyIqtPxnXn+nOXWrVqmb+vW7fuju577733mkcct2zZkm7brVu3mr+7I6TcKivWRfL2SJI6d+6cZjvn851Sk1nv0+TXdceOHbpx40aa7c6ePatjx4653Mcqt55nlFowKlu2rMt5RsltAgMDLQ922dWlS5fUrVs3JSUlqUSJEtqxY4cqV64sSRowYIA54MrduNN9mYMHD5rvtzuVvC8THh5uHiVMzd9//33bc67wfwhGNhUXF6f9+/dLuvltifMG9OGHH5Z0cyc6JCQkzWXMnz9f0dHRLve5W84jscXHx6fZ7oEHHjBrnThxomJiYu74sWrXrm12CUmvS9mJEyeybAeuXbt2km4euUgeXOFOJSQkSEr/PIDFixfr5MmT6S4neV2ktx6kmyd8Jktv5yb5fJh/6tFHHzVPmv7yyy/N55uZfHx8zEET/umJ4D/88EOa50xcuXLFDF7VqlVL8+hJ5cqVzZ2jb7/9VtOnT1dSUpIcDod69uz5j+rL6P9cZsmK9ZfZatasaY4mN2XKFF2+fDnD9/X09DTX3e+//67IyMg0206ZMkXSze5vWdH9qXLlyqpWrZokad68eTp+/HimP4bz+k1rm5SUlKRvvvkm3eVk1vs0+TPq0qVLWrBgQZrtpk6dan4m/NPPtX/K+b0wd+5cHTp0yOX8olvbrV692gzw/+T8oox+BvxbDRw40Px/DAkJ0b333qtZs2bJ09NTFy5cUI8ePe76C8qMvnbJn/mS9Omnn97VYznvq82YMSPNdqkN4IO0EYxykMuXL6t+/fpasmRJul2bkpKS9PLLL5sn57dr187lW44nn3zSPJo0YsSIVIdQjYyM1H/+8x9JN0+6/Kc7ac47hqkNIZ7M29vbfNzTp0+rc+fO6XbVio2N1ddff+0yLfk8K+nmcLyfffZZivslJCSob9++un79+h09j7v15ptvmn3uBwwYcNtvUZctW6bdu3e7TKtSpYok6eeff071CM6RI0f0wgsv3LaW5HWR3nqQbn6bmhxSv/7661Q/CObOnZvuTsidKFOmjLnedu3apf79+6e7c3327FlzhzPZr7/+mmIURmfR0dHmt/fOwe9unD59Wm+88Uaq815//XXzaOzAgQPTXU7y8MHr16/XV199JenmTk/yeRl3q2jRouZ5ardb15khM9ZfVvPw8NCbb74pSYqKilK3bt3S3CYkJSWl+NLhxRdflHTz6HqvXr1SvW9ISIj5BUzHjh2zrIvhu+++K0m6du2aOnTo4DLc863i4+M1YcIEc7j1jEjeHklKdch5SRoyZMhtj2Bn9LPhdnr27GkewXvjjTdSDaq7du3SiBEjJN18v7Zv3/6uHy8zlC5d2jySMW7cOEmu5xclSw5Gs2fPzpTzizL6GfBvNGvWLM2bN0/SzW1v8qiI9evXN/8nVqxYobFjx97V8pNfu6NHj6YbRjp27Kj77rtPkvS///3PPD84LXv37tXPP//sMq19+/bm43388cep9s7Yv3+/ORQ5MsjdF0pC1omNjTUv4lWmTBnjxRdfNGbNmmWsX7/e2Llzp7FmzRrjyy+/NC8MKd28WnJqF+dcsmSJ4XA4DElGgQIFjGHDhhkbNmwwNm/ebHzxxRdGiRIlzGVMmDAh1XqS59/uInGGYRgxMTHmBddq1apl/Prrr8Zff/1lHDp0yDh06JARFxdntk1ISDBatGhhLr98+fLGiBEjjNWrVxs7duww1q1bZ0yePNl47rnnjPz58xtFixZN8XiXLl0yypYtay6jS5cuxvLly43t27cbc+fONerWrWtIMn/qNhcxTU9GL1DofHHGPHnyGL179zZ+/PFHY/v27caWLVuMBQsWGG+99ZZRqVIlQ5Lx888/u9z/s88+M+8fEBBghISEGFu2bDHWrl1rfPDBB4avr6/h7e1t1KpVK90L4D333HOGdPPq3xMnTjT27NljroczZ864tH377bfNx2zUqJGxaNEiIywszFi2bJnRs2dPw8PDw2jYsKHZJr0LvKZ3Qb5ksbGxRvXq1c3lVatWzRg7dqyxfv16Y8eOHcbq1auNr7/+2mjfvr2RJ08eo3bt2ikeK3fu3EabNm2MsWPHGitWrDDCwsKMtWvXGuPHjzfuu+8+c9ljx469bT23cr7gYZ06dQxJRuvWrY1FixYZ27dvNxYtWmS0atXKbBMcHGzcuHEj3WVevXrVvCBx8m3GjBl3XFtqGjdubEgyihYtasyZM8fYv3+/ua7Pnz9vtsusi/n+0/VnGBm/wGv37t3v9OVIVWJiotGyZUvzMf39/Y2xY8caGzZsMN/r77//vlGlSpVUt3WdOnUy7xsUFGTMnDnT2LZtm/H7778bvXv3NrezRYoUMaKiolLcP6Ov/Z22NQzXiysXK1bMeOedd4zffvvN2LFjh7FhwwZj+vTpRp8+fYwiRYoYklJc4Da9C7xevnzZ/Jzw9PQ0XnjhBeOXX34xtm3bZsybN8/chie/B9N6zxw6dMic/8gjjxhr1641Dh48aL5Pnf9/brctGT9+vLmsEiVKGF988YWxefNm448//jCGDRtmFChQwJBkOBwOY+nSpSnufycXRc6s92GfPn1c/vdffPHFFG0iIyNd2jgcjlQvlpvsdhchzehnwJ1c4PdO9gdS4/x/HxISYuzZs+e2t1OnTrksIyIiwvDx8TEkGVWrVk1x8eiEhASjQYMG5nPfs2dPunWkZvLkyeb8QYMGGdu2bTNfu4iICJe2u3fvNt9zkoxWrVoZ06dPNzZv3mxs377dWL58uTFixAijUaNGhiTjjTfeSPF48+fPN+9fqFAhY+TIkcamTZuMjRs3GiNGjDB8fX0NX19fo0qVKumuc/wfglEOcvXqVaNkyZIuG8j0blWqVDG2bduW5vKmTZtmeHl5pXn/XLlyGSNGjEjz/ne6IRw8eHCaj3XrRjcuLs7o1q1bhp5nxYoVU328vXv3pvt69ezZ8453NFJzJ1dunzdvnrnhTu/m4eFhrFq1yuW+169fNx555JE075M3b17j+++/v+3Ow44dO9Jc77d+yF+5csX8IEnt1rRpU2PPnj3p7kzcSTAyDMM4f/680bp16wyt++bNm6f6WLe7vfjii0ZiYmKG6nHmvKPw66+/prs+AgICjBMnTmRouS+99JJ5P19fX5cvCv4J5y9Abr05/99mVjAyjH+2/gwj64ORYdx8nz/11FO3rTe1bd3Vq1eNJ598Mt37lS5d2tixY0eqj+3OYJSQkGAMHjzYyJUr122fW/78+VO87263Y/zLL7+YX3ildmvWrJmxd+/e275nnn766TSX4fw8M7It+eSTTwwPD480l+fl5WVMnz491ftaEYxmzpzpUt8PP/yQart7773XbFOjRo10l3m7YJTRzwCrglFGb6+++qp5/8TERKNJkyaGJCN37txp7vscPnzYDCuBgYHGtWvX0qwjNbGxsS7rwvmW2vty165dZmi53W3YsGGpPuZnn32W5ns6X758xtKlS2+7zvF/6EqXg3h7e+vEiRP6448/NGzYMD366KO69957lT9/fuXKlUs+Pj4KCAjQM888ozlz5mjv3r0uV6S+Vffu3XXgwAG9+uqruu+++5Q/f37lzZtXlSpVUt++fbVjxw4NGTIk0+ofNWqUJk+erCZNmqhIkSLpDjebN29eTZ8+Xdu2bdPAgQN1//33y9fXV56enipUqJCCgoLUu3dvzZ8/X3/++Weqy7j//vu1b98+DR48WFWqVJGXl5eKFSum5s2ba86cOemeX+UuzzzzjCIiIjRq1Cg1a9ZMJUqUUO7cuZUvXz7de++9evzxx/XFF18oIiLCHI0oWe7cubV06VL997//VZ06dZQvXz7lzZtXlStX1oABAxQWFpah0XaCgoK0adMmdenSReXLl0/z6u7SzW6Uq1at0ieffKIaNWoob9685nk6X3/9tVauXOlyod3MUKRIES1fvlwrV65Uz549VaVKFRUoUECenp4qUqSI6tatqxdffFHLli0zBypINnbsWC1YsEADBgxQnTp1VKZMGeXJk0d58+aVv7+/evTooQ0bNujrr7/+RwMbSDdHTVy+fLkmTJigBg0aqFChQsqXL59q1Kih4cOHKywsLMPXzHj++efN3zt37pzm4Bp3qm3btlq5cqWeeOIJlS5dOt0LX2aWf7L+rJIvXz798MMPWrVqlZ5//nlVrFhRefPmVcGCBRUQEKAOHTpozpw5Zrc7Z97e3lq4cKEWL16sDh06qHTp0sqTJ48KFy6s+vXra+TIkfrrr78UFBSU5c8rV65cGj16tPbv36833nhDwcHBKly4sHLlyqWCBQvq/vvv13PPPafp06fr1KlTd/y+a9WqlbZt26auXbua76/ixYuradOm+uabb7Ry5Urlz5//tsuZNWuWPv30U9WrV0++vr7/6H9z6NCh2rFjh/r27atKlSopb968yp8/v+677z69+uqrOnDggLp163bXy89szl3iUju/KJnz58E/vX7RnXwG/FuMGjXKvL7Thx9+mOa+T6VKlcxui7t379bQoUPv6HEKFCigjRs3mvtNt7vkQ2BgoPbv36/p06erffv2KleunLy9vZUnTx6VKlVKzZo107vvvqvt27fr/fffT3UZ//nPf7R+/Xp16NBBJUqUkJeXlypUqKBevXpp27ZtatOmzR09B7tzGAZnZAFAdjZ16lTzXKMtW7aoXr16FlcEAEDOwxEjAMjmko9eVq9enVAEAICbEIwAIBvbuHGjNm7cKOnmiIUAAMA96EoHANnMsWPHFB8fr/379+v1119XeHi4SpQoofDw8Nv2WQcAAHfH0+oCAACumjZtmuJq6F999RWhCAAAN6IrHQBkUwULFlTDhg21ZMkSPf3001aXAwBAjuaWYHTo0CE1atRI/v7+qlevnvbv35+izaZNmxQUFKSgoCDdf//96t+/v+Lj4835S5YsUUBAgCpXrqyOHTvq8uXL7igVALKdiIgIGYahmJgYbdy40bw6OwAAcB+3nGP00EMPqVu3burRo4fmz5+vMWPGaNOmTS5t4uLilDt3buXOnVtJSUl66qmn1KxZM73yyiu6fPmyKlWqpLVr1yogIEAvvfSSChYsqJEjR2bo8ZOSknTy5EkVLFhQDocjs58eAAAAgH8JwzAUGxur0qVLp38ttMy+YuyZM2cMX19f48aNG4ZhGEZSUpJxzz33pHsV7qtXrxqtW7c2vvrqK8MwDOP777832rRpY87ft29fuleyvlVkZOQdXyGZGzdu3Lhx48aNGzduOfcWGRmZbobI9MEXIiMjVbp0aXl63ly0w+FQ+fLldfz4cfn5+bm0jYiIUPv27XX48GG1bdtW/fr1kyQdP35cFSpUMNv5+fnpxIkTSkpKSjXlxcfHu3TDM/7/QbDIyEj5+Phk9lMEAAAA8C8RExOjcuXKqWDBgum2c8uodLd2XzPS6K3n5+ennTt36vLly+ratasWLlyozp07p7qM9IwcOVLDhg1LMd3Hx4dgBAAAAOC2+SLTB18oV66coqKilJCQIOlmKIqMjFT58uXTvE+BAgXUuXNnzZ49W5JUvnx5RUREmPMjIiJUpkyZNPsEDhkyRNHR0eYtMjIy854QAAAAgBwv04NRiRIlFBwcrFmzZkmSFixYID8/vxTd6I4cOaIbN25Ikq5fv66FCxcqMDBQktS6dWuFhobqwIEDkqQJEyaYR5JS4+XlZR4d4igRAAAAgDvlluG6J02apEmTJsnf31+jRo3S1KlTJUl9+vTR4sWLJUlr1qxRcHCwatasqeDgYN1zzz167733JN28dseUKVPUvn17Va5cWSdOnNDQoUPdUSoAAAAAuGe4bqvFxMTI19dX0dHRHD0CAAAAbCyj2cAtR4wAAAAA4N+EYAQAAADA9ghGAAAAAGyPYAQAAADA9ghGAAAAAGyPYAQAAADA9ghGAAAAAGyPYAQAAADA9ghGAAAAAGyPYAQAAADA9jytLuDfyu/tpVaX8I9FjGprdQkAAABAtsARIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC255ZgdOjQITVq1Ej+/v6qV6+e9u/fn6LNqlWrVL9+fVWrVk3Vq1fXO++8I8MwJEkRERHy9PRUUFCQeTty5Ig7SgUAAAAAebpjof3791e/fv3Uo0cPzZ8/X71799amTZtc2hQuXFhz587Vvffeq2vXrunhhx/W3Llz9eyzz0qSChUqpJ07d7qjPAAAAABwkelHjM6ePauwsDB17dpVktSxY0eFh4crIiLCpV1wcLDuvfdeSZK3t7eCgoJ09OjRu3rM+Ph4xcTEuNwAAAAAIKMyPRhFRkaqdOnS8vS8eTDK4XCofPnyOn78eJr3OX36tObPn682bdqY02JiYlS3bl3VqlVLH330kRITE9O8/8iRI+Xr62veypUrl3lPCAAAAECO55ZzjBwOh8vfyecOpSYmJkaPP/64Bg8erFq1akmSSpUqpaioKIWGhmrFihVav369xowZk+YyhgwZoujoaPMWGRmZOU8EAAAAgC1kejAqV66coqKilJCQIOlmKIqMjFT58uVTtI2NjVXr1q3Vrl07vf766+Z0Ly8vlShRQpJUpEgR9erVS+vXr0/zMb28vOTj4+NyAwAAAICMyvRgVKJECQUHB2vWrFmSpAULFsjPz09+fn4u7S5fvqzWrVurVatWeu+991zmnT17Vjdu3JB08/yhhQsXKjg4OLNLBQAAAABJbupKN2nSJE2aNEn+/v4aNWqUpk6dKknq06ePFi9eLEkaN26ctm7dqh9//NEckvuTTz6RJG3YsEHBwcGqWbOmatWqpZIlS+qdd95xR6kAAAAAIIeR3glA/1IxMTHy9fVVdHS027rV+b291C3LzUoRo9paXQIAAADgVhnNBm45YgQAAAAA/yYEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHsEIwAAAAC2RzACAAAAYHtuCUaHDh1So0aN5O/vr3r16mn//v0p2qxatUr169dXtWrVVL16db3zzjsyDMOcv2TJEgUEBKhy5crq2LGjLl++7I5SAQAAAMA9wah///7q16+fDh48qMGDB6t3794p2hQuXFhz587V/v37tW3bNq1du1Zz586VJF2+fFm9e/fWokWLdPjwYZUqVUqffPKJO0oFAAAAgMwPRmfPnlVYWJi6du0qSerYsaPCw8MVERHh0i44OFj33nuvJMnb21tBQUE6evSoJGn58uWqU6eOAgICJEkvvPCCGZoAAAAAILN5ZvYCIyMjVbp0aXl63ly0w+FQ+fLldfz4cfn5+aV6n9OnT2v+/PlatmyZJOn48eOqUKGCOd/Pz08nTpxQUlKSPDxSZrn4+HjFx8ebf8fExGTiMwIAAACQ07mlK53D4XD52/ncoVvFxMTo8ccf1+DBg1WrVq00l5GekSNHytfX17yVK1fuzosGAAAAYFuZHozKlSunqKgoJSQkSLoZiiIjI1W+fPkUbWNjY9W6dWu1a9dOr7/+ujm9fPnyLl3vIiIiVKZMmVSPFknSkCFDFB0dbd4iIyMz90kBAAAAyNEyPRiVKFFCwcHBmjVrliRpwYIF8vPzS9GN7vLly2rdurVatWql9957z2Ve69atFRoaqgMHDkiSJkyYoM6dO6f5mF5eXvLx8XG5AQAAAEBGuaUr3aRJkzRp0iT5+/tr1KhRmjp1qiSpT58+Wrx4sSRp3Lhx2rp1q3788UcFBQUpKCjIHHmuYMGCmjJlitq3b6/KlSvrxIkTGjp0qDtKBQAAAAA5jPROAPqXiomJka+vr6Kjo9129Mjv7aVuWW5WihjV1uoSAAAAALfKaDZwyxEjAAAAAPg3IRgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD23BKNDhw6pUaNG8vf3V7169bR///4UbSIiItSsWTP5+vqqTp06KeZ5enoqKCjIvB05csQdpQIAAACAe4JR//791a9fPx08eFCDBw9W7969U7Tx8fHR8OHDNWfOnFSXUahQIe3cudO8VapUyR2lAgAAAEDmB6OzZ88qLCxMXbt2lSR17NhR4eHhioiIcGlXpEgRPfDAA8qfP/8/fsz4+HjFxMS43AAAAAAgozI9GEVGRqp06dLy9PSUJDkcDpUvX17Hjx+/o+XExMSobt26qlWrlj766CMlJiam2XbkyJHy9fU1b+XKlftHzwEAAACAvbilK53D4XD52zCMO7p/qVKlFBUVpdDQUK1YsULr16/XmDFj0mw/ZMgQRUdHm7fIyMi7qhsAAACAPWV6MCpXrpyioqKUkJAg6WYoioyMVPny5TO8DC8vL5UoUULSzS53vXr10vr169Nt7+Pj43IDAAAAgIzK9GBUokQJBQcHa9asWZKkBQsWyM/PT35+fhlextmzZ3Xjxg1JN88fWrhwoYKDgzO7VAAAAACQ5KaudJMmTdKkSZPk7++vUaNGaerUqZKkPn36aPHixZJuBp6yZcuqU6dO2r17t8qWLashQ4ZIkjZs2KDg4GDVrFlTtWrVUsmSJfXOO++4o1QAAAAAkMO40xOA/gViYmLk6+ur6Ohot3Wr83t7qVuWm5UiRrW1ugQAAADArTKaDdxyxAgAAAAA/k0IRgAAAABsj2AEAAAAwPYIRgAAAABsj2AEAAAAwPYIRgAAAABsz9PqAoC79W8fMp3h0gEAALIPjhgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADbIxgBAAAAsD2CEQAAAADb87S6AAD/Xn5vL7W6hH8kYlRbq0sAAADZBEeMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7bklGB06dEiNGjWSv7+/6tWrp/3796doExERoWbNmsnX11d16tRJMX/JkiUKCAhQ5cqV1bFjR12+fNkdpQIAAACAe4JR//791a9fPx08eFCDBw9W7969U7Tx8fHR8OHDNWfOnBTzLl++rN69e2vRokU6fPiwSpUqpU8++cQdpQIAAABA5gejs2fPKiwsTF27dpUkdezYUeHh4YqIiHBpV6RIET3wwAPKnz9/imUsX75cderUUUBAgCTphRde0Ny5c9N8zPj4eMXExLjcAAAAACCjMj0YRUZGqnTp0vL09JQkORwOlS9fXsePH8/wMo4fP64KFSqYf/v5+enEiRNKSkpKtf3IkSPl6+tr3sqVK/fPngQAAAAAW3FLVzqHw+Hyt2EY/3gZ6RkyZIiio6PNW2Rk5B0/HgAAAAD78szsBZYrV05RUVFKSEiQp6enDMNQZGSkypcvn+FllC9fXqtWrTL/joiIUJkyZeThkXqO8/LykpeX1z+uHQAAAIA9ZfoRoxIlSig4OFizZs2SJC1YsEB+fn7y8/PL8DJat26t0NBQHThwQJI0YcIEde7cObNLBQAAAABJbjhiJEmTJk1Sjx49NGLECPn4+Gj69OmSpD59+qhdu3Zq166d4uPjValSJcXHxys6Olply5bV888/r5EjR6pgwYKaMmWK2rdvr4SEBNWoUcNcBgAAAABkNrcEo6pVq2rTpk0ppk+ZMsX83cvLS1FRUWkuIzlAAQAAAIC7uWXwBQAAAAD4NyEYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA23NLMDp06JAaNWokf39/1atXT/v370+13dSpU1WlShVVqlRJ/fr1U0JCgiQpIiJCnp6eCgoKMm9HjhxxR6kAAAAA4J5g1L9/f/Xr108HDx7U4MGD1bt37xRtwsPD9d5772nDhg06fPiwTp8+ralTp5rzCxUqpJ07d5q3SpUquaNUAAAAAMj8YHT27FmFhYWpa9eukqSOHTsqPDxcERERLu3mz5+vJ598Uvfcc48cDocGDBiguXPn3tVjxsfHKyYmxuUGAAAAABmV6cEoMjJSpUuXlqenpyTJ4XCofPnyOn78uEu748ePq0KFCubffn5+Lm1iYmJUt25d1apVSx999JESExPTfMyRI0fK19fXvJUrVy6TnxUAAACAnMwtXekcDofL34Zh3Ladc5tSpUopKipKoaGhWrFihdavX68xY8ak+XhDhgxRdHS0eYuMjPyHzwAAAACAnWR6MCpXrpyioqLMgRQMw1BkZKTKly/v0q58+fIu3euOHTtmtvHy8lKJEiUkSUWKFFGvXr20fv36NB/Ty8tLPj4+LjcAAAAAyKhMD0YlSpRQcHCwZs2aJUlasGCB/Pz85Ofn59KuY8eO+vHHH3XmzBkZhqGJEyeqc+fOkm6ep3Tjxg1JN88fWrhwoYKDgzO7VAAAAACQ5KaudJMmTdKkSZPk7++vUaNGmaPN9enTR4sXL5Yk3XvvvRo2bJgaN26sSpUqqUSJEubodRs2bFBwcLBq1qypWrVqqWTJknrnnXfcUSoAAAAAyNMdC61atao2bdqUYvqUKVNc/u7bt6/69u2bol2HDh3UoUMHd5QGAAAAACm45YgRAAAAAPybEIwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2B7BCAAAAIDtEYwAAAAA2J6n1QUAAO6e39tLrS7hH4kY1dbqEgAAkMQRIwAAAAAgGAEAAAAAwQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7RGMAAAAANgewQgAAACA7Xm6Y6GHDh1S9+7dde7cORUqVEjTpk1TtWrVUrSbOnWqRo0apaSkJLVo0UITJkyQp+fNkpYsWaL//Oc/SkhIUM2aNTV9+nQVKFDAHeUCAHDX/N5eanUJ/0jEqLZWlwAA2YJbjhj1799f/fr108GDBzV48GD17t07RZvw8HC999572rBhgw4fPqzTp09r6tSpkqTLly+rd+/eWrRokQ4fPqxSpUrpk08+cUepAAAAAJD5wejs2bMKCwtT165dJUkdO3ZUeHi4IiIiXNrNnz9fTz75pO655x45HA4NGDBAc+fOlSQtX75cderUUUBAgCTphRdeMOelJj4+XjExMS43AAAAAMioTO9KFxkZqdKlS5td4hwOh8qXL6/jx4/Lz8/PbHf8+HFVqFDB/NvPz0/Hjx9Pc96JEyeUlJQkD4+UWW7kyJEaNmxYZj+VdNH1wHqsA+uxDqzHOrAe68Ba//aujNK//z3EOrAe6yBzuKUrncPhcPnbMIzbtru1za3LSM+QIUMUHR1t3iIjI++gWgAAAAB2l+lHjMqVK6eoqCglJCTI09NThmEoMjJS5cuXd2lXvnx5l+51x44dM9uUL19eq1atMudFRESoTJkyqR4tkiQvLy95eXll9lMBAAAAYBOZfsSoRIkSCg4O1qxZsyRJCxYskJ+fn0s3OunmuUc//vijzpw5I8MwNHHiRHXu3FmS1Lp1a4WGhurAgQOSpAkTJpjzAAAAACCzuaUr3aRJkzRp0iT5+/tr1KhR5mhzffr00eLFiyVJ9957r4YNG6bGjRurUqVKKlGihDl6XcGCBTVlyhS1b99elStX1okTJzR06FB3lAoAAAAA7rmOUdWqVbVp06YU06dMmeLyd9++fdW3b99Ul9GuXTu1a9fOHeUBAAAAgAu3HDECAAAAgH8TghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9T6sLAAAAuFsRo9paXQKAHIIjRgAAAABsj2AEAAAAwPYIRgAAAABsj2AEAAAAwPYIRgAAAABsj2AEAAAAwPYIRgAAAABsj2AEAAAAwPYIRgAAAABsj2AEAAAAwPYIRgAAAABsj2AEAAAAwPY8rS4AAAAA/14Ro9paXQKQKThiBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbC9Tg1FcXJy6dOmiypUry9/fXwsXLkyz7ZYtWxQUFCR/f3+1aNFCp06dMuf5+fkpICBAQUFBCgoK0nfffZeZZQIAAACAC8/MXNjnn38uLy8vHT58WOHh4WrYsKGaN2+uwoULu7QzDEPPPfecpkyZombNmunzzz/X66+/rrlz55pt5s+fr+rVq2dmeQAAAECOEzGqrdUl5AiZGoy+++47TZs2TZJUsWJFPfjgg/rpp5/Uo0cPl3bbtm2Tl5eXmjVrJknq37+/SpQooRs3bih37tx3/Ljx8fGKj483/46JibnbpwAAAADAhjK1K93x48dVoUIF828/Pz8dP378tu0KFiyoggULunSne+6551SjRg316dNHf//9d7qPO3LkSPn6+pq3cuXKZcKzAQAAAGAXdxSMmjRpomLFiqV6i4yMlCQ5HA6zvWEYaS7Lud2tbdetW6ddu3YpLCxMRYsWVffu3dOta8iQIYqOjjZvybUAAAAAQEbcUVe69evXpzu/fPnyioiIUPHixSVJx44dU5s2bdJslyw2NlaxsbEqVaqUOV+ScufOrUGDBsnf3z/dx/Xy8pKXl9edPBUAAAAAMGVqV7pOnTpp/PjxkqTw8HCtXbtW7dq1S9Gudu3aunbtmtasWSNJmjRpktq3b6/cuXPrypUrunTpktl27ty5Cg4OzswyAQAAAMBFpg6+8Oabb6pXr16qXLmyPDw8NH78eBUpUkSSNHHiRJ08eVIfffSRPDw8NGvWLA0YMEBXr15VmTJlNGvWLEnSmTNn1LFjRyUmJsowDN17772aMWNGZpYJAAAAAC4cRnonAv1LxcTEyNfXV9HR0fLx8bG6HAAAAAAWyWg2yNSudAAAAADwb0QwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtkcwAgAAAGB7BCMAAAAAtudpdQHuYBiGpJtXuQUAAABgX8mZIDkjpCVHBqPY2FhJUrly5SyuBAAAAEB2EBsbK19f3zTnO4zbRad/oaSkJJ08eVIFCxaUw+Gwupw7FhMTo3LlyikyMlI+Pj5Wl2NLrAPrsQ6sxzqwHuvAeqwD67EOrJUTXn/DMBQbG6vSpUvLwyPtM4ly5BEjDw8PlS1b1uoy/jEfH59/7Rswp2AdWI91YD3WgfVYB9ZjHViPdWCtf/vrn96RomQMvgAAAADA9ghGAAAAAGyPYJQNeXl56YMPPpCXl5fVpdgW68B6rAPrsQ6sxzqwHuvAeqwDa9np9c+Rgy8AAAAAwJ3giBEAAAAA2yMYAQAAALA9ghEAAAAA2yMYAQAAALA9ghEAAICTxMREjRs3Ti+99JIk6ciRI1q1apXFVQFwN0+rCwAAZ0eOHNHKlSsVFRWlvHnzqmbNmnrooYfk7e1tdWlAlkpMTNSxY8d07do1c1q1atUsrMg+Xn75Zd24cUMbNmyQJBUtWlSdO3dWaGioxZXZS2JioiIjI+Xn52d1KbAJjhhlEydOnFD79u1Vu3ZtSdLOnTs1duxYa4uyqYULF+r999/X4MGDzRvcb/PmzWrevLkef/xxbdmyRYmJiTp37py+/vprBQQE6IMPPtDVq1etLtMWJk2apLi4OKvLsLXly5erfPnyqlatmpo0aaIaNWqobdu2VpdlGxs3btTkyZPNL2QKFSqk69evW1yVvaxfv14VKlTQgw8+KEkKDQ3V888/b3FVyOkIRtlE//799dRTTykhIUGSVL16dU2dOtXiquxn0KBB+vbbbzVlyhQlJiZq3rx5On/+vNVl2cKYMWP0xRdfaP/+/Zo6dao++eQTjRkzRsuWLdO+fftUunRpzZ492+oybWHt2rWqWLGiXnvtNR0+fNjqcmzpnXfe0caNG3Xffffp/Pnzmj59up5++mmry7KNW49QJyYmKikpyaJq7Gnw4MFau3atihYtKkmqW7euwsLCLK7Kft5//31dunRJhmGobdu2KlasmBYsWGB1WW5DMMomTp8+ra5du8rD4+Yq8fT0lKcnPR2z2sqVK/XTTz+pePHiGjNmjEJDQ3X27Fmry7KFH374QcHBwanOy58/v/r3768+ffpkcVX2NGfOHO3atUuFChVS8+bN1aZNGy1btszqsmzF4XCoQoUK5pdlXbt21aZNmyyuyj4CAwM1e/ZsGYahiIgIvfDCC+aRC2SNhIQEVapUyWVanjx5LKrGvn766ScVKlRIK1askKenp/744w998sknVpflNgSjbMLT01OGYZh/X7x4kW+nLODt7S0PDw85HA7duHFD99xzj06cOGF1WbZ25swZvfrqq1aXYTslS5bUBx98oNmzZ2vv3r3q2rWrAgICtHLlSqtLs4XcuXNLksqWLauff/5Zu3fvVlRUlMVV2ccXX3yhdevW6dSpU6pfv76SkpL06aefWl2WrXh7e+vy5ctyOBySpH379nGuqQWSv7Bfu3atOnXqpKpVq1pckXtxSCKb6NSpkwYMGKDY2FhNmzZNEyZMUO/eva0uy3YKFiyouLg4PfDAA+revbtKlixp7qDAvf7++28NGzZMx44dU+fOndWpUye9//77Gj9+PF2Isti1a9c0Z84cjR8/Xt7e3vrss8/01FNPafv27Xr66acVERFhdYk53quvvqqLFy9q+PDh6ty5sy5dusR5p1kkMTFRH3/8sSZNmqRJkyZZXY5tvffee2rVqpVOnjypHj166JdfftGsWbOsLst28ufPr1GjRmnevHn6448/lJSUlKPPt3MYzocpYKm5c+dq0aJFMgxD7dq1U9euXa0uyXbOnDmjwoULKzExUV988YUuXryoV199VeXKlbO6tByvffv2yps3r5o2baolS5bo4sWLio+P15QpUxQUFGR1ebZSsmRJtWzZUq+88orq1q3rMq9v376aPHmyRZXZx7lz51SsWLHbToN7NG/eXKtXr7a6DNsyDEOnT5/WtWvX9Msvv8gwDD3yyCOqXLmy1aXZzuHDh/X111/rwQcfVIcOHXT48GH98MMPGjJkiNWluQXBCHASEhKiXr163XYaMt/999+vffv2SZLi4+NVvHhxHT9+XIUKFbK2MJtJTEzU7Nmz1a1bN6tLsbVatWqlONE8tWlwjy+++EI3btxQz549VaBAAXN6vnz5LKzKPgzDUHBwsHbu3Gl1KbZnt/0iglE20alTJ7MfbTJfX181bNhQPXr0MPt4wr3YGbHOra8zr7t1GjdurD/++MPqMmwpISFB169fV6NGjbRp0ybz3NPo6Gg1b95cBw4csLhCe3D+zHU4HDIMQw6HQ4mJiRZWZS8dO3bUN998Y45KB2vYbb+Ic4yyiRIlSigsLExdunSRJH333XcqWbKkvv/+e+3cuVP//e9/La4wZ9u2bZu2bNmic+fOacKECeb06OjoHN2XNjs5ceKEyzWjbv2bE5+zTp06dbRp0yY1bNjQ6lJs55NPPtGwYcPkcDiUP39+c7qPj4/eeOMNCyuzFwY/sl7+/PkVHBysxx57zOWoHZ8FWcOu+0UEo2xi9+7dWrNmjby8vCRJ/fr10+OPP66lS5dyfkUWOHHihLZt26YrV664XNncx8dH06ZNs64wG3nhhRfS/RtZZ926dRo/frz8/f1ddki2bt1qYVX28MEHH+iDDz7QwIED9b///c/qcgDLVKpUKcVw3cg6dt0voitdNlG1alUdOHDA7E6XmJio6tWr688//1RwcLB27NhhcYX2sHz5cj366KNWlwFYau3atalOb9q0aRZXAlgj+bINt6IrHezGbvtFHDHKJpo2baq2bdvq+eefl8Ph0MyZM/XAAw/o8uXL5lEkuM8ff/yhxo0byzCMVC9k2aZNGwuqshfnQ/XSzX79JUqUUOPGjVWyZEmLqrInApB1WrRooZUrV6p48eIuO+bJ57hwwemsERsba/5+9epVzZgxI0d3H8qOYmNjNXToUK1YsUIOh0MtW7bU8OHDVbBgQatLs5XNmzerXr165rle586d0/jx4/XBBx9YXJl7cMQom7hx44YmTpyoNWvWSJKaNWumUqVK6amnnrK2MJtIHoK4efPmKeY5HA6tWrXKgqrspWfPnimmnTt3TqGhoZo1a5YefvhhC6qyp3PnzmnYsGHatWuXrl27Zk6nK537nTp1SqVKldKxY8dSnV+hQoUsrgjJmjVrZn5Gw/2ef/555cuXTwMHDpQkffPNN4qNjdXMmTMtrsxegoKCUowOmJN7MhGMspkDBw4oJCREM2bMUJkyZbR9+3arSwIsdeDAAfXs2VObNm2yuhTbaNeunRo3bqypU6dqzJgxmjRpkoKDg/Xxxx9bXZqtXL16Vbt27ZLD4VBgYKDy5s1rdUm2dejQIbVu3VpHjhyxuhTbqFmzpnbt2nXbaXCv1F7z6tWra+/evRZV5F50pcsG4uLi9P3332vq1Kk6cuSIrl69qg0bNuj++++3ujRbmj9/vsuh+w4dOlhdkq0FBAQoPj7e6jJs5fjx41q8eLFmz56txx9/XK1atbJVH/PsYPXq1Xr22WdVqlQpGYahM2fOaO7cuXRzzCLOXRkTExOVkJDA6LBZLDExUbGxsWbXuStXrjBaoAX8/f31xRdf6LXXXpNhGPryyy8VEBBgdVluQzCyWL9+/bRgwQI1adJEb775ptq0aaMqVaoQiiwyePBgrV69Wl27dpUkjR49Wlu3btWoUaMsrsy+rl27xgnPWSxPnjySJC8vL124cEGFChVSVFSUxVXZyyuvvKJFixapfv36km52Y+zdu7f27NljcWX2sG3bNvN3T09PlSxZUrly5bKwIvvp1q2bGjRooOeee04Oh0Pz5s1T9+7drS7LdsaNG6euXbtq6NChcjgceuCBBzRjxgyry3IbgpHF5s6dq9q1a6t///5q3bq1HA5HqiPhIGv89NNP2rlzp9llpV+/fgoKCiIYZYHUBr04d+6cpk2bpo4dO1pQkX1VrVpVFy5cUNeuXdWgQQP5+voqODjY6rJsJX/+/GYokqR69eq5XNcI7sW5XNYbPHiwAgMDtWLFChmGodGjR6t169ZWl2U7pUuX1qpVq3TlyhVJyvHbIc4xstjly5c1b948TZ06VVFRUerWrZtmzJihyMhIq0uzpebNm2vlypXmVc8TExP18MMPa/Xq1RZXlvPdOvCFw+FQ8eLF1apVK/Xs2ZMvDCyyYcMGXbp0SY8++ijfmGehN998UzVr1jSPXs+ePVuHDx/OsSNBZTdhYWEaOnSojh49qoSEBHNUwKNHj1pdmm1cu3ZNXl5e5rY/KSlJ169fl7e3t8WV2cu6detSnf7ggw9mcSVZg2CUjezbt08hISGaNWuWKlWqpK5du3KRyyz22muv6c8//zQP18+cOVO1atVSo0aNJDFsN4CsUbx4cZ0/f968XEN8fLw5XC7DdrtfjRo19NJLL6lhw4YuXwjQzT3rNGrUSMuXL5evr68kKTo6Wm3bttWGDRssrsxe6tata/5+7do1/fXXX6pevbrCwsIsrMp9CEbZUEJCghYtWqSQkJBUuxfBfVIbrjsZw3a716xZs8xvxzdv3qwGDRqY88aNG6dXX33VqtJs49Zr5yTjGjpZL63hupPR1cu9UhuiGFnLbsNE/1ts3bpV06dP1/jx460uxS0IRgCyhVq1apnfQDn/ntrfcA92xoGbXnzxRfXp04dz6ywUGBioTZs2mee0xMbGqmHDhjl2mOh/k4YNG+bYS2gw+AJwizNnzmjfvn0uF7akC537OX9Hc+v3NXx/kzUIPtnHkSNHNGjQoBQX2eWoXdbYuHGjpkyZoqpVq7qc08JFjrPOc889p0ceecS8wOv//vc/RqWzwP79+83fExMTtWXLFsXGxlpYkXsRjAAn06ZN07Bhw3T+/HlVqVJFu3btUoMGDQhGWcC5C9et3bkYeCFrsVNuvT59+mjAgAE6evSoli5dqq+++kp+fn5Wl2UbY8eOtboE23vrrbdUsmRJLV68WJI0cOBAs7s1sk7btm3N3z09PVW5cmVNnz7dworci650gJPAwECtXbtWDz30kHbs2KF169ZpxowZmjJlitWl5XjlypXTkCFDJEkjR440f5ekUaNG6fjx41aVZjvNmzfXgAED9NFHH2nevHnmTvnQoUOtLs02kruP1qhRQ3v27JFhGHrooYcYITMLJSYmKjIykkAK2IiH1QUA2Unu3LlVuHBhJSQkSLo5HKXzYWS4z8MPP6zQ0FCFhoa6/B4aGqoWLVpYXZ6tREdH65lnnpGHh4dq1KihSZMm6ffff7e6LFvJnTu3JKlgwYI6duyY4uPjb3sOGDLP+vXrVaFCBXNI4tDQUD3//PMWV2UP8+bN05EjR8y/X375ZRUqVEi1a9fWn3/+aWFl9rJ///50bzkVXekAJ15eXjIMQ/7+/vrqq69UoUIFnTt3zuqybOHbb7+1ugT8f7fulN9zzz3slGexpk2b6sKFC3rppZdUp04deXl56amnnrK6LNsYPHiw1q5da77mdevWZQCYLDJixAjzxP6lS5dq0aJF+vXXX7Vt2za9/vrrWr58ucUV2kPbtm3lcDhkGIaOHz8uHx8fSTe/OKtQoYLCw8MtrtA9CEaAk+HDhysmJkaffvqpBgwYoEuXLmnChAlWl2ULkZGRKleuXLptTp48qdKlS2dRRfaV2k55p06drC7LVj799FNJ0rPPPqsmTZooOjpa1atXt7gq+0hISFClSpVcpuXJk8eiauzF4XCYI9H98ssv6tmzp+rXr6/69etr8uTJFldnH8nB5+WXX9aDDz5ofgbMnz9f27Zts7I0t6IrHeDkoYcekq+vrypVqqTff//d7NYF9+vSpYv69Omj1atXu5zwHxERoQkTJqh+/fravHmzhRXax6effqoiRYro2WefVVhYmH755Rd9+eWXVpdlKz///LMuXbok6eb5d2XKlNGSJUusLcpGvL29dfnyZXPgl3379rmMTgf3SUpKMn/fsmWLeYH1W+cha4SGhrp8MfbUU09pzZo11hXkZgQjwEmfPn10/vx58+9z586pf//+FlZkHxs2bFCrVq30ySefqEiRIipcuLDy5s2rhx56SOHh4frhhx/UoUMHq8u0laNHj2rBggU5tstEdvbee++pUKFC5t+FChXSe++9Z11BNtGlSxdJ0rvvvqtWrVrp5MmT6tGjh1q0aKGPP/7Y4ursISgoSG+++abGjh2ro0ePqmnTppJkflGArBUXF6f169ebf2/YsEFxcXEWVuRedKUDnGzfvl1FixY1/y5WrJhCQ0MtrMheOnXqpE6dOikhIUHnzp1Tvnz5zH7NcL+WLVvqs88+U1BQkE6ePKk6deqofv36Cg8P1/79+/XWW29ZXaJtORwOvi3PAgcOHJAktWrVSv7+/vrll19kGIbeffddVa5c2eLq7GH8+PF69913tWbNGs2fP1958+aVdPPIRY8ePawtzobGjx+vLl26KH/+/DIMQ9euXdPcuXOtLsttCEaAk8TERJe/DcNQfHy8RdXYl6enp0qWLGl1GbZz4sQJBQUFSZLmzJmjpk2b6scff9TFixfVtGlTglEW8vHx0ZYtW1S/fn1J0ubNm1WwYEGLq8r5nK+ZVrFiRfPiosg6Pj4++u9//6vdu3crMDDQnN6yZUvdc889FlZmT02aNNHRo0f1119/yTAMBQQE5Ojz7QhGgJP69evr1Vdf1eDBg2UYhj777DM1bNjQ6rKALOF8DsXGjRvNCxsXLlxYnp58XGSl0aNHq3379rr//vslSX/++ad+/PFHi6vK+fbs2aMSJUqkmG4YhhwOBxc5zkI9evRIMRJgatPgfrt379bKlSvlcDh048YN1a5d2+qS3IZPOsDJmDFjNGjQIAUHB8vhcKhdu3acdA7b8PDwUFRUlAoXLqy1a9dq1KhR5ryc3Kc8O2rYsKH2799vDlvcqFEjl3OOwsPDVbFiRYuqy7n8/f21bNkyq8uwtXPnzuns2bO6du2a/vzzTxmGIenmMNFXrlyxuDr7mTx5sj7++GPzHN8OHTrovffeU58+fSyuzD0cRvI7DsBtjRs3Tq+++qrVZQBusXDhQg0cOFC5c+dWgwYNNH/+fEk3jx599NFH+uWXXyyuEMlq1arFN+duwOtqvXHjxmns2LEpLs/g6+url19+Wb1797awOvsJDAzUypUrVbx4cUnS33//rRYtWmj37t0WV+YeBCPgDvCh6X4xMTF6//33FR4erp9++kn79+/Xrl27zNGi4F5nzpzRqVOnVLNmTfN8i5MnTyohIUHly5e3uDokCw4O1o4dO6wuI8fhdc0+Pv74Y0ZizAYCAwNThKCaNWtq165dFlXkXgzXDdwBvkdwvwEDBqhYsWI6cuSIpJsnQI8ePdriquwhLi5OBQsWlL+/v65evaq4uDjFxcWpUKFCKlasmNXlwYnzIAHIPIQi6yUPePTGG2+Y2yDnG7JW5cqV9c477+jkyZM6deqUhg0bluLixzkJwQi4A+yMuN+BAwf07rvvKnfu3JKkvHnzEkizSIECBVSwYME0bwDgbskDHiVvjwoUKGDe2A5lvYkTJ+rIkSMKDAxUYGCgDhw4oIkTJ1pdltsw+AKAbOXWYUCvXr1KMMoiydfJGT58uLy8vNSvXz8ZhqEpU6YwKl02w/8Ecqrk7upct8t6iYmJGjNmjObNm2d1KVmGI0bAHWBnxP2aN2+uESNGKD4+XmvWrNEzzzyj9u3bW12WrSxfvlxvvvmmfH19VahQIf3nP/8xB2KA+yUmJmrcuHHptkkeIQrIaVLrPkdXOmvkypVLW7dutbqMLEUwAv6/xMREtWzZMt0206ZNy5pibOzjjz+Ww+FQwYIFNXjwYNWrV0/vv/++1WXZyoULF3T48GHz78OHD+vcuXMWVmQvuXLl0oIFC9Jtw0npyKno0pu9PP744xo9erTOnj1ri4DKqHSAk4cffli//vqrcuXKZXUpgGUWLlyofv36mRfx27Fjh7755huO3GWhjz/+WP7+/nrmmWesLgWwRFpdegcNGmR1abbi4fF/x1AcDod5sePExEQLq3IfghHg5N1339WuXbv0/PPPq0CBAub0Nm3aWFiVvfTp00ejR49W0aJFJd282N8777yjSZMmWVyZvfz999/avHmzDMNQw4YNzWtYIGsUL15c58+fV968eZU/f35zZ+Ts2bNWlwZkicaNG+uPP/5wmfbAAw9ow4YNFlUEO+BsWsBJ8kb4f//7nznN4XAQjLLQ9u3bzVAkScWKFVNoaKiFFdnTiRMnFBsbq2effVaXLl3SqVOnVKpUKavLso1t27ZZXQJgqeQuvZUrV5ZEl96sFhUVpUGDBungwYOqU6eOPv/8cxUpUsTqstyOI0YAspVbLyZnGIaqV6+uffv2WViVvUycOFH/+9//dPnyZR05ckRHjhxR3759tWrVKqtLs5WzZ8/qr7/+UpMmTZSQkKCkpKQUozYCORVdeq3Vtm1bVa1aVS1bttQPP/wgDw8PTZkyxeqy3I5gBDhJTEzU119/rcOHD+urr77SkSNHdOzYMT300ENWl2Ybffv2Vb58+TR48GAZhqHPPvtMV65cscUGObsIDg7Wxo0b1ahRI/OCl9WrV9fevXstrsw+Fi5cqNdff12SFBERoV27dmnIkCFatmyZxZUBWYcuvdapUaOG9uzZI+nmvlHdunXNodRzMkalA5y8/PLL2rt3r1asWCFJKlq0qN566y2Lq7KXMWPGKDY2VsHBwapdu7bi4uL05ZdfWl2WreTJk0d58+Z1mcZ1jLLWiBEjtH37dhUuXFiSVLNmTR07dsziqoCsldylt127dsqdO7dOnTpldUm2kXyRdUm2GpCKTzrAycaNG7Vz504FBwdLkgoVKqTr169bXJW9+Pj4KCQkxOoybK148eI6ePCgHA6HJGnmzJkqV66cxVXZi4eHh8u5dlLKix8DOZlzl95nn31W58+fp0tvFgoPD9fTTz+d5t/ff/+9FWW5HcEIcOLt7e3yd2JiIlfftsCWLVt05MgRJSQkmNO6detmYUX2MnbsWD377LP666+/5Ofnp3z58unnn3+2uixbKViwoM6cOWOG09WrV5tHjwA7mDRpkjZv3qxGjRpJkipVqsSojFlo7NixLn+3bdvWmkKyGMEIcBIYGKjZs2fLMAxFRERo5MiRevDBB60uy1YGDhyoX3/9VUFBQebhe4fDQTDKQpUrV9bmzZv1119/yTAMVa1a1VZdKbKD0aNHq02bNgoPD1ezZs106NAhwilshS691tq2bZu++uorzZo1S127drW6nCzDOwxw8sUXX+iNN97QqVOnVK9ePT3xxBP69NNPrS7LVlasWKH9+/enOHqHrNO+fXstWrRI9913X4ppyBp16tTRqlWrtHHjRhmGoUaNGqlQoUJWlwVkGbr0Wmv9+vWSbu4XEYwAmypQoIAmTZrExUQtVKpUKUKRxY4fP55i2pEjRyyoxN4uX76s6OhoORwOxcXFEYxgK3TptVbZsmVVo0YNhYeHq169einmb9261YKq3I9gBDi5ceOGxo0bpxUrVsjhcKhly5Z66aWXOOk5CzVq1EhPP/20Onfu7BKQuMiu+02ePFnffPONDh486PJBGB0drapVq1pYmf3MmzdPL7/8spo0aSLDMPTyyy/r66+/djn5GcipEhMTtWvXLrr0WujHH39UWFiYunbtqs8++8zqcrIM1zECnPTq1Uvnz59Xr169JEnffvutihYtqqlTp1pcmX00b948xTSHw8FIRFng2LFjCg8P18CBAzVx4kRzuo+PjwIDA9kpyUIBAQFavny5KlasKOnmtYxat26tAwcOWFwZkDUaN26sP/74w+oybO/PP/906Vad03HECHCyefNm7du3z+zT/Nhjj6lGjRoWV2Uvq1evtroE26pQoYIqVKigP//80+pSbK9YsWJmKJIkPz8/FStWzMKKgKxVp04dbdq0SQ0bNrS6FNvau3evPvvsM/PCrrVq1dKbb76p6tWrW1yZ+xCMACelS5fWtWvXzJFwrl+/rrJly1pclf2cOXNG+/bt07Vr18xpdKXLOufOndOwYcO0a9cul3WQU/uUZ0ctW7bU8OHD1adPHxmGoZCQELVv315xcXGSpHz58llcIeBe69at0/jx4+Xv768CBQqY09kOZY1NmzapXbt26tWrlz7++GMZhqFNmzapefPmWrx4cY4NrHSlA5z069dPW7du1TPPPCNJ+uGHH/Tggw/K399fkvTCCy9YWZ4tTJs2TcOGDdP58+dVpUoV7dq1Sw0aNNCGDRusLs022rVrp8aNG2vq1KkaM2aMJk2apODgYH388cdWl2YbHh4eac5zOBxKTEzMwmqArBMTE6MLFy7o2LFjLtOPHDmiokWL6oknnrCoMntp3ry53nvvPT300EMu01etWqVhw4Zp7dq1FlXmXgQjwEnPnj3TnOdwOBQSEpKF1dhTYGCg1q5dq4ceekg7duzQunXrNGPGDE2ZMsXq0mwjKChIO3fuVGBgoHbv3q3r16/r0Ucf1cqVK60uDf9feHi4S1c7IKcYMGCAWrZsqY4dO7pMnzlzpjZt2qQJEyZYVJm9VK1aVX/99dcdz/u3oysd4OTbb79Nd/64ceP06quvZlE19pQ7d24VLlxYCQkJkqQHH3xQb7/9tsVV2UvyKIxeXl66cOGCChUqpKioKIurgrOOHTua/f6BnGTdunUug78ke/755zV69GgLKrKn5M/g1Ny4cSMLK8laaR+rB5DC9OnTrS4hx/Py8pJhGPL399dXX32lxYsX69y5c1aXZStVq1bVhQsX1LVrVzVo0ED169dXcHCw1WXBCZ09kFOlN/pl8sBIcL969erp888/TzH9s88+U926dS2oKGtwxAi4A+yMuN/w4cMVExOjTz/9VAMGDNClS5foOpHFZs6cKUl69dVXVadOHV28eFGPPvqoxVXBGTuIyKkSEhIUExMjHx8fl+nR0dE5+khFdjNmzBg1a9ZM8+fPV8OGDeVwOLRx40adP39ea9assbo8t+EcI+AO1KpVi+4rsI3r16+7dKdgJLTsg20RcqqPPvpI27dv17Rp01S4cGFJ0sWLF9W7d28FBgbqww8/tLZAG4mLi9PcuXNdhuvu0qVLjv4s4IgRgGzhdkeFGBEw63z33Xd6/fXXdfr0aUk3j5QyElr2wneayKneeecd9erVS+XKlVOVKlUkSYcOHVLHjh313nvvWVydvXh5eWnNmjVmLwI7IBgBd4CdEfcJDQ1Ncx7dhrLW22+/rUWLFql27drpDhsN63To0MHqEgC3yJUrl6ZPn67333/f5UhFpUqVLK7MfnLlyqUTJ05YXUaWoisdcIurV69q165dcjgcCgwMNC/2Kkm7du1SzZo1LawOcL/GjRvrjz/+sLoMW4uOjtbQoUO1cuVKORwOPfzwwxo+fLh8fX2tLg2AjYwYMUKRkZHq2bOny4V2q1WrZmFV7kMwApysXr1azz77rEqVKiXDMHTmzBnNnTtXTZs2tbo0WwkLC9POnTt17do1cxpd6bLOd999p3379ql9+/by9vY2p+fUD8LsqH379ipTpoz69esnSZo6daqOHz+uRYsWWVsYAFtJ7XppDodDR48etaAa9yMYAU5q1KihKVOmqH79+pKkrVu3qnfv3tqzZ4/FldnH6NGj9d133+n48eNq2rSpfv/9d7Vo0UI//vij1aXZxueff673339fxYoVM4fOzckfhNlRjRo1Umx3UpsGAMg8dB4HnOTPn98MRdLNcfzz589vYUX2M3PmTG3cuFFly5bVggULFBoaal5wFFnjq6++0pEjR3T8+HGFh4crPDycUJTFypcv73L9rnPnzqX6zS0AuNtPP/1kXlz35MmTOfoLGoIR4KRJkyaaNWuW+ffs2bO5fksW8/b2lre3t5KSkmQYhqpWraqIiAiry7IVPz8/lSpVyuoybK1AgQKqWbOm+vfvr/79+ysoKEiFCxfW4MGDNXjwYKvLA2ATH374oSZOnKipU6dKutl7YMCAARZX5T6MSgc4mTZtms6fP6++fftKkuLj41W0aFF9/fXXcjgcOnv2rMUV5nz58uXTjRs3FBQUpLfeektly5ZVXFyc1WXZSv369dWlSxd16tTJ5RyjNm3aWFiVvVSrVs3lnK7kbRIAZKVFixZp+/btqlOnjiSpVKlSio2Ntbgq9+EcI8DJsWPH0p1foUKFLKrEvvbu3auKFSsqLi5OQ4cO1cWLF/Xuu+8qKCjI6tJso3nz5immORwOrVq1yoJqAABWadCggTZv3qzg4GDt2LFDkhQYGKjdu3dbXJl7cMQIcELwsV716tUl3Tzfa/LkyRZXY0+rV6+2ugTbul03uU8//TSLKgGAm/tFGzZskMPhUFJSkkaMGKEaNWpYXZbbEIwAScWLF0/3IqJ0oXO/q1evatq0aSpcuLCefvppDR48WL/++qsCAgI0duxYlSlTxuoSc7zw8HBVrFhR+/fvT3U+w3W7X/JgL4cPH9a6devMC7n++OOPeuSRR6wsDYAN/fe//1X37t21d+9e5cuXT02aNNHs2bOtLstt6EoH6P+60E2ZMkUXLlxQv379ZBiGQkJCVKZMGb311lsWV5jzde3aVdHR0bpy5Yo8PDzk5+enjh07atWqVTpw4IB+/vlnq0vM8R577DEtWbLEdtetyI4effRRzZo1S0WLFpUkXbhwQc8//7yWLl1qcWUA7CguLk5JSUkuF3nNiQhGgJOmTZtq7dq1LtMefPBBrVu3zqKK7KNatWrav3+/rl27ppIlS+rChQvy8Lg5cGb16tW1d+9eiysEsk5q73n+DwBYYf78+VqxYoUcDodatmxpHsnOiRiuG3By8uTJFNcOOXXqlIUV2YeXl5ekm8N1V6xY0QxFkriOURZr3759hqbBfe677z716dNHmzZt0qZNm9SvXz8FBARYXRYAmxk8eLBGjx6t++67TwEBARo9erTefvttq8tyG84xApwMGjRIQUFBatu2rSRp2bJlGjp0qMVV2UN8fLz+/PNPGYbh8rskXbt2zeLq7OX48eMpph05csSCSuxr6tSpGjZsmF566SUZhqEWLVro888/t7osADbz008/aefOncqbN68kqV+/fgoKCtKoUaMsrsw96EoH3GL37t1au3atDMNQ8+bNc/ToK9mJn59fmgNgcH5L1pg8ebK++eYb/fnnny4DLURHR6tq1apavHixhdUBALJa8+bNtXLlSrMXR2Jioh5++OEcO3opwQgAIOnmICTh4eEaOHCgJk6caE738fFRYGCgcuXKZWF19pLasN2+vr5q2LChHnroIQsqAmAny5YtkyT9/vvv+vPPP9W9e3dJ0syZM1WrVi0NHz7cyvLchmAEOElt2O7knZFPP/1UJUuWtKgywBrR0dGKjIw0ry+FrNGtWzdt2LDBPLfrp59+0gMPPKCdO3fq6aef1jvvvGNtgQBytNQu9J0sJ1/wm2AEOHn//fcVGxurnj17yjAMzZgxQwULFpTD4dC2bdsYMhq20Lp1a82bN0+enp5mIOrWrZs++ugjiyuzj4cffljz589XoUKFJEmXLl1S165dNXfuXNWrV09//vmntQUCQA7E4AuAk19++UVbt241/x4zZow5hPf9999vYWVA1jlz5owKFSqk77//Xk888YQ+//xz1a5dm2CUhU6ePGmGIkkqVKiQIiIiVLBgQXl7e1tXGADbOXz4sI4ePaqEhARzWps2bSysyH0IRoCTS5cu6fz58+ZFFc+fP28O182Q0bCLGzduSJLWrVun1q1bK3fu3GkOjAH3qFatmvr166eePXvK4XBo2rRpqlq1quLj4znXC0CWeeONNzRr1ixVrVrV3PY4HA6CEWAHr7zyioKCgtSmTRs5HA4tW7ZMgwcP1uXLl9W4cWOrywOyRPXq1dWqVSv99ddf+vTTTxUXF0cwymIhISH66KOPzOG6mzdvrtGjRytXrlxavny51eUBsImffvpJ4eHhypcvn9WlZAnOMQJusWfPHq1Zs0aGYahZs2YKDAw058XHx5sXIgVymgMHDiggIEDXrl3T4sWLVbduXVWsWFEnTpzQtGnTOOE/G3nrrbc0evRoq8sAkMO1aNFCv/76qzw97XEshWAE3IFatWopLCzM6jIAt3B+f9/6Xue9n72wPgBkhZ07d2rIkCFq2bKly/mNL7zwgoVVuY894h+QSfgeATmZ8/v71vc67/3shfUBICuMHDlSp06d0s6dO13OMcqpCEbAHcjJGwPA+f1963ud9372wvoAkBXCwsJ08OBB22xzCEYAAEnStWvX9Oeff8owDJffk+cBAOwlICBAV65cUYECBawuJUsQjIA7QPcV5GRxcXEuQ7A6/26Xbwv/LdgWAcgKBQsWVO3atdWqVSuXc4w+/fRTC6tyH4IRcAcaNGhgdQmA20RERFhdAv6/SZMm6fnnn09ziNzffvstiysCYEdVq1ZV1apVrS4jyzAqHeCkQYMGeuWVV9SpUyflzp3b6nIA2NSzzz6rlStX6tlnn9WLL76oypUrW10SAOR4BCPAya+//qrx48dr+/bt6tWrlwYMGKAyZcpYXRYAGzp9+rQmTZqkKVOmqEaNGnrppZdy7NXmAWRPH330UarT33///SyuJGt4WF0AkJ20atVKixcv1h9//KH4+HjVrl1bnTp10h9//GF1aQBspmTJkvrggw80e/Zs7d27V127dlVAQIBWrlxpdWkAbCI2Nta8/f3335o+fbr++usvq8tyG84xAlIRGxur6Oho5cmTR6VKldKLL76oBx54QF9//bXVpQGwgWvXrmnOnDkaP368vL299dlnn+mpp57S9u3b9fTTT3M+GIAs8dlnn7n8/eGHH6pv374WVeN+BCPAybx58zR+/HhdunRJr7zyisaOHau8efMqMTFRlSpVIhgByBJ+fn5q2bKlJk6cqLp165rT69Wrp5YtW1pYGQA7K1q0qI4cOWJ1GW7DOUaAk8cee0yDBg3Sww8/nGLezz//rMcff9yCqgDYSWJiombPnq1u3bpZXQoAm5swYYL5e2JiorZs2aJTp07l2C69BCPg/0tMTFSPHj00c+ZMq0sBYHONGzfm3EYAluvZs6f5u6enpypVqqT+/furcOHCFlblPnSlA/6/XLly6cSJE1aXAQCqU6eONm3apIYNG1pdCgAbWrdunSTXYJRsz549evDBB7O6pCzBESPAyYgRIxQZGamePXuqQIEC5vRq1apZWBUAuwkODtaePXvk7+/vsi3aunWrhVUBsAvncxuTORwOnTx5UqdOnVJiYqIFVbkfwQhwUrFixRTTHA6Hjh49akE1AOxq7dq1qU5v2rRpFlcCANKFCxc0fPhwzZo1S6+88oreffddq0tyC7rSAU7Cw8OtLgEAdOTIEfXq1ctlWkhICMEIQJa6du2avvzyS40dO1ZdunTR/v37VaxYMavLchsu8Ao4ad++fYamAYA7pXZpAC4XACCrJCUl6ZtvvlGVKlW0b98+bd68WWPHjs3RoUjiiBHg4vjx4ymm5eTx+gFkL9u2bdOWLVt07tw5l2Fyo6Ojdf36dQsrA2An1atXV3x8vEaOHKlatWrp6tWr2r9/vzk/p557TTACJE2ePFnffPONDh48qHr16pnTo6OjVbVqVQsrA2AnJ06c0LZt23TlyhWFhoaa0318fDRt2jTrCgNgK3FxcXI4HHrvvffkcDjkPCRBTj73msEXAEnHjh1TeHi4Bg4cqIkTJ5rTfXx8FBgYqFy5cllYHQC7Wb58uR599FGrywAAWyEYAQCQDW3ZskVHjhxRQkKCOa1bt24WVgQAORvBCHASERGh0aNHp9gZWbVqlYVVAbCbF154Qb/88ouCgoLMI9YOh0Pff/+9xZUBQM5FMAKc1KtXTy1atFDDhg1dus+1bdvWwqoA2E2VKlW0Z88eeXt7W10KANgGgy8ATq5du6aRI0daXQYAmytVqhShCACyGMEIcFK9enVFRUWpbNmyVpcCwMYaNWqkp59+Wp07d3YJSG3atLGwKgDI2ehKBzhp3bq1tm7dqgceeMBlZ4R+/QCyUvPmzVNMczgcnO8IAG5EMAKcTJ8+PdXp3bt3z+JKAAAAkJUIRgAAZDPr1q1LdfqDDz6YxZUAgH0QjAAnvXr1SnV6SEhIFlcCwM7q1q1r/n7t2jX99ddfql69usLCwiysCgByNgZfAJzUrl3b/P3atWtasGCBgoODLawIgB2Fhoa6/L1169Y0u/oCADIHR4yAdMTFxempp57SsmXLrC4FgM01bNhQmzZtsroMAMixOGIEpCNv3ryKiIiwugwANrN//37z98TERG3ZskWxsbEWVgQAOR/BCHAyePBg8/fExERt27ZN1apVs7AiAHbUtm1b83dPT09VrlyZrnQA4GYEI8BJ/vz5zd89PT01cOBAdezY0cKKANhReHi41SUAgO1wjhEAANnQtm3btHLlSjkcDrVo0cJlcBgAQObzsLoAIDuJjY3Vyy+/rPvuu0/VqlXTK6+8Qr9+AFlu8uTJ6tChg06dOqWTJ0+qQ4cOmjJlitVlAUCOxhEjwMnzzz+vfPnyaeDAgZKkb775RrGxsZo5c6bFlQGwk8DAQK1cuVLFixeXJP39999q0aKFdu/ebXFlAJBzcY4R4GT37t3atWuX+feECRNUs2ZNCysCYFfJoSj5d4fDYWE1AJDz0ZUOcJKYmOjSde7y5ctKSkqysCIAdlS5cmW98847OnnypE6dOqVhw4apUqVKVpcFADkaR4wAJ926dVODBg303HPPyeFwaN68eerevbvVZQGwmYkTJ+qVV15RYGCgHA6HHn74YU2cONHqsgAgR+McI0BSTEyMLly4ID8/Py1fvlwrV66UYRiqVKmSunbtKh8fH6tLBAAAgBsRjABJAwYMUMuWLVNcs2jmzJnatGmTJkyYYFFlAOwkJCRECQkJ6tevn8v0cePGydfXVz169LCmMACwAYIRIKlatWrav39/qvOqV6+uvXv3ZnFFAOyoTp06WrFihQoVKuQy/cKFC2rVqpVCQ0OtKQwAbIDBFwBJuXLlSnMeI0EByCo3btxIEYokqUiRIrpx40bWFwQANkIwAiQlJCQoJiYmxfTo6Gh2RgBkmcuXL6c5j4tNA4B7EYwASV26dNHzzz+vixcvmtMuXryonj17qnPnzhZWBsBOateurZCQkBTTp0+fruDgYAsqAgD74BwjQDevX9SrVy8tWLBAVapUkSQdOnRIHTt2VEhISLpd7QAgsxw9elQPPPCAHnzwQTVs2FCStHHjRq1bt04bNmzgWkYA4EYEI8DJkSNHFBYWJkmqVasWOyEAstyZM2c0fvx4bd++XdLNo0gvvPCCSpYsaXFlAJCzEYwAAPiXef755zVz5kyrywCAHIVzjAAA+JfZt2+f1SUAQI5DMAIAAABgewQjAAAAALZHMAIAAABgewQjAAD+ZbiEAABkPoIRAADZ0E8//aTRo0dLkk6cOKE9e/aY80JDQ60qCwByLIbrBgAgm/nwww+1ZcsWHTlyRAcPHtSpU6f01FNP6Y8//rC6NADIsThiBABANrNo0SItWbJE+fPnlySVKlVKsbGxFlcFADkbwQgAgGzG29ub84gAIIt5Wl0AAABwVaFCBW3YsEEOh0NJSUkaMWKEatSoYXVZAJCjcY4RAADZzJkzZ9S9e3etWrVKHh4eatKkiWbPnq0SJUpYXRoA5FgEIwAAsqm4uDglJSWpQIECVpcCADkeXekAAMiGDh8+rKNHjyohIcGc1qZNGwsrAoCcjSNGAABkM2+88YZmzZqlqlWrmoMwOBwOrVq1yuLKACDnIhgBAJDNVK5cWbt371a+fPmsLgUAbIPhugEAyGYqVKigPHnyWF0GANgKR4wAAMhmdu7cqSFDhqhly5by9vY2p7/wwgsWVgUAORuDLwAAkM2MHDlSp06d0s6dO13OMQIAuA9HjAAAyGaqVKmigwcPEoYAIAtxjhEAANlMQECArly5YnUZAGArdKUDACCbKViwoGrXrq1WrVq5nGP06aefWlgVAORsBCMAALKZqlWrqmrVqlaXAQC2wjlGAAAAAGyPI0YAAGRDCxcu1M6dO3Xt2jVzGl3pAMB9GHwBAIBsZtCgQfr22281ZcoUJSYmat68eTp//rzVZQFAjkYwAgAgm1m5cqV++uknFS9eXGPGjFFoaKjOnj1rdVkAkKMRjAAAyGa8vb3l4eEhh8OhGzdu6J577tGJEyesLgsAcjTOMQIAIJspWLCg4uLi9MADD6h79+4qWbKkcufObXVZAJCjMSodAADZzJkzZ1S4cGElJibqiy++0MWLF/XKK6+ofPnyVpcGADkWwQgAAACA7dGVDgCAbCYiIkKjR4/WkSNHlJCQYE5ftWqVhVUBQM7GESMAALKZevXqqUWLFmrYsKFy5cplTm/btq2FVQFAzkYwAgAgmwkMDNTu3butLgMAbIXhugEAyGaqV6+uqKgoq8sAAFvhHCMAALKZCxcuKDAwUA888IC8vb3N6d9//72FVQFAzkYwAgAgm+nSpYu6dOlidRkAYCucYwQAQDaSmJio1q1b6/fff7e6FACwFc4xAgAgG8mVK5cMw1BSUpLVpQCArdCVDgCAbKZBgwZ64okn9Pzzz6tAgQLm9DZt2lhYFQDkbHSlAwAgm2nevHmKaQ6Hgwu8AoAbEYwAAAAA2B5d6QAAyGbWrVuX6vQHH3wwiysBAPvgiBEAANlM3bp1zd+vXbumv/76S9WrV1dYWJiFVQFAzsYRIwAAspnQ0FCXv7du3arp06dbVA0A2APDdQMAkM3Vq1ePo0UA4GYcMQIAIJvZv3+/+XtiYqI2b96s2NhYCysCgJyPYAQAQDbTtm1b8/fcuXOrUqVKdKUDADdj8AUAALKpI0eOaPHixapcubIef/xxq8sBgByNc4wAAMgmWrZsqZ07d0qSTp48qbp16+q3337T4MGDNXr0aGuLA4AcjmAEAEA2ceLECQUFBUmS5syZo6ZNm2r58uXauHGjZs+ebW1xAJDDEYwAAMgmvL29zd83btyoNm3aSJIKFy4sT09OCwYAdyIYAQCQTXh4eCgqKkpXrlzR2rVr1bRpU3NeXFychZUBQM7H108AAGQTQ4cOVe3atZU7d241b95c/v7+km4ePfLz87O2OADI4RiVDgCAbOTMmTM6deqUatasKYfDIenmQAwJCQkqX768xdUBQM5FMAIAAABge5xjBAAAAMD2CEYAAAAAbI9gBAAAAMD2CEYAAAAAbI9gBADINHPmzNHYsWPdsmw/Pz/16NHjtu0iIiLUtm1bFSlSRA6HQ4MGDXJLPe58rgCArMd1jAAAmWbOnDnau3ev28JIRrz22mvasmWLQkJCVLJkSZUqVcotj5MdnisAIPMQjAAA/1hcXJzy5ctndRmSpL1796pevXpq37691aXclez0WgKAndCVDgBs4O+//1a/fv1Urlw5eXl5qXjx4mrcuLFWrFjh0i4kJEQ1a9aUt7e3ihQpoieffFJ//vmnS5sePXqoQIEC2rNnjx555BEVLFhQLVq0ULNmzbR06VIdO3ZMDofDvCW7fv26hg8froCAALOGnj176u+//3ZZ/o0bNzR48GCVLFlS+fLl0wMPPKCtW7fe9jmuWbNGDodDhw8f1vLly83Hj4iIkCTFxMToP//5jypWrKg8efKoTJkyGjRokK5cueKynPHjx+vBBx9UiRIllD9/ftWoUUOffvqpbty4YbZJ77km17FmzRqX5UZERMjhcGjatGm3fS3v5PVatWqVmjVrpqJFiypv3rwqX768OnbsqLi4uNu+ZgCA/8MRIwCwgeeff15hYWH65JNP5O/vr0uXLiksLEznz58324wcOVJDhw5Vly5dNHLkSJ0/f14ffvihGjZsqNDQUFWpUsVse/36dbVr1079+/fX22+/rYSEBJUtW1b9+vXTkSNH9OOPP7o8flJSkp544gmtX79egwcPVqNGjXTs2DF98MEHatasmbZt26a8efNKkvr27asZM2boP//5j1q2bKm9e/eqQ4cOio2NTfc51qpVS5s2bdKTTz6pSpUq6fPPP5cklSpVSnFxcWratKmioqI0dOhQBQYGat++fXr//fe1Z88erVixwgw2R44c0bPPPmsGqF27dumTTz7RgQMHFBISIkmaMGFCms/1TqX2Wmb09Uo+n6pJkyYKCQlRoUKFdOLECf3yyy+6fv06R54A4E4YAIAcr0CBAsagQYPSnH/x4kUjb968Rps2bVymHz9+3PDy8jKeffZZc1r37t0NSUZISEiK5bRt29aoUKFCiulz5841JBkLFixwmR4aGmpIMiZMmGAYhmH8+eefhiTjtddec2k3e/ZsQ5LRvXv32z1Vo0KFCkbbtm1dpo0cOdLw8PAwQkNDXabPnz/fkGQsW7Ys1WUlJiYaN27cMGbMmGHkypXLuHDhwm2f6+rVqw1JxurVq12mh4eHG5KMb7/91pyW1muZ0dcruf6dO3emWj8AIOPoSgcANlCvXj1NmzZNw4cP1+bNm126hUnSpk2bdPXq1RSjvpUrV04PPfSQVq5cmWKZHTt2zPDjL1myRIUKFdLjjz+uhIQE8xYUFKSSJUua3c5Wr14tSXruuedc7v/000/L0/PuOzksWbJE1atXV1BQkMvjt2rVKkW3tx07dqhdu3YqWrSocuXKpdy5c6tbt25KTEzUwYMH77qG9Nz6Wmb09QoKClKePHnUr18/TZ8+XUePHnVLfQBgBwQjALCB7777Tt27d9eUKVPUsGFDFSlSRN26ddPp06clyexSl9oIbqVLl3bpcidJ+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-<h3 id="plotting">Plotting<a class="anchor-link" href="#plotting">¶</a></h3><p>Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="plotting">Plotting<a class="anchor-link" href="#plotting">&#182;</a></h3><p>Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset</p>
+
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<span class="notice" hidden="">Copied!</span>
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-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">distributions_in_binary_cls</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-6">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">distributions_in_binary_cls</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">cont_cols</span><span class="p">],</span>
     <span class="n">target</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span>
     <span class="n">low_per_cut</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
     <span class="n">high_per_cut</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-6">fig = distributions_in_binary_cls(
+<div id="cell-6" class="clipboard-copy-txt">fig = distributions_in_binary_cls(
     df=df_pd[cont_cols],
     target=df_pd[target_col],
     low_per_cut=0,
     high_per_cut=1,
 )</div>
+    
+        </div>
+    </div>
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UgYvW68Xq9YtGhRzjYNDQ1GTwm32y02b95csK0AxFFHHZXzvIUQ4tZbbzW2eeWVV4q2uZArr7zSOMZ1112Xs17TNHH66acb2zz44IM527TXC6ojSult+sMPPwi32230winU41HvsSvLsli+fHnO+kI91nSPP/640Zb33nsv7zalvs+7ukekfn59/vnnBY918cUXG++tBQsW5N3GfC6eccYZRZ9DPl9++aXRnnvuuafgdvrPQpKknB5PmqaJFStWFH2cG264wdg/38+tu3pEHnPMMQKAGDp0qPjuu+/yHuOzzz4zendnv6euv/56o/dcsZ53zc3NQlXVom3Ntq3fA+ZevsOHD8/bE/iDDz4wekrutddeOes72yNSV+rPSYjSv7c0TRMTJ04UAMSuu+6a9/NWCCHeeusto7fjo48+mrEuHo8b75NC31WLFy82vqtK/T7Nx/waAcjbc15RFHHkkUca28yYMSNjfTAYNM7BCy64oOBjXXLJJQKAsFqteX/e7TH/vI877jixePHidktra2veY91+++3Gse644w5j+fTp043l11xzTbvt6Mx5Z3bvvfcKAMJms4k333wz7zaNjY1il112EUCqV2q2yy+/vOjPL5FIZFwBU0q7iIio83iPSCKiImpra416U1NTyftt2LDBqB900EEFt5Mkqct6acmyjEcffRQOh6NLjnf99ddj5513zlm+yy674NprrwWQ6jn573//u0ser1xeffVVY+Tna6+9FrvttlvONsOHD8c999wDIHXPsuzePGZOpxNPPPFE3t6Ol1xyibF89uzZHW5rPB437u03bty4vPdzlCQJDz74oHFuTp8+vcOP09X0nqD19fV4+OGHYbXmv+Di5ptvxuDBg6FpGv7xj3/krB89enTRxznnnHOMQaRee+21bW53V7vqqquw66675l23ZcsW42d7yy23YK+99sq73fDhw3H99dcDAF544QVEIpEOtWHChAlGr9B//vOfBbd77rnnAAAHHngghg0blrFOkiTsuOOORR/nhhtuQF1dHYQQOb2/ustXX32FGTNmAEi9D0aNGpV3u9133x3Tpk0DkOq5a6Z/Vo8ZMwYDBgwo+FiBQCCjR1gpuvJ74L777sPAgQNzlh9yyCG44IILAACffvopFixY0KE2VsLMmTPx5ZdfAgD+8Y9/oK6uLu92P/nJT/Dzn/8cAHI+k1977TWjd1uh76rx48cb31VdZeLEibj66qtzllutVjz66KNGr83sAe58Ph9+8YtfACj8vk4kEsY9GY866qi8P++O+Pe//218HhQrhc6Zq6++2jhvb7jhBixcuBBff/01/u///g8AsOeee2bcQ7gcFEUxes9ffPHFmDJlSt7tqqurje/wOXPm4NtvvzXWxeNx4/wp9POz2Wx47LHHOCAbEVE3YRBJRFSE1+s16qFQqOT99EsxAeQd4KIc9t9//3YHfiiVJEmYOnVqwfX6ACgA8N5773XJY5aL3j5Jkope6nbSSScZl5cVe06HH354wcubfT6fEabpl1Z3xMKFC42BSs4++2zjkutsfr8fJ598MoDUJbal3KS/nPQw+thjj4XT6Sy4ndVqNS5JLDbgEZC6PHjDhg345ptv8NVXXxlFv4RPv6y4J8kesMHs7bffNgZM0X92hei//CuKgoULF3a4Hfql1p9//nnegUzmzp2LlStXtttmnX4Z8fLly42fw7Jly4zLviv1s9DPO7fbjaOPPrrotvprum7dOqxevdpYrn9WL126FPPnz+/S9nXV90B1dXXG7RWymT/XevrnMdD2c9tpp50wceLEotvqP7cFCxZkDARj/lwv9buqK0ydOrVgID1kyBAcccQRAFKDsmQPXKPfuiIYDOKVV17J2f+NN97Ali1bAFTusmwz/bLrqqoqKIqC008/Haeddhqi0Sjcbjf++c9/lj24mz9/vvH9VurnJpD5/bJw4ULjD8ml/vyIiKi8GEQSERVhDh/9fn/J+40cORIHHnggAOCPf/wjdtllF9xwww344IMPOtzDqVTt/ULXESNHjizYSwUA+vXrZ4SeX331VZc9bjno7RsxYkTR+yPa7Xajt12x5zR27Niij1dTUwOgY8G1zvy4++67b9Ftzesr+TNoaWkxep/87W9/yzuyrrm89NJLADJ7i5nNnDkTxxxzDAKBAAYNGoSddtopo/fOzJkzAcD4hb2n8Hq9BXvkAanearpBgwYVfY3Gjx9vbFvodSrm1FNPNcKXfL0i9WV2u93ocZZNCIFnnnkGhxxyCLxeLwYPHoyxY8dm/Cz0+89W6mehv6aRSARWq7Xoa3rMMccY+5lf01NPPRU2mw3xeBz7778/jj32WDz88MNYsmRJp0fJ1nXV98Duu+9esJcxAOy2225GT+ye/nkMtP3cli9f3u7nxcUXXwwg1VuwsbHROIZ+L7+OfFd1hb333rvoev0enZFIJOePUZMmTcIuu+wCILeHp3nZgAED2g3WSzF16lQIIdot+n018xk2bJhxj+Dly5cb96+8//77sdNOO21zG9tj/tycNGlS0XPF/Idj83vcfN/HUn9+RERUXgwiiYiKMP+CrQdMpXruueeM3l9Lly7FH/7wBxx66KGoqqrC5MmT8fDDDxs9pLpCVw7EUcqAJvpljOZfDnsivX3FLrvU6ZfCFXtObre76DH03hbZvWFKYX7c9tprvmyvkj+DTZs2dWq/7CBGCIHzzz8fxxxzDGbOnNlukBuNRjv1uOVSVVVVdH1XvU6lGDp0qNE7SL/UU5dMJvHiiy8CAKZMmZL3cy0Wi+Hoo4/GmWeeiY8++qjd17pSP4uueE3Hjh2L5557DtXV1Ugmk5gxYwYuuugijB8/Hv3798eZZ57Zqdss6Lrie6C9z2Or1Wr8HHv65zHQNT83vYdbR76rukJ7j2d+rHw/C71X5IcffoiGhgZj+fr16/Gf//wHAHDWWWcVDZ672ymnnIJDDz3UmD/ssMPwy1/+slseuyvPFaBjPz8iIiqfnvMtR0TUA+l//QfQ4b/+Dx48GHPnzsX777+PV155BbNmzcLSpUuhKAo+/vhjfPzxx7j33nvx5ptvYsyYMdvc1kKX8XZGKZeybWtvoe7W255Te+3tKW01B66XXXYZzjvvvJL2y77H5uOPP47HHnsMQKqH12WXXYZ9990XgwcPhtvtNs7vs846C08//XSPef669t5/+utkt9s7dLl1oVGv23P66adj1qxZWLlyZcYove+8847xB5ZCl2XfdttteOuttwAAkydPxrRp07DHHntg4MCBcLlcRth+0EEHYfbs2RX7Weiv6ciRIzt0n0rziLwAcOKJJ+Kwww7DCy+8gLfffhuzZ8/G5s2bsWXLFjzzzDN45plnMHXqVDz++OMdvk9kV3wP9LbPrvboP7f9998fDz/8cMn7mUdW1p9vd7822/q5fOaZZ+J3v/sd4vE4nnrqKdx4440AUvfK1F+XnnBZttnnn3+eEcZ/8cUX2LhxY7eEdubvl48++ijjvt3FmANH88+kt3yvEhH1dQwiiYiKePfdd436AQcc0KljHHrooUZvgq1bt+K9997D3//+d3zwwQf47rvv8Itf/CIj8OwJNm7c2O42ek+F7B5V5l/UNU0ruH84HO5k6zpGb18pl7jqz7ujvV+7ivlxN2zYUDSgNv+MKtVeIHNAp0gkknFZcUc88sgjAIAddtgBc+fOhcvlyrtdRwaNKqa7z1P9dUokEqitrc24f2A5nHTSSfjNb36DeDyOf/7zn0YQqV+W7fP5Mi5X1gkhjEF1DjjgAHzwwQcFw7dt+Vnoxyz22gPFX3/9Nd24cSPGjh27Tb3IAoEALrzwQlx44YUAUr0XX3/9dTzwwANYt24dnnrqKey+++649NJLO3X8bfkeaO/zOJlMGj+LSn4WlKq2thYbN27E5s2bO/15oT/PjnxXdYWNGzcW/Vw2P1a+n0VtbS2OP/54vPDCC3jyySdxww03QJIk4x6ikyZNavf2H90pFovh9NNPRyKRgMfjQTQaxebNm3HeeecZA0WVk/n7xW63d+p8Mf8cOvLzIyKi8ukVQaR+k3Sfz9elN5wmou2H+ZfZeDyOYDDY7j5Lly7F+++/DyDVK2nMmDEF9yv1mDabDVOmTMGUKVNw6qmn4s0338Tnn3+ORYsWYYcddjC20385V1W1pOOW2gbzJZShUChne733wcqVK9HQ0FDwl9otW7YYl5Vlvy7mz+m1a9cWbJN+f7lCbTdfrpivrWbxeNyoZ283ZswYzJs3Dw0NDfjuu+/Qr1+/vMdQFMUIAsaOHdvpn7X+GnbkZ6cz99SaNWtW3hG+dXPmzDHqw4cPz3gs82XN0Wi0w+0wK/baAoDD4UB9fT3WrVuHd955By0tLZ36rl6yZAmA1OXCiqJAUZScbYQQRm9CIUS750Sx9eYejKtXry54nzlzOJTvtdTb2V57zOHCv//9b5x22mkFt+0Ksizj8MMPx4wZM/DCCy/glltuQSKRMAYK+elPf5r3dW5sbDRC+5/+9KdobW3Ne/zW1lYsX74cQP5zvb1z0OFwAACam5uLvm7m+7tlfw6MGzcOb775JiKRCN55551O/7EonyFDhuDXv/41fvrTn2KfffZBOBzGc889h3POOWebj13q94DeO2vRokVobGwsGLQuWrQIiUQCALDjjjt26LOgve+EZDJptKW9z5FSv7fGjx+PpUuXGgNRZY/aXoqddtoJn3zySYe+qzrzmQxkvkazZ8/GrrvuWnBbfZAUt9uNurq6vI936qmn4oUXXkBDQwNmzpwJh8OBr7/+2li3LZ/X5p+3oijbdCwA+L//+z8sXboUAHDvvfdi+fLl+NOf/oSZM2fi/vvvNy41L9aOfOddqd/v5tDwjTfeMO6x2RHme/eW+vNrr11ERL2ZEAKhUAj19fUdvtKjq0iiF/RBX7NmDYYOHVrpZhAREREREREREfVqq1ev7vQtgLZVr+gR6fP5AKReqI6MWktEpJs9e7ZxGeLvfvc7XHPNNQW3/frrr3HhhRfiiy++AJC6PPG1116DzWbL2TYQCOQ95pdffgmg8EjWQgiceuqpeOuttyBJEr7++uuMwUemTZuGZ555Bv369cOKFSuK9jAr1IZ8/vnPf+LXv/610cbhw4dnrD/66KONnnZVVVV47733MHr06Ixtli9fjsMPPxwtLS0YOHAgFi9enHO/v/333x9fffUV+vfvj//97385vVX++9//4rjjjjN6Y+Vr+3//+18cddRRAIB//etfOOKIIwo+rzvuuAN33nkngNQozmaKomDChAlYv349fD4f3nrrLUyYMCFjmzVr1uCwww7D+vXr4Xa78dVXX+Xci6rU11l/DQ844ABjhOeOuO666/DAAw8AAK666ipce+21GeuFELjooovw3HPPAQDuu+++nF4pq1atMs69Bx98sOC9AEtR7LXVNTQ0YJ999kE8Hkd1dTVeeeUV7LHHHgWP+c4776C+vj7jMrv99tsPS5YsQX19PebOnZsz+NL333+Po446CuvXrweQGs3V3FtON3HiRKxatQrHH388nnrqqYJtSCaT2HHHHdHU1IRx48bho48+Mnrp6V566aWMe17mey0vuugiPPvsswXbY3bppZcal2CeccYZ+POf/1ywl9vmzZvx5ptvYurUqUWPWUw8Hsfo0aPR0tKCk08+Gc3NzXjnnXcwYMAALFu2LO99LTVNw4gRI9DS0oIJEybggw8+yHl/L1y4MKO3ZL5zvb1z8IsvvjAG1Jk6dSr+8pe/ZKwXQuDyyy/H448/bizL95n1s5/9DB988AEA4Oqrr8bvf//7gq/HqlWrsGDBgoyRwmfMmIEDDjig4GBDa9aswV577YVoNIpDDz0Ur7zySsHjZ9vW74EJEybghx9+AJA6399///2cwTbmzJmD4447DslkErvtthtmzZqV85yL/Rza+0646667cPvtt0OWZfzwww/G/8nzKfV7S1VVTJo0yRg1+y9/+QvOOuusgsddunQpVq1ahSlTphjLEokEJkyYgA0bNhT8rvr6669xxBFHGJ9bnf1MNr9GAHDzzTfjsssuy9gmmUzilFNOMW7n8sILL+AnP/lJwWPec889uPXWW43734ZCIZx++ul48MEHO9w+M/PP+7TTTjNGvO6orVu3YtKkSdi4cWPOZ/I333yDgw46CNFoFLvtthvee++9nP8ftXfedeT7/b777sMtt9wCADj88MPx1FNPwePx5N02FArhueeeM26xoLv22msxffp0AKX9/ID87wcior4gGAxi6NChRb/Ty070Ai0tLQKAaGlpqXRTiKiX+vDDDwUAAUBcdNFFYvHixUb55JNPxFtvvSXuuusuccQRRwhJkoxtf/SjH4nNmzcXPK6+3Y033pix/IknnhAAxN577y1uueUWMWPGDPHpp5+KefPmiWeffVYcfvjhxr7HH398znEfeeQRY/1ll10mPv30U7FixQqxYsUK0dDQUFIb8tHbBUCsXLkyZ/3kyZMFADF69GgRCAREVVWVuOOOO8S8efPEvHnzxB133CECgYBxjJdeeinv4zz88MPGNuPGjRPPPfec+Oyzz8R7770nLrvsMuFwOMSkSZOKtj0YDAqn0ykAiD322EO8/fbbYvny5cbrEIlEjG1vvPFG41j5zJgxw/i5er1ecfPNN4s5c+aITz75RNx///2if//+xv4PPvhg3mOU+jrrr+HkyZOLbldIMBgUo0aNMh7vZz/7mXjjjTfEwoULxUsvvSQOPvhgY92kSZNEMpnMOcbKlSuNbZ544olOtUPX3murM59bdrtdnHfeeeLVV18VCxcuFP/73//Eyy+/LK6++mqxww47CADijTfeyNj/nnvuMfYfO3asePzxx8X//vc/MWvWLHHjjTeKQCAgnE6n2GOPPQQAMXz48LztOP300wUA4XA4xMMPPywWL15snDMbN27M2PZ3v/ud8Zj77befeO2118Rnn30m3nzzTXHOOecIWZYzztN8r+XUqVOLtscsFAqJ8ePHZ7w3/vSnP4nZs2eLRYsWiQ8//FBMnz5dHH/88cJut4s999yz3WO257zzzhMAhMfjETabTQAQv/3tb4vuM23aNKON++yzj3juuefEggULxHvvvScuv/xy4XQ6RV1dnRgzZkzBc72Uc/BHP/qRsc3UqVPFBx98IBYuXCief/554zw3v/75PrPWrl0rBg0aZGyz7777ir/97W9i7ty54rPPPhPvvvuuuO+++8Thhx8uLBaLOPHEEzP2nzx5snC73eKkk04SDz30kPjoo4/EokWLxAcffCDuvvtuMXToUOPYr732WqkvuxBi278Hhg8fLgCIXXfdVdhsNjF48GAxffp0MX/+fDF79mxxzTXXGJ+RVqtVfPLJJx3+ObT3nfDuu+8a60877TQxb9488c033xjvKbOOfG99+eWXwuv1GtsfeeSR4qmnnhKffPKJWLhwoXjrrbfE7bffLvbbbz8BQFxxxRU5bXvppZeM/c3fVXPnzhW33367CAQCIhAIiNGjR2/TZ7L5Ndprr70EAHHqqaeKt956yzhf99lnH2ObY489tt1jrl27VlgsFmMfAOLjjz/uVPvMzD/v4447LuP/OoXK0qVLc45z3HHHCQBCkiTx3nvv5ayfPn268Ti///3vi7Yj33nXke/3ZDIpDj30UON4w4YNE7fffrv48MMPxaJFi8THH38sHnnkEXH66acLj8cjamtrcx6vublZDBkyxDiG+ef33HPPib333tt4rxZ7PxAR9QU9IV9jEElE2wVzEFlK6devn7jtttuEoihFj9teENleOeCAA8TWrVtzjhsKhTLCKHPJDjxKDciy21UsiJw8ebKYMWOGcLvdedsgy7K49957Cz6Oqqri+OOPL/i8x48fL9atW9du26+66qqCx/jwww+N7UoJy5588knhcDgKHs9isYjbb7+94P6lvs7bGkQKkfolbuzYsUXPnf333z/vuaPv314IVKpSg0ghhHj++eeF3+9v97yXZVl88MEHGfsmEglxxBFHFNzH5XKJF198sd3gb9GiRQV/zlOnTs3YNhwOZ4Rh2WXy5Mli8eLFRV/LjgSRQgixdetW8ZOf/KSkz4dDDjmkpGMW88EHH+Qc99NPPy26T3Nzs9htt90KtqumpkbMmjWr6Lleyjm4bNmyjD8CZJfLL7+83c8sIYRoaGjICBCKlXPOOSdjX/05FCvtfTYUsq3fA3oQOXXqVPHII48Iq9Wad3+73S6ee+65vG3Y1iBSVdWi7xGzjnxvCSHEF198YYSE7ZWbb7457/O75557hCzLefdxu91i5syZ2/yZbH6NPvvsM7H77rsXbOf+++8vgsFgScc95phjjP3GjBnTqbZlM/+8Sy2BQCDjGH//+9+NdcX+aDFlyhQBpD7PZ8+eXbAdhd7/pX6/CyFEJBIRZ511VknPZ+TIkXkf76uvvhIDBw4s+tlQyucNEVFv1xPytcrcmZKIqIeQZRmBQADDhg3DgQceiMsuuwwvv/wy1qxZg9///vedHoX1tNNOw4cffojf//73OPDAAzFy5Ei43W7Y7XYMGTIEP/3pT/Hss89i1qxZeW+y7/V6MXfuXFx66aXYeeed4Xa7t/WpdtjRRx+NTz/9FOeccw6GDx8Ou92O/v3748QTT8ScOXNwxRVXFNxXlmW89NJL+Otf/4q9994bHo8HHo8HEydOxG233Yb//e9/JY0afOedd+KRRx7BgQceiJqamryXkpZq6tSp+Prrr43X1OPxwOVyYYcddsAFF1yARYsWtXtpe3cZMWIEvvjiC0yfPh2TJ09GbW0tbDYbBgwYgJ/85Cd4+umn8fHHH/e4EXJ/8YtfoKGhAXfeeScOPvhg9O/fHzabDW63G6NGjcKxxx6L+++/Hw0NDTjkkEMy9rXZbJg5cyb+8pe/YK+99oLb7YbL5cKOO+6IX/3qV/jss89w0kkntduG3XbbDfPmzcOpp56KYcOG5VxubeZ2u/HBBx/gtttuw4QJE+ByueD3+7H33ntj+vTpeP/99+H1erf5dTGrqanBW2+9hffffx/nnHMORo8eDa/XC6vVipqaGuy9996YNm0a3nzzzYzLBDtr8uTJGff/GTNmDPbcc8+i+wQCAfz3v//FH/7wB0yYMAFOpxNerxc777wzrrzyyozLqrfF2LFj8dlnn+Giiy4yPmP69euHn/zkJ5g5cybuu+++ko4zfPhw/O9//8Orr76KU045xfi8tdls6NevH/bbbz9cccUVmDVrFh577LGMfV988UX885//xNlnn43ddtsNAwcOhNVqhdfrxfjx4/HrX/+6058N2/o9YHb++edj9uzZOPnkk1FfXw+73Y7BgwfjrLPOwqJFi3DKKad0uH2lkGUZ77zzDq677jrsuuuu8Hq9BS+57uj31sSJE7F06VI89dRTOP744zF06FA4nU7Y7XYMGjQIBx98MK677josXLgQN9xwQ95jXHnllZg9ezZOOOEE9O/fHw6HA8OHD8e5556LTz/91Lj8t6tUV1dj7ty5uOOOO7DbbrvB5/PB6/Vi7733xgMPPIBZs2aVfKnbmWeeadS7YhCkrrBixQr89re/BZC6NcAdd9xRcNsnnngC/fr1g6ZpOPPMMzs8sEtHvt9dLheeeuopfPrpp7jooouwyy67IBAIwGq1oqqqCrvtthvOO+88vPTSS1i2bFneY+yyyy5YsmQJrrrqKowePRoOhwN1dXU45JBD8Oyzz2bcBoKIiMqrVwxWEwwGEQgE0NLSwntEEhEREVGfN2LECKxatQpTp0417i1Kfcf111+PW2+9FRaLBatXry7pj3NERETbqifka+wRSURERERE1E1UVTUG05oyZQpDSCIi2q4wiCQiIiIiIuomL7zwAlavXg0A+NWvflXh1hAREXWvzt38jIiIiIiIiEry7bffIplM4tNPP824D2NX38eSiIiop2MQSUREREREAFKXDSuKAkVRkEgkkEgkkEwmoSgKVFVFMpnMW8zrVFWFqqrQNC1v3Vw0TcsoQgioqgohRN51mqYBgDFvrptLR0mSBFmWS5paLBZYrVZYrda8dfMym80Gu92OI444IuPxbDYbrr/+eqxbtw52ux12u90YrKfQgEBERER9AYNIIiIiIqIeLJlMIhaLIRqNIhaLZdSzlyUSCcTj8bwle10ikTBCRz141IM+Kg99VPYRI0bgr3/9K/76179mrJckCU6nE06nEy6XCy6Xy5jXlzmdTrjdbrjdbng8Hng8HqNunnq9XjidTsgy78ZFREQ9B0fNJiIiIiLqQqqqIhqNIhKJIBKJIBwOIxqNIhwOIxKJZKzT583L9Lq+XFGUij0XIVkAWU5NJQmQLIAkQ0hyel4GJAuEbJ6XIaDXJQCpads+bcsgSeltJaDANHM9kP6nbZlRR2pb03xpTzL165AEYdRh1AUg2uZT22hGkUTmfGqZXheQNBUQKqCpRl3SUvOpejI1LdOvZJIkGaGkz+czitfrzbvM5/PB7/cjEAjA6/WydyYRUR/TE/I19ogkIiIiou2aEAKKomSEf9nBYXt1c4nFYuVpJyTAYoWQrYBsgzDXZatpnQVCSk/lzClkK4Q+leTUMsmSWZctRqDYoUCPOk9o6bBSAdSkaZpZh5aEpCqpuppI1VUlXc+aR+oy9XA4jHA4jI0bN3aoSbIsG6Fk9lSvV1VVobq6GlVVVaiqqoLL5SrTC0RERH0Fg0giIiIi6jU0TSt6mXKhoq/PDhv18FBV1S5vq5AkQLZDWGxGgcUGIWfWoa+XbekwMXvbVNjIYLAPk2TAIqd+7rZ0J8xtIUQ62ExASiZSwWQyngors6ZIJiCpcUjGNA5JS0LTNDQ3N6O5ubnkh3U6nUYoaQ4pq6urUV1djZqaGtTW1qKmpgY+n489LomItkMMIomIiIioy+mBYfblx+bAMDs8zF6Xbz4ej5e13UKypENAPRC0ZwWFplAxvd4IDbO2gX45M1F3k9K9Zy1WCJu74/tryVQgmYylp/nqMUiKaSpUxGIxbNiwARs2bGj3IaxWK2pqajLCSfN8XV0d6urqUFVVBauVv7YSEfUV/EQnIiIiIoMQAvF4HK2trQiHw2htbc0o+jL9cs/sex2a62VtJ5C+zLgtNGwLD62mdanLlvVehakA0ZobLMpWwJLudUi0vZOtEHYrhN1T2vZCpMJLJWoEk3Iyagoqo0aRlQgkNTUa+6ZNm7Bp06biTZHlnHCytrYW/fr1M6Z1dXW8pyURUS/BIJKIiIioj4rH42hpaUFLSwuCwWBGCYVCaGlpQSgUylnelZcpp+5rmHmJsXlqvt9hxtQcHmbc/9DK3oZEPY0ktYX6SA1+UPRTRFNzw0ljPpKaT6SWaZqGLVu2YMuWLVi+fHnBQzqdTvTr169oCQQCDCuJiCqMQSQRERFRL6FpGlpaWtDY2IimpiY0NzejqakJLS0txry+rLm5GZFIpNOPZQSIVjuExZG+HNkOWOzpZfolyfY89zTk5clEVIRsgXB4IRxeAEVCS6GlelWmg8lUYBmBlAgbYaWciEBS44jFYli9ejVWr15d8GFtNhv69euH/v37550yrCQiKj8GkUREREQVFolE0NjYmFO2bt2aMd/U1ARN0zp0bCFJEFZnKky0thVYnalAMXudxZ5aL1sZIBJRZUkyhN0NYXcDniKBpZqEpIRToWQiDDkRhqSE00FlOLUsGYOiKFi3bh3WrVtX8CHtdntOOJmvZ6Us8zYORESdwSCSiIiIqAyEEIhEItiyZYsRKuarb9mypcP3U9SsTgibE8LqSgWJNieELV1Pz2vpeVjsDBSJqG+zWCEsAajOQOFtNDV12bceTKan5rqcjCGRSGDt2rVYu3ZtwUPZbDbj/pR60e9fab6Ppd1uL8OTJSLq3RhEEhEREXWAEAKtra0Fg0VzPRaLlX5cOTW6rWZzpUJFo5iXuVPhInviEBF1jGyBcPigOnyFt9FU02Xf2WFlJNXLUolCUZSSRgf3+/3GoDp1dXVGXR8lvLa2FtXV1bDZbF38ZImIei4GkURERERIDezS1NSU9xLp7JJIJEo+rrDYoNncqRBRDxbt2XV3asRmIiKqHNkC4fRDdfoLb5MeaCf38u9IeqCdMKREFJJQjUHAvvvuu6IP6/f7UVNTY4ST5ml1dbVR/H4/LBZLFz9pIqLuxSCSiIiI+iQhBMLhMJqbm9HY2JgxiEtTU1NOPRQKdez4FnsqYLTrvRZNYaNdn3cxYCQi6kvSA+2o6YF28hICUOPpXpSmgXaMsNI0SrjQjMCyoaGh+EPLMgKBAKqqqjICyurqamOZvj4QCMDj8XDgHSLqcRhEEhERUa+QTCYRDAbR3NyMlpaWjGJeptebm5uhKEqHHkNIsnFJtGb0YHTlXQaZ/40iIqI8JAmwOqFZnQBqCm+nB5ZKNB1SpsLJjKBSiUJKxiAlY9A0zfjj2cqVK9tths1mQyAQMMJJvZjDSr/fb2wTCARgtfK7jYjKi58yRERE1K00TUM4HEYoFDJ6geilpaXFWN7S0pKxLhwOd+rxUvdedEFYXdDMg7rYXG0Dvtic0GxuDuxCRETdxxxYuqqLbyu0VCCpRCEpsbaAUolCNi9Ph5aSloSiKNiyZQu2bNlScpM8Hk9GMNle8Xq9DC+JqEP4iUFEREQdJoRAPB5HKBRCKBRCa2urESCa6+Zl+nxrays0Tevc4wIQVkfb6NBWRzpMdGYus7YFjrDwvztERNTLSXL69h/u0rZXk22hZDKWGVIa9XjmFEA4HEY4HMa6detKbprP5+tweClz0DWi7Rb/Z05ERLSdEkIgFotlBInmunmZPm8OEzt62XPO48tWCIvdFCw6TMU0b0nNw+qAsNoBib+8EBERFWWxQli8EMXuZWkmNEBNQFLiqR6W5oBSiUFS4+kAM94WaKqpgdv0/yesWbOmpIeSZTnjknD9MvHsefOl5A6Ho7OvBBH1MAwiiYiIejF9QJZ84WH2sux1XRImQoKwOgCLHcJqT4eGdlOA2FZghI6pdZA58icREVGPIMlA+soCFYHS9hFaOpjM6lmZjEFS4pk9MvV1qgJN09Dc3Izm5uaSm+d0OvPe5zJfCQQCcLlcHKiHqIdiEElERFRhyWTSCAaLhYbZvRP10tnLnHUFw0TTFFaHqfei3QgZIVt5T0UiIqLtkWmAt5JpamZgaVwqHs9/GXkyDkloiMVi2LBhAzZs2FDSwzgcjrwhpT6yuD7SuL6MPS6Jug+DSCIioi6QSCSKhobFeilGIpFtfnwhWUyhoT0VGmb0TkwtywgU01OGiURERNQtZAuE3Q1hL/Fel0IAqpIRUqYuHY/mv++lEoMkVMTjcWzcuBEbN24s6WFcLpcRTmaHldlTji5OtG347iEiIkpTVTUjNNTvh1joHonmeiwW2+bHF7I1IyAUFnvb5czZ4WFW70XI/EonIiKiPkaSUn9gtdoh4AcAqMW2FwLQkm0jjJvDS9N8xlSoiEajiEajJQ/S4/f7CwaV+rSmpgZVVVXweDy8TJzIhL+1EBFRn6Nf6tzS0mKEhea6OWA011tbW7f5sfWehzmBYp4wUaTDRFg4CAsRERHRNpMkwGKDsNggHL72txcC0JRUKKlE2wbqUfQel6ael0o0dak4BILBIILBIFatWtXuQ9hsNiOgLBRWmtextyX1dTzDiYioR4vH42hpaTGK/h8/vW5ergeO4XB4mx5TyLaseyEWv09iWy9FG8NEIiIiot5CklJXmVjsEE4/2r3rtjFAT8wILzN7X6bDTH2dpkBRFGzevBmbN28uqUk+n88IJosFljU1NRyUh3olBpFERNRtksmkER42NzdnBIyFlsXj8U4/njCP0pw9inPWiM5t91Z0ADLDRCIiIiLKYh6gp5QxerRkTmAp670szSGm3usSwrhS54cffmj38Ha7vWhgaV4XCARgsVi2/TUg2kYMIomIqNP03orNzc1GKTTf0tKCUCjUqccRkgRhdaZDQ6ep7sit6z0XeakzEREREVWSbIVweCEc3va3FSLd29IcVGZeIi6bl2lJJBKJkgflkSQJgUCg4KXh2QP1uN1u9raksmAQSUREhng8nhMiNjU15a03Nzd3arRnAWSFig4ImzMrYHRmrINs46jORERERNR3SVLq/8Q2Z2m9LfXRxNM9K2UlT4Cp97pMxgAhjP/jNzQ0tHt4/d6W5qIHlfp8IBAwitfrhcyriqgEDCKJiPooIQQikUhGD8X2ei92ZuTntt6KTlOg6MoMFzOCRvZUJCIiIiLaJlmD8hQfTTx9b8ucy8HTPS2T0cxRxbVkh+9tKcsyfD5fRjipF7/fD6/XC5/PB5/PZ9S9Xi88Hg8DzO0Mg0giol4iFotlDNISDAYz7qmY7/6KiqJ0+HGEJOeGhzZXxjLNVIfFzt6KREREREQ9lfnelqVQk0ZPSnNAKWcv0wfuURVommb8DtIRsizD4/EYwaTX64XL5YLb7Ybb7TbqLpcrZ7nL5YLNZoPdbjemet1qtVb80nIhBDRNg6qqGVMhRN4CwFivs1qtRrFYLEap9HPbFgwiiYi6WTweRygUQjAYRGtrqzHas77MHDSa5xOJRKceT8iW/D0Tbamei1rW8tTIz733i42IiIiIiLaBxQphKfHelgCgqZDUOCQl3hZgmkcXV+OQkgkgPdXnJZEK5vTfhbpadjgpSRJkWS461et6IKhPVVXNmNc0LWNddtGDx3Ixh5NWqxU2mw1utxsejwculwsej8cIbfXi8XgyQs5KYRBJRNRBqqoiEomgtbW15GIOG7dpFGhJzhqkJeueirbseyw6AQs/6omIiIiIqExkC4TshrC5O7afloSkJlIhZTKeqqsJSKoCaAokVUnXk6Z6ermmAGoSECokTU1dfi60jMMrigJFURAOh7vwyZZHKh6UACk9Tct+TrpkMolkMtnhx+nMPl2Nv50S0XZBCIF4PI5IJIJoNJox1Us0GkVraysikQjC4TDC4bAROJqXRaPRbW8PJAirA7DY24JFcz17MJd0HbKVvRWJiIiIiKj3k60QsrXjAWYhQgBCTfXQFBqgqRlBJYSAEfnpdaOHoKkutPTvXFLOVBi/i2Wtk+T0ulS94DLo8+ljAMV/vzPaqRnPQTLqWlsAq6k5Aa6kJlKDGhnBrQLRtKZrXuttwCCSiHoUVVURj8cRi8UySjweRzQaNeb1ejQazSjZ68who6bl/2tSZwnZkgoPLXqgaE/Pp+ow1YXFYQocHbz8mYiIiIiIqCtJEiBZUwGnaXHlL0beBuYgMy37+XTk+dmWzuyKVm0TBpFE1C4hBJLJJGKxGBKJhBEUllKPx+NG0efNy83L9H3L/nxkK4TFlhppTk6NNgfZlg4R0/NGPRUmGqPSGXU7IFvK3lYiIiIiIiKivoJBJFEvJISAoigZIV920cNA87we+uVb1958V/cmLOl5prvq6132YdHnbekwMV23tC1LhYvmdamwUQ8SeWkzERERERERUWUwiCTqQpqmFez9V2iar8dge8vi8XjFRrsSQFswKFuMKSQrhCU9lS2mbdKhoGTJDBMlSzostEJIlvRyW/qYtvQxGRgS0XZOaJASPf8G60REPZmwezIuayQiosphEEl9nn5ZcXZvP/0y4HyXCeebby9I7K7LinOeH6SMQFAP/1L17GAwaypZMsNEOTtEtKS3aVvedpNdIiIqNykRhvfLf1W6GUREvVrrxJMgHL5KN4OIiMAgcrshhEAsFqt0M8oiHo/jL3/5C9atW2cEjeZLixOJREV6DwrIpkuKLaZLjLODP723oCUjSBQZYaKpN6Gkh4tWQJa7NxjUR+YiIqLuoSYr3QIiot5PTQKqUulWEBFVXoWurDTrVUHkc889B5fLVelm9EqKouDpp5+udDO2KxI0QEtA0hIAf48kIiKiPu6YY47BySefjBdffBEzZ86s2G1kiLJ5l7xa6SYQEfUIyWTlw4leFUQ+/fTTsFp7VZOJiIiIiLYLJ598MoYNG4aTTz4ZM2bMqHRziIiIqAdiqkcVJUHAIgEWCZClzLpVSl15bJEErOmpRYJRlyXAKrftY00v490LiYior4glJczf4qh0M4hK8uKLLxo9IiVJYo9I6jH2qYvDaeX5SES0aFPlE5NeFUTuuuuusNls0DQtbxFCQFVVCCEylhWbZm8HwJjPXkZdT0BCUgBJAXRFhCihLbS0yoA1HVam6rlTiwzYTOttctv2tvR2tqz9bXLbdjbTepsk2upZ28iVf68TEVEvtDkqM4ikXmPmzJmYMWMGQ0jqcX4xOoJ+Lv4+R0R0Z7TytzvsVUHkHXfcAb/fX7HHzw4nVVXNWF6sAG0Bp76PfpzsurkUWl5KyRe8mtuRPc0OcfOFvNn1fFO9FFquqiqSyWTGtNA6c11RFKiqCkVRjHXZ/8kVkKBogAIJUMt8QnSARTKHlm11W05omV4mCdgsmdtY08vsGftlBp/61G4+VnobhqFERL1PjUPDfZOaKt0MopI0xyVsiVlQ51RR5WAQST1HjYMhJBFRT9GrgshKkyQJkiRBlmUA4P0qewBzcGkOKPW6oigZdfMy87pEIpGzXC/Z6/LN68vMU3NIqgoJqgrE1MqlgVYpM5xsCzoF7Flhpl3ODULtedbrgafdkrmN3dJ2TKtcsadMRNTrWWSwFw/1Gv1cwOie9JdgIiIi6nGYpFGvZrFYYLFYKt2MHHqv0UIhZXbdXBRFQTwez7tP9nZ63by9eZk5DE0KCUlVQrSbfz+QpVQoaZdFOqBsCy4d6UAzNRVwGCGmgMOSKvb0Mocltb3Dkr0N4LAI9vgkIiIiIiIi6uEYRBKVgSRJsFqtsFqtcLvdFWlDdhiaXbLDy3g8nrO+0Lxej8fjOcv1eZ0mJMT03qBK+Z6vTRZwWtoCTKcppGybb6s7LQIui4DTqs/DVE8VC3tzEhERERERUR8gBKD1gDunMIgk6qMqGYYKITLCyVgsllOPxWJGcGku+vJYLGbUo9FowWV6r09Fk6BoEkJdGHbqvS5d1lRo6bK21Z3pqduqZS4zlqfqbisvTyciIiIiIqKO0QQQVyXE1fRUk9LzEhKmZbGkhKgqIZpMF1VCLD2fsU6VkFAqfwsVBpFE1OUkSYLD4YDD4YDP5yvb4+iBZzQaRTQaRSwWy6mbl2WXSCSCSCRi1PVpMpkEACQ0CYkuCDftsh5KanBbM0PKtpJa57EKuG0CHqsGjy01zyCTiIiIiIioe2gCSGrp25tpQFKTkBSpzi+qBijG8sx15n30eb3DjGLUU9NEnmWK1hYwJtLzfRGDSCLqtcyBZ1VVVZcdV1GUnJAyEokgHA4jHA4b9eypXm9tbTX2AdKBZkJCS6JziaJdFvDYtFRIaRXw2FLBpTcdVHptqbo3HWDqdadFQOqb311ERERERNRHJbVUGKdo6d+lVAkJDelpuqip8E7fzhzoJc11kR0K5gkP0/P6VBM965coSZLgdDoLFrfbDbfbDY/H0+40mUxi0KBBFX0+DCKJiLLYbDYEAgEEAoFtOo6qqkYwqZdwOJx3qpdQKGSUcDgMIP3lG7egKd6xx5elzKDSYxXw2TR47ampz5Za57O1LfdYOfAPERERERGVLqnBuPQ3kr4cOGK6TDiaTAWGMTVzqtdj+mXG6Xm1hwWB+i3PbDabUex2e86yfMVutxvF4XBkzBcqLpcLDocDTqcTLpcLdrsdUhf1MAkGg11ynG3BIJKIqEwsFgt8Pl+nL09XVdUIKvVwMjusDIVCCAaDCAaDGfV4PA5NSAgpEkJK6T0xJYh0j0o9oBTw2zX47Br86brfpsFnT9W9Vg7qQ0RERETUm2kiFSRGkhLCSQlhRUI4KaenEsKKjEhSQmt6PpqUM+5JmCjTJcSyLBsBntPpNOr5ijnIyxcE6nXzcpvNBqvVmhMqZi/rqhCQUhhEEhH1UBaLBX6/H36/v8P7xuPxnJCypaWlaAmHwxBoCy/Xl/A4enDps2nw2/WgUiBg1+C3awjYNQQcAgFbat5u6fjrQEREREREpUlqQKsioVWR0Zr+f31qvq3etiw1jSQlCGx72OZ0Oo3LgL1er1HXi8vlMnr5ZZfs5Q6HgyFgH8UgkoioD9L/MlhXV1fyPoqiIBgM5gSUTU1NaG5uRnNzM5qamoxlwWAQQsAILtdF2n8MtzUVWOrBZMChocqeCi6rjPnUNrxEnIiIiIi2d0kNCCoyggkJoYSMkCIhaJoGFRmhhGRMo2rnL1fSBxv1er3w+Xzw+/1G3Vw8Hg+8Xq9x30E9cLRaGTFR+3iWEBERgNS9MWtra1FbW1vS9slkEqFQKCeobGpqQmNjY05dURREkjIiSWADineNlCDgt4uMcLLKrqHKoSFgF6hyaKhOr7Px0nAiIiIi6iWEAGKqhJZEKkhsSaRCxpZ0qGjU08s7EyzKsmwEifq97/W6fsWVvtwcODocjjI8Y6JMDCKJiKhTrFYrqqurUV1d3e62Qgi0trbmDSgbGxuxdetWY9rc3AxN09CijzTeWvzYXltbKFmdDiurHMIILhlYEhEREVG5xVWgOZ4KFlNFMtXljOBR6eA9FS0WCwKBAKqrq41pVVVVRl2fr6qqgtfrhSzzP7/UMzGIJCKispMkybiUY/jw4UW3VVUVzc3NOQFldn3r1q1QFCV9bxsZq8PF2+CzpUNKU0BZ5dBQnQ4uq9P3tbTy/2xEREREBEDVgKCSChT1kLEtbJTQbPRolBFTOxYuulwu44/6+UpNTY0RMDJYpL6EQSQREfUoFovFuER89OjRBbcTQiAUCmHLli1obGzEli1bjIAye5miKAgpMkKKjNVFHltCaqTwVK/Kwj0tAwwsiYiIiHqtaBKZ4WJCDxmltvl46j6MHRnExeFwoLa2FjU1NaipqTECRb2uz1dXV8PpdJbxGRL1XAwiiYioV5IkybjHzahRowpuJ4RAMBjMCCnzTbdu3QpVVRFUUjf7/qHIJeH6aOHVph6WVekRwvWgMpBe7uBI4URERERlp6UHUWyJp4LEVMgomULGtsAx3oHei7Iso7q6OiNgLFRcLhdHeSZqB4NIIiLq0yRJMm7GXSyw1DQNLS0tGcFkvh6WjY2NUFXVGC38h3Ye32lJD7BjGngnFVSmRgv3p+s+O+9jSURERJRNv/eiOVDU77+o91xMDfQiQROlh4Aulws1NTVGwJgdNOrzgUAAFgv/skzUVRhEEhERoe2v3dXV1UUvCdcDy+ywUr+HpV62bt2KeDyOmCojFgU2Rtv/D6zbmgol/bZUQOk3hZX+9HKfXYPPJuC2Csj8gzsRERH1QgkVqYFbFBkt8cxBXYLbcO9FSZJQVVWVEybmCxvdbncZnyERFcIgkoiIqAPMgeWOO+5YcDshBCKRSMagO9l1fQTxpqYmaJqGSFJGJAmsR/uhpSwJeK3CCCZ9Ng0+e3pqE/DaUuGl16bBY01NnRaAVwsRERFRVxMCiKXDxaCSChP1IDGojxittNUjyY5dBuJ0OvP2VsyuV1VVwWplzEHUk/EdSkREVAaSJMHj8cDj8WDo0KFFt9U0DaFQyBgtvLm5OSOk1EtzczNaWloQDoehCcm4n2WpLJIwQkmPLXWfS49Vg9dU99hS27htAm5rKsR0WwUH5yEiItrOxFUglL7kOaTICKXDRGNZeqqHjQmtY3/ttNlsOYO5ZA/wos+73W7ee5Goj2AQSUREVGGyLBv3sRw+fHi72ycSCQSDQSOY1Kd6MS8PhUJoaWmBoihQOxFe6uyyHlBqcKfDSWPeIuBKL8s7taQG7eHvD0RERJWhakA4mQoUW03BolFXJLSaQ0alYwO66JxOp3HliF70S6XN9ZqaGni9XoaLRNshBpFERES9jN1uR11dHerq6kreJxaLIRgMIhQKIRgM5tTN862trUYJh8MAgIQmIZGQ0JToXNdICdnhpIAzXXdZ2pY706GmHmBmb2OTGWgSEdH2S4hUT8VWRUZrMhUehpVUoNi2TELYFDi2KhIiSQkCHf8CtdlsCAQCqKqqQnV1tVE3F3Po6HK5yvCsiagvYRBJRES0HXA6nXA6nejfv3+H9lNVFZFIBKFQKCOg1OdDoRDC4bBR9PDSXFRVhUDql6BIctueh0XKDiszA0097HRaUpeW5++hKWDhpeZERFRBSQ2I6KFhUkZEkdCaTAWIbctT91LUg0Q9dEx2YGTobD6fz7gKIxAIwO/3Z9SzA0ePx8Nei0TUpRhEEhERUUEWiwU+nw8+n69T+wshEI/H84aUkUgkY5q9rLW1NWNeCAFVpH4xC29joGmX9WCy7VLzzJK6X6ZxGTrvmUlERCb64CypgeZSQWFYDxXT9UiyLUyMKPr3V2q+M5c9m9lsNvj9/oyih4w+n89YZg4dfT4fB3IhoorjpxARERGVjSRJRm/M2traTh9H0zTEYrGc4DJfkKkHnua6XmKxGIC2S82bO3mpuX7PTE96VHKPra2ePfCPx6all6V6ZMrsWEJEVHGaAKJJyQgR24psLA+blysyoqpkBIqdvdQ5m8fjMYJDr9ebUdfDRfNyPVB0Op3sqUhEvRKDSCIiIurxZFmG2+2G2+3epuMkk8m8l5Kbp4UuQ++Ke2ZKEMYo5d50QOk1Ri5vG8HcZ8tcxh6YRERt9BAxmpQQUaW2unmqyhnz2fWY2jUfrFar1QgKvV6vUcwBYrH1FoulS9pBRNRbMIgkIiKi7YbVajUuUesM/Z6ZekCpF32wn+y6eT4Wi0FAQkiREFIAoPRfPt3psFIPKH02Db50UOmz5y732Njzkoh6HlUDYqqEqCohlkxNo0kptSwdFEbVdFBoWh8xbdOVISKQGgDO6/XC4/EYIaHeS9EcHBYqdrudPROJiDqAQSQRERFRicz3zBw0aFCH9k0kEgiFQmhpaUFLS0vGiOX6vHl5S0sLQqEQhBDpe5ABm6KlhZdtPS81+G0CXnt6agoufabg0mfX4GCnHCLKktSAuJoKAfUSTyJzXq/rYaEpZIyZAsSYKiGhdW1gZ7PZ4PF4jKKHiG6326ibl5sDRH2d3W7v0jYREVFxDCKJiIiIuoHdbkdtbW2H7pWpqipaW1uN8LK90tzcjHA4bOp5KWN9qe2T00GlzTS1twWWmT0xU9vYGV4SVZQQgKIBcU1CQpUQV1O3joinA8JUHel1bcFhPB0wZpeYiox9lS4ODnU2mw0ulwtut9sIBM1183yxbRwOR1naR0RE5cMgkoiIiKiHslgsHb6UPJlM5oST5mm+ejKZREKT0Bi3oDFeevv08LKU+156TCOR856X1NcJASRFKgBMaOkgUJOgpEPARDroS5iCQ2NbI1TM3Fdf37ZtKmTsigFT2qMHh/mK2+3OmPd4PMZUX2euu91u9kIkItqOMYgkIiIi6kOsVmuHel4KIRCNRkvudakXTdM6FV4CgMOSNbK4VcCtj0CeHmncnR5l3JhaUlOOPE7bIrsHoR4EtoWBbUGgHvopWmYoaASFGtLBYvZ+3RcQmtlsNjgcDjidzoxiXuZyuTLq2fPZ2+jhoc1m69bnQkREfReDSCIiIqLtmCRJxojkpd73UgiBcDicc4/L7Hn9nph6XR91PHXpZ8cDTJ3TorWFlJa2sNJpEXBaBRyW1HKHJbXMYQFc1rZ5p2mdTQY4zkTlCAGoAlA0CYq556Ap9CtUN4eI2UFg9jo9UOzqexSWQpZlIxDUQ0G9bp7Pnpayzhw2Wq381Y6IiHo+flsRERERUYdIkmQM+FBfX1/yfvo9L0OhUM7I4/nmw+GwUVpbW6EoCgAgpsqIqUBTJ4PMbHZZwCanwkmbjPQ0FWC2LU/NW6XUNhZZwCYBVjl1qblVSk9lAWvWcouUCjstEiBLqR6dqXpq3iIBMgBZBmRk9viUjH9SE3OMJklt85oABFLBnsiYl6CZlgsBaAA0IUEVqVGMjboA1HRd0+ta2/KkBiT1qZYKD/UQMSmApKavk6Ck5/WA0ZgagWDbsu7uOajTexDmCwgLhYXZoaDdbs8JCe12O1wul7G91WrlqMpERERpDCKJiIiIqFt05p6XZolEIiOYNAeV4XAYkUgE0Wi03RKJRBCPt6WYes+5cLKrnil1lt1uNwI8cz276Ouyw8BC4WH2crvdzh6EREREFcBvXyIiIiLqFex2O+x2O6qrq7f5WKqqIhaLIR6P5y2xWAyJRCJnG0VR8pZkMolEIoFkMglFUTLqmqZBVVVjml3PXtYVJEmCJEmQZRmSJMFisRjzQCoUtlqtsFgsGXXzsux1Npstp1itViPUs9lsOfWOFJvNZrSPiIiI+iYGkURERES03bFYLPB4PPB4PJVuSl5CCKPo84XWATACR33KS4GJiIioJ2IQSURERETUwzBMJCIior6I1z4QERERERERERFR2TGIJCIiIiIiIiIiorJjEElERERERERERERlxyCSiIiIiIiIiIiIyo5BJBEREREREREREZUdg0giIiIiIiIiIiIqOwaRREREREREREREVHbWSjegFEIIAEAwGKxwS4iIiIiIiIiIiHofPVfTc7ZK6BVBZCgUAgAMHTq0wi0hIiIiIiIiIiLqvUKhEAKBQEUeWxKVjEFLpGka1q1bB5/PB0mSKt0cKrNgMIihQ4di9erV8Pv9lW4OEXUhvr+J+i6+v4n6Lr6/ifouvr+3L0IIhEIh1NfXQ5Yrc7fGXtEjUpZlDBkypNLNoG7m9/v5QUjUR/H9TdR38f1N1Hfx/U3Ud/H9vf2oVE9IHQerISIiIiIiIiIiorJjEElERERERERERERlxyCSehyHw4Ebb7wRDoej0k0hoi7G9zdR38X3N1Hfxfc3Ud/F9zd1t14xWA0RERERERERERH1buwRSURERERERERERGXHIJKIiIiIiIiIiIjKjkEkERERERERERERlR2DSCIiIiIiIiIiIio7BpHU7e644w5IkoTLLrvMWCaEwE033YT6+nq4XC4cfPDBWLJkScZ+8Xgcv/nNb1BXVwePx4Of/vSnWLNmTTe3noiy3XTTTZAkKaMMHDjQWM/3N1HvtnbtWpxxxhmora2F2+3GbrvthoULFxrr+R4n6p1GjBiR8/0tSRKmTZsGgO9tot4smUziuuuuw8iRI+FyuTBq1Cjccsst0DTN2IbvcaoUBpHUrRYsWIC///3vmDhxYsbyu+++G/fffz+mT5+OBQsWYODAgTj88MMRCoWMbS677DK8+uqreP755zFnzhy0trbimGOOgaqq3f00iCjLLrvsgvXr1xtl8eLFxjq+v4l6r6amJuy///6w2Wx46623sHTpUtx3332oqqoytuF7nKh3WrBgQcZ397vvvgsAOOmkkwDwvU3Um9111114+OGHMX36dCxbtgx333037rnnHjzwwAPGNnyPU8UIom4SCoXE6NGjxbvvvismT54sLr30UiGEEJqmiYEDB4o777zT2DYWi4lAICAefvhhIYQQzc3Nwmazieeff97YZu3atUKWZfGf//ynW58HEWW68cYbxa677pp3Hd/fRL3b1VdfLQ444ICC6/keJ+o7Lr30UrHDDjsITdP43ibq5Y4++mhx7rnnZiw74YQTxBlnnCGE4Pc3VRZ7RFK3mTZtGo4++mgcdthhGctXrlyJDRs24IgjjjCWORwOTJ48GXPnzgUALFy4EIqiZGxTX1+P8ePHG9sQUeWsWLEC9fX1GDlyJE455RR8//33APj+JurtXn/9dey111446aST0L9/f+y+++545JFHjPV8jxP1DYlEAs888wzOPfdcSJLE9zZRL3fAAQfg/fffxzfffAMA+OKLLzBnzhwcddRRAPj9TZVlrXQDaPvw/PPP47PPPsOCBQty1m3YsAEAMGDAgIzlAwYMwKpVq4xt7HY7qqurc7bR9yeiyth3333xj3/8A2PGjMHGjRtx6623Yr/99sOSJUv4/ibq5b7//ns89NBDuPzyy/H73/8e8+fPxyWXXAKHw4GzzjqL73GiPuK1115Dc3Mzzj77bAD8/zlRb3f11VejpaUFY8eOhcVigaqquO2223DqqacC4HucKotBJJXd6tWrcemll+Kdd96B0+ksuJ0kSRnzQoicZdlK2YaIymvKlClGfcKECZg0aRJ22GEHPPXUU/jRj34EgO9vot5K0zTstddeuP322wEAu+++O5YsWYKHHnoIZ511lrEd3+NEvdtjjz2GKVOmoL6+PmM539tEvdMLL7yAZ555Bs8++yx22WUXfP7557jssstQX1+PqVOnGtvxPU6VwEuzqewWLlyITZs2Yc8994TVaoXVasWsWbPwl7/8BVar1fgrTPZfVTZt2mSsGzhwIBKJBJqamgpuQ0Q9g8fjwYQJE7BixQpj9Gy+v4l6p0GDBmHcuHEZy3beeWf88MMPAMD3OFEfsGrVKrz33ns4//zzjWV8bxP1bv/3f/+H3/3udzjllFMwYcIEnHnmmfjtb3+LO+64AwDf41RZDCKp7A499FAsXrwYn3/+uVH22msvnH766fj8888xatQoDBw40BipD0jdp2bWrFnYb7/9AAB77rknbDZbxjbr16/HV199ZWxDRD1DPB7HsmXLMGjQIIwcOZLvb6JebP/998fy5cszln3zzTcYPnw4APA9TtQHPPHEE+jfvz+OPvpoYxnf20S9WyQSgSxnxj0WiwWapgHge5wqrFKj5ND2zTxqthBC3HnnnSIQCIhXXnlFLF68WJx66qli0KBBIhgMGtv86le/EkOGDBHvvfee+Oyzz8SPf/xjseuuu4pkMlmBZ0BEuiuuuEJ89NFH4vvvvxeffPKJOOaYY4TP5xMNDQ1CCL6/iXqz+fPnC6vVKm677TaxYsUK8c9//lO43W7xzDPPGNvwPU7Ue6mqKoYNGyauvvrqnHV8bxP1XlOnThWDBw8WM2bMECtXrhSvvPKKqKurE1dddZWxDd/jVCkMIqkisoNITdPEjTfeKAYOHCgcDoc46KCDxOLFizP2iUaj4uKLLxY1NTXC5XKJY445Rvzwww/d3HIiyvaLX/xCDBo0SNhsNlFfXy9OOOEEsWTJEmM9399Evdsbb7whxo8fLxwOhxg7dqz4+9//nrGe73Gi3uvtt98WAMTy5ctz1vG9TdR7BYNBcemll4phw4YJp9MpRo0aJa699loRj8eNbfgep0qRhBCi0r0yiYiIiIiIiIiIqG/jPSKJiIiIiIiIiIio7BhEEhERERERERERUdkxiCQiIiIiIiIiIqKyYxBJREREREREREREZccgkoiIiIiIiIiIiMqOQSQRERERERERERGVHYNIIiIiIiIiIiIiKjsGkURERERERERERFR2DCKJiIiIqCBJkvDaa68BABoaGiBJEj7//POKtomIiIiIeicGkURERES9zIYNG/Cb3/wGo0aNgsPhwNChQ3Hsscfi/fffL+vjDh06FOvXr8f48eMBAB999BEkSUJzc3PGdps2bcIvf/lLDBs2DA6HAwMHDsSRRx6JefPmlbV9RERERNSzWSvdACIiIiIqXUNDA/bff39UVVXh7rvvxsSJE6EoCt5++21MmzYNX3/9dc4+iqLAZrNt82NbLBYMHDiw3e1OPPFEKIqCp556CqNGjcLGjRvx/vvvo7GxcZvbUEgikYDdbi/b8YmIiIho27FHJBEREVEv8utf/xqSJGH+/Pn4+c9/jjFjxmCXXXbB5Zdfjk8++QRA6nLqhx9+GMcddxw8Hg9uvfVWAMAbb7yBPffcE06nE6NGjcLNN9+MZDJpHHvFihU46KCD4HQ6MW7cOLz77rsZj22+NLuhoQGHHHIIAKC6uhqSJOHss89Gc3Mz5syZg7vuuguHHHIIhg8fjn322QfXXHMNjj76aONYzc3NuPDCCzFgwAA4nU6MHz8eM2bMMNa//PLL2GWXXeBwODBixAjcd999GW0ZMWIEbr31Vpx99tkIBAK44IILAABz587FQQcdBJfLhaFDh+KSSy5BOBzuwp8AEREREXUWg0giIiKiXqKxsRH/+c9/MG3aNHg8npz1VVVVRv3GG2/Ecccdh8WLF+Pcc8/F22+/jTPOOAOXXHIJli5dir/97W948skncdtttwEANE3DCSecAIvFgk8++QQPP/wwrr766oJtGTp0KF5++WUAwPLly7F+/Xr8+c9/htfrhdfrxWuvvYZ4PJ53X03TMGXKFMydOxfPPPMMli5dijvvvBMWiwUAsHDhQpx88sk45ZRTsHjxYtx00024/vrr8eSTT2Yc55577sH48eOxcOFCXH/99Vi8eDGOPPJInHDCCfjyyy/xwgsvYM6cObj44os78jITERERUZlIQghR6UYQERERUfvmz5+PfffdF6+88gp+9rOfFdxOkiRcdtll+OMf/2gsO+iggzBlyhRcc801xrJnnnkGV111FdatW4d33nkHRx11FBoaGjBkyBAAwH/+8x9MmTIFr776Ko4//ng0NDRg5MiRWLRoEXbbbTd89NFHOOSQQ9DU1JQRgr788su44IILEI1Gsccee2Dy5Mk45ZRTMHHiRADAO++8gylTpmDZsmUYM2ZMTvtPP/10bN68Ge+8846x7KqrrsLMmTOxZMkSAKkekbvvvjteffVVY5uzzjoLLpcLf/vb34xlc+bMweTJkxEOh+F0Okt9qYmIiIioDNgjkoiIiKiX0P9+LElSu9vutddeGfMLFy7ELbfcYvRY9Hq9uOCCC7B+/XpEIhEsW7YMw4YNM0JIAJg0aVKn2nniiSdi3bp1eP3113HkkUfio48+wh577GH0aPz8888xZMiQvCEkACxbtgz7779/xrL9998fK1asgKqqRZ/jk08+mfEcjzzySGiahpUrV3bquRARERFR1+FgNURERES9xOjRoyFJEpYtW4bjjz++6LbZl25rmoabb74ZJ5xwQs62TqcT+S6SKSXwLMTpdOLwww/H4YcfjhtuuAHnn38+brzxRpx99tlwuVxF9xVC5Dx2vvble46//OUvcckll+RsO2zYsE48CyIiIiLqSgwiiYiIiHqJmpoaHHnkkfjrX/+KSy65JCeIa25uzrhE2myPPfbA8uXLseOOO+ZdP27cOPzwww9Yt24d6uvrAQDz5s0r2h59lGpzL8VCxo0bh9deew0AMHHiRKxZswbffPNN3l6R48aNw5w5czKWzZ07F2PGjDHuI5nPHnvsgSVLlhR8jkRERERUWbw0m4iIiKgXefDBB6GqKvbZZx+8/PLLWLFiBZYtW4a//OUvRS+lvuGGG/CPf/wDN910E5YsWYJly5bhhRdewHXXXQcAOOyww7DTTjvhrLPOwhdffIHZs2fj2muvLdqW4cOHQ5IkzJgxA5s3b0Zrayu2bt2KH//4x3jmmWfw5ZdfYuXKlfjXv/6Fu+++G8cddxwAYPLkyTjooINw4okn4t1338XKlSvx1ltv4T//+Q8A4IorrsD777+PP/zhD/jmm2/w1FNPYfr06bjyyiuLtufqq6/GvHnzMG3aNHz++edYsWIFXn/9dfzmN7/pyEtMRERERGXCIJKIiIioFxk5ciQ+++wzHHLIIbjiiiswfvx4HH744Xj//ffx0EMPFdzvyCOPxIwZM/Duu+9i7733xo9+9CPcf//9GD58OABAlmW8+uqriMfj2GeffXD++ecbI2oXMnjwYNx888343e9+hwEDBuDiiy+G1+vFvvvuiz/+8Y846KCDMH78eFx//fW44IILMH36dGPfl19+GXvvvTdOPfVUjBs3DldddZXRs3KPPfbAiy++iOeffx7jx4/HDTfcgFtuuQVnn3120fZMnDgRs2bNwooVK3DggQdi9913x/XXX49BgwaV+OoSERERUTlx1GwiIiIiIiIiIiIqO/aIJCIiIiIiIiIiorJjEElERERERERERERlxyCSiIiIiIiIiIiIyo5BJBEREREREREREZUdg0giIiIiIiIiIiIqOwaRREREREREREREVHYMIomIiIiIiIiIiKjsGEQSERERERERERFR2TGIJCIiIiIiIiIiorJjEElERERERERERERlxyCSiIiIiIiIiIiIyo5BJBEREREREREREZUdg0giIiIiIiIiIiIqOwaRREREREREREREVHYMIomIiIiIiIiIiKjsGEQSERERERERERFR2TGIJCIiIiIiIiIiorJjEElERERERERERERlxyCSiIiIiIiIiIiIyo5BJBEREREREREREZUdg0giIiIiIiIiIiIqOwaRREREREREREREVHYMIomIiIiIiIiIiKjsGEQSERERERERERFR2TGIJCIiIiIiIiIiorJjEElERERERERERERlxyCSiIiIiIiIiIiIyo5BJBEREREREREREZUdg0giIiIiIiIiIiIqOwaRREREREREREREVHYMIomIiIiIiIiIiKjsGEQSERERERERERFR2TGIJCIiIiIiIiIiorJjEElERERERERERERlxyCSiIiIiIiIiIiIyo5BJBEREREREREREZUdg0giIiIiIiIiIiIqOwaRREREREREREREVHYMIomIiIiIiIiIiKjsGEQSERERERERERFR2TGIJCIiIiIiIiIiorJjEElERERERERERERlxyCSiIiIiIiIiIiIys5a6QaUQtM0rFu3Dj6fD5IkVbo5REREREREREREvYoQAqFQCPX19ZDlyvRN7BVB5Lp16zB06NBKN4OIiIiIiIiIiKhXW716NYYMGVKRx+4VQaTP5wOQeqH8fn+FW0NERERERERERNS7BINBDB061MjZKqFXBJH65dh+v59BJBERERERERERUSdV8raHHKyGiIiIiIiIiIiIyo5BJBEREREREREREZUdg0giIiIiIiIiIiIqOwaRREREREREREREVHYMIomIiIiIiIiIiKjsesWo2URE5aKqasGSTCahqio0TYMkSUaRZdmYAjDmzeusVivsdjusVmtFRyQjIiIiIiIi6ikYRBJRj6eqKsLhMEKhEEKhEFpbW9Ha2opQKIRwOIxoNIp4PI5YLGZMzXXzslgsZgSMqqpCCFHWtsuyDLvdnlEcDkfeqdfrhdfrhc/nM0r2vNvthsViKWubiYiIiIiIiMqBQSQRdbtIJILGxsaM0tTUhMbGRgSDwZzAMRwOd3sbhSQDkgRIMgC9R6MAjOBSACI9Tdcl5IaamqYZAWhXkCQJbrcbPp8PVVVVqKurQ11dHWpra426XrxeL3tjEhERERERUY/BIJKIukwikcDmzZuxceNGbNy4EZs3b84JHBsbGzsdygnZCmGxQ1gdxhQWG4RsA2QrhMUKyBYI2Zqaz55arBBSahs9ZBSSBEBOBY568Lgt4Z3Qg0kN0FRImgqI9FRLppYJNb3ONK8mIakJSGocUjIBqAlIyXhqmT7VkhBCIBwOIxwOY8OGDUWb4nA4cgLK+vp6DB48GPX19RgwYACsVn4NEBERERERUffgb6BEVLLW1lZs2rQJGzZsMMJGc2lsbCz5UmchWyFsLmg2F4TNDWFzQlhdqWlO2GiHsNhTAWJPJ0kA0oGmbIWp/+S201RIagJIpgNLJQZZCUNKRCApEcjmqRpHPB7HunXrsG7duryHs1gsGDhwIAYPHmwUPagcNGgQ7HZ7V7SaiIiIiIiICACDSCLKEgqFsHbtWqxZsyajrF27FqFQqN39hWyBZvdC2L3Q7B4IuxvC6kyHjXrw6AIstm54Nn2MbIGQXYDNZQSbaqFttSSkRASykgonpUQEciIMKR6CHA9CjoWgqirWrl2LtWvX5uwuSRL69++PoUOHYuTIkRgxYoQxdbvd5XqGRERERERE1IcxiCTaDsViMaxevRpr167F6tWrjaBxzZo1aG5uLrqvsDigOdIho8PbFjo6vBB2D4TVuW2XNlPXkK0QTj9Upz//eiFSvSdjQcjxEKRYMBVQxoOQY0FASxo9XT/99NOMXQcOHGgEk3oZNmwYHA5HNzwxIiIiIiIi6q0kUe4hY7tAMBhEIBBAS0sL/P4Cv1QTUY5YLIYffvgBDQ0NWLlyJVatWoWGhgasX7++6CXUms0FzeGH5vRDOP3QHAFoTj80h489GbcHQkBKxlIhZawFcrSprSjRvLvIsozBgwdj5MiR2HHHHbHjjjtizJgxqK2t5YA5REREREREPUBPyNcYRBL1AZ0JHIXFAc2ZDhid/nTwmJpn2EgFJWOwRJtToWSkCXKsGZZIY+relXlUV1dj9OjRGDNmjBFODho0iOEkERERERFRN+sJ+RqDSKJeRAiBLVu24LvvvsN3332Hb7/9Ft9++y3WrFlTMHDUrE5oripozqrU1FUNzVWVuk8jUVcQApISNXpNWiJbIUe2Qo62QMozTI/H48kJJ4cNGwZZlivQeCIiIiIiou1DT8jXGEQS9VDJZBKrVq3KCBy/++47tLS05N3eCByN0JGBI1WYmoQcbUwFk+GtqWm0CZLQcjZ1u90YM2YMxo4di7Fjx2KnnXbCwIED2XOSiIiIiIioi/SEfI1BJFEPEI/H8e233+Kbb77BN998gxUrVmDVqlVQFCVnWwEpdQm1uwaauwZqeipsHMmYegFNhRxrTgeTjbBEtkCONELSkjmbVlVVGaGkHlBWV1dXoNFERERERES9X0/I1xhEEnWzWCyG7777DsuXLzeCx4aGBmhabi8xYbFBddVAc9e2hY6uKkDmgPfUhwgNcrQZcngLLOHNsIS3QI42Qsrz9TRgwACMHTsWO++8M8aOHYsxY8bA7WYIT0RERERE1J6ekK8xiCQqI72nox46Ll++HKtWrcobOmpWJzRPHVR3LTRPLVR3LYTdC/DSVNoeaUnIkUZY0uGkHN4COdaC7HeDLMsYMWJERjg5cuRIWK0M64mIiIiIiMx6Qr7GIJKoiySTSaxcuRJff/21UVauXFkgdHSlwkZPHTR3aipsboaORMWoCVjCW41g0tK6GbISztnM4XBgzJgxRjC58847836TRERERES03esJ+RqDSKJO0DQNq1evxvLly43Q8dtvv0UikcjdVu/p6KmD6q6D5qll6EjURaREJB1MbjYu65bU3HurVlVVGfea3GmnnbDTTjuhtra2Ai0mIiIiIiKqjJ6QrzGIJGqHEAIbNmzA8uXLjeDxm2++QTic2xNLWOxQPbVQ3f2M8FHYPQwdibqLEJBjLW3BZOsWyNGtee832a9fv4xRunfaaSf4fL4KNJqIiIiIiKj8ekK+xiCSyEQIgc2bNxuho16CwWDutrIlfT/Hfqnejp46CIefoSNRT5Nxv8kt6ftNNufcbxIABg8ebISTY8aMwY477giPx9PtTSYiIiIiIupqPSFfYxBJ27WtW7fmhI5NTU052wlJhuaqSd3TMR06aq4qQJK7v9FEtO1UxQglLeEtsES2QI6H8m46ePBgjB49OqNUV1d3c4OJiIiIiIi2TU/I1xhE0nZBCIH169djxYoVGaWxsTF3W0jQ3NXp+znqoWM1IFsq0HIi6jbJWEavSUtkK+RE7i0YAKCurg5jxozJCCf79+/PAXGIiIiIiKjH6gn5GoNI6nOSySRWr16dEzrmvacjJGiuADR3nXF5teauAWRrBVpORD2NpMQgR7amQsn0VIoF817W7ff7MXLkSIwaNQo77LADRo4ciZEjR8Ltdnd7u4mIiIiIiLL1hHyNQST1aqFQCCtXrsR3332H77//Ht9++y2+++67vKNXpy6vrk7f17E2NXVVAxZbBVpORL2WqqSDycbUNLwVcqwp74A4ADBo0CCMGjUqowwePBhWK//gQURERERE3acn5GsMIqlXSCaTWLNmjRE46tNNmzbl3V7I1lTQ6K6F6qmF5q6B5qwGZN7TkYjKQFMhR5shRxthiTZBjjRBjjZCVqJ5N7fZbBgxYgSGDx+eURhQEhERERFRufSEfI2/7VCPIoTApk2b0NDQgJUrVxq9HVetWgVFUfLuo9k9qYFk3NXpAWVqOXo1EXUv2QLNk+ptnTQtlpQY5Gg6lIw0pULKaBMURTFuG2FmsVgwePBgI5gcNmyYMXW5XN37nIiIiIiIiLoYe0RSRWQHjqtWrUJDQwMaGhoQjebvQSRka1vg6K5JXWbtqgasjm5uPRHRNhACUjyUDiWbIceaIUdbIMeaIWnJgrsNGDAAw4YNw+DBgzFkyBCjDBw4kL0oiYiIiIioXT0hX2MQSWWlqio2bNiA1atXG0GjHjoWDBwlCZojAM1VlRE4CoePvRyJqO8SAlIiDDnW0hZQ6vVkrOBusixj0KBBGQGlXh8wYABDSiIiIiIiAtAz8jUGkdQlIpEIfvjhh4yyevVqrFmzpuAl1RmBo6s6Pa2C5vADsqWbnwERUQ+WjMESbYEca4EUa4EcD0KOBSHHg5A0teBuFosFAwYMwKBBg/KWQCAAiX/gISIiIiLaLvSEfI3dJKhkyWQSGzZswNq1a7FmzRojbPzhhx+wZcuWgvsJyQLN6YfmDGQFjgEOHkNEVAqrE6rPCdU3IHO5EJCUiBFKyrGgKagMQVVVrFu3DuvWrct7WKfTmRFMDhw4EIMGDUL//v3Rv39/BpVERERERNSlGERSBnPYqAeO+nTDhg1Q1cI9bzSbKxU2OgPQnFWp8NFVBWH3ABIDRyKiLidJEHYPVLsHKgZlrtMv9Y6HICVaIcdDqXo8XVciiMVixsBg+TgcDvTr188IJvMVt9vdDU+UiIiIiIj6AgaR26FIJIL169dnFD14XL9+fdGwUcgWaA5/qrgCpuAxwEFjiIh6EkmCcHihOrz512tJSPEw5EQoHVK2QtKniTDkZBTxeBxr1qzBmjVrCj6Mx+NBv379UFtbi7q6OtTW1hpFn6+pqYHDwe8IIiIiIqLtHYPIPiiZTGLjxo05YaNeWlpaiu5vXErt8ENz+iHMdZubA8YQEfUFshXCFYDqCiDvn580NRVIJsKmaWvmvJpAOBxGOBxGQ0ND0Yfz+/2oqakxwsnq6mpUVVUZ05qaGlRVVaGqqgo2m60cz5iIiIiIiCqMQWQvI4RAMBjEpk2bsHHjRmzatCmjvnHjRjQ2NkLTtKLH0awOCLsPmsMHzeGFcPjSPRsZNhIREQDZAuH0Q3UWuYm1qkBOtEJKRFL3qlQikBLRtro+L1QEg0EEg8F2A0sA8Hq9qK6uzggrA4EAAoEA/H5/xjQQCMDlcvFelkREREREvQCDyB5E0zQ0Nzdj69at2LJlizHVw0a9xGKxdo8lJAs0hw/C4U2Hjb5U2Jieh8XeDc+IiIj6NIsNmqsacFUX3kYIQE2kQ0o9sIxCUqKQkjFISixdj6bqEGhtbUVraytWr15dUjOsVmtOQOn3++Hz+eD1euHz+Yxinvd4PLBYLF30YhARERERUXsYRHaDZDKJlpYWNDU1oampKSNkNE+3bt1a9P6MZprVCWH3QnN4UlO7B8LugWb3Qtg9EDYXezUSEVHlSRJgdUCzOooHloAptNRDSlNgmYxBUuKQ1PQ0GU8tEyqSySQaGxvR2NjYwaZJcLvd8Pl88Pv98Hq98Hg8RvF6vXmXmad2O/+wR0RERERUKgaRnSCEQDQaRTAYRHNzMxobG9Hc3IympqacelNTE4LBIIQQpR0bgLC5IGxuCJsbmj09dXhNQaMbkPmjIyKiPsYcWpZKTaaDynjuVE1ASsaB9FQyT7UkhBDGPS43bNjQqSZbrVa43W64XC643e68xbzO5XLB6XQWLXa7nZeaExEREVGftN2nWaqqIhwOIxQKIRQKoaWlBcFgMGOaXW9paYGiKB16HAFAWJ1tIaPdDS09FTZT3eoCZLk8T5aIiKivsVghLF6IQqODF6KpkNQ4pGQC0MNJvSQTqfBSVYxl2fOSmvp/QDKZNO5/2VVkWYbD4YDT6YTL5YLdbm+3OBwOo26z2WCz2WC1WmG1Wo26eWqxWHLmLRYLZFk26vnm9dIXg1LzH40L1c0kSTJeh774ehARERGVQ68PIvXeiXqPBnPRw8XW1lajbp4PBoOIRCIl91bMeWzJkg4X9YDRlbpkOl0XGXUHIDFgpD5GaJAS4Uq3gqjXEnYPvxsqRbZAyKk/BnaKEICqQNKUdFCppIJKTWmrqwqgZdeTqammprdNQtKSgJaEJFIDzWmahmg0img0iqampi580l1HD+FkWc6YFlpmDu3yHasQIYTx/zS93pn5QuvKITuczH4N5PQfnPO9PvleP1mW8y6TJMkIhc3L9QBZX59dNxdz2GwOnfOtLzWsLjRvPmahxzC3Ld9zL/RamF/77HOtvfOvq37e+ZaX83GJiIh6q14VRN50001IJpMIh8NobW1FJBJBOBxud4ToUgjZCmFxQFgdEDZHKkS06lNzvW0K2cr7MNJ2TUqE4f3yX5VuBlGv1TrxJAiHr9LNoM6QJMBqh4AdAp6uOabQUoGkmg4m9YBSU9NBpQpoqaLXJU0FRHqqqYBIby+01B+LNC1dTy2ThAakl0npZdA0ACK1TqTqEMIIRvM2NR3kdcX/wfoac9hJBOT2ntXD03whdKEgupSA2hxMt7d/dvCdL9zV57Pb2pF5vZ79WhRbVug1Mq8r9hzMbe9IuN/e8o4sy/czyf75FNtWL0REfVGvCiLnz58PqzV/kwUAyHYIqw3CYoewpKawOtLz6aLPW+2AxZ4KHy02QO7EqJlacpueD1Gvp/I9QLRN1CSgduxWH9TXSan/l1hs6BExlkiFkoBm1CVjHkjffEbfOL1tqp7aTpi2y/sAJTRCaptKmctEnmWpqpS7DFnLpOz1uY8ppNxl0B9ZtNVzCdNikTsVmcskUWCd8fqbtzMt07c1TSX9Z5W9vxCQsvcVmml57n6S/vM1/6yFlmpHxnmhmdpmWmZ+nIzjtp1P5uMYx8h5bm3t7o1dALJ74JY6OCVRe0HltoSepRy3I6UzwW2hoLhYj3v9dSklhC6lFPrDgDkALxTKFwreOzLfkW3yTdtbV8r6Uo9L1FV6VRBZjAQAWgJSIgGAl4oSUe92zDHH4OSTT8aLL76ImTNnsmdNH+Vd8mqlm0BE1K6e9A20LW3hr9LU22iaZvQ87+gYBURE+SSTle9MxP7eREQ90Mknn4xhw4bh5JNPZghJREQVJfWRQkRERJXXq3pEVldXQ5ZlqKoKVVWRTCahqmqvvz+RBQJ2i4BdBuyygNUi4JABm0XALov0FLBLqe1sMmCT2/axyln7y5l1/seLyqUpLuP+xYFKN6NPevHFF40ekZIkMYzsoy6f0IJqR+/+DiOivs8m87bo2QSApCYhrkmIqxISKpDQ61rbvJKuK5oERdOn5npqu6QmIakBSQFTPTXl/+bLw3wZc/ZAS5IkwWq1ZlzGnG9QJfN8qZc3F7s3Z3v3wCylAMi43FifFqrnm7a3LLuebz7f691dtuX/zcX2zV5X6raFtit0vGIDqxVaVuiYw4cPx6BBg4rev7jQ/qW0u5TtSz1WKcuL6Y7fl4QQHT6Xu/LcL3SsjjxGa2srJk+e3FVN6hRJ9ILfboPBIAKBAFpaWuD3+zu0rxACqqpCURQkk8mcaSKRgKIoead6yZ5PJBKIx+M59XxTvVQqLJWQG1zq4aYtXbebQs/MuoDNAqNut6SPk67b08d0yEgfW/A/qtuZzVEZV8yrrnQz+iQ9fGQI2bfdN6kJ/VwMIomIyk0IIKYC0aSMqCohkpQQTZeYmi7mugrEc5ZlBo09NSC0Wq2wWq1GqGauZxdz+FZsnTl0K7RdoVBPlmWjDaU8nnn7Ym0v9vx4Tzsiovy2JV/rKr2qR2Rn6H/NKjTITXdJJpMZwWQikUAsFssbWna0ZO+fSCSM4FNAQiL9l9ZwN9wKQEJbSOmwtIWWDlnAYRVwyAJOi4DDAjgsel1k1J0WAadVwGUVcFlSUzsDzh6pxqHhvklNlW5Gn9Qcl7AlZkGdU0WVg0FkX1XD3pBERCVJBYkSwoqEcDIVJIaTMsKKXk9NI4qMsClkjCQlRNVUvVzBodVqhdPphNPphMPhMKZ6sdvtJRWbzQar1WpM7XZ7xrzNZjPqegBnDh2tVmvGoBpEREQ9UZ8PInsK/T8JHo+n7I8lhMgIPvP10swXXuZb1t68HqbqN08WkBBP/wU51IX3U5altlDSaWkLKZ1WAY9VwG0VcFs1eGx6PVU8Vg3u9DIb74ja5Swy2JurTPq5gNHgqJpERNS3aAKIJiW0KhJa00FiqyKhNR0ghk317OVdESTKsgyv1wuPx2MUl8sFl8sFt9udd5pdsgPHSnd4ICIi6k34rdkHSZJk/MXU6/V2y2Pql7nrwWT2NBqNGiUWi+XUzcsikUhGEUJAE6m/dG9Lr06bLOC1CnhsqcDSaxXwpuseqwavTaSXt9V9Ng12S9e9TkRERER9haoB4aSEkCKjVUlNQ4m2kFEPEltNYWPrNgaKNpsNPp8PXq8XPp/PqOvzet3j8RhTt9tt1B0OB3sMEhERVRCDSOoSeo9Pt9vdpcfVNA2xWAyRSAThcDgjoAyHw0YJhUJobW01ink+HA5DCAFFk9CUkNCU6FjXSLucCiy9trapL6Pets6XnndYeCk5ERER9R5FQ0VFRihr2qqkLo3uLKfTiUAgAL/fD5/PlzMNBAIZ83pxOBxd+KyJiIiouzGIpB5NlmW43W643W7U1dV16hiapiESiSAUCiEUCiEYDOZMs5e1tLQgGAxCVVUkNAmNcQsa46U/pk1uCybN4aXPFF767JnhJXteEhERUVfo7lBRDw79fn/OtNAyu93ehc+YiIiIegsGkdTn6fcC8nq9GDRoUMn7CSEQiUSMULKlpcWo6/Pm5XpRFCXV+zJuQVMHwku7nD+gLBRcem0CVt73koiIqE/r7lAxX3BYVVWVs1wvXq+X90gkIiKikvF/DUQFSJJk3MS8vr6+pH2EEIjFYjnhZHbJDjCTySQSmoStcQu2diC8dFr0YFLAa28LKY0A054ZZnptAjIvGSciIqoIfaCWkClQzAgY9VDRtHxbQ8V8AWJ2mMhQkYiIiLoL/6dB1IUkSTJGVBw4cGBJ+5h7XmaX5ubmnN6Yel3TNMRUGTEV2BwrsX3QB+FJ9a702wR8di0dXur1th6Yfl4yTkREVJAmgFZFQjAhI6hICCVkU8iYZ5kiQROd+4ugfvmzuRQLFxkqEhERUU/E/50QVVhnel5qmoZwOFw0tDQvb2lpQSgUgoB+KRcAlJYwOiypQNKfDin9dg1+e9syc52XixMRUW8mBBBJSgjq4WI6RNSDxmCiLWAMJjo/ArTb7c4JFauqqvIGjVVVVQwViYiIqM/g/2iIeiFZlo3RI4cMGVLSPslk0hiIRw8nzVO9mJcnk0nEVQmbVQs2x0oLLj1WDVWOVG/LgENDwK4XYaqnQk0LQ0siIiozVYMpTJTRkmgLGYMJCS1KWz2YkJHsYI9FSZLg8/lQVVVlFHO4aF6m32uRIz8TERHR9opBJNF2wmq1orq6GtXV1SVtL4RAOBw2QsmmpiY0NzcXnOqXi4eTMsJJGWvbOb6E1CXiVQ4NVXYtc+oQGcvYy5KIiMziKtrCREVCizlYNC3vbK9Ft9ttfGcGAgFUV1ejqqoqZ6oHi+ytSERERFQa/q+JiPKSJMkYbbyUXpeqqiIYDKKpqQlNTU1obGzMmepFDy2DioSgIuOHdo7ts6UCyWpHZqnR604NXquAxIF4iIh6JUVDxuAteu/EYEa9rSdjQuvYB74syxmBYk1NjREoZpeqqir2WCQiIiIqEwaRRNQlLBZLyT0uVVVFS0sLGhsbsXXr1qIlmUymb/AvY3W48DFtskC1vS2YrEkHlbVOFdUODbXO1OXgHDWciKi8hABiKlKjP6cHawlm1FMjQwcTbSNHR9WOd3232+0Z4aE5XMwOGv1+PywWjr5GREREVGkMIomo21ksFtTU1KCmpgY77rhjwe2EEAgGg9i6dSu2bNlilM2bN2Pz5s3GfHNzMxRNwqaYBZtiFqAl//GskkgFlHpQ6dRQ41BR60wFlbUODW72rCQiyqBqQGsyc/TnYCIdJioSWrNGhW5VZCgd7LEIpL4b9HsrmgPGQlOXywWJH9hEREREvQqDSCLqsSRJMn4pHTVqVMHtEokEtm7daoSTelC5adMmY9rY2IikQFtYWYDTohnBZJ1TQ21WUFnl4D0riah30wQQ1u+rqGQO3BJU5JzAMZLs3MjQdru9pMFb9OL1ehksEhEREfVxDCKJqNez2+0YNGgQBg0aVHAbRVFyQkq9bN68GRs3bkRLSwtiqoy1YRlrC1wGLkEYl3rXGYGlagSVdU4VTn6yElE3EwIIJyU0x2U0J2S0xCU0J1L1UCI9UnR6dOiQIkHr5MjQ+cJEczEHjE6ns0zPloiIiIh6K/66TETbBZvN1m5YGYvFjHBy48aNeaeKoqAxbkFj3IIVBS4B91jNQaVqBJZ6aOm38fJvIiqNEEAkKaEpLqMxLqMpXVJho4zm9CjRLYmOXw7t9/tz7q+Y3VtRn/p8Po4MTURERETbjP+jJCJKczqdGDZsGIYNG5Z3vaZpaGpqwsaNGwuW1tZWhJMywq0yfmjN/zg2WaR7UOaGlLXp+1fy8m+ivk8TQHNCRlMsFTKag0Zz6cgI0X6/37gHb21tLWpqavIO3lJVVcVgkYiIiIi6Hf8HSkRUIlmWUVtbi9raWowbNy7vNuFwGBs3bsSGDRuMnpTmsnXrVigasCFiwYZI/ntVShAI2AVqTZd816Z7V+rzXvaqJOrRNAG0JCQ0xixojMvYGk8FjlvT4eLWWKpXY6mXSPv9fvTr1w91dXWora1FXV1dRtioT+12e5mfGRERERFR5zGIJCLqQh6PB6NGjSo4uI6iKMY9Kc1h5YYNG4zliUQCzYnU/d2+C+Z/HLuculdlavRvFbVZo4FzBHCi8tEEEExIqYAxZkkFi3EZjXrPxnTIqJYQMsqybASM+jS7XldXB4fD0Q3PjIiIiIiovBhEEhF1I5vNhvr6etTX1+ddL4RAS0uLcU9Kc1ipT5uampDQJGyMWrAxagFgy3ssh0WgxqGhOl2q7Ka6Q0O1naOAE2VLakBzXEZTItVzsTHWdom0+fLpUkNGPVTs378/+vXrZxR9vqamBhZL/t7RRERERER9DYNIIqIeRJIkY7CInXbaKe828XjcGAFcH/XbPBL45s2b0dLSgrgqYX3EgvUFLgHX+WxtAaXfriFgFwjYNQTsbfN+uwYve1hSL6b3Ymwb5KVt0Bfz/RiDCQkCpYWMNTU1RqioB4vmwLGmpob3YSQiIiIiMuH/jomIehmHw4HBgwdj8ODBBbeJx+NGQLlly5aMsnXrVmOqKApCioyQUnhwHZ1FEvDbUqFkwKHBZ0vdq9JnE/Cm616bljFvY29LKiMhgKgqoSUhIZiQ06VtFGnz6NItilTy/RitVmvGJdLmy6T10JEhIxERERFRx/F/0EREfZDD4cCQIUMwZMiQgtvol4HroeTmzZvR1NRklMbGRqMeCoWgCglNCQlNCRloJ7TUOS2pUNJjFXBbBdw2DW6LgNuWmndZRXqdBpe+jVXAaUkVmwz2wtyOaAIIKxJaFQmtSRmhhIRWRUZrUkJIDxmVtsAxmJCRLDFcBNp6HOuDTuklO3AMBAKQZaboRERERERdjUEkEdF2ynwZ+I477lh0W0VRckLKlpYWBINBtLS0ZNSDwSCCwSA0TUNMlRBTLdjSyTbKUiqQdKSDSZclde9LpymstFsEHLKAPb3OLqe2T9WRWp/expaet8mpdRZmTV1O1YCIKiGiSIgkJUSTEsJJGZFk23wkKSGclBBWZLQqEkLpaSRZ2mXR2dxuN6qqqlBTU4OqqipUV1dnBI01NTWoq6tDVVUVezESEREREVUQ/zdORETtstlsxiWppdA0DeFw2AgpW1tbM0o4HM6pm5fFYrHUcYSUDrDK87wsUiqcTAWTqTDTJrcFlbZ0eGlNL7PlLEvNW2XAKqWmFilz3ioJWGS01aXUNrIEWCRAlgEZqeVy1jpJAiR0rFeoJrIKJKgidRlzallqPqkBiiZBERIUFUgKCYq+TAOSWtt8XE2VVLAMo25Mk/o6CQlt27uwejweBAIB+P1++P1+o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yvanzyVu3+vPP3tsge7xpx1EwqFdNhhh2nNmjVasWKFDj30UC1cuJBrlQMAZtzw8LDbizH99OlUGRgYGPfxxvLICZTJBMqT4WKgTMZf7oaP8vgP+hsfzoYgksObBxPLlgmGFQ+GM+uNkRUfkj00cg3K1HC4R3Z0wL1z07PPPjvmKevq6sYElE1NTVq4cKF8vtnd5RgAUBhDQ0PasWOHtm/frm3btmnLli3asmWLhofHXtvY8YWUKF0gp3SBEmULlCipPSiOJgNznmXJBMoVD5QrXnNosi4Rlad/30g4mSxDQ0N66qmn9NRTT7kPLS0t1SGHHKLly5fr0EMP1fLly7Vs2TIOYAMAXpNYLKb29nbt3bs3Z9jY2dk54XM4vtBIj8ZU2Dg6bnwlB33QOBcQRM4HliXjK1HCV6JExcLMeYlYxh287eFe2ZGRu3knompvb1d7e3vGzqeUPNV7wYIFWrhwoRoaGrRw4cKM8ZqaGm6aAwBzXCQS0a5du7R9+3a1tLS4wz179ijXCRXG9ipRWpsRPBp/aRGWHMAB8fiVCC9SIrwoOW2c5HXK+9vlGdgne7BT9lCXBgYGtGnTJm3atMl9qG3bam5udoPJQw89VEuXLlVtbS37hAAASVJ/f7/a29vV1tamvXv3Zozv3btXnZ2dOfcx0xmPT85IL8bMsDF5KrU8xFyzHX+h+c7jk1NaK6c068LkxsiKR2RFenOGlI4Td38scvH5fGpoaHBLKqSsra3VggULVFtbS49KAJgFjDHq7Ox0r6PT2tqqlpYWtbS0qLW1Ne+1dBxvUE6oSk6oMnlzmdIFckJh7mgNHEwsW05JtZySasW0OlnnOMkzawY75RnslD3UIXuwS4oPa/v27dq+fbv++te/uk8RCAS0aNEi97I/6WfYVFdXc4o3ABwEjDHq7e3V/v37tX//fu3bt88dT023tbVNeOq0lHb6tD/tWo1pgeN8OH36YEcQidwsS8YXlPEF5ZTVZc4zRlZsSFakT3a0X3akLzkeGRmPDigWi2nXrl3atWtX3peoqqrSggUL3JIeUqaGJSUlBX6jAHDwGxgYyLgzYGo8NYxG819H2Hj8SoSqRkPHkSHXdQTmKXs0nIyn6oyRFRscDScHO+UZ6pA13KdIJKJt27Zp27ZtY54qFAplhJOLFi1SXV2duy/IfiAAFFcikVBvb697ObdUyRU2jrc/mc7xBpIho790pBdjKnQsk/GXyniDBI0HOYJITJ1lyfhLZPwlclQ/dr5xZEUHZEf6RwLKZEhpRftlxwZlRQdkGcf9EXvppZfyvlQwGFR1dbUqKytVVVXlDlPj6fMqKio49QfAvBKJRNTZ2emWjo4OdXR0uOOdnZ1qa2tTb2/vuM9jZMn4S0ePNruBY1UycGRnECkj23ggl+RvR6VUc8hIhSMrNjBysLpfVnTkwHW0T1Z0UENDQ3r55Zf18ssv53y+UCik6upq1dTUZAzTS1lZ2cy9QQCY4xKJhAYGBtTf3+8Oe3t71dPTo97eXnV3d2eM9/X1TXiqdDrH45fxhmT8ITm+kIw3JPlGxv0lcnyl45867cRlRfun4Z0ir0Si2EtAEIkCsGyZQLkSgXJJC8fON0ZWfFjWSChpR0eGqZAyOig72i/LiWt4eFi7d+/W7t27J3xZ27ZVUVGh8vLyKZWSkhKFQiFODQJQVPF4XP39/err61Nvb6+7Y9jX1+fW9fT0ZISOfX19k35+xxvMOLXFBMrlpE558ZdJHMjBJFjRAZU9+7/FXgzME0NDQ2ptbVVra2uxFwUAMAl2IiololKkp9iLgjzi8fjEjQpsbgWRTkJKxIq9FJgGxvbKBCqkQIXy5vGJ2EhgOSQ7PiwrNpycHqmzRurs2LAsJyrHcdTd3a3u7u4DWqZQKKSSkhIFg8GMYfp4KBRSKBRSIBCQz+dTIBBQIBCQ3+93x1PzgsGgO+71zq2vGoDxJRIJxeNxRaNRt0QiEQ0NDWloaEjDw8MZw1x1g4ODGhgYUF9fn/r7+zU4OHhAy2KUvCGZ8YXk+ILuuHGPPpfK8ZdJnnGuy2sSs+LoKOaARPF3XgEAADB3zal0JPjqE/K2PV3sxcBBKhUWAMBcYsnIig1IsQF5ir0wmFfOOOMMnX322br55pt1++23T+nULQAAAMxPnIcFAACAKTv77LPV3Nyss88+mxASAAAAkzKnekT6LKOQxyn2YgCviZEUcyzFDdekBGaK3zbyWgQlwGvlGGnYSR7Hvvnmm90ekZZlEUYCAABgQnMqiDx3xaDetpRrE6Hw4o40nLA0nLAUSQ3jmXVRJzmMJJQ2bik6UhdJr3OkaMJSzEk+bjbyeDzy+XzyeDzyer05i8fjceenxsebtm3bHabG06fz1dm2Lcuy5PF4ZFlWRl12m9QwVbKn0+slZYynplPD9BsWpU+/1hsZGWPcf9DT/1HPVe84jlufXhzHyZif3s5xHHc6kUiMW5dIJJRIJNzx7GH2/NS1ENPHUyVfXSwWUzweVywWyyjFFHUsJSwp6DHJ4jWj4x4p6DUKeNLrjELeZClJDT2OO+2zuZE05qd9Q7Yue6RKknT77bfrj3/8IyEkAAAAJm1OBZFAPo5JhoSDcUvD8WRYODQyTB8filsaSoy2SRYpMjIvFTomZqi3osfjybjJTfrNb6ZSfD6f/H6/vF6vO50a93q98vl8Y0oqXEyFj9w1HIWUCkRzhZTRaNQd5iqRSCRn3fDw8Jhhdt3Q0JAb4iaMpYG4pYG4pMhrez8eKy2g9BqFPEYlPkdlXqNSn1Gp1xkZJqfLfI47HvIYQkzMWdUBR988oUuS1B2xtH/Yo9pgQpUBgkjMfY5J7hMOxCwNJpLbjMGYpaGEraHY6D7kUEIajCfrh+Mj+58JS0bT/+NuWVbGPmLqZoi59hdT+4PZ4+l12fuA6fuJ6XUeD1cdRnL/LRaLuftwucaj0ah7A79YLKZIJJJzHy+9LrVvl9pnS42nSjQaLcj78dupg82Oe9A56DUKeeQedC7xGpX6HIU8RqU+qcSbrPNyUTscJH64KaCHirwMBJEoumhqZy5uaTBhJ4cj0+54YnR67Lit4URh/qv3+/3unbJTJRgMunfOzh7PV3KFjYFAgLtpY96wLMv9x2emxeNx92ZUqbtVZ4+n16Xf0Tq9pO5sbYxRwljqi1nqO4COnvZIiFnqTQaUFf6RoS//dJDwErOEx5YWhJLh/oKQtELcbR2zkzHJ/cvemKW+qK3emK2+qKW+mK2+mKX+mKX+mK2BmKX+uK3+WDKAfK1hosfjUVlZmUpLSxUKhVRaWqqSkpIJx1P7mKl9y9S4z+fjYDHmHcdxMg4y59pvyx7PNd3f36/+/n4NDw9LSp4hE3Us9camnir67eR+WZlvdB+uzGdU7k/us5X7HJX7jSp8jsr9jsp9hJeYnfyz4DgTKQhek4QjDY30RBxMCxAH43ZG3WBaqDgYzwwbp/NaiT6fTyUlJVMq2UFjeiEoBOY+r9er8vJylZeXv+bnchxHQ0NDbjCZHlT29fVllN7eXvX396u3t9eti0QickzqH2Bp79Dk9gS8llF5amfX7yjsNyNDR2Gfo3BgdLrcZ2TzPyuAg1DckXpjtnoilnqitnqitnqj1kjImDUeO/AzXILBoMLhsMrLy1VRUeFuQ8rKyiYsgUCA4BB4jWzbdv8fmw7xeDxj3y19mF5S+2+pYWrccRxFHUudEY86p3BWTYk3uV9WMbJ/Vu5zFA4k9+PCI/ttlSPDIP92Yh5hdZ/HjJEiCWX0Qhx0T0FJHhUeSth5wsTp7YloWZZKSkrcI8ipkppOzSspKXHnpY+npv1+/7QsDwDkYtu2+5tTV1c35cdHIpGMoLK3t1fd3d3q6elRT0+PO55eNzw8rLix1BW11BWd+NC6pWRoGR4JLSsDRpV+R1UBR5UBxx0P+51ZcUQUAIbiUlfEVnfEVvdIwOiWkdCxO5rstTjVHoslJSWqrKzMKBUVFW5JDxxT4+xPAgcXr9ercDiscDg85cc6juMecE4PKXt6etz9uPSS2n9zHGekc87kDjz77eT+WnhkH63Cb9yQMrUPVx1I9sLkgDPmOoLIOSrmKPO6h2mnLKeulTiU3fswYWWe9pyw5ExTb8RgMOgGh6nwMH2YXZddHwqFMm4gAgAHo9RlGWprayf9mOHhYXentqurK6N0dnZmDHt6emSM1BsbOe1oYPznLvUmd2yrRnZ8q0ZCy+qgo+pAQtWB5I4wO7wADkTckbqjthsydmWV1LypHNi2bVvV1dVuqaysVFVV1ZiwsaqqSuFwWIFAoIDvEMDBzrZtt1d0Y2PjpB7jOI76+voyDjCnSmdnZ0bp6urS0NCQoo6l9mGP2ofHDy09lskIJ6tG9uMqAyY5PhJYhrxcuxmzF0HkDEg4St412Um7A3PWDVMiiZE7Mscz79Q87F4Ue3Q43acz27adN0CcTJBYVlbGKcwAUCCp64TV19dP2DYej2cElp2dnero6HDL/v373fFoNKqBuK2BuK3WcQJLjzXao7I6OLqDW502Xel3uA4SMM/EnWQvxs6IrY7hZKDYEbHVOexR50jQ2DOJXtwppaWlqqmpUU1NTUbQmCqp+oqKCg5eA5jVbNueUg/MwcFBd78t/SBzR0eHO9y3b5+6u7uVMBo5RXz8wDLoGd1XS99vSx/nBoooloMiPTJGMmlDZyT8TxjJMZYSRoobS46j0XGTHE84yfnJYinmSDHHUnxkGHOHuepGLnibsEZCxrTphKWII0UT0xsaZkv1REydppy67mF2aJhrOlWCwSDXsgGAg4DX63X/kR+PMUb9/f0ZwWRqfP/+/dq3b5/27dunzs5OJRxHHRGPOiIeqTf381ky7ilDNUFHNSPD6qCjmkBCNcHktZHY1ABzg2OSPRk7h5MhY8dI2NiZFjRONmRM/S4tWLBANTU1qq2tHVNqampUUlJS4HcFALNT6n/4RYsWjdsuHo+7weT+/fsz9t3S9+H6+/s1n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@@ -50,13 +40,12 @@
     
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-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
-    
   </head>
   
   
@@ -67,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -118,39 +109,31 @@
       </div>
     </div>
     
-      
-        <form class="md-header__option" data-md-component="palette">
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
-    
-      <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
-      </label>
-    
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
-    
-      <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
-      </label>
-    
-  
-</form>
-      
-    
-    
-      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,60 +184,46 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
@@ -266,35 +232,14 @@
   
     
   
-  
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link md-tabs__link--active">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
     </div>
@@ -388,19 +334,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,19 +349,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../installation.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+        installation
       </a>
     </li>
   
@@ -428,323 +364,228 @@
     
       
       
-  
+      
+
   
   
   
     
-    
-      
-        
-      
-        
-      
-        
+    <li class="md-nav__item md-nav__item--nested">
       
-        
       
-    
-    
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
       
       
-    
-    
-    <li class="md-nav__item md-nav__item--nested">
       
         
+          
         
           
         
-        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" type="checkbox" id="__nav_3" >
+          
         
           
-          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-            
-  
-  <span class="md-ellipsis">
-    churn_pred
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_3">
-            <span class="md-nav__icon md-icon"></span>
-            churn_pred
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+        code_profiling
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/preprocessing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    preprocessing
-  </span>
-  
-
+        preprocessing
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Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( 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.83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
-</style>
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=93ab1f50">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+    init_mathjax();
+    </script>
+    <!-- End of mathjax configuration --><div class="jupyter-wrapper">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="models">Models<a class="anchor-link" href="#models">¶</a></h1>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="models">Models<a class="anchor-link" href="#models">&#182;</a></h1>
 </div>
 </div>
 </div>
 </div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=9280b194">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="imports">Imports<a class="anchor-link" href="#imports">&#182;</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=6e28ff71">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 
 <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
-<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">".."</span><span class="p">)</span>
+<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;..&quot;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
-<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_columns"</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
-<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_rows"</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_columns&quot;</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-1">import os
+<div id="cell-1" class="clipboard-copy-txt">import os
 import sys
 
 sys.path.append(os.getcwd())
@@ -1403,31 +1809,36 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
 
 pd.set_option("display.max_columns", 200)
 pd.set_option("display.max_rows", 300)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=9ea38cc4">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">dill</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">dill</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 
@@ -1452,7 +1863,7 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
 <span class="c1"># from typing import Optional, Dict</span>
 <span class="c1"># tracemalloc.start()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-2">import dill
+<div id="cell-2" class="clipboard-copy-txt">import dill
 import numpy as np
 import matplotlib.pyplot as plt
 
@@ -1476,92 +1887,113 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
 # import warnings
 # from typing import Optional, Dict
 # tracemalloc.start()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
 </div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=e590c40c">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h1>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="dataset">Dataset<a class="anchor-link" href="#dataset">&#182;</a></h1>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=01227d03">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">valid_size</span> <span class="o">=</span> <span class="mf">0.2</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">valid_size</span> <span class="o">=</span> <span class="mf">0.2</span>
 <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.5</span>
 <span class="n">random_state</span> <span class="o">=</span> <span class="mi">1</span>
 <span class="n">test_n_valid_combined</span> <span class="o">=</span> <span class="kc">True</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-3">valid_size = 0.2
+<div id="cell-3" class="clipboard-copy-txt">valid_size = 0.2
 test_size = 0.5
 random_state = 1
 test_n_valid_combined = True</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=1be80c66-1622-49eb-9f76-6b4fc32f6422">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">"data/dataset_auxiliary_features_cleaned.parquet"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">&quot;data/dataset_auxiliary_features_cleaned.parquet&quot;</span><span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-4">df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
+<div id="cell-4" class="clipboard-copy-txt">df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
 df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -1575,249 +2007,255 @@ <h1 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h1>
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>CustomerId</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment5</th>
-<th>Surname_Country</th>
-<th>Surname_Country_region</th>
-<th>Surname_Country_subregion</th>
-<th>Country_region</th>
-<th>Country_subregion</th>
-<th>is_native</th>
-<th>Country_hemisphere</th>
-<th>Country_gdp_per_capita</th>
-<th>Country_IncomeGroup</th>
-<th>Surname_Country_gdp_per_capita</th>
-<th>Surname_Country_IncomeGroup</th>
-<th>working_class</th>
-<th>stage_of_life</th>
-<th>generation</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>15787619</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>160980.03</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>145936.28</td>
-<td>0</td>
-<td>neutral</td>
-<td>4 stars</td>
-<td>Taiwan</td>
-<td>Asia</td>
-<td>Eastern Asia</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>32756.00000</td>
-<td>None</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>1</th>
-<td>15770309</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>151762.74</td>
-<td>1</td>
-<td>0</td>
-<td>1</td>
-<td>127014.32</td>
-<td>0</td>
-<td>neutral</td>
-<td>1 star</td>
-<td>United States</td>
-<td>Americas</td>
-<td>Northern America</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>76329.58227</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>2</th>
-<td>15569178</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>82767.42</td>
-<td>1</td>
-<td>1</td>
-<td>0</td>
-<td>71811.90</td>
-<td>0</td>
-<td>neutral</td>
-<td>2 stars</td>
-<td>Russian Federation</td>
-<td>Europe</td>
-<td>Eastern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>3</th>
-<td>15795519</td>
-<td>716</td>
-<td>Germany</td>
-<td>Female</td>
-<td>18</td>
-<td>3</td>
-<td>128743.80</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>197322.13</td>
-<td>0</td>
-<td>neutral</td>
-<td>2 stars</td>
-<td>Russian Federation</td>
-<td>Europe</td>
-<td>Eastern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>66616.02225</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>4</th>
-<td>15621893</td>
-<td>727</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>4</td>
-<td>133550.67</td>
-<td>1</td>
-<td>1</td>
-<td>1</td>
-<td>46941.41</td>
-<td>0</td>
-<td>positive</td>
-<td>1 star</td>
-<td>Italy</td>
-<td>Europe</td>
-<td>Southern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>55442.07843</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>CustomerId</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment5</th>
+      <th>Surname_Country</th>
+      <th>Surname_Country_region</th>
+      <th>Surname_Country_subregion</th>
+      <th>Country_region</th>
+      <th>Country_subregion</th>
+      <th>is_native</th>
+      <th>Country_hemisphere</th>
+      <th>Country_gdp_per_capita</th>
+      <th>Country_IncomeGroup</th>
+      <th>Surname_Country_gdp_per_capita</th>
+      <th>Surname_Country_IncomeGroup</th>
+      <th>working_class</th>
+      <th>stage_of_life</th>
+      <th>generation</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>15787619</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>160980.03</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>4 stars</td>
+      <td>Taiwan</td>
+      <td>Asia</td>
+      <td>Eastern Asia</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>32756.00000</td>
+      <td>None</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>15770309</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>151762.74</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>1 star</td>
+      <td>United States</td>
+      <td>Americas</td>
+      <td>Northern America</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>76329.58227</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>15569178</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
+      <td>82767.42</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0</td>
+      <td>71811.90</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>2 stars</td>
+      <td>Russian Federation</td>
+      <td>Europe</td>
+      <td>Eastern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>15795519</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>128743.80</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>197322.13</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>2 stars</td>
+      <td>Russian Federation</td>
+      <td>Europe</td>
+      <td>Eastern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>66616.02225</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>15621893</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>133550.67</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1</td>
+      <td>46941.41</td>
+      <td>0</td>
+      <td>positive</td>
+      <td>1 star</td>
+      <td>Italy</td>
+      <td>Europe</td>
+      <td>Southern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>55442.07843</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8072a4da-97e5-451b-99f8-3b16f7efbe2e">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-5">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">target_col</span> <span class="o">=</span> <span class="s2">"Exited"</span>
-<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"CustomerId"</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-5">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">target_col</span> <span class="o">=</span> <span class="s2">&quot;Exited&quot;</span>
+<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CustomerId&quot;</span><span class="p">]</span>
 <span class="n">cat_cols</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">"Country"</span><span class="p">,</span>
-    <span class="s2">"Gender"</span><span class="p">,</span>
-    <span class="s2">"HasCreditCard"</span><span class="p">,</span>
-    <span class="s2">"IsActiveMember"</span><span class="p">,</span>
-    <span class="s2">"CustomerFeedback_sentiment3"</span><span class="p">,</span>
-    <span class="s2">"CustomerFeedback_sentiment5"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_region"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_subregion"</span><span class="p">,</span>
-    <span class="s2">"Country_region"</span><span class="p">,</span>
-    <span class="s2">"Country_subregion"</span><span class="p">,</span>
-    <span class="s2">"is_native"</span><span class="p">,</span>
-    <span class="s2">"Country_hemisphere"</span><span class="p">,</span>
-    <span class="s2">"Country_IncomeGroup"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_IncomeGroup"</span><span class="p">,</span>
-    <span class="s2">"working_class"</span><span class="p">,</span>
-    <span class="s2">"stage_of_life"</span><span class="p">,</span>
-    <span class="s2">"generation"</span><span class="p">,</span>
+    <span class="s2">&quot;Country&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Gender&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;HasCreditCard&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;IsActiveMember&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CustomerFeedback_sentiment3&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CustomerFeedback_sentiment5&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_region&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_subregion&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;is_native&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_hemisphere&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_IncomeGroup&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_IncomeGroup&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;working_class&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;stage_of_life&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;generation&quot;</span><span class="p">,</span>
 <span class="p">]</span>
 <span class="n">cont_cols</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
     <span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span> <span class="o">+</span> <span class="n">cat_cols</span> <span class="o">+</span> <span class="p">[</span><span class="n">target_col</span><span class="p">]</span>
 <span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-5">target_col = "Exited"
+<div id="cell-5" class="clipboard-copy-txt">target_col = "Exited"
 id_cols = ["CustomerId"]
 cat_cols = [
     "Country",
@@ -1842,125 +2280,155 @@ <h1 id="dataset">Dataset<a class="anchor-link" href="#dataset">¶</a></h1>
 cont_cols = df_pd.drop(
     columns=id_cols + cat_cols + [target_col]
 ).columns.values.tolist()</div>
+    
+        </div>
+    </div>
+
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-6">df_pd[cat_cols] = df_pd[cat_cols].astype(str)</div>
-</div>
-</div>
+<div id="cell-6" class="clipboard-copy-txt">df_pd[cat_cols] = df_pd[cat_cols].astype(str)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
 </div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=c08eb903-8738-4ec8-bd15-1c961c687454">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>check possible class imbalance</p>
+
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 </div>
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-<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=59dabfe1-bc72-4bf4-b31c-ba9d7adb543b">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-7">df_pd[target_col].value_counts()</div>
-</div>
-</div>
+<div id="cell-7" class="clipboard-copy-txt">df_pd[target_col].value_counts()</div>
+    
+        </div>
+    </div>
+
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+
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+
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-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>Exited
 0    7963
 1    2037
 Name: count, dtype: int64</pre>
 </div>
+
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+
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+
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+
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 </div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=78d2c2b2">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="preprocessing">Preprocessing<a class="anchor-link" href="#preprocessing">¶</a></h1><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="preprocessing">Preprocessing<a class="anchor-link" href="#preprocessing">&#182;</a></h1><ul>
 <li>divide dataset into train, test, valid</li>
 <li>scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...)</li>
 </ul>
+
 </div>
 </div>
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_valid</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_train</span><span class="p">,</span> <span class="n">df_valid</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
     <span class="n">df_pd</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="n">valid_size</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
 <span class="p">)</span>
 <span class="n">df_valid</span><span class="p">,</span> <span class="n">df_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
@@ -1975,9 +2443,9 @@ <h1 id="preprocessing">Preprocessing<a class="anchor-link" href="#preprocessing"
 <span class="n">df_test</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="n">scaler_mapper_def</span> <span class="o">=</span> <span class="p">{</span>
-    <span class="s2">"cont_cols"</span><span class="p">:</span> <span class="n">StandardScaler</span><span class="p">,</span>
-    <span class="s2">"cat_cols"</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
-    <span class="s2">"id_cols"</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="s2">&quot;cont_cols&quot;</span><span class="p">:</span> <span class="n">StandardScaler</span><span class="p">,</span>
+    <span class="s2">&quot;cat_cols&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="s2">&quot;id_cols&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
 <span class="p">}</span>
 <span class="n">scaler</span> <span class="o">=</span> <span class="n">scaler_mapper</span><span class="p">(</span>
     <span class="n">cont_cols</span><span class="o">=</span><span class="n">cont_cols</span><span class="p">,</span>
@@ -1990,7 +2458,7 @@ <h1 id="preprocessing">Preprocessing<a class="anchor-link" href="#preprocessing"
 <span class="n">df_test_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_test</span><span class="p">)</span>
 <span class="n">df_valid_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_valid</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-8">df_train, df_valid = train_test_split(
+<div id="cell-8" class="clipboard-copy-txt">df_train, df_valid = train_test_split(
     df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state
 )
 df_valid, df_test = train_test_split(
@@ -2019,42 +2487,47 @@ <h1 id="preprocessing">Preprocessing<a class="anchor-link" href="#preprocessing"
 df_train_scaled = scaler.fit_transform(df_train)
 df_test_scaled = scaler.transform(df_test)
 df_valid_scaled = scaler.transform(df_valid)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
 </div>
-</div>
-</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=43bcbf4e">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#logistic-regression">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#logistic-regression">&#182;</a></h2>
 </div>
 </div>
 </div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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 </div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">LR_clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">class_weight</span><span class="o">=</span><span class="s2">"balanced"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">LR_clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">class_weight</span><span class="o">=</span><span class="s2">&quot;balanced&quot;</span><span class="p">)</span>
 <span class="n">LR_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span>
     <span class="n">df_train_scaled</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">id_cols</span> <span class="o">+</span> <span class="n">cat_cols</span><span class="p">),</span>
     <span class="n">df_train_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span>
@@ -2064,11 +2537,11 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
 <span class="p">)</span>
 
 <span class="nb">print</span><span class="p">(</span>
-    <span class="s2">"LR classification report :</span><span class="se">\n</span><span class="s2">"</span>
+    <span class="s2">&quot;LR classification report :</span><span class="se">\n</span><span class="s2">&quot;</span>
     <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span> <span class="n">LR_predicted</span><span class="p">))</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-9">LR_clf = LogisticRegression(class_weight="balanced")
+<div id="cell-9" class="clipboard-copy-txt">LR_clf = LogisticRegression(class_weight="balanced")
 LR_clf.fit(
     df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols),
     df_train_scaled[target_col],
@@ -2081,17 +2554,27 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
     "LR classification report :\n"
     + str(classification_report(df_test_scaled[target_col], LR_predicted))
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>LR classification report :
               precision    recall  f1-score   support
 
@@ -2105,29 +2588,34 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=eb376d7d-fa9c-494e-9148-aba3df20fc24">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders?</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders?</span>
 <span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span>
 
 <span class="n">criterion</span> <span class="o">=</span> <span class="n">f1_score</span>
@@ -2142,27 +2630,27 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
         <span class="o">&gt;=</span> <span class="n">t</span>
     <span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
     <span class="n">threshold_score</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
-        <span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">criterion</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span> <span class="n">preds_bin</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">"weighted"</span><span class="p">))</span>
+        <span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">criterion</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span> <span class="n">preds_bin</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s2">&quot;weighted&quot;</span><span class="p">))</span>
     <span class="p">)</span>
 
 <span class="n">threshold_score</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">threshold_score</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">best_threshold</span><span class="p">,</span> <span class="n">best_score</span> <span class="o">=</span> <span class="n">threshold_score</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">threshold_score</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The best threshold</span><span class="se">\n</span><span class="si">{</span><span class="n">best_threshold</span><span class="si">}</span><span class="se">\n</span><span class="s2">, with score:</span><span class="se">\n</span><span class="si">{</span><span class="n">best_score</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;The best threshold</span><span class="se">\n</span><span class="si">{</span><span class="n">best_threshold</span><span class="si">}</span><span class="se">\n</span><span class="s2">, with score:</span><span class="se">\n</span><span class="si">{</span><span class="n">best_score</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-10"># which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders?
+<div id="cell-10" class="clipboard-copy-txt"># which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders?
 from sklearn.metrics import f1_score
 
 criterion = f1_score
 
 threshold_score = []
 for t in np.arange(0.2, 0.8, 0.01):
-    # preds_bin = [int(p &gt; t) for p in y_pred]
+    # preds_bin = [int(p > t) for p in y_pred]
     preds_bin = (
         LR_clf.predict_proba(
             df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols)
         )[:, 1]
-        &gt;= t
+        >= t
     ).astype(int)
     threshold_score.append(
         (t, criterion(df_test_scaled[target_col], preds_bin, average="weighted"))
@@ -2172,17 +2660,27 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
 best_threshold, best_score = threshold_score[0][0], threshold_score[0][1]
 
 print(f"The best threshold\n{best_threshold}\n, with score:\n{best_score}")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
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+
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>The best threshold
 0.6100000000000003
 , with score:
@@ -2190,29 +2688,34 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=3c4fb5ae">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">y_onehot_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">y_onehot_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
 <span class="n">y_score</span> <span class="o">=</span> <span class="n">LR_clf</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span>
     <span class="n">df_test_scaled</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span> <span class="o">+</span> <span class="n">id_cols</span> <span class="o">+</span> <span class="n">cat_cols</span><span class="p">)</span>
 <span class="p">)</span>
@@ -2226,10 +2729,10 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
 <span class="n">micro_roc_auc_weighted</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span>
     <span class="n">y_onehot_test</span><span class="p">,</span>
     <span class="n">y_score</span><span class="p">,</span>
-    <span class="n">average</span><span class="o">=</span><span class="s2">"weighted"</span><span class="p">,</span>
+    <span class="n">average</span><span class="o">=</span><span class="s2">&quot;weighted&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-11">y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values
+<div id="cell-11" class="clipboard-copy-txt">y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values
 y_score = LR_clf.predict_proba(
     df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols)
 )
@@ -2245,46 +2748,61 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
     y_score,
     average="weighted",
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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 <div class="jp-InputArea jp-Cell-inputArea">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Per class AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_per_class</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Weighted AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_weighted</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Per class AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_per_class</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Weighted AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_weighted</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-12">print(f"Per class AUC:\n{micro_roc_auc_per_class}")
+<div id="cell-12" class="clipboard-copy-txt">print(f"Per class AUC:\n{micro_roc_auc_per_class}")
 print(f"Weighted AUC:\n{micro_roc_auc_weighted}")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Per class AUC:
 [0.76928761 0.76928761]
 Weighted AUC:
@@ -2292,42 +2810,48 @@ <h2 id="logistic-regression">Logistic Regression<a class="anchor-link" href="#lo
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4c6ea9fe">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></h1><p><strong>NOTE</strong>:</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">&#182;</a></h1><p><strong>NOTE</strong>:</p>
 <p><a href="https://docs.h2o.ai/h2o/latest-stable/h2o-docs/welcome.html#java-requirements">java is required</a>: <code>sudo apt install openjdk-17-jdk</code></p>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=542a4ad0">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># initialize H2O</span>
-<span class="n">h2o</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="s2">"h2o_logs"</span><span class="p">,</span> <span class="n">log_level</span><span class="o">=</span><span class="s2">"WARN"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># initialize H2O</span>
+<span class="n">h2o</span><span class="o">.</span><span class="n">init</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="s2">&quot;h2o_logs&quot;</span><span class="p">,</span> <span class="n">log_level</span><span class="o">=</span><span class="s2">&quot;WARN&quot;</span><span class="p">)</span>
 
 <span class="c1"># read as h2o file</span>
 <span class="n">h2o_train</span> <span class="o">=</span> <span class="n">h2o</span><span class="o">.</span><span class="n">H2OFrame</span><span class="p">(</span><span class="n">df_train_scaled</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span><span class="p">))</span>
@@ -2356,17 +2880,17 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
 <span class="n">m</span> <span class="o">=</span> <span class="n">aml</span><span class="o">.</span><span class="n">get_best_model</span><span class="p">()</span>
 
 <span class="c1"># Leaderboard, show and save</span>
-<span class="n">lb</span> <span class="o">=</span> <span class="n">h2o</span><span class="o">.</span><span class="n">automl</span><span class="o">.</span><span class="n">get_leaderboard</span><span class="p">(</span><span class="n">aml</span><span class="p">,</span> <span class="n">extra_columns</span><span class="o">=</span><span class="s2">"ALL"</span><span class="p">)</span>
+<span class="n">lb</span> <span class="o">=</span> <span class="n">h2o</span><span class="o">.</span><span class="n">automl</span><span class="o">.</span><span class="n">get_leaderboard</span><span class="p">(</span><span class="n">aml</span><span class="p">,</span> <span class="n">extra_columns</span><span class="o">=</span><span class="s2">&quot;ALL&quot;</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">lb</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">model_performance</span><span class="p">(</span><span class="n">h2o_valid</span><span class="p">))</span>
 <span class="n">predictions</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">h2o_test</span><span class="p">)</span>
 
 <span class="c1"># save results and model</span>
-<span class="c1"># h2o.export_file(lb, path="h2o_logs/leaderboard.csv", force=True)</span>
+<span class="c1"># h2o.export_file(lb, path=&quot;h2o_logs/leaderboard.csv&quot;, force=True)</span>
 <span class="c1"># MOJO is h2o version agnostic</span>
-<span class="c1"># m.save_mojo("h2o_logs/bestmodel.zip")</span>
+<span class="c1"># m.save_mojo(&quot;h2o_logs/bestmodel.zip&quot;)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-13"># initialize H2O
+<div id="cell-13" class="clipboard-copy-txt"># initialize H2O
 h2o.init(log_dir="h2o_logs", log_level="WARN")
 
 # read as h2o file
@@ -2405,20 +2929,30 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
 # h2o.export_file(lb, path="h2o_logs/leaderboard.csv", force=True)
 # MOJO is h2o version agnostic
 # m.save_mojo("h2o_logs/bestmodel.zip")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Checking whether there is an H2O instance running at http://localhost:54321..... not found.
 Attempting to start a local H2O server...
-  Java Version: openjdk version "17.0.10" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing)
+  Java Version: openjdk version &#34;17.0.10&#34; 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing)
   Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar
   Ice root: /tmp/tmpaylgzon8
   JVM stdout: /tmp/tmpaylgzon8/h2o_unknownUser_started_from_python.out
@@ -2428,9 +2962,16 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output" data-mime-type="text/html" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output " data-mime-type="text/html">
+
 <style>
 
 #h2o-table-1.h2o-container {
@@ -2467,12 +3008,12 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
   /* background: #F5F5F5 */
 }
 
-</style>
-<div class="h2o-container" id="h2o-table-1">
-<table class="h2o-table">
-<caption></caption>
-<thead></thead>
-<tbody><tr><td>H2O_cluster_uptime:</td>
+</style>      
+<div id="h2o-table-1" class="h2o-container">
+  <table class="h2o-table">
+    <caption></caption>
+    <thead></thead>
+    <tbody><tr><td>H2O_cluster_uptime:</td>
 <td>01 secs</td></tr>
 <tr><td>H2O_cluster_timezone:</td>
 <td>Etc/UTC</td></tr>
@@ -2502,13 +3043,20 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
 <td>False</td></tr>
 <tr><td>Python_version:</td>
 <td>3.10.11 final</td></tr></tbody>
-</table>
+  </table>
 </div>
+
 </div>
+
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
 Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
 Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
@@ -2646,30 +3194,35 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=340ff74f">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">y_onehot_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
-<span class="n">y_score</span> <span class="o">=</span> <span class="n">h2o</span><span class="o">.</span><span class="n">as_list</span><span class="p">(</span><span class="n">predictions</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">"predict"</span><span class="p">]))</span><span class="o">.</span><span class="n">values</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">y_onehot_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
+<span class="n">y_score</span> <span class="o">=</span> <span class="n">h2o</span><span class="o">.</span><span class="n">as_list</span><span class="p">(</span><span class="n">predictions</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s2">&quot;predict&quot;</span><span class="p">]))</span><span class="o">.</span><span class="n">values</span>
 
 <span class="n">micro_roc_auc_per_class</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span>
     <span class="n">y_onehot_test</span><span class="p">,</span>
@@ -2680,10 +3233,10 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
 <span class="n">micro_roc_auc_weighted</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span>
     <span class="n">y_onehot_test</span><span class="p">,</span>
     <span class="n">y_score</span><span class="p">,</span>
-    <span class="n">average</span><span class="o">=</span><span class="s2">"weighted"</span><span class="p">,</span>
+    <span class="n">average</span><span class="o">=</span><span class="s2">&quot;weighted&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-14">y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values
+<div id="cell-14" class="clipboard-copy-txt">y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values
 y_score = h2o.as_list(predictions.drop(["predict"])).values
 
 micro_roc_auc_per_class = roc_auc_score(
@@ -2697,64 +3250,89 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
     y_score,
     average="weighted",
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>/opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using:
 
 with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True):
     pandas_df = h2o_df.as_data_frame()
 
-  warnings.warn("Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using"
+  warnings.warn(&#34;Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using&#34;
 </pre>
 </div>
 </div>
+
 </div>
+
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+
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Per class AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_per_class</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Weighted AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_weighted</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Per class AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_per_class</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Weighted AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_weighted</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-15">print(f"Per class AUC:\n{micro_roc_auc_per_class}")
+<div id="cell-15" class="clipboard-copy-txt">print(f"Per class AUC:\n{micro_roc_auc_per_class}")
 print(f"Weighted AUC:\n{micro_roc_auc_weighted}")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Per class AUC:
 [0.90083383 0.90083383]
 Weighted AUC:
@@ -2762,41 +3340,46 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
 </pre>
 </div>
 </div>
+
 </div>
+
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+
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=78e38bea">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-16">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
 <span class="n">n_classes</span> <span class="o">=</span> <span class="mi">2</span>
 
 <span class="k">for</span> <span class="n">class_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
     <span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_predictions</span><span class="p">(</span>
         <span class="n">y_onehot_test</span><span class="p">[:,</span> <span class="n">class_id</span><span class="p">],</span>
         <span class="n">y_score</span><span class="p">[:,</span> <span class="n">class_id</span><span class="p">],</span>
-        <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">"ROC curve for class </span><span class="si">{</span><span class="n">class_id</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span>
+        <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;ROC curve for class </span><span class="si">{</span><span class="n">class_id</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
         <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span>
         <span class="c1"># plot_chance_level=(class_id == 2),</span>
     <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-16">fig, ax = plt.subplots(figsize=(6, 6))
+<div id="cell-16" class="clipboard-copy-txt">fig, ax = plt.subplots(figsize=(6, 6))
 n_classes = 2
 
 for class_id in range(n_classes):
@@ -2807,64 +3390,95 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
         ax=ax,
         # plot_chance_level=(class_id == 2),
     )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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+
+
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+
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"
+class="
+"
+>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=ed56a3dc-226c-45c8-924d-de25c110e9e0">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-17">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span>
-    <span class="s2">"Classification report:</span><span class="se">\n</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-        <span class="n">classification_report</span><span class="p">(</span><span class="n">df_test</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span> <span class="n">h2o</span><span class="o">.</span><span class="n">as_list</span><span class="p">(</span><span class="n">predictions</span><span class="p">[</span><span class="s2">"predict"</span><span class="p">]))</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-17">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="nb">print</span><span class="p">(</span>
+    <span class="s2">&quot;Classification report:</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+        <span class="n">classification_report</span><span class="p">(</span><span class="n">df_test</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span> <span class="n">h2o</span><span class="o">.</span><span class="n">as_list</span><span class="p">(</span><span class="n">predictions</span><span class="p">[</span><span class="s2">&quot;predict&quot;</span><span class="p">]))</span>
     <span class="p">)</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-17">print(
+<div id="cell-17" class="clipboard-copy-txt">print(
     "Classification report:\n{}".format(
         classification_report(df_test[target_col], h2o.as_list(predictions["predict"]))
     )
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Classification report:
               precision    recall  f1-score   support
 
@@ -2878,53 +3492,63 @@ <h1 id="h2o-automl">H2O AutoML<a class="anchor-link" href="#h2o-automl">¶</a></
 </pre>
 </div>
 </div>
+
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+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>/opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using:
 
 with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True):
     pandas_df = h2o_df.as_data_frame()
 
-  warnings.warn("Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using"
+  warnings.warn(&#34;Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using&#34;
 </pre>
 </div>
 </div>
+
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+
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+
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">&#182;</a></h1>
 </div>
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
-<span class="n">config</span><span class="p">[</span><span class="s2">"objective"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"binary"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[20]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-18">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">config</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="n">config</span><span class="p">[</span><span class="s2">&quot;objective&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;binary&quot;</span>
 
 <span class="n">df_train_scaled_enc</span> <span class="o">=</span> <span class="n">df_train_scaled</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 <span class="n">df_valid_scaled_enc</span> <span class="o">=</span> <span class="n">df_valid_scaled</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
@@ -2964,7 +3588,7 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
     <span class="n">free_raw_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-18">config = {}
+<div id="cell-18" class="clipboard-copy-txt">config = {}
 config["objective"] = "binary"
 
 df_train_scaled_enc = df_train_scaled.copy()
@@ -3004,56 +3628,71 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
     reference=flgbtrain,
     free_raw_data=False,
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">lgbm</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-19">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">lgbm</span><span class="o">.</span><span class="n">train</span><span class="p">(</span>
     <span class="n">config</span><span class="p">,</span>
     <span class="n">lgbtrain</span><span class="p">,</span>
     <span class="n">valid_sets</span><span class="o">=</span><span class="p">[</span><span class="n">lgbvalid</span><span class="p">],</span>
-    <span class="n">valid_names</span><span class="o">=</span><span class="p">[</span><span class="s2">""</span><span class="p">],</span>
+    <span class="n">valid_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;&quot;</span><span class="p">],</span>
     <span class="n">feval</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-19">model = lgbm.train(
+<div id="cell-19" class="clipboard-copy-txt">model = lgbm.train(
     config,
     lgbtrain,
     valid_sets=[lgbvalid],
     valid_names=[""],
     feval=None,
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+
+
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+
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines
 [LightGBM] [Info] Number of positive: 1630, number of negative: 6370
 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002671 seconds.
@@ -3067,47 +3706,52 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
 </pre>
 </div>
 </div>
+
 </div>
+
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+
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-20">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="n">predict_cls_lgbm_from_raw</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-20">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">res</span> <span class="o">=</span> <span class="n">predict_cls_lgbm_from_raw</span><span class="p">(</span>
     <span class="n">preds_raw</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">lgbtest</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">raw_score</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
-    <span class="n">task</span><span class="o">=</span><span class="s2">"binary"</span><span class="p">,</span>
+    <span class="n">task</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="n">result</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span>
     <span class="p">{</span>
-        <span class="s2">"predicted"</span><span class="p">:</span> <span class="n">res</span><span class="p">,</span>
-        <span class="s2">"ground_truth"</span><span class="p">:</span> <span class="n">df_test</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
+        <span class="s2">&quot;predicted&quot;</span><span class="p">:</span> <span class="n">res</span><span class="p">,</span>
+        <span class="s2">&quot;ground_truth&quot;</span><span class="p">:</span> <span class="n">df_test</span><span class="p">[</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
     <span class="p">}</span>
 <span class="p">)</span>
 
 <span class="nb">print</span><span class="p">(</span>
-    <span class="s2">"Classification report:</span><span class="se">\n</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
-        <span class="n">classification_report</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s2">"ground_truth"</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s2">"predicted"</span><span class="p">])</span>
+    <span class="s2">&quot;Classification report:</span><span class="se">\n</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+        <span class="n">classification_report</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s2">&quot;ground_truth&quot;</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s2">&quot;predicted&quot;</span><span class="p">])</span>
     <span class="p">)</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-20">res = predict_cls_lgbm_from_raw(
+<div id="cell-20" class="clipboard-copy-txt">res = predict_cls_lgbm_from_raw(
     preds_raw=model.predict(lgbtest.data, raw_score=True),
     task="binary",
 )
@@ -3124,17 +3768,27 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
         classification_report(result["ground_truth"], result["predicted"])
     )
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Classification report:
               precision    recall  f1-score   support
 
@@ -3148,32 +3802,37 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
 </pre>
 </div>
 </div>
+
 </div>
+
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+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-21">
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-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">y_onehot_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">y_onehot_test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">df_test_scaled</span><span class="p">[</span><span class="n">target_col</span><span class="p">])</span><span class="o">.</span><span class="n">values</span>
 <span class="n">y_score</span> <span class="o">=</span> <span class="n">predict_proba_lgbm_from_raw</span><span class="p">(</span>
     <span class="n">preds_raw</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">lgbtest</span><span class="o">.</span><span class="n">data</span><span class="p">),</span>
-    <span class="n">task</span><span class="o">=</span><span class="s2">"binary"</span><span class="p">,</span>
+    <span class="n">task</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span>
     <span class="n">binary2d</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
@@ -3186,13 +3845,13 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
 <span class="n">micro_roc_auc_weighted</span> <span class="o">=</span> <span class="n">roc_auc_score</span><span class="p">(</span>
     <span class="n">y_onehot_test</span><span class="p">,</span>
     <span class="n">y_score</span><span class="p">,</span>
-    <span class="n">average</span><span class="o">=</span><span class="s2">"weighted"</span><span class="p">,</span>
+    <span class="n">average</span><span class="o">=</span><span class="s2">&quot;weighted&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Per class AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_per_class</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Weighted AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_weighted</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Per class AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_per_class</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Weighted AUC:</span><span class="se">\n</span><span class="si">{</span><span class="n">micro_roc_auc_weighted</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-21">y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values
+<div id="cell-21" class="clipboard-copy-txt">y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values
 y_score = predict_proba_lgbm_from_raw(
     preds_raw=model.predict(lgbtest.data),
     task="binary",
@@ -3213,17 +3872,27 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
 
 print(f"Per class AUC:\n{micro_roc_auc_per_class}")
 print(f"Weighted AUC:\n{micro_roc_auc_weighted}")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Per class AUC:
 [0.89585797 0.89585797]
 Weighted AUC:
@@ -3231,62 +3900,62 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
 </pre>
 </div>
 </div>
+
+</div>
+
 </div>
+
 </div>
 </div>
 </div>
-</div> <!-- jp-Notebook -->
-</div> <!-- jupyter-wrapper -->
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-        <style>
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+        <a href="04_model_training_pipeline.html" class="md-footer__link md-footer__link--next" aria-label="Next: 04_model_training_pipeline" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              04_model_training_pipeline
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+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
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-        <footer class="md-footer">
+    </nav>
   
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
@@ -3312,12 +3981,10 @@ <h1 id="lightgbm">LightGBM<a class="anchor-link" href="#lightgbm">¶</a></h1>
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-    <script id="__config" type="application/json">{"base": "..", "features": ["navigation.tabs", "navigation.tabs.sticky", "navigation.indexes", "navigation.expand", "toc.integrate"], "search": "../assets/javascripts/workers/search.b8dbb3d2.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
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-      <script src="../assets/javascripts/bundle.dd8806f2.min.js"></script>
+      <script src="../assets/javascripts/bundle.6c7ad80a.min.js"></script>
       
         <script src="../stylesheets/extra.js"></script>
       
diff --git a/docs/site/notebooks/04_model_training_pipeline.html b/docs/site/notebooks/04_model_training_pipeline.html
index 67625a8..6ad3477 100644
--- a/docs/site/notebooks/04_model_training_pipeline.html
+++ b/docs/site/notebooks/04_model_training_pipeline.html
@@ -8,34 +8,24 @@
       
       
       
-      
-        <link rel="prev" href="03_models.html">
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-      <meta name="generator" content="mkdocs-1.6.0, mkdocs-material-9.5.20">
+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>04_model_training_pipeline - ecovadis_assignment</title>
+        <title>04_model_training_pipeline - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.1d29e8d0.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.06af60db.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
+        
       
       
-
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-      
-    
     
       
         
@@ -50,13 +40,12 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
-    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    <script>__md_scope=new URL("..",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
-    
   </head>
   
   
@@ -67,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
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     <div class="md-header__title" data-md-component="header-title">
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
           </span>
         </div>
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@@ -118,39 +109,31 @@
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-      
-        <form class="md-header__option" data-md-component="palette">
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-    
-    
-    
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-    
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-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
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+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
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+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,60 +184,46 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
@@ -266,35 +232,14 @@
   
     
   
-  
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link md-tabs__link--active">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
     </div>
@@ -388,19 +334,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,19 +349,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../installation.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+        installation
       </a>
     </li>
   
@@ -428,344 +364,242 @@
     
       
       
+      
+
   
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--nested">
       
-        
-      
-        
-      
-        
-      
-        
       
-    
-    
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
       
       
-    
-    
-    <li class="md-nav__item md-nav__item--nested">
       
         
+          
         
           
         
-        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" type="checkbox" id="__nav_3" >
+          
         
           
-          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-            
-  
-  <span class="md-ellipsis">
-    churn_pred
-  </span>
-  
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-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_3">
-            <span class="md-nav__icon md-icon"></span>
-            churn_pred
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+        code_profiling
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/preprocessing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    preprocessing
-  </span>
-  
-
+        preprocessing
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/training.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    training
-  </span>
-  
-
+        training
       </a>
     </li>
   
 
-              
             
-          </ul>
-        </nav>
-      
+          
+        </ul>
+      </nav>
     </li>
   
 
     
       
       
+      
+
   
   
     
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-        
-      
-        
       
-        
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" checked>
       
-        
       
-        
       
         
-      
+          
         
-      
-    
-    
-      
+          
         
+          
         
-      
-      
-    
-    
-    <li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
-      
+          
         
+          
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
+          
         
           
-          <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="">
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-  
-  <span class="md-ellipsis">
-    notebooks
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-  
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-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_4">
-            <span class="md-nav__icon md-icon"></span>
-            notebooks
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_4">
+          notebooks
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="notebooks" data-md-level="1">
+        <label class="md-nav__title" for="__nav_4">
+          <span class="md-nav__icon md-icon"></span>
+          notebooks
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
-  </span>
-  
-
+        TBD_00_auxiliary_features_surname_origin_country_classification.ipynb
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
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var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-scrollbar-background-color: #3f4244;--jp-scrollbar-thumb-color: 88, 96, 97;--jp-scrollbar-endpad: 3px;--jp-scrollbar-thumb-margin: 3.5px;--jp-scrollbar-thumb-radius: 9px;--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-500);--jp-console-icon-background-color: var(--md-blue-500);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-200);--jp-terminal-icon-color: var(--md-grey-800);--jp-text-editor-icon-color: var(--md-grey-200);--jp-inspector-icon-color: var(--md-grey-200);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-700)}.jupyter-wrapper 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.83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
-</style>
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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-<h1 id="example-usage-notebook">Example usage notebook<a class="anchor-link" href="#example-usage-notebook">¶</a></h1><p>Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="example-usage-notebook">Example usage notebook<a class="anchor-link" href="#example-usage-notebook">&#182;</a></h1><p>Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation</p>
+
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="imports">Imports<a class="anchor-link" href="#imports">&#182;</a></h2>
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-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 
 <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
-<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">".."</span><span class="p">)</span>
+<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;..&quot;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">mlflow</span>
 
-<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_columns"</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
-<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_rows"</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_columns&quot;</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-1">import os
+<div id="cell-1" class="clipboard-copy-txt">import os
 import sys
 
 sys.path.append(os.getcwd())
@@ -1414,31 +1819,36 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
 
 pd.set_option("display.max_columns", 200)
 pd.set_option("display.max_rows", 300)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-2">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">json</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Literal</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
@@ -1455,7 +1865,7 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
 
 <span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-2">import os
+<div id="cell-2" class="clipboard-copy-txt">import os
 import json
 from typing import Literal
 import pandas as pd
@@ -1471,61 +1881,77 @@ <h2 id="imports">Imports<a class="anchor-link" href="#imports">¶</a></h2>
 from sklearn.model_selection import train_test_split
 
 from pprint import pprint</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="train-a-testing-model">Train a testing model<a class="anchor-link" href="#train-a-testing-model">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="train-a-testing-model">Train a testing model<a class="anchor-link" href="#train-a-testing-model">&#182;</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-3">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># 1. get the data</span>
-<span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">"data/dataset_auxiliary_features_cleaned.parquet"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># 1. get the data</span>
+<span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">&quot;data/dataset_auxiliary_features_cleaned.parquet&quot;</span><span class="p">)</span>
 <span class="n">df_pd</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-3"># 1. get the data
+<div id="cell-3" class="clipboard-copy-txt"># 1. get the data
 df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
 df_pd.head()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[3]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -1539,318 +1965,339 @@ <h2 id="train-a-testing-model">Train a testing model<a class="anchor-link" href=
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>CustomerId</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment5</th>
-<th>Surname_Country</th>
-<th>Surname_Country_region</th>
-<th>Surname_Country_subregion</th>
-<th>Country_region</th>
-<th>Country_subregion</th>
-<th>is_native</th>
-<th>Country_hemisphere</th>
-<th>Country_gdp_per_capita</th>
-<th>Country_IncomeGroup</th>
-<th>Surname_Country_gdp_per_capita</th>
-<th>Surname_Country_IncomeGroup</th>
-<th>working_class</th>
-<th>stage_of_life</th>
-<th>generation</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>15787619</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>160980.03</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>145936.28</td>
-<td>0</td>
-<td>neutral</td>
-<td>4 stars</td>
-<td>Taiwan</td>
-<td>Asia</td>
-<td>Eastern Asia</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>32756.00000</td>
-<td>None</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>1</th>
-<td>15770309</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>151762.74</td>
-<td>1</td>
-<td>0</td>
-<td>1</td>
-<td>127014.32</td>
-<td>0</td>
-<td>neutral</td>
-<td>1 star</td>
-<td>United States</td>
-<td>Americas</td>
-<td>Northern America</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>76329.58227</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>2</th>
-<td>15569178</td>
-<td>570</td>
-<td>France</td>
-<td>Female</td>
-<td>18</td>
-<td>4</td>
-<td>82767.42</td>
-<td>1</td>
-<td>1</td>
-<td>0</td>
-<td>71811.90</td>
-<td>0</td>
-<td>neutral</td>
-<td>2 stars</td>
-<td>Russian Federation</td>
-<td>Europe</td>
-<td>Eastern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>3</th>
-<td>15795519</td>
-<td>716</td>
-<td>Germany</td>
-<td>Female</td>
-<td>18</td>
-<td>3</td>
-<td>128743.80</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>197322.13</td>
-<td>0</td>
-<td>neutral</td>
-<td>2 stars</td>
-<td>Russian Federation</td>
-<td>Europe</td>
-<td>Eastern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>66616.02225</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>4</th>
-<td>15621893</td>
-<td>727</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>4</td>
-<td>133550.67</td>
-<td>1</td>
-<td>1</td>
-<td>1</td>
-<td>46941.41</td>
-<td>0</td>
-<td>positive</td>
-<td>1 star</td>
-<td>Italy</td>
-<td>Europe</td>
-<td>Southern Europe</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>55442.07843</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>CustomerId</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment5</th>
+      <th>Surname_Country</th>
+      <th>Surname_Country_region</th>
+      <th>Surname_Country_subregion</th>
+      <th>Country_region</th>
+      <th>Country_subregion</th>
+      <th>is_native</th>
+      <th>Country_hemisphere</th>
+      <th>Country_gdp_per_capita</th>
+      <th>Country_IncomeGroup</th>
+      <th>Surname_Country_gdp_per_capita</th>
+      <th>Surname_Country_IncomeGroup</th>
+      <th>working_class</th>
+      <th>stage_of_life</th>
+      <th>generation</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>15787619</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>160980.03</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>4 stars</td>
+      <td>Taiwan</td>
+      <td>Asia</td>
+      <td>Eastern Asia</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>32756.00000</td>
+      <td>None</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>15770309</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>151762.74</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>1 star</td>
+      <td>United States</td>
+      <td>Americas</td>
+      <td>Northern America</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>76329.58227</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>2</th>
+      <td>15569178</td>
+      <td>570</td>
+      <td>France</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>4</td>
+      <td>82767.42</td>
+      <td>1</td>
+      <td>1</td>
+      <td>0</td>
+      <td>71811.90</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>2 stars</td>
+      <td>Russian Federation</td>
+      <td>Europe</td>
+      <td>Eastern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>3</th>
+      <td>15795519</td>
+      <td>716</td>
+      <td>Germany</td>
+      <td>Female</td>
+      <td>18</td>
+      <td>3</td>
+      <td>128743.80</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>197322.13</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>2 stars</td>
+      <td>Russian Federation</td>
+      <td>Europe</td>
+      <td>Eastern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>66616.02225</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>4</th>
+      <td>15621893</td>
+      <td>727</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>4</td>
+      <td>133550.67</td>
+      <td>1</td>
+      <td>1</td>
+      <td>1</td>
+      <td>46941.41</td>
+      <td>0</td>
+      <td>positive</td>
+      <td>1 star</td>
+      <td>Italy</td>
+      <td>Europe</td>
+      <td>Southern Europe</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>55442.07843</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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 <div class="jp-InputArea jp-Cell-inputArea">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">target_col</span> <span class="o">=</span> <span class="s2">"Exited"</span>
-<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"CustomerId"</span><span class="p">]</span>
-<span class="n">cat_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Country"</span><span class="p">,</span> <span class="s2">"Gender"</span><span class="p">,</span> <span class="s2">"HasCreditCard"</span><span class="p">,</span> <span class="s2">"IsActiveMember"</span><span class="p">,</span><span class="s2">"CustomerFeedback_sentiment3"</span><span class="p">,</span> <span class="s2">"CustomerFeedback_sentiment5"</span><span class="p">,</span> <span class="s2">"Surname_Country"</span><span class="p">,</span> <span class="s2">"Surname_Country_region"</span><span class="p">,</span> <span class="s2">"Surname_Country_subregion"</span><span class="p">,</span> <span class="s2">"Country_region"</span><span class="p">,</span> <span class="s2">"Country_subregion"</span><span class="p">,</span> <span class="s2">"is_native"</span><span class="p">,</span> <span class="s2">"Country_hemisphere"</span><span class="p">,</span> <span class="s2">"Country_IncomeGroup"</span><span class="p">,</span> <span class="s2">"Surname_Country_IncomeGroup"</span><span class="p">,</span> <span class="s2">"working_class"</span><span class="p">,</span> <span class="s2">"stage_of_life"</span><span class="p">,</span> <span class="s2">"generation"</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">target_col</span> <span class="o">=</span> <span class="s2">&quot;Exited&quot;</span>
+<span class="n">id_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;CustomerId&quot;</span><span class="p">]</span>
+<span class="n">cat_cols</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Country&quot;</span><span class="p">,</span> <span class="s2">&quot;Gender&quot;</span><span class="p">,</span> <span class="s2">&quot;HasCreditCard&quot;</span><span class="p">,</span> <span class="s2">&quot;IsActiveMember&quot;</span><span class="p">,</span><span class="s2">&quot;CustomerFeedback_sentiment3&quot;</span><span class="p">,</span> <span class="s2">&quot;CustomerFeedback_sentiment5&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">,</span> <span class="s2">&quot;Country_region&quot;</span><span class="p">,</span> <span class="s2">&quot;Country_subregion&quot;</span><span class="p">,</span> <span class="s2">&quot;is_native&quot;</span><span class="p">,</span> <span class="s2">&quot;Country_hemisphere&quot;</span><span class="p">,</span> <span class="s2">&quot;Country_IncomeGroup&quot;</span><span class="p">,</span> <span class="s2">&quot;Surname_Country_IncomeGroup&quot;</span><span class="p">,</span> <span class="s2">&quot;working_class&quot;</span><span class="p">,</span> <span class="s2">&quot;stage_of_life&quot;</span><span class="p">,</span> <span class="s2">&quot;generation&quot;</span><span class="p">]</span>
 <span class="n">cont_cols</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">id_cols</span> <span class="o">+</span> <span class="n">cat_cols</span> <span class="o">+</span> <span class="p">[</span><span class="n">target_col</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-4">target_col = "Exited"
+<div id="cell-4" class="clipboard-copy-txt">target_col = "Exited"
 id_cols = ["CustomerId"]
 cat_cols = ["Country", "Gender", "HasCreditCard", "IsActiveMember","CustomerFeedback_sentiment3", "CustomerFeedback_sentiment5", "Surname_Country", "Surname_Country_region", "Surname_Country_subregion", "Country_region", "Country_subregion", "is_native", "Country_hemisphere", "Country_IncomeGroup", "Surname_Country_IncomeGroup", "working_class", "stage_of_life", "generation"]
 cont_cols = df_pd.drop(columns=id_cols + cat_cols + [target_col]).columns.values.tolist()</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-5">df_pd[cat_cols] = df_pd[cat_cols].astype(str)</div>
-</div>
-</div>
+<div id="cell-5" class="clipboard-copy-txt">df_pd[cat_cols] = df_pd[cat_cols].astype(str)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
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-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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 <div class="jp-InputArea jp-Cell-inputArea">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">valid_size</span> <span class="o">=</span> <span class="mf">0.2</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">valid_size</span> <span class="o">=</span> <span class="mf">0.2</span>
 <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.5</span>
 <span class="n">random_state</span> <span class="o">=</span> <span class="mi">1</span>
 <span class="n">df_train</span><span class="p">,</span> <span class="n">df_valid</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">df_pd</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="n">valid_size</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">df_pd</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
 <span class="n">df_valid</span><span class="p">,</span> <span class="n">df_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">df_valid</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="n">test_size</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">df_valid</span><span class="p">[</span><span class="n">target_col</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-6">valid_size = 0.2
+<div id="cell-6" class="clipboard-copy-txt">valid_size = 0.2
 test_size = 0.5
 random_state = 1
 df_train, df_valid = train_test_split(df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state)
 df_valid, df_test = train_test_split(df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
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-</div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">prepare_data</span> <span class="o">=</span> <span class="n">PreprocessData</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-7">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">prepare_data</span> <span class="o">=</span> <span class="n">PreprocessData</span><span class="p">(</span>
         <span class="n">id_cols</span><span class="o">=</span><span class="n">id_cols</span><span class="p">,</span>
         <span class="n">target_col</span><span class="o">=</span><span class="n">target_col</span><span class="p">,</span>
         <span class="n">cat_cols</span><span class="o">=</span><span class="n">cat_cols</span><span class="p">,</span>
@@ -1859,7 +2306,7 @@ <h2 id="train-a-testing-model">Train a testing model<a class="anchor-link" href=
 <span class="c1"># this should be fitted only on training data</span>
 <span class="n">_</span> <span class="o">=</span> <span class="n">prepare_data</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-7">prepare_data = PreprocessData(
+<div id="cell-7" class="clipboard-copy-txt">prepare_data = PreprocessData(
         id_cols=id_cols,
         target_col=target_col,
         cat_cols=cat_cols,
@@ -1867,32 +2314,37 @@ <h2 id="train-a-testing-model">Train a testing model<a class="anchor-link" href=
     )
 # this should be fitted only on training data
 _ = prepare_data.fit(df=df_pd)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
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-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">LGBOptunaOptimizer</span><span class="p">(</span>
-        <span class="n">objective</span><span class="o">=</span><span class="s2">"binary"</span><span class="p">,</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-8">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">LGBOptunaOptimizer</span><span class="p">(</span>
+        <span class="n">objective</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span>
         <span class="n">n_class</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
     <span class="p">)</span>
 
@@ -1900,7 +2352,7 @@ <h2 id="train-a-testing-model">Train a testing model<a class="anchor-link" href=
         <span class="n">cat_cols</span><span class="o">=</span><span class="n">prepare_data</span><span class="o">.</span><span class="n">cat_cols</span><span class="p">,</span>
         <span class="n">target_col</span><span class="o">=</span><span class="n">prepare_data</span><span class="o">.</span><span class="n">target_col</span><span class="p">,</span>
         <span class="n">id_cols</span><span class="o">=</span><span class="n">id_cols</span><span class="p">,</span>
-        <span class="n">objective</span><span class="o">=</span><span class="s2">"binary"</span><span class="p">,</span>
+        <span class="n">objective</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span>
         <span class="n">n_class</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
         <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span><span class="p">,</span>
         <span class="n">preprocessors</span><span class="o">=</span><span class="p">[</span><span class="n">prepare_data</span><span class="p">],</span>
@@ -1912,7 +2364,7 @@ <h2 id="train-a-testing-model">Train a testing model<a class="anchor-link" href=
     <span class="n">df_test</span><span class="o">=</span><span class="n">df_test</span><span class="p">,</span>
     <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-8">optimizer = LGBOptunaOptimizer(
+<div id="cell-8" class="clipboard-copy-txt">optimizer = LGBOptunaOptimizer(
         objective="binary",
         n_class=2,
     )
@@ -1932,1386 +2384,2177 @@ <h2 id="train-a-testing-model">Train a testing model<a class="anchor-link" href=
     df_valid=df_valid,
     df_test=df_test,
     )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>[I 2024-06-18 21:05:47,850] A new study created in memory with name: no-name-72ade7dc-1867-41c2-b5a4-df3b58765bc7
 feature_fraction, val_score: inf:   0%|          | 0/7 [00:00&lt;?, ?it/s]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Training until validation scores don't improve for 50 rounds
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction, val_score: 0.388064:  14%|#4        | 1/7 [00:08&lt;00:48,  8.11s/it][I 2024-06-18 21:05:55,971] Trial 0 finished with value: 0.38806351066689354 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 0.38806351066689354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction, val_score: 0.388064:  14%|#4        | 1/7 [00:08&lt;00:48,  8.11s/it][I 2024-06-18 21:05:55,971] Trial 0 finished with value: 0.38806351066689354 and parameters: {&#39;feature_fraction&#39;: 0.4}. Best is trial 0 with value: 0.38806351066689354.
 feature_fraction, val_score: 0.388064:  14%|#4        | 1/7 [00:08&lt;00:48,  8.11s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[241]	valid_0's binary_logloss: 0.388064
-Training until validation scores don't improve for 50 rounds
+[241]	valid_0&#39;s binary_logloss: 0.388064
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction, val_score: 0.384552:  29%|##8       | 2/7 [00:15&lt;00:39,  7.81s/it][I 2024-06-18 21:06:03,568] Trial 1 finished with value: 0.38455247747199556 and parameters: {'feature_fraction': 0.5}. Best is trial 1 with value: 0.38455247747199556.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction, val_score: 0.384552:  29%|##8       | 2/7 [00:15&lt;00:39,  7.81s/it][I 2024-06-18 21:06:03,568] Trial 1 finished with value: 0.38455247747199556 and parameters: {&#39;feature_fraction&#39;: 0.5}. Best is trial 1 with value: 0.38455247747199556.
 feature_fraction, val_score: 0.384552:  29%|##8       | 2/7 [00:15&lt;00:39,  7.81s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[184]	valid_0's binary_logloss: 0.384552
-Training until validation scores don't improve for 50 rounds
+[184]	valid_0&#39;s binary_logloss: 0.384552
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction, val_score: 0.381327:  43%|####2     | 3/7 [00:20&lt;00:25,  6.48s/it][I 2024-06-18 21:06:08,471] Trial 2 finished with value: 0.3813271028603074 and parameters: {'feature_fraction': 0.8}. Best is trial 2 with value: 0.3813271028603074.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction, val_score: 0.381327:  43%|####2     | 3/7 [00:20&lt;00:25,  6.48s/it][I 2024-06-18 21:06:08,471] Trial 2 finished with value: 0.3813271028603074 and parameters: {&#39;feature_fraction&#39;: 0.8}. Best is trial 2 with value: 0.3813271028603074.
 feature_fraction, val_score: 0.381327:  43%|####2     | 3/7 [00:20&lt;00:25,  6.48s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[154]	valid_0's binary_logloss: 0.381327
-Training until validation scores don't improve for 50 rounds
+[154]	valid_0&#39;s binary_logloss: 0.381327
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction, val_score: 0.381327:  57%|#####7    | 4/7 [00:22&lt;00:13,  4.64s/it][I 2024-06-18 21:06:10,281] Trial 3 finished with value: 0.40303264639386804 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 2 with value: 0.3813271028603074.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction, val_score: 0.381327:  57%|#####7    | 4/7 [00:22&lt;00:13,  4.64s/it][I 2024-06-18 21:06:10,281] Trial 3 finished with value: 0.40303264639386804 and parameters: {&#39;feature_fraction&#39;: 0.8999999999999999}. Best is trial 2 with value: 0.3813271028603074.
 feature_fraction, val_score: 0.381327:  57%|#####7    | 4/7 [00:22&lt;00:13,  4.64s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[9]	valid_0's binary_logloss: 0.403033
-Training until validation scores don't improve for 50 rounds
+[9]	valid_0&#39;s binary_logloss: 0.403033
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction, val_score: 0.381327:  71%|#######1  | 5/7 [00:29&lt;00:10,  5.45s/it][I 2024-06-18 21:06:17,181] Trial 4 finished with value: 0.385808915879594 and parameters: {'feature_fraction': 1.0}. Best is trial 2 with value: 0.3813271028603074.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction, val_score: 0.381327:  71%|#######1  | 5/7 [00:29&lt;00:10,  5.45s/it][I 2024-06-18 21:06:17,181] Trial 4 finished with value: 0.385808915879594 and parameters: {&#39;feature_fraction&#39;: 1.0}. Best is trial 2 with value: 0.3813271028603074.
 feature_fraction, val_score: 0.381327:  71%|#######1  | 5/7 [00:29&lt;00:10,  5.45s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[196]	valid_0's binary_logloss: 0.385809
-Training until validation scores don't improve for 50 rounds
+[196]	valid_0&#39;s binary_logloss: 0.385809
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction, val_score: 0.381327:  86%|########5 | 6/7 [00:35&lt;00:05,  5.57s/it][I 2024-06-18 21:06:22,970] Trial 5 finished with value: 0.3905588715180784 and parameters: {'feature_fraction': 0.7}. Best is trial 2 with value: 0.3813271028603074.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction, val_score: 0.381327:  86%|########5 | 6/7 [00:35&lt;00:05,  5.57s/it][I 2024-06-18 21:06:22,970] Trial 5 finished with value: 0.3905588715180784 and parameters: {&#39;feature_fraction&#39;: 0.7}. Best is trial 2 with value: 0.3813271028603074.
 feature_fraction, val_score: 0.381327:  86%|########5 | 6/7 [00:35&lt;00:05,  5.57s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[152]	valid_0's binary_logloss: 0.390559
-Training until validation scores don't improve for 50 rounds
+[152]	valid_0&#39;s binary_logloss: 0.390559
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41&lt;00:00,  5.97s/it][I 2024-06-18 21:06:29,778] Trial 6 finished with value: 0.38245734022417816 and parameters: {'feature_fraction': 0.6}. Best is trial 2 with value: 0.3813271028603074.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41&lt;00:00,  5.97s/it][I 2024-06-18 21:06:29,778] Trial 6 finished with value: 0.38245734022417816 and parameters: {&#39;feature_fraction&#39;: 0.6}. Best is trial 2 with value: 0.3813271028603074.
 feature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41&lt;00:00,  5.99s/it]
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[201]	valid_0's binary_logloss: 0.382457
+[201]	valid_0&#39;s binary_logloss: 0.382457
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>num_leaves, val_score: 0.381327:   0%|          | 0/20 [00:00&lt;?, ?it/s]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Training until validation scores don't improve for 50 rounds
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.381327:   5%|5         | 1/20 [00:06&lt;02:12,  6.99s/it][I 2024-06-18 21:06:36,772] Trial 7 finished with value: 0.3867130065464061 and parameters: {'num_leaves': 28}. Best is trial 7 with value: 0.3867130065464061.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.381327:   5%|5         | 1/20 [00:06&lt;02:12,  6.99s/it][I 2024-06-18 21:06:36,772] Trial 7 finished with value: 0.3867130065464061 and parameters: {&#39;num_leaves&#39;: 28}. Best is trial 7 with value: 0.3867130065464061.
 num_leaves, val_score: 0.381327:   5%|5         | 1/20 [00:06&lt;02:12,  6.99s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[225]	valid_0's binary_logloss: 0.386713
-Training until validation scores don't improve for 50 rounds
+[225]	valid_0&#39;s binary_logloss: 0.386713
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.370474:  10%|#         | 2/20 [00:14&lt;02:15,  7.51s/it][I 2024-06-18 21:06:44,655] Trial 8 finished with value: 0.3704743259843756 and parameters: {'num_leaves': 92}. Best is trial 8 with value: 0.3704743259843756.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.370474:  10%|#         | 2/20 [00:14&lt;02:15,  7.51s/it][I 2024-06-18 21:06:44,655] Trial 8 finished with value: 0.3704743259843756 and parameters: {&#39;num_leaves&#39;: 92}. Best is trial 8 with value: 0.3704743259843756.
 num_leaves, val_score: 0.370474:  10%|#         | 2/20 [00:14&lt;02:15,  7.51s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[64]	valid_0's binary_logloss: 0.370474
-Training until validation scores don't improve for 50 rounds
+[64]	valid_0&#39;s binary_logloss: 0.370474
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.370474:  15%|#5        | 3/20 [00:26&lt;02:42,  9.53s/it][I 2024-06-18 21:06:56,583] Trial 9 finished with value: 0.3718260260505307 and parameters: {'num_leaves': 149}. Best is trial 8 with value: 0.3704743259843756.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.370474:  15%|#5        | 3/20 [00:26&lt;02:42,  9.53s/it][I 2024-06-18 21:06:56,583] Trial 9 finished with value: 0.3718260260505307 and parameters: {&#39;num_leaves&#39;: 149}. Best is trial 8 with value: 0.3704743259843756.
 num_leaves, val_score: 0.370474:  15%|#5        | 3/20 [00:26&lt;02:42,  9.53s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[49]	valid_0's binary_logloss: 0.371826
-Training until validation scores don't improve for 50 rounds
+[49]	valid_0&#39;s binary_logloss: 0.371826
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.370474:  20%|##        | 4/20 [00:44&lt;03:21, 12.59s/it][I 2024-06-18 21:07:13,876] Trial 10 finished with value: 0.37095297012345396 and parameters: {'num_leaves': 248}. Best is trial 8 with value: 0.3704743259843756.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.370474:  20%|##        | 4/20 [00:44&lt;03:21, 12.59s/it][I 2024-06-18 21:07:13,876] Trial 10 finished with value: 0.37095297012345396 and parameters: {&#39;num_leaves&#39;: 248}. Best is trial 8 with value: 0.3704743259843756.
 num_leaves, val_score: 0.370474:  20%|##        | 4/20 [00:44&lt;03:21, 12.59s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[41]	valid_0's binary_logloss: 0.370953
-Training until validation scores don't improve for 50 rounds
+[41]	valid_0&#39;s binary_logloss: 0.370953
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.370474:  25%|##5       | 5/20 [00:59&lt;03:25, 13.71s/it][I 2024-06-18 21:07:29,563] Trial 11 finished with value: 0.3738263141664088 and parameters: {'num_leaves': 252}. Best is trial 8 with value: 0.3704743259843756.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.370474:  25%|##5       | 5/20 [00:59&lt;03:25, 13.71s/it][I 2024-06-18 21:07:29,563] Trial 11 finished with value: 0.3738263141664088 and parameters: {&#39;num_leaves&#39;: 252}. Best is trial 8 with value: 0.3704743259843756.
 num_leaves, val_score: 0.370474:  25%|##5       | 5/20 [00:59&lt;03:25, 13.71s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[46]	valid_0's binary_logloss: 0.373826
-Training until validation scores don't improve for 50 rounds
+[46]	valid_0&#39;s binary_logloss: 0.373826
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.370474:  30%|###       | 6/20 [01:08&lt;02:48, 12.01s/it][I 2024-06-18 21:07:38,283] Trial 12 finished with value: 0.38055573216311783 and parameters: {'num_leaves': 96}. Best is trial 8 with value: 0.3704743259843756.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.370474:  30%|###       | 6/20 [01:08&lt;02:48, 12.01s/it][I 2024-06-18 21:07:38,283] Trial 12 finished with value: 0.38055573216311783 and parameters: {&#39;num_leaves&#39;: 96}. Best is trial 8 with value: 0.3704743259843756.
 num_leaves, val_score: 0.370474:  30%|###       | 6/20 [01:08&lt;02:48, 12.01s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[52]	valid_0's binary_logloss: 0.380556
-Training until validation scores don't improve for 50 rounds
+[52]	valid_0&#39;s binary_logloss: 0.380556
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  35%|###5      | 7/20 [01:25&lt;02:58, 13.73s/it][I 2024-06-18 21:07:55,556] Trial 13 finished with value: 0.36708245318754934 and parameters: {'num_leaves': 253}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  35%|###5      | 7/20 [01:25&lt;02:58, 13.73s/it][I 2024-06-18 21:07:55,556] Trial 13 finished with value: 0.36708245318754934 and parameters: {&#39;num_leaves&#39;: 253}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  35%|###5      | 7/20 [01:25&lt;02:58, 13.73s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[39]	valid_0's binary_logloss: 0.367082
-Training until validation scores don't improve for 50 rounds
+[39]	valid_0&#39;s binary_logloss: 0.367082
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  40%|####      | 8/20 [01:36&lt;02:33, 12.81s/it][I 2024-06-18 21:08:06,381] Trial 14 finished with value: 0.3760206407150123 and parameters: {'num_leaves': 132}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  40%|####      | 8/20 [01:36&lt;02:33, 12.81s/it][I 2024-06-18 21:08:06,381] Trial 14 finished with value: 0.3760206407150123 and parameters: {&#39;num_leaves&#39;: 132}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  40%|####      | 8/20 [01:36&lt;02:33, 12.81s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[51]	valid_0's binary_logloss: 0.376021
-Training until validation scores don't improve for 50 rounds
+[51]	valid_0&#39;s binary_logloss: 0.376021
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  45%|####5     | 9/20 [01:44&lt;02:05, 11.43s/it][I 2024-06-18 21:08:14,782] Trial 15 finished with value: 0.378543416834411 and parameters: {'num_leaves': 64}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  45%|####5     | 9/20 [01:44&lt;02:05, 11.43s/it][I 2024-06-18 21:08:14,782] Trial 15 finished with value: 0.378543416834411 and parameters: {&#39;num_leaves&#39;: 64}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  45%|####5     | 9/20 [01:44&lt;02:05, 11.43s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[103]	valid_0's binary_logloss: 0.378543
-Training until validation scores don't improve for 50 rounds
+[103]	valid_0&#39;s binary_logloss: 0.378543
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  50%|#####     | 10/20 [01:56&lt;01:55, 11.58s/it][I 2024-06-18 21:08:26,682] Trial 16 finished with value: 0.37346546696167743 and parameters: {'num_leaves': 184}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  50%|#####     | 10/20 [01:56&lt;01:55, 11.58s/it][I 2024-06-18 21:08:26,682] Trial 16 finished with value: 0.37346546696167743 and parameters: {&#39;num_leaves&#39;: 184}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  50%|#####     | 10/20 [01:56&lt;01:55, 11.58s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[32]	valid_0's binary_logloss: 0.373465
-Training until validation scores don't improve for 50 rounds
+[32]	valid_0&#39;s binary_logloss: 0.373465
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  55%|#####5    | 11/20 [02:08&lt;01:45, 11.67s/it][I 2024-06-18 21:08:38,573] Trial 17 finished with value: 0.3716035258824437 and parameters: {'num_leaves': 178}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  55%|#####5    | 11/20 [02:08&lt;01:45, 11.67s/it][I 2024-06-18 21:08:38,573] Trial 17 finished with value: 0.3716035258824437 and parameters: {&#39;num_leaves&#39;: 178}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  55%|#####5    | 11/20 [02:08&lt;01:45, 11.67s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[33]	valid_0's binary_logloss: 0.371604
-Training until validation scores don't improve for 50 rounds
+[33]	valid_0&#39;s binary_logloss: 0.371604
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  60%|######    | 12/20 [02:10&lt;01:08,  8.58s/it][I 2024-06-18 21:08:40,079] Trial 18 finished with value: 0.4090573635738217 and parameters: {'num_leaves': 6}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  60%|######    | 12/20 [02:10&lt;01:08,  8.58s/it][I 2024-06-18 21:08:40,079] Trial 18 finished with value: 0.4090573635738217 and parameters: {&#39;num_leaves&#39;: 6}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  60%|######    | 12/20 [02:10&lt;01:08,  8.58s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[219]	valid_0's binary_logloss: 0.409057
-Training until validation scores don't improve for 50 rounds
+[219]	valid_0&#39;s binary_logloss: 0.409057
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  65%|######5   | 13/20 [02:19&lt;01:00,  8.68s/it][I 2024-06-18 21:08:48,983] Trial 19 finished with value: 0.3704743259843756 and parameters: {'num_leaves': 92}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  65%|######5   | 13/20 [02:19&lt;01:00,  8.68s/it][I 2024-06-18 21:08:48,983] Trial 19 finished with value: 0.3704743259843756 and parameters: {&#39;num_leaves&#39;: 92}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  65%|######5   | 13/20 [02:19&lt;01:00,  8.68s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[64]	valid_0's binary_logloss: 0.370474
-Training until validation scores don't improve for 50 rounds
+[64]	valid_0&#39;s binary_logloss: 0.370474
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  70%|#######   | 14/20 [02:34&lt;01:03, 10.58s/it][I 2024-06-18 21:09:03,972] Trial 20 finished with value: 0.37479451809569353 and parameters: {'num_leaves': 211}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  70%|#######   | 14/20 [02:34&lt;01:03, 10.58s/it][I 2024-06-18 21:09:03,972] Trial 20 finished with value: 0.37479451809569353 and parameters: {&#39;num_leaves&#39;: 211}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  70%|#######   | 14/20 [02:34&lt;01:03, 10.58s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[38]	valid_0's binary_logloss: 0.374795
-Training until validation scores don't improve for 50 rounds
+[38]	valid_0&#39;s binary_logloss: 0.374795
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  75%|#######5  | 15/20 [02:43&lt;00:50, 10.14s/it][I 2024-06-18 21:09:13,084] Trial 21 finished with value: 0.3772431537086629 and parameters: {'num_leaves': 90}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  75%|#######5  | 15/20 [02:43&lt;00:50, 10.14s/it][I 2024-06-18 21:09:13,084] Trial 21 finished with value: 0.3772431537086629 and parameters: {&#39;num_leaves&#39;: 90}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  75%|#######5  | 15/20 [02:43&lt;00:50, 10.14s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[69]	valid_0's binary_logloss: 0.377243
-Training until validation scores don't improve for 50 rounds
+[69]	valid_0&#39;s binary_logloss: 0.377243
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  80%|########  | 16/20 [02:53&lt;00:40, 10.13s/it][I 2024-06-18 21:09:23,178] Trial 22 finished with value: 0.3728508215083201 and parameters: {'num_leaves': 94}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  80%|########  | 16/20 [02:53&lt;00:40, 10.13s/it][I 2024-06-18 21:09:23,178] Trial 22 finished with value: 0.3728508215083201 and parameters: {&#39;num_leaves&#39;: 94}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  80%|########  | 16/20 [02:53&lt;00:40, 10.13s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[76]	valid_0's binary_logloss: 0.372851
-Training until validation scores don't improve for 50 rounds
+[76]	valid_0&#39;s binary_logloss: 0.372851
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  85%|########5 | 17/20 [03:01&lt;00:28,  9.57s/it][I 2024-06-18 21:09:31,462] Trial 23 finished with value: 0.3741261012500593 and parameters: {'num_leaves': 65}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  85%|########5 | 17/20 [03:01&lt;00:28,  9.57s/it][I 2024-06-18 21:09:31,462] Trial 23 finished with value: 0.3741261012500593 and parameters: {&#39;num_leaves&#39;: 65}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  85%|########5 | 17/20 [03:01&lt;00:28,  9.57s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[130]	valid_0's binary_logloss: 0.374126
-Training until validation scores don't improve for 50 rounds
+[130]	valid_0&#39;s binary_logloss: 0.374126
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  90%|######### | 18/20 [03:11&lt;00:19,  9.61s/it][I 2024-06-18 21:09:41,151] Trial 24 finished with value: 0.3809976671401048 and parameters: {'num_leaves': 116}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  90%|######### | 18/20 [03:11&lt;00:19,  9.61s/it][I 2024-06-18 21:09:41,151] Trial 24 finished with value: 0.3809976671401048 and parameters: {&#39;num_leaves&#39;: 116}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  90%|######### | 18/20 [03:11&lt;00:19,  9.61s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[61]	valid_0's binary_logloss: 0.380998
-Training until validation scores don't improve for 50 rounds
+[61]	valid_0&#39;s binary_logloss: 0.380998
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082:  95%|#########5| 19/20 [03:18&lt;00:08,  8.92s/it][I 2024-06-18 21:09:48,483] Trial 25 finished with value: 0.3773659816174925 and parameters: {'num_leaves': 45}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082:  95%|#########5| 19/20 [03:18&lt;00:08,  8.92s/it][I 2024-06-18 21:09:48,483] Trial 25 finished with value: 0.3773659816174925 and parameters: {&#39;num_leaves&#39;: 45}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082:  95%|#########5| 19/20 [03:18&lt;00:08,  8.92s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[150]	valid_0's binary_logloss: 0.377366
-Training until validation scores don't improve for 50 rounds
+[150]	valid_0&#39;s binary_logloss: 0.377366
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>num_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27&lt;00:00,  8.98s/it][I 2024-06-18 21:09:57,579] Trial 26 finished with value: 0.379797423356925 and parameters: {'num_leaves': 148}. Best is trial 13 with value: 0.36708245318754934.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>num_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27&lt;00:00,  8.98s/it][I 2024-06-18 21:09:57,579] Trial 26 finished with value: 0.379797423356925 and parameters: {&#39;num_leaves&#39;: 148}. Best is trial 13 with value: 0.36708245318754934.
 num_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27&lt;00:00, 10.39s/it]
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[32]	valid_0's binary_logloss: 0.379797
+[32]	valid_0&#39;s binary_logloss: 0.379797
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>bagging, val_score: 0.367082:   0%|          | 0/10 [00:00&lt;?, ?it/s]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Training until validation scores don't improve for 50 rounds
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  10%|#         | 1/10 [00:15&lt;02:17, 15.28s/it][I 2024-06-18 21:10:12,864] Trial 27 finished with value: 0.3754694447762392 and parameters: {'bagging_fraction': 0.6017118946353502, 'bagging_freq': 3}. Best is trial 27 with value: 0.3754694447762392.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  10%|#         | 1/10 [00:15&lt;02:17, 15.28s/it][I 2024-06-18 21:10:12,864] Trial 27 finished with value: 0.3754694447762392 and parameters: {&#39;bagging_fraction&#39;: 0.6017118946353502, &#39;bagging_freq&#39;: 3}. Best is trial 27 with value: 0.3754694447762392.
 bagging, val_score: 0.367082:  10%|#         | 1/10 [00:15&lt;02:17, 15.28s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[54]	valid_0's binary_logloss: 0.375469
-Training until validation scores don't improve for 50 rounds
+[54]	valid_0&#39;s binary_logloss: 0.375469
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  20%|##        | 2/10 [00:32&lt;02:10, 16.29s/it][I 2024-06-18 21:10:29,858] Trial 28 finished with value: 0.37140708223458174 and parameters: {'bagging_fraction': 0.9697760068067878, 'bagging_freq': 7}. Best is trial 28 with value: 0.37140708223458174.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  20%|##        | 2/10 [00:32&lt;02:10, 16.29s/it][I 2024-06-18 21:10:29,858] Trial 28 finished with value: 0.37140708223458174 and parameters: {&#39;bagging_fraction&#39;: 0.9697760068067878, &#39;bagging_freq&#39;: 7}. Best is trial 28 with value: 0.37140708223458174.
 bagging, val_score: 0.367082:  20%|##        | 2/10 [00:32&lt;02:10, 16.29s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[51]	valid_0's binary_logloss: 0.371407
-Training until validation scores don't improve for 50 rounds
+[51]	valid_0&#39;s binary_logloss: 0.371407
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  30%|###       | 3/10 [00:40&lt;01:27, 12.51s/it][I 2024-06-18 21:10:37,880] Trial 29 finished with value: 0.3763001794627212 and parameters: {'bagging_fraction': 0.4336686702739523, 'bagging_freq': 1}. Best is trial 28 with value: 0.37140708223458174.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  30%|###       | 3/10 [00:40&lt;01:27, 12.51s/it][I 2024-06-18 21:10:37,880] Trial 29 finished with value: 0.3763001794627212 and parameters: {&#39;bagging_fraction&#39;: 0.4336686702739523, &#39;bagging_freq&#39;: 1}. Best is trial 28 with value: 0.37140708223458174.
 bagging, val_score: 0.367082:  30%|###       | 3/10 [00:40&lt;01:27, 12.51s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[35]	valid_0's binary_logloss: 0.3763
-Training until validation scores don't improve for 50 rounds
+[35]	valid_0&#39;s binary_logloss: 0.3763
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  40%|####      | 4/10 [00:57&lt;01:25, 14.27s/it][I 2024-06-18 21:10:54,855] Trial 30 finished with value: 0.37399860180838584 and parameters: {'bagging_fraction': 0.9955847594220013, 'bagging_freq': 7}. Best is trial 28 with value: 0.37140708223458174.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  40%|####      | 4/10 [00:57&lt;01:25, 14.27s/it][I 2024-06-18 21:10:54,855] Trial 30 finished with value: 0.37399860180838584 and parameters: {&#39;bagging_fraction&#39;: 0.9955847594220013, &#39;bagging_freq&#39;: 7}. Best is trial 28 with value: 0.37140708223458174.
 bagging, val_score: 0.367082:  40%|####      | 4/10 [00:57&lt;01:25, 14.27s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[46]	valid_0's binary_logloss: 0.373999
-Training until validation scores don't improve for 50 rounds
+[46]	valid_0&#39;s binary_logloss: 0.373999
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  50%|#####     | 5/10 [01:13&lt;01:15, 15.04s/it][I 2024-06-18 21:11:11,265] Trial 31 finished with value: 0.36851832518824595 and parameters: {'bagging_fraction': 0.7559826069760041, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  50%|#####     | 5/10 [01:13&lt;01:15, 15.04s/it][I 2024-06-18 21:11:11,265] Trial 31 finished with value: 0.36851832518824595 and parameters: {&#39;bagging_fraction&#39;: 0.7559826069760041, &#39;bagging_freq&#39;: 4}. Best is trial 31 with value: 0.36851832518824595.
 bagging, val_score: 0.367082:  50%|#####     | 5/10 [01:13&lt;01:15, 15.04s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[46]	valid_0's binary_logloss: 0.368518
-Training until validation scores don't improve for 50 rounds
+[46]	valid_0&#39;s binary_logloss: 0.368518
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  60%|######    | 6/10 [01:29&lt;01:00, 15.17s/it][I 2024-06-18 21:11:26,675] Trial 32 finished with value: 0.3695574686675923 and parameters: {'bagging_fraction': 0.7373615298502163, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  60%|######    | 6/10 [01:29&lt;01:00, 15.17s/it][I 2024-06-18 21:11:26,675] Trial 32 finished with value: 0.3695574686675923 and parameters: {&#39;bagging_fraction&#39;: 0.7373615298502163, &#39;bagging_freq&#39;: 4}. Best is trial 31 with value: 0.36851832518824595.
 bagging, val_score: 0.367082:  60%|######    | 6/10 [01:29&lt;01:00, 15.17s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[51]	valid_0's binary_logloss: 0.369557
-Training until validation scores don't improve for 50 rounds
+[51]	valid_0&#39;s binary_logloss: 0.369557
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  70%|#######   | 7/10 [01:44&lt;00:45, 15.17s/it][I 2024-06-18 21:11:41,852] Trial 33 finished with value: 0.37298644850012186 and parameters: {'bagging_fraction': 0.7616544959864899, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  70%|#######   | 7/10 [01:44&lt;00:45, 15.17s/it][I 2024-06-18 21:11:41,852] Trial 33 finished with value: 0.37298644850012186 and parameters: {&#39;bagging_fraction&#39;: 0.7616544959864899, &#39;bagging_freq&#39;: 4}. Best is trial 31 with value: 0.36851832518824595.
 bagging, val_score: 0.367082:  70%|#######   | 7/10 [01:44&lt;00:45, 15.17s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[38]	valid_0's binary_logloss: 0.372986
-Training until validation scores don't improve for 50 rounds
+[38]	valid_0&#39;s binary_logloss: 0.372986
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  80%|########  | 8/10 [01:58&lt;00:29, 14.94s/it][I 2024-06-18 21:11:56,282] Trial 34 finished with value: 0.3745164899611851 and parameters: {'bagging_fraction': 0.7510199039916521, 'bagging_freq': 5}. Best is trial 31 with value: 0.36851832518824595.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  80%|########  | 8/10 [01:58&lt;00:29, 14.94s/it][I 2024-06-18 21:11:56,282] Trial 34 finished with value: 0.3745164899611851 and parameters: {&#39;bagging_fraction&#39;: 0.7510199039916521, &#39;bagging_freq&#39;: 5}. Best is trial 31 with value: 0.36851832518824595.
 bagging, val_score: 0.367082:  80%|########  | 8/10 [01:58&lt;00:29, 14.94s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[35]	valid_0's binary_logloss: 0.374516
-Training until validation scores don't improve for 50 rounds
+[35]	valid_0&#39;s binary_logloss: 0.374516
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082:  90%|######### | 9/10 [02:13&lt;00:14, 14.95s/it][I 2024-06-18 21:12:11,256] Trial 35 finished with value: 0.3790678087224149 and parameters: {'bagging_fraction': 0.6376411296778922, 'bagging_freq': 3}. Best is trial 31 with value: 0.36851832518824595.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082:  90%|######### | 9/10 [02:13&lt;00:14, 14.95s/it][I 2024-06-18 21:12:11,256] Trial 35 finished with value: 0.3790678087224149 and parameters: {&#39;bagging_fraction&#39;: 0.6376411296778922, &#39;bagging_freq&#39;: 3}. Best is trial 31 with value: 0.36851832518824595.
 bagging, val_score: 0.367082:  90%|######### | 9/10 [02:13&lt;00:14, 14.95s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[41]	valid_0's binary_logloss: 0.379068
-Training until validation scores don't improve for 50 rounds
+[41]	valid_0&#39;s binary_logloss: 0.379068
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>bagging, val_score: 0.367082: 100%|##########| 10/10 [02:28&lt;00:00, 14.82s/it][I 2024-06-18 21:12:25,777] Trial 36 finished with value: 0.36759282443526115 and parameters: {'bagging_fraction': 0.8438639692641409, 'bagging_freq': 5}. Best is trial 36 with value: 0.36759282443526115.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>bagging, val_score: 0.367082: 100%|##########| 10/10 [02:28&lt;00:00, 14.82s/it][I 2024-06-18 21:12:25,777] Trial 36 finished with value: 0.36759282443526115 and parameters: {&#39;bagging_fraction&#39;: 0.8438639692641409, &#39;bagging_freq&#39;: 5}. Best is trial 36 with value: 0.36759282443526115.
 bagging, val_score: 0.367082: 100%|##########| 10/10 [02:28&lt;00:00, 14.82s/it]
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[30]	valid_0's binary_logloss: 0.367593
+[30]	valid_0&#39;s binary_logloss: 0.367593
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>feature_fraction_stage2, val_score: 0.367082:   0%|          | 0/6 [00:00&lt;?, ?it/s]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Training until validation scores don't improve for 50 rounds
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction_stage2, val_score: 0.367082:  17%|#6        | 1/6 [00:14&lt;01:11, 14.28s/it][I 2024-06-18 21:12:40,067] Trial 37 finished with value: 0.3751194682458601 and parameters: {'feature_fraction': 0.8480000000000001}. Best is trial 37 with value: 0.3751194682458601.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction_stage2, val_score: 0.367082:  17%|#6        | 1/6 [00:14&lt;01:11, 14.28s/it][I 2024-06-18 21:12:40,067] Trial 37 finished with value: 0.3751194682458601 and parameters: {&#39;feature_fraction&#39;: 0.8480000000000001}. Best is trial 37 with value: 0.3751194682458601.
 feature_fraction_stage2, val_score: 0.367082:  17%|#6        | 1/6 [00:14&lt;01:11, 14.28s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[31]	valid_0's binary_logloss: 0.375119
-Training until validation scores don't improve for 50 rounds
+[31]	valid_0&#39;s binary_logloss: 0.375119
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction_stage2, val_score: 0.367082:  33%|###3      | 2/6 [00:31&lt;01:03, 15.82s/it][I 2024-06-18 21:12:56,969] Trial 38 finished with value: 0.37511946824586 and parameters: {'feature_fraction': 0.88}. Best is trial 38 with value: 0.37511946824586.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction_stage2, val_score: 0.367082:  33%|###3      | 2/6 [00:31&lt;01:03, 15.82s/it][I 2024-06-18 21:12:56,969] Trial 38 finished with value: 0.37511946824586 and parameters: {&#39;feature_fraction&#39;: 0.88}. Best is trial 38 with value: 0.37511946824586.
 feature_fraction_stage2, val_score: 0.367082:  33%|###3      | 2/6 [00:31&lt;01:03, 15.82s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[31]	valid_0's binary_logloss: 0.375119
-Training until validation scores don't improve for 50 rounds
+[31]	valid_0&#39;s binary_logloss: 0.375119
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction_stage2, val_score: 0.367082:  50%|#####     | 3/6 [00:48&lt;00:50, 16.73s/it][I 2024-06-18 21:13:14,771] Trial 39 finished with value: 0.37230053743309227 and parameters: {'feature_fraction': 0.7520000000000001}. Best is trial 39 with value: 0.37230053743309227.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction_stage2, val_score: 0.367082:  50%|#####     | 3/6 [00:48&lt;00:50, 16.73s/it][I 2024-06-18 21:13:14,771] Trial 39 finished with value: 0.37230053743309227 and parameters: {&#39;feature_fraction&#39;: 0.7520000000000001}. Best is trial 39 with value: 0.37230053743309227.
 feature_fraction_stage2, val_score: 0.367082:  50%|#####     | 3/6 [00:48&lt;00:50, 16.73s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[46]	valid_0's binary_logloss: 0.372301
-Training until validation scores don't improve for 50 rounds
+[46]	valid_0&#39;s binary_logloss: 0.372301
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction_stage2, val_score: 0.367082:  67%|######6   | 4/6 [01:05&lt;00:33, 16.76s/it][I 2024-06-18 21:13:31,580] Trial 40 finished with value: 0.37230053743309227 and parameters: {'feature_fraction': 0.7200000000000001}. Best is trial 39 with value: 0.37230053743309227.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction_stage2, val_score: 0.367082:  67%|######6   | 4/6 [01:05&lt;00:33, 16.76s/it][I 2024-06-18 21:13:31,580] Trial 40 finished with value: 0.37230053743309227 and parameters: {&#39;feature_fraction&#39;: 0.7200000000000001}. Best is trial 39 with value: 0.37230053743309227.
 feature_fraction_stage2, val_score: 0.367082:  67%|######6   | 4/6 [01:05&lt;00:33, 16.76s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[46]	valid_0's binary_logloss: 0.372301
-Training until validation scores don't improve for 50 rounds
+[46]	valid_0&#39;s binary_logloss: 0.372301
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction_stage2, val_score: 0.366281:  83%|########3 | 5/6 [01:21&lt;00:16, 16.48s/it][I 2024-06-18 21:13:47,556] Trial 41 finished with value: 0.3662812971282284 and parameters: {'feature_fraction': 0.8160000000000001}. Best is trial 41 with value: 0.3662812971282284.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction_stage2, val_score: 0.366281:  83%|########3 | 5/6 [01:21&lt;00:16, 16.48s/it][I 2024-06-18 21:13:47,556] Trial 41 finished with value: 0.3662812971282284 and parameters: {&#39;feature_fraction&#39;: 0.8160000000000001}. Best is trial 41 with value: 0.3662812971282284.
 feature_fraction_stage2, val_score: 0.366281:  83%|########3 | 5/6 [01:21&lt;00:16, 16.48s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[30]	valid_0's binary_logloss: 0.366281
-Training until validation scores don't improve for 50 rounds
+[30]	valid_0&#39;s binary_logloss: 0.366281
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>feature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38&lt;00:00, 16.66s/it][I 2024-06-18 21:14:04,578] Trial 42 finished with value: 0.3670824531875493 and parameters: {'feature_fraction': 0.784}. Best is trial 41 with value: 0.3662812971282284.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>feature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38&lt;00:00, 16.66s/it][I 2024-06-18 21:14:04,578] Trial 42 finished with value: 0.3670824531875493 and parameters: {&#39;feature_fraction&#39;: 0.784}. Best is trial 41 with value: 0.3662812971282284.
 feature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38&lt;00:00, 16.47s/it]
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[39]	valid_0's binary_logloss: 0.367082
+[39]	valid_0&#39;s binary_logloss: 0.367082
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>regularization_factors, val_score: 0.366281:   0%|          | 0/20 [00:00&lt;?, ?it/s]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Training until validation scores don't improve for 50 rounds
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.366281:   5%|5         | 1/20 [00:13&lt;04:12, 13.29s/it][I 2024-06-18 21:14:17,877] Trial 43 finished with value: 0.36913074695699283 and parameters: {'lambda_l1': 0.26740014043884963, 'lambda_l2': 0.001760870184331642}. Best is trial 43 with value: 0.36913074695699283.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.366281:   5%|5         | 1/20 [00:13&lt;04:12, 13.29s/it][I 2024-06-18 21:14:17,877] Trial 43 finished with value: 0.36913074695699283 and parameters: {&#39;lambda_l1&#39;: 0.26740014043884963, &#39;lambda_l2&#39;: 0.001760870184331642}. Best is trial 43 with value: 0.36913074695699283.
 regularization_factors, val_score: 0.366281:   5%|5         | 1/20 [00:13&lt;04:12, 13.29s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[32]	valid_0's binary_logloss: 0.369131
-Training until validation scores don't improve for 50 rounds
+[32]	valid_0&#39;s binary_logloss: 0.369131
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.366281:  10%|#         | 2/20 [00:28&lt;04:21, 14.52s/it][I 2024-06-18 21:14:33,256] Trial 44 finished with value: 0.3744642189562516 and parameters: {'lambda_l1': 1.1442585672872564e-08, 'lambda_l2': 4.849247272391423e-08}. Best is trial 43 with value: 0.36913074695699283.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.366281:  10%|#         | 2/20 [00:28&lt;04:21, 14.52s/it][I 2024-06-18 21:14:33,256] Trial 44 finished with value: 0.3744642189562516 and parameters: {&#39;lambda_l1&#39;: 1.1442585672872564e-08, &#39;lambda_l2&#39;: 4.849247272391423e-08}. Best is trial 43 with value: 0.36913074695699283.
 regularization_factors, val_score: 0.366281:  10%|#         | 2/20 [00:28&lt;04:21, 14.52s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[37]	valid_0's binary_logloss: 0.374464
-Training until validation scores don't improve for 50 rounds
+[37]	valid_0&#39;s binary_logloss: 0.374464
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.366281:  15%|#5        | 3/20 [00:45&lt;04:23, 15.52s/it][I 2024-06-18 21:14:49,962] Trial 45 finished with value: 0.3703169935094016 and parameters: {'lambda_l1': 9.96832677827828e-07, 'lambda_l2': 6.3332349736895}. Best is trial 43 with value: 0.36913074695699283.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.366281:  15%|#5        | 3/20 [00:45&lt;04:23, 15.52s/it][I 2024-06-18 21:14:49,962] Trial 45 finished with value: 0.3703169935094016 and parameters: {&#39;lambda_l1&#39;: 9.96832677827828e-07, &#39;lambda_l2&#39;: 6.3332349736895}. Best is trial 43 with value: 0.36913074695699283.
 regularization_factors, val_score: 0.366281:  15%|#5        | 3/20 [00:45&lt;04:23, 15.52s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[75]	valid_0's binary_logloss: 0.370317
-Training until validation scores don't improve for 50 rounds
+[75]	valid_0&#39;s binary_logloss: 0.370317
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.366281:  20%|##        | 4/20 [00:58&lt;03:54, 14.69s/it][I 2024-06-18 21:15:03,375] Trial 46 finished with value: 0.3672993919740384 and parameters: {'lambda_l1': 1.5127153863704856, 'lambda_l2': 4.478393628964223e-08}. Best is trial 46 with value: 0.3672993919740384.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.366281:  20%|##        | 4/20 [00:58&lt;03:54, 14.69s/it][I 2024-06-18 21:15:03,375] Trial 46 finished with value: 0.3672993919740384 and parameters: {&#39;lambda_l1&#39;: 1.5127153863704856, &#39;lambda_l2&#39;: 4.478393628964223e-08}. Best is trial 46 with value: 0.3672993919740384.
 regularization_factors, val_score: 0.366281:  20%|##        | 4/20 [00:58&lt;03:54, 14.69s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[49]	valid_0's binary_logloss: 0.367299
-Training until validation scores don't improve for 50 rounds
+[49]	valid_0&#39;s binary_logloss: 0.367299
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.366281:  25%|##5       | 5/20 [01:12&lt;03:36, 14.40s/it][I 2024-06-18 21:15:17,268] Trial 47 finished with value: 0.3673221145565776 and parameters: {'lambda_l1': 2.3927205612539093, 'lambda_l2': 5.0167732343674086e-08}. Best is trial 46 with value: 0.3672993919740384.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.366281:  25%|##5       | 5/20 [01:12&lt;03:36, 14.40s/it][I 2024-06-18 21:15:17,268] Trial 47 finished with value: 0.3673221145565776 and parameters: {&#39;lambda_l1&#39;: 2.3927205612539093, &#39;lambda_l2&#39;: 5.0167732343674086e-08}. Best is trial 46 with value: 0.3672993919740384.
 regularization_factors, val_score: 0.366281:  25%|##5       | 5/20 [01:12&lt;03:36, 14.40s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[73]	valid_0's binary_logloss: 0.367322
-Training until validation scores don't improve for 50 rounds
+[73]	valid_0&#39;s binary_logloss: 0.367322
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.366281:  30%|###       | 6/20 [01:25&lt;03:15, 13.96s/it][I 2024-06-18 21:15:30,372] Trial 48 finished with value: 0.38611937264133733 and parameters: {'lambda_l1': 6.054665616489558, 'lambda_l2': 1.800720533496738e-08}. Best is trial 46 with value: 0.3672993919740384.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.366281:  30%|###       | 6/20 [01:25&lt;03:15, 13.96s/it][I 2024-06-18 21:15:30,372] Trial 48 finished with value: 0.38611937264133733 and parameters: {&#39;lambda_l1&#39;: 6.054665616489558, &#39;lambda_l2&#39;: 1.800720533496738e-08}. Best is trial 46 with value: 0.3672993919740384.
 regularization_factors, val_score: 0.366281:  30%|###       | 6/20 [01:25&lt;03:15, 13.96s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[156]	valid_0's binary_logloss: 0.386119
-Training until validation scores don't improve for 50 rounds
+[156]	valid_0&#39;s binary_logloss: 0.386119
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  35%|###5      | 7/20 [01:41&lt;03:08, 14.53s/it][I 2024-06-18 21:15:46,069] Trial 49 finished with value: 0.36481873368079354 and parameters: {'lambda_l1': 0.018981605475779886, 'lambda_l2': 4.496278591408666e-06}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  35%|###5      | 7/20 [01:41&lt;03:08, 14.53s/it][I 2024-06-18 21:15:46,069] Trial 49 finished with value: 0.36481873368079354 and parameters: {&#39;lambda_l1&#39;: 0.018981605475779886, &#39;lambda_l2&#39;: 4.496278591408666e-06}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  35%|###5      | 7/20 [01:41&lt;03:08, 14.53s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[32]	valid_0's binary_logloss: 0.364819
-Training until validation scores don't improve for 50 rounds
+[32]	valid_0&#39;s binary_logloss: 0.364819
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  40%|####      | 8/20 [01:58&lt;03:02, 15.22s/it][I 2024-06-18 21:16:02,773] Trial 50 finished with value: 0.3676098962913463 and parameters: {'lambda_l1': 0.005938438143650016, 'lambda_l2': 1.6486829620090715e-05}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  40%|####      | 8/20 [01:58&lt;03:02, 15.22s/it][I 2024-06-18 21:16:02,773] Trial 50 finished with value: 0.3676098962913463 and parameters: {&#39;lambda_l1&#39;: 0.005938438143650016, &#39;lambda_l2&#39;: 1.6486829620090715e-05}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  40%|####      | 8/20 [01:58&lt;03:02, 15.22s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[32]	valid_0's binary_logloss: 0.36761
-Training until validation scores don't improve for 50 rounds
+[32]	valid_0&#39;s binary_logloss: 0.36761
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  45%|####5     | 9/20 [02:13&lt;02:46, 15.12s/it][I 2024-06-18 21:16:17,683] Trial 51 finished with value: 0.3693663505867317 and parameters: {'lambda_l1': 0.14870260759215176, 'lambda_l2': 8.827878170241735e-07}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  45%|####5     | 9/20 [02:13&lt;02:46, 15.12s/it][I 2024-06-18 21:16:17,683] Trial 51 finished with value: 0.3693663505867317 and parameters: {&#39;lambda_l1&#39;: 0.14870260759215176, &#39;lambda_l2&#39;: 8.827878170241735e-07}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  45%|####5     | 9/20 [02:13&lt;02:46, 15.12s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[32]	valid_0's binary_logloss: 0.369366
-Training until validation scores don't improve for 50 rounds
+[32]	valid_0&#39;s binary_logloss: 0.369366
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  50%|#####     | 10/20 [02:21&lt;02:11, 13.17s/it][I 2024-06-18 21:16:26,468] Trial 52 finished with value: 0.3995526935417914 and parameters: {'lambda_l1': 9.914097223337537, 'lambda_l2': 2.507766754016921e-06}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  50%|#####     | 10/20 [02:21&lt;02:11, 13.17s/it][I 2024-06-18 21:16:26,468] Trial 52 finished with value: 0.3995526935417914 and parameters: {&#39;lambda_l1&#39;: 9.914097223337537, &#39;lambda_l2&#39;: 2.507766754016921e-06}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  50%|#####     | 10/20 [02:21&lt;02:11, 13.17s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[108]	valid_0's binary_logloss: 0.399553
-Training until validation scores don't improve for 50 rounds
+[108]	valid_0&#39;s binary_logloss: 0.399553
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  55%|#####5    | 11/20 [02:36&lt;02:02, 13.60s/it][I 2024-06-18 21:16:41,058] Trial 53 finished with value: 0.37135627608204047 and parameters: {'lambda_l1': 0.004317296224576123, 'lambda_l2': 1.0694628305152963e-08}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  55%|#####5    | 11/20 [02:36&lt;02:02, 13.60s/it][I 2024-06-18 21:16:41,058] Trial 53 finished with value: 0.37135627608204047 and parameters: {&#39;lambda_l1&#39;: 0.004317296224576123, &#39;lambda_l2&#39;: 1.0694628305152963e-08}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  55%|#####5    | 11/20 [02:36&lt;02:02, 13.60s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[32]	valid_0's binary_logloss: 0.371356
-Training until validation scores don't improve for 50 rounds
+[32]	valid_0&#39;s binary_logloss: 0.371356
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  60%|######    | 12/20 [02:50&lt;01:50, 13.82s/it][I 2024-06-18 21:16:55,362] Trial 54 finished with value: 0.3656757773203038 and parameters: {'lambda_l1': 0.1528681952118711, 'lambda_l2': 0.0003078435151438368}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  60%|######    | 12/20 [02:50&lt;01:50, 13.82s/it][I 2024-06-18 21:16:55,362] Trial 54 finished with value: 0.3656757773203038 and parameters: {&#39;lambda_l1&#39;: 0.1528681952118711, &#39;lambda_l2&#39;: 0.0003078435151438368}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  60%|######    | 12/20 [02:50&lt;01:50, 13.82s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[38]	valid_0's binary_logloss: 0.365676
-Training until validation scores don't improve for 50 rounds
+[38]	valid_0&#39;s binary_logloss: 0.365676
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  65%|######5   | 13/20 [03:03&lt;01:34, 13.48s/it][I 2024-06-18 21:17:08,074] Trial 55 finished with value: 0.3683612678266087 and parameters: {'lambda_l1': 0.026844520069898837, 'lambda_l2': 0.0010041201106681954}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  65%|######5   | 13/20 [03:03&lt;01:34, 13.48s/it][I 2024-06-18 21:17:08,074] Trial 55 finished with value: 0.3683612678266087 and parameters: {&#39;lambda_l1&#39;: 0.026844520069898837, &#39;lambda_l2&#39;: 0.0010041201106681954}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  65%|######5   | 13/20 [03:03&lt;01:34, 13.48s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[30]	valid_0's binary_logloss: 0.368361
-Training until validation scores don't improve for 50 rounds
+[30]	valid_0&#39;s binary_logloss: 0.368361
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  70%|#######   | 14/20 [03:18&lt;01:24, 14.03s/it][I 2024-06-18 21:17:23,369] Trial 56 finished with value: 0.367596997615459 and parameters: {'lambda_l1': 0.0003242973314637712, 'lambda_l2': 5.561136449228501e-05}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  70%|#######   | 14/20 [03:18&lt;01:24, 14.03s/it][I 2024-06-18 21:17:23,369] Trial 56 finished with value: 0.367596997615459 and parameters: {&#39;lambda_l1&#39;: 0.0003242973314637712, &#39;lambda_l2&#39;: 5.561136449228501e-05}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  70%|#######   | 14/20 [03:18&lt;01:24, 14.03s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[30]	valid_0's binary_logloss: 0.367597
-Training until validation scores don't improve for 50 rounds
+[30]	valid_0&#39;s binary_logloss: 0.367597
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  75%|#######5  | 15/20 [03:31&lt;01:07, 13.57s/it][I 2024-06-18 21:17:35,879] Trial 57 finished with value: 0.36589916181880006 and parameters: {'lambda_l1': 0.2057156258629452, 'lambda_l2': 0.024885518547422405}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  75%|#######5  | 15/20 [03:31&lt;01:07, 13.57s/it][I 2024-06-18 21:17:35,879] Trial 57 finished with value: 0.36589916181880006 and parameters: {&#39;lambda_l1&#39;: 0.2057156258629452, &#39;lambda_l2&#39;: 0.024885518547422405}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  75%|#######5  | 15/20 [03:31&lt;01:07, 13.57s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[31]	valid_0's binary_logloss: 0.365899
-Training until validation scores don't improve for 50 rounds
+[31]	valid_0&#39;s binary_logloss: 0.365899
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  80%|########  | 16/20 [03:45&lt;00:55, 13.85s/it][I 2024-06-18 21:17:50,372] Trial 58 finished with value: 0.36736185428940016 and parameters: {'lambda_l1': 0.0002150419853087141, 'lambda_l2': 0.034107683119902095}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  80%|########  | 16/20 [03:45&lt;00:55, 13.85s/it][I 2024-06-18 21:17:50,372] Trial 58 finished with value: 0.36736185428940016 and parameters: {&#39;lambda_l1&#39;: 0.0002150419853087141, &#39;lambda_l2&#39;: 0.034107683119902095}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  80%|########  | 16/20 [03:45&lt;00:55, 13.85s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[34]	valid_0's binary_logloss: 0.367362
-Training until validation scores don't improve for 50 rounds
+[34]	valid_0&#39;s binary_logloss: 0.367362
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  85%|########5 | 17/20 [04:00&lt;00:42, 14.20s/it][I 2024-06-18 21:18:05,384] Trial 59 finished with value: 0.3702898033719683 and parameters: {'lambda_l1': 0.0583471784381045, 'lambda_l2': 0.3274496387110711}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  85%|########5 | 17/20 [04:00&lt;00:42, 14.20s/it][I 2024-06-18 21:18:05,384] Trial 59 finished with value: 0.3702898033719683 and parameters: {&#39;lambda_l1&#39;: 0.0583471784381045, &#39;lambda_l2&#39;: 0.3274496387110711}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  85%|########5 | 17/20 [04:00&lt;00:42, 14.20s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[40]	valid_0's binary_logloss: 0.37029
-Training until validation scores don't improve for 50 rounds
+[40]	valid_0&#39;s binary_logloss: 0.37029
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  90%|######### | 18/20 [04:18&lt;00:30, 15.10s/it][I 2024-06-18 21:18:22,586] Trial 60 finished with value: 0.3651493917986752 and parameters: {'lambda_l1': 0.001458069822509784, 'lambda_l2': 0.008248248782706902}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  90%|######### | 18/20 [04:18&lt;00:30, 15.10s/it][I 2024-06-18 21:18:22,586] Trial 60 finished with value: 0.3651493917986752 and parameters: {&#39;lambda_l1&#39;: 0.001458069822509784, &#39;lambda_l2&#39;: 0.008248248782706902}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  90%|######### | 18/20 [04:18&lt;00:30, 15.10s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[43]	valid_0's binary_logloss: 0.365149
-Training until validation scores don't improve for 50 rounds
+[43]	valid_0&#39;s binary_logloss: 0.365149
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819:  95%|#########5| 19/20 [04:33&lt;00:15, 15.10s/it][I 2024-06-18 21:18:37,676] Trial 61 finished with value: 0.3696068718370661 and parameters: {'lambda_l1': 0.0006748569060520587, 'lambda_l2': 0.01603839211588336}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819:  95%|#########5| 19/20 [04:33&lt;00:15, 15.10s/it][I 2024-06-18 21:18:37,676] Trial 61 finished with value: 0.3696068718370661 and parameters: {&#39;lambda_l1&#39;: 0.0006748569060520587, &#39;lambda_l2&#39;: 0.01603839211588336}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819:  95%|#########5| 19/20 [04:33&lt;00:15, 15.10s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[41]	valid_0's binary_logloss: 0.369607
-Training until validation scores don't improve for 50 rounds
+[41]	valid_0&#39;s binary_logloss: 0.369607
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>regularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48&lt;00:00, 15.16s/it][I 2024-06-18 21:18:52,985] Trial 62 finished with value: 0.3700319935632785 and parameters: {'lambda_l1': 0.004313680126647664, 'lambda_l2': 0.008355812202217454}. Best is trial 49 with value: 0.36481873368079354.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>regularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48&lt;00:00, 15.16s/it][I 2024-06-18 21:18:52,985] Trial 62 finished with value: 0.3700319935632785 and parameters: {&#39;lambda_l1&#39;: 0.004313680126647664, &#39;lambda_l2&#39;: 0.008355812202217454}. Best is trial 49 with value: 0.36481873368079354.
 regularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48&lt;00:00, 14.42s/it]
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[38]	valid_0's binary_logloss: 0.370032
+[38]	valid_0&#39;s binary_logloss: 0.370032
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
 <pre>min_child_samples, val_score: 0.364819:   0%|          | 0/5 [00:00&lt;?, ?it/s]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Training until validation scores don't improve for 50 rounds
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>min_child_samples, val_score: 0.364819:  20%|##        | 1/5 [00:10&lt;00:40, 10.19s/it][I 2024-06-18 21:19:03,252] Trial 63 finished with value: 0.3897736671401978 and parameters: {'min_child_samples': 50}. Best is trial 63 with value: 0.3897736671401978.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>min_child_samples, val_score: 0.364819:  20%|##        | 1/5 [00:10&lt;00:40, 10.19s/it][I 2024-06-18 21:19:03,252] Trial 63 finished with value: 0.3897736671401978 and parameters: {&#39;min_child_samples&#39;: 50}. Best is trial 63 with value: 0.3897736671401978.
 min_child_samples, val_score: 0.364819:  20%|##        | 1/5 [00:10&lt;00:40, 10.19s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[51]	valid_0's binary_logloss: 0.389774
-Training until validation scores don't improve for 50 rounds
+[51]	valid_0&#39;s binary_logloss: 0.389774
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>min_child_samples, val_score: 0.364819:  40%|####      | 2/5 [00:29&lt;00:46, 15.39s/it][I 2024-06-18 21:19:22,277] Trial 64 finished with value: 0.3683263542462511 and parameters: {'min_child_samples': 5}. Best is trial 64 with value: 0.3683263542462511.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>min_child_samples, val_score: 0.364819:  40%|####      | 2/5 [00:29&lt;00:46, 15.39s/it][I 2024-06-18 21:19:22,277] Trial 64 finished with value: 0.3683263542462511 and parameters: {&#39;min_child_samples&#39;: 5}. Best is trial 64 with value: 0.3683263542462511.
 min_child_samples, val_score: 0.364819:  40%|####      | 2/5 [00:29&lt;00:46, 15.39s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[43]	valid_0's binary_logloss: 0.368326
-Training until validation scores don't improve for 50 rounds
+[43]	valid_0&#39;s binary_logloss: 0.368326
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>min_child_samples, val_score: 0.364819:  60%|######    | 3/5 [00:36&lt;00:23, 11.88s/it][I 2024-06-18 21:19:29,977] Trial 65 finished with value: 0.3938280500430312 and parameters: {'min_child_samples': 100}. Best is trial 64 with value: 0.3683263542462511.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>min_child_samples, val_score: 0.364819:  60%|######    | 3/5 [00:36&lt;00:23, 11.88s/it][I 2024-06-18 21:19:29,977] Trial 65 finished with value: 0.3938280500430312 and parameters: {&#39;min_child_samples&#39;: 100}. Best is trial 64 with value: 0.3683263542462511.
 min_child_samples, val_score: 0.364819:  60%|######    | 3/5 [00:36&lt;00:23, 11.88s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[113]	valid_0's binary_logloss: 0.393828
-Training until validation scores don't improve for 50 rounds
+[113]	valid_0&#39;s binary_logloss: 0.393828
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>min_child_samples, val_score: 0.364819:  80%|########  | 4/5 [00:53&lt;00:13, 13.86s/it][I 2024-06-18 21:19:46,881] Trial 66 finished with value: 0.36712449039705003 and parameters: {'min_child_samples': 10}. Best is trial 66 with value: 0.36712449039705003.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>min_child_samples, val_score: 0.364819:  80%|########  | 4/5 [00:53&lt;00:13, 13.86s/it][I 2024-06-18 21:19:46,881] Trial 66 finished with value: 0.36712449039705003 and parameters: {&#39;min_child_samples&#39;: 10}. Best is trial 66 with value: 0.36712449039705003.
 min_child_samples, val_score: 0.364819:  80%|########  | 4/5 [00:53&lt;00:13, 13.86s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[32]	valid_0's binary_logloss: 0.367124
-Training until validation scores don't improve for 50 rounds
+[32]	valid_0&#39;s binary_logloss: 0.367124
+Training until validation scores don&#39;t improve for 50 rounds
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>min_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07&lt;00:00, 13.80s/it][I 2024-06-18 21:20:00,561] Trial 67 finished with value: 0.3719291335673048 and parameters: {'min_child_samples': 25}. Best is trial 66 with value: 0.36712449039705003.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>min_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07&lt;00:00, 13.80s/it][I 2024-06-18 21:20:00,561] Trial 67 finished with value: 0.3719291335673048 and parameters: {&#39;min_child_samples&#39;: 25}. Best is trial 66 with value: 0.36712449039705003.
 min_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07&lt;00:00, 13.50s/it]</pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Early stopping, best iteration is:
-[38]	valid_0's binary_logloss: 0.371929
+[38]	valid_0&#39;s binary_logloss: 0.371929
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
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+
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+
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+
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># save/load trainer</span>
-<span class="c1"># dill_dump("lgbm_trainer.dill", trainer)</span>
-<span class="n">trainer</span> <span class="o">=</span> <span class="n">dill_load</span><span class="p">(</span><span class="s2">"lgbm_trainer.dill"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[8]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># save/load trainer</span>
+<span class="c1"># dill_dump(&quot;lgbm_trainer.dill&quot;, trainer)</span>
+<span class="n">trainer</span> <span class="o">=</span> <span class="n">dill_load</span><span class="p">(</span><span class="s2">&quot;lgbm_trainer.dill&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-9"># save/load trainer
+<div id="cell-9" class="clipboard-copy-txt"># save/load trainer
 # dill_dump("lgbm_trainer.dill", trainer)
 trainer = dill_load("lgbm_trainer.dill")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">pprint</span><span class="p">(</span><span class="n">metrics_dict</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[11]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">pprint</span><span class="p">(</span><span class="n">metrics_dict</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-10">pprint(metrics_dict)</div>
-</div>
-</div>
+<div id="cell-10" class="clipboard-copy-txt">pprint(metrics_dict)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>{'cls_report': {'0': {'f1-score': 0.9244823386114495,
-                      'precision': 0.8971631205673759,
-                      'recall': 0.9535175879396985,
-                      'support': 796},
-                '1': {'f1-score': 0.6536312849162011,
-                      'precision': 0.7597402597402597,
-                      'recall': 0.5735294117647058,
-                      'support': 204},
-                'accuracy': 0.876,
-                'macro avg': {'f1-score': 0.7890568117638252,
-                              'precision': 0.8284516901538178,
-                              'recall': 0.7635234998522022,
-                              'support': 1000},
-                'weighted avg': {'f1-score': 0.8692287236576187,
-                                 'precision': 0.8691288569586443,
-                                 'recall': 0.876,
-                                 'support': 1000}},
- 'cm': [[759, 37], [87, 117]],
- 'prec_rec_curve': [[0.204, 0.7597402597402597, 1.0],
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>{&#39;cls_report&#39;: {&#39;0&#39;: {&#39;f1-score&#39;: 0.9244823386114495,
+                      &#39;precision&#39;: 0.8971631205673759,
+                      &#39;recall&#39;: 0.9535175879396985,
+                      &#39;support&#39;: 796},
+                &#39;1&#39;: {&#39;f1-score&#39;: 0.6536312849162011,
+                      &#39;precision&#39;: 0.7597402597402597,
+                      &#39;recall&#39;: 0.5735294117647058,
+                      &#39;support&#39;: 204},
+                &#39;accuracy&#39;: 0.876,
+                &#39;macro avg&#39;: {&#39;f1-score&#39;: 0.7890568117638252,
+                              &#39;precision&#39;: 0.8284516901538178,
+                              &#39;recall&#39;: 0.7635234998522022,
+                              &#39;support&#39;: 1000},
+                &#39;weighted avg&#39;: {&#39;f1-score&#39;: 0.8692287236576187,
+                                 &#39;precision&#39;: 0.8691288569586443,
+                                 &#39;recall&#39;: 0.876,
+                                 &#39;support&#39;: 1000}},
+ &#39;cm&#39;: [[759, 37], [87, 117]],
+ &#39;prec_rec_curve&#39;: [[0.204, 0.7597402597402597, 1.0],
                     [1.0, 0.5735294117647058, 0.0],
                     [0.0, 1.0]]}
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [32]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">pprint</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span><span class="n">df_test</span><span class="p">,</span> <span class="n">with_dynamic_binary_threshold</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[32]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-11">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">pprint</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">compute_metrics</span><span class="p">(</span><span class="n">df_test</span><span class="p">,</span> <span class="n">with_dynamic_binary_threshold</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-11">pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True))</div>
-</div>
-</div>
+<div id="cell-11" class="clipboard-copy-txt">pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True))</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>{'cls_report': {'0': {'f1-score': 0.9244823386114495,
-                      'precision': 0.8971631205673759,
-                      'recall': 0.9535175879396985,
-                      'support': 796},
-                '1': {'f1-score': 0.6536312849162011,
-                      'precision': 0.7597402597402597,
-                      'recall': 0.5735294117647058,
-                      'support': 204},
-                'accuracy': 0.876,
-                'macro avg': {'f1-score': 0.7890568117638252,
-                              'precision': 0.8284516901538178,
-                              'recall': 0.7635234998522022,
-                              'support': 1000},
-                'weighted avg': {'f1-score': 0.8692287236576187,
-                                 'precision': 0.8691288569586443,
-                                 'recall': 0.876,
-                                 'support': 1000}},
- 'cm': [[759, 37], [87, 117]],
- 'prec_rec_curve': [[0.204, 0.7597402597402597, 1.0],
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
+<pre>{&#39;cls_report&#39;: {&#39;0&#39;: {&#39;f1-score&#39;: 0.9244823386114495,
+                      &#39;precision&#39;: 0.8971631205673759,
+                      &#39;recall&#39;: 0.9535175879396985,
+                      &#39;support&#39;: 796},
+                &#39;1&#39;: {&#39;f1-score&#39;: 0.6536312849162011,
+                      &#39;precision&#39;: 0.7597402597402597,
+                      &#39;recall&#39;: 0.5735294117647058,
+                      &#39;support&#39;: 204},
+                &#39;accuracy&#39;: 0.876,
+                &#39;macro avg&#39;: {&#39;f1-score&#39;: 0.7890568117638252,
+                              &#39;precision&#39;: 0.8284516901538178,
+                              &#39;recall&#39;: 0.7635234998522022,
+                              &#39;support&#39;: 1000},
+                &#39;weighted avg&#39;: {&#39;f1-score&#39;: 0.8692287236576187,
+                                 &#39;precision&#39;: 0.8691288569586443,
+                                 &#39;recall&#39;: 0.876,
+                                 &#39;support&#39;: 1000}},
+ &#39;cm&#39;: [[759, 37], [87, 117]],
+ &#39;prec_rec_curve&#39;: [[0.204, 0.7597402597402597, 1.0],
                     [1.0, 0.5735294117647058, 0.0],
                     [0.0, 1.0]]}
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h1 id="mlflow">Mlflow<a class="anchor-link" href="#mlflow">¶</a></h1><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="mlflow">Mlflow<a class="anchor-link" href="#mlflow">&#182;</a></h1><ul>
 <li>tracking</li>
 <li>model registration in Minio</li>
 </ul>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">mlflow_host</span> <span class="o">=</span> <span class="s2">"10.152.183.54"</span>
-<span class="n">mlflow_host_url</span> <span class="o">=</span> <span class="s2">"mlflow.mlflow.svc.cluster.local"</span>
-<span class="n">mlflow_port</span> <span class="o">=</span> <span class="s2">"5000"</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"AWS_ACCESS_KEY_ID"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"minioadmin"</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"AWS_SECRET_ACCESS_KEY"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"minioadmin"</span>
-<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"MLFLOW_S3_ENDPOINT_URL"</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"http://10.152.183.156:9000"</span>
-
-<span class="n">mlflow</span><span class="o">.</span><span class="n">set_tracking_uri</span><span class="p">(</span><span class="s2">"http://"</span> <span class="o">+</span> <span class="n">mlflow_host</span> <span class="o">+</span> <span class="s2">":"</span> <span class="o">+</span> <span class="n">mlflow_port</span><span class="p">)</span>
-<span class="n">experiment_id</span> <span class="o">=</span> <span class="n">get_or_create_experiment</span><span class="p">(</span><span class="s2">"ecovadis"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">mlflow_host</span> <span class="o">=</span> <span class="s2">&quot;10.152.183.54&quot;</span>
+<span class="n">mlflow_host_url</span> <span class="o">=</span> <span class="s2">&quot;mlflow.mlflow.svc.cluster.local&quot;</span>
+<span class="n">mlflow_port</span> <span class="o">=</span> <span class="s2">&quot;5000&quot;</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;AWS_ACCESS_KEY_ID&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;minioadmin&quot;</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;AWS_SECRET_ACCESS_KEY&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;minioadmin&quot;</span>
+<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;MLFLOW_S3_ENDPOINT_URL&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;http://10.152.183.156:9000&quot;</span>
+
+<span class="n">mlflow</span><span class="o">.</span><span class="n">set_tracking_uri</span><span class="p">(</span><span class="s2">&quot;http://&quot;</span> <span class="o">+</span> <span class="n">mlflow_host</span> <span class="o">+</span> <span class="s2">&quot;:&quot;</span> <span class="o">+</span> <span class="n">mlflow_port</span><span class="p">)</span>
+<span class="n">experiment_id</span> <span class="o">=</span> <span class="n">get_or_create_experiment</span><span class="p">(</span><span class="s2">&quot;ecovadis&quot;</span><span class="p">)</span>
 <span class="n">mlflow</span><span class="o">.</span><span class="n">set_experiment</span><span class="p">(</span><span class="n">experiment_id</span><span class="o">=</span><span class="n">experiment_id</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-12">mlflow_host = "10.152.183.54"
+<div id="cell-12" class="clipboard-copy-txt">mlflow_host = "10.152.183.54"
 mlflow_host_url = "mlflow.mlflow.svc.cluster.local"
 mlflow_port = "5000"
 os.environ["AWS_ACCESS_KEY_ID"] = "minioadmin"
@@ -3321,101 +4564,129 @@ <h1 id="mlflow">Mlflow<a class="anchor-link" href="#mlflow">¶</a></h1><ul>
 mlflow.set_tracking_uri("http://" + mlflow_host + ":" + mlflow_port)
 experiment_id = get_or_create_experiment("ecovadis")
 mlflow.set_experiment(experiment_id=experiment_id)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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 </div>
+
+
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-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>&lt;Experiment: artifact_location='s3://mlflow/6', creation_time=1718745770093, experiment_id='6', last_update_time=1718745770093, lifecycle_stage='active', name='ecovadis', tags={}&gt;</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&lt;Experiment: artifact_location=&#39;s3://mlflow/6&#39;, creation_time=1718745770093, experiment_id=&#39;6&#39;, last_update_time=1718745770093, lifecycle_stage=&#39;active&#39;, name=&#39;ecovadis&#39;, tags={}&gt;</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">metrics_dict_flattened</span> <span class="o">=</span> <span class="n">flatten_dict</span><span class="p">(</span><span class="n">metrics_dict</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
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+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">metrics_dict_flattened</span> <span class="o">=</span> <span class="n">flatten_dict</span><span class="p">(</span><span class="n">metrics_dict</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-13">metrics_dict_flattened = flatten_dict(metrics_dict)</div>
-</div>
-</div>
+<div id="cell-13" class="clipboard-copy-txt">metrics_dict_flattened = flatten_dict(metrics_dict)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># mlflow metrics can be only int, float not list</span>
-<span class="k">del</span> <span class="n">metrics_dict_flattened</span><span class="p">[</span><span class="s2">"cm"</span><span class="p">]</span>
-<span class="k">del</span> <span class="n">metrics_dict_flattened</span><span class="p">[</span><span class="s2">"prec_rec_curve"</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[14]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-14">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># mlflow metrics can be only int, float not list</span>
+<span class="k">del</span> <span class="n">metrics_dict_flattened</span><span class="p">[</span><span class="s2">&quot;cm&quot;</span><span class="p">]</span>
+<span class="k">del</span> <span class="n">metrics_dict_flattened</span><span class="p">[</span><span class="s2">&quot;prec_rec_curve&quot;</span><span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-14"># mlflow metrics can be only int, float not list
+<div id="cell-14" class="clipboard-copy-txt"># mlflow metrics can be only int, float not list
 del metrics_dict_flattened["cm"]
 del metrics_dict_flattened["prec_rec_curve"]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">run_name</span> <span class="o">=</span> <span class="s2">"init_run"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-15">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">run_name</span> <span class="o">=</span> <span class="s2">&quot;init_run&quot;</span>
 <span class="k">with</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">start_run</span><span class="p">(</span><span class="n">experiment_id</span><span class="o">=</span><span class="n">experiment_id</span><span class="p">,</span> <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">,</span> <span class="n">nested</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">run</span><span class="p">:</span>
     <span class="n">mlflow</span><span class="o">.</span><span class="n">log_params</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">best</span><span class="p">)</span>
     <span class="n">mlflow</span><span class="o">.</span><span class="n">log_metrics</span><span class="p">(</span><span class="n">metrics_dict_flattened</span><span class="p">)</span>
@@ -3423,25 +4694,25 @@ <h1 id="mlflow">Mlflow<a class="anchor-link" href="#mlflow">¶</a></h1><ul>
     <span class="c1"># Log tags</span>
     <span class="n">mlflow</span><span class="o">.</span><span class="n">set_tags</span><span class="p">(</span>
         <span class="n">tags</span><span class="o">=</span><span class="p">{</span>
-            <span class="s2">"project"</span><span class="p">:</span> <span class="s2">"ecovadis"</span><span class="p">,</span>
-            <span class="s2">"optimizer_engine"</span><span class="p">:</span> <span class="s2">"optuna"</span><span class="p">,</span>
-            <span class="s2">"model_family"</span><span class="p">:</span> <span class="s2">"ligtgbm"</span><span class="p">,</span>
-            <span class="s2">"feature_set_version"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+            <span class="s2">&quot;project&quot;</span><span class="p">:</span> <span class="s2">&quot;ecovadis&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;optimizer_engine&quot;</span><span class="p">:</span> <span class="s2">&quot;optuna&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;model_family&quot;</span><span class="p">:</span> <span class="s2">&quot;ligtgbm&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;feature_set_version&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
         <span class="p">}</span>
     <span class="p">)</span>
     <span class="c1"># Log figure - for future fun</span>
-    <span class="c1"># mlflow.log_figure(figure=correlation_plot, artifact_file="correlation_plot.png")</span>
+    <span class="c1"># mlflow.log_figure(figure=correlation_plot, artifact_file=&quot;correlation_plot.png&quot;)</span>
 
-    <span class="n">artifact_path</span> <span class="o">=</span> <span class="s2">"ecovadis_model"</span>
-    <span class="n">registered_model_name</span> <span class="o">=</span> <span class="s2">"ecovadis_lgbm_model"</span>
+    <span class="n">artifact_path</span> <span class="o">=</span> <span class="s2">&quot;ecovadis_model&quot;</span>
+    <span class="n">registered_model_name</span> <span class="o">=</span> <span class="s2">&quot;ecovadis_lgbm_model&quot;</span>
     <span class="n">mlflow</span><span class="o">.</span><span class="n">pyfunc</span><span class="o">.</span><span class="n">log_model</span><span class="p">(</span><span class="n">python_model</span><span class="o">=</span><span class="n">trainer</span><span class="p">,</span> 
                             <span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">,</span> 
                             <span class="n">registered_model_name</span><span class="o">=</span><span class="n">registered_model_name</span><span class="p">,</span>
-                            <span class="n">code_paths</span><span class="o">=</span><span class="p">[</span><span class="s2">"churn_pred"</span><span class="p">])</span>  
+                            <span class="n">code_paths</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;churn_pred&quot;</span><span class="p">])</span>  
     <span class="n">model_uri</span> <span class="o">=</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">get_artifact_uri</span><span class="p">(</span><span class="n">artifact_path</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Run ID:</span><span class="se">\n</span><span class="si">{</span><span class="n">run</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">run_id</span><span class="si">}</span><span class="se">\n</span><span class="s2">Model uri:</span><span class="se">\n</span><span class="si">{</span><span class="n">model_uri</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Run ID:</span><span class="se">\n</span><span class="si">{</span><span class="n">run</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">run_id</span><span class="si">}</span><span class="se">\n</span><span class="s2">Model uri:</span><span class="se">\n</span><span class="si">{</span><span class="n">model_uri</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-15">run_name = "init_run"
+<div id="cell-15" class="clipboard-copy-txt">run_name = "init_run"
 with mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run:
     mlflow.log_params(trainer.optimizer.best)
     mlflow.log_metrics(metrics_dict_flattened)
@@ -3466,28 +4737,43 @@ <h1 id="mlflow">Mlflow<a class="anchor-link" href="#mlflow">¶</a></h1><ul>
                             code_paths=["churn_pred"])  
     model_uri = mlflow.get_artifact_uri(artifact_path)
     print(f"Run ID:\n{run.info.run_id}\nModel uri:\n{model_uri}")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: &lt;class 'pandas.core.frame.DataFrame'&gt;, skipping schema inference
-2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: &lt;class 'pandas.core.frame.DataFrame'&gt;, skipping schema inference
-2024/06/18 21:23:58 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpfsv2c_xg/model, flavor: python_function). Fall back to return ['cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback. 
-Successfully registered model 'ecovadis_lgbm_model'.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: &lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;, skipping schema inference
+2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: &lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;, skipping schema inference
+2024/06/18 21:23:58 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpfsv2c_xg/model, flavor: python_function). Fall back to return [&#39;cloudpickle==2.2.1&#39;]. Set logging level to DEBUG to see the full traceback. 
+Successfully registered model &#39;ecovadis_lgbm_model&#39;.
 2024/06/18 21:27:30 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: ecovadis_lgbm_model, version 1
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Run ID:
 761906b692e94248940c6dbf6b12c23c
 Model uri:
@@ -3495,87 +4781,110 @@ <h1 id="mlflow">Mlflow<a class="anchor-link" href="#mlflow">¶</a></h1><ul>
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>Created version '1' of model 'ecovadis_lgbm_model'.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Created version &#39;1&#39; of model &#39;ecovadis_lgbm_model&#39;.
 </pre>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="screenshots-from-mlflow-gui-nodeport-service-in-kubernetes">Screenshots from MLflow GUI (Nodeport service in Kubernetes)<a class="anchor-link" href="#screenshots-from-mlflow-gui-nodeport-service-in-kubernetes">¶</a></h2><p><img alt="mlflow_parameters" src="img/mlflow_parameters.png"/>
-<img alt="mlflow_parameters" src="img/mlflow_metrics.png"/></p>
-<p><img alt="mlflow_parameters" src="img/mlflow_artifacts.png"/></p>
-<p><img alt="mlflow_parameters" src="img/mlflow_artifacts_registered_model.png"/></p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="screenshots-from-mlflow-gui-nodeport-service-in-kubernetes">Screenshots from MLflow GUI (Nodeport service in Kubernetes)<a class="anchor-link" href="#screenshots-from-mlflow-gui-nodeport-service-in-kubernetes">&#182;</a></h2><p><img src="img/mlflow_parameters.png" alt="mlflow_parameters">
+<img src="img/mlflow_metrics.png" alt="mlflow_parameters"></p>
+<p><img src="img/mlflow_artifacts.png" alt="mlflow_parameters"></p>
+<p><img src="img/mlflow_artifacts_registered_model.png" alt="mlflow_parameters"></p>
+
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="predictions-testing">Predictions testing<a class="anchor-link" href="#predictions-testing">¶</a></h2><p><strong>IMPORTANT</strong>: the predictions are in raw_score format(for testing to see if I get same predictions), i.e. strange numbers and not classes in the output</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="predictions-testing">Predictions testing<a class="anchor-link" href="#predictions-testing">&#182;</a></h2><p><strong>IMPORTANT</strong>: the predictions are in raw_score format(for testing to see if I get same predictions), i.e. strange numbers and not classes in the output</p>
+
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="example-data">Example data<a class="anchor-link" href="#example-data">¶</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="example-data">Example data<a class="anchor-link" href="#example-data">&#182;</a></h3>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-16">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-</svg>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-16">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-16">df_pd.iloc[:2]</div>
-</div>
-</div>
+<div id="cell-16" class="clipboard-copy-txt">df_pd.iloc[:2]</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3589,184 +4898,207 @@ <h3 id="example-data">Example data<a class="anchor-link" href="#example-data">¶
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>CustomerId</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment5</th>
-<th>Surname_Country</th>
-<th>Surname_Country_region</th>
-<th>Surname_Country_subregion</th>
-<th>Country_region</th>
-<th>Country_subregion</th>
-<th>is_native</th>
-<th>Country_hemisphere</th>
-<th>Country_gdp_per_capita</th>
-<th>Country_IncomeGroup</th>
-<th>Surname_Country_gdp_per_capita</th>
-<th>Surname_Country_IncomeGroup</th>
-<th>working_class</th>
-<th>stage_of_life</th>
-<th>generation</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>15787619</td>
-<td>844</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>2</td>
-<td>160980.03</td>
-<td>1</td>
-<td>0</td>
-<td>0</td>
-<td>145936.28</td>
-<td>0</td>
-<td>neutral</td>
-<td>4 stars</td>
-<td>Taiwan</td>
-<td>Asia</td>
-<td>Eastern Asia</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>32756.00000</td>
-<td>None</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-<tr>
-<th>1</th>
-<td>15770309</td>
-<td>656</td>
-<td>France</td>
-<td>Male</td>
-<td>18</td>
-<td>10</td>
-<td>151762.74</td>
-<td>1</td>
-<td>0</td>
-<td>1</td>
-<td>127014.32</td>
-<td>0</td>
-<td>neutral</td>
-<td>1 star</td>
-<td>United States</td>
-<td>Americas</td>
-<td>Northern America</td>
-<td>Europe</td>
-<td>Western Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>57594.03402</td>
-<td>High income</td>
-<td>76329.58227</td>
-<td>High income</td>
-<td>working_age</td>
-<td>teen</td>
-<td>gen_z</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>CustomerId</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment5</th>
+      <th>Surname_Country</th>
+      <th>Surname_Country_region</th>
+      <th>Surname_Country_subregion</th>
+      <th>Country_region</th>
+      <th>Country_subregion</th>
+      <th>is_native</th>
+      <th>Country_hemisphere</th>
+      <th>Country_gdp_per_capita</th>
+      <th>Country_IncomeGroup</th>
+      <th>Surname_Country_gdp_per_capita</th>
+      <th>Surname_Country_IncomeGroup</th>
+      <th>working_class</th>
+      <th>stage_of_life</th>
+      <th>generation</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>15787619</td>
+      <td>844</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>2</td>
+      <td>160980.03</td>
+      <td>1</td>
+      <td>0</td>
+      <td>0</td>
+      <td>145936.28</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>4 stars</td>
+      <td>Taiwan</td>
+      <td>Asia</td>
+      <td>Eastern Asia</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>32756.00000</td>
+      <td>None</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>15770309</td>
+      <td>656</td>
+      <td>France</td>
+      <td>Male</td>
+      <td>18</td>
+      <td>10</td>
+      <td>151762.74</td>
+      <td>1</td>
+      <td>0</td>
+      <td>1</td>
+      <td>127014.32</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>1 star</td>
+      <td>United States</td>
+      <td>Americas</td>
+      <td>Northern America</td>
+      <td>Europe</td>
+      <td>Western Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>57594.03402</td>
+      <td>High income</td>
+      <td>76329.58227</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>teen</td>
+      <td>gen_z</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<div>
-<span class="notice" hidden="">Copied!</span>
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-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-17">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-17">df_pd.iloc[:2].transpose().to_dict(orient='index')</div>
-</div>
-</div>
+<div id="cell-17" class="clipboard-copy-txt">df_pd.iloc[:2].transpose().to_dict(orient='index')</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>{'CustomerId': {0: 15787619, 1: 15770309},
- 'CreditScore': {0: 844, 1: 656},
- 'Country': {0: 'France', 1: 'France'},
- 'Gender': {0: 'Male', 1: 'Male'},
- 'Age': {0: 18, 1: 18},
- 'Tenure': {0: 2, 1: 10},
- 'Balance (EUR)': {0: 160980.03, 1: 151762.74},
- 'NumberOfProducts': {0: 1, 1: 1},
- 'HasCreditCard': {0: '0', 1: '0'},
- 'IsActiveMember': {0: '0', 1: '1'},
- 'EstimatedSalary': {0: 145936.28, 1: 127014.32},
- 'Exited': {0: 0, 1: 0},
- 'CustomerFeedback_sentiment3': {0: 'neutral', 1: 'neutral'},
- 'CustomerFeedback_sentiment5': {0: '4 stars', 1: '1 star'},
- 'Surname_Country': {0: 'Taiwan', 1: 'United States'},
- 'Surname_Country_region': {0: 'Asia', 1: 'Americas'},
- 'Surname_Country_subregion': {0: 'Eastern Asia', 1: 'Northern America'},
- 'Country_region': {0: 'Europe', 1: 'Europe'},
- 'Country_subregion': {0: 'Western Europe', 1: 'Western Europe'},
- 'is_native': {0: '0', 1: '0'},
- 'Country_hemisphere': {0: 'northern', 1: 'northern'},
- 'Country_gdp_per_capita': {0: 57594.03402, 1: 57594.03402},
- 'Country_IncomeGroup': {0: 'High income', 1: 'High income'},
- 'Surname_Country_gdp_per_capita': {0: 32756.0, 1: 76329.58227},
- 'Surname_Country_IncomeGroup': {0: 'None', 1: 'High income'},
- 'working_class': {0: 'working_age', 1: 'working_age'},
- 'stage_of_life': {0: 'teen', 1: 'teen'},
- 'generation': {0: 'gen_z', 1: 'gen_z'}}</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>{&#39;CustomerId&#39;: {0: 15787619, 1: 15770309},
+ &#39;CreditScore&#39;: {0: 844, 1: 656},
+ &#39;Country&#39;: {0: &#39;France&#39;, 1: &#39;France&#39;},
+ &#39;Gender&#39;: {0: &#39;Male&#39;, 1: &#39;Male&#39;},
+ &#39;Age&#39;: {0: 18, 1: 18},
+ &#39;Tenure&#39;: {0: 2, 1: 10},
+ &#39;Balance (EUR)&#39;: {0: 160980.03, 1: 151762.74},
+ &#39;NumberOfProducts&#39;: {0: 1, 1: 1},
+ &#39;HasCreditCard&#39;: {0: &#39;0&#39;, 1: &#39;0&#39;},
+ &#39;IsActiveMember&#39;: {0: &#39;0&#39;, 1: &#39;1&#39;},
+ &#39;EstimatedSalary&#39;: {0: 145936.28, 1: 127014.32},
+ &#39;Exited&#39;: {0: 0, 1: 0},
+ &#39;CustomerFeedback_sentiment3&#39;: {0: &#39;neutral&#39;, 1: &#39;neutral&#39;},
+ &#39;CustomerFeedback_sentiment5&#39;: {0: &#39;4 stars&#39;, 1: &#39;1 star&#39;},
+ &#39;Surname_Country&#39;: {0: &#39;Taiwan&#39;, 1: &#39;United States&#39;},
+ &#39;Surname_Country_region&#39;: {0: &#39;Asia&#39;, 1: &#39;Americas&#39;},
+ &#39;Surname_Country_subregion&#39;: {0: &#39;Eastern Asia&#39;, 1: &#39;Northern America&#39;},
+ &#39;Country_region&#39;: {0: &#39;Europe&#39;, 1: &#39;Europe&#39;},
+ &#39;Country_subregion&#39;: {0: &#39;Western Europe&#39;, 1: &#39;Western Europe&#39;},
+ &#39;is_native&#39;: {0: &#39;0&#39;, 1: &#39;0&#39;},
+ &#39;Country_hemisphere&#39;: {0: &#39;northern&#39;, 1: &#39;northern&#39;},
+ &#39;Country_gdp_per_capita&#39;: {0: 57594.03402, 1: 57594.03402},
+ &#39;Country_IncomeGroup&#39;: {0: &#39;High income&#39;, 1: &#39;High income&#39;},
+ &#39;Surname_Country_gdp_per_capita&#39;: {0: 32756.0, 1: 76329.58227},
+ &#39;Surname_Country_IncomeGroup&#39;: {0: &#39;None&#39;, 1: &#39;High income&#39;},
+ &#39;working_class&#39;: {0: &#39;working_age&#39;, 1: &#39;working_age&#39;},
+ &#39;stage_of_life&#39;: {0: &#39;teen&#39;, 1: &#39;teen&#39;},
+ &#39;generation&#39;: {0: &#39;gen_z&#39;, 1: &#39;gen_z&#39;}}</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>after some notepad magic:</p>
+
 <pre><code>{
     "CustomerId": [15787619, 15770309],
     "CreditScore": [844, 656],
@@ -3796,57 +5128,70 @@ <h3 id="example-data">Example data<a class="anchor-link" href="#example-data">¶
     "working_class": ["working_age", "working_age"],
     "stage_of_life": ["teen", "teen"],
     "generation": ["gen_z", "gen_z"],
-}
-</code></pre>
+}</code></pre>
+
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="trainer">Trainer<a class="anchor-link" href="#trainer">¶</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="trainer">Trainer<a class="anchor-link" href="#trainer">&#182;</a></h3>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-18">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">trainer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"Exited"</span><span class="p">]),</span> <span class="n">context</span><span class="o">=</span><span class="p">{})</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[16]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-18">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">trainer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Exited&quot;</span><span class="p">]),</span> <span class="n">context</span><span class="o">=</span><span class="p">{})</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-18">trainer.predict(df=df_pd.iloc[:2].drop(columns=["Exited"]), context={})</div>
-</div>
-</div>
+<div id="cell-18" class="clipboard-copy-txt">trainer.predict(df=df_pd.iloc[:2].drop(columns=["Exited"]), context={})</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[16]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -3860,238 +5205,324 @@ <h3 id="trainer">Trainer<a class="anchor-link" href="#trainer">¶</a></h3>
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>Exited</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>-9.958064</td>
-</tr>
-<tr>
-<th>1</th>
-<td>-12.560959</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Exited</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-9.958064</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-12.560959</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h3 id="downloaded-trainer">Downloaded Trainer<a class="anchor-link" href="#downloaded-trainer">¶</a></h3>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="downloaded-trainer">Downloaded Trainer<a class="anchor-link" href="#downloaded-trainer">&#182;</a></h3>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [17]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-19">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">model_uri</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[17]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">model_uri</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-19">model_uri</div>
-</div>
-</div>
+<div id="cell-19" class="clipboard-copy-txt">model_uri</div>
+    
+        </div>
+    </div>
+
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+
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-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>'s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model'</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[17]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&#39;s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model&#39;</pre>
 </div>
+
 </div>
+
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+
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># loaded_trainer = mlflow.pyfunc.load_model(model_uri)</span>
-<span class="n">loaded_trainer</span> <span class="o">=</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">pyfunc</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="s1">'s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model'</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[18]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-20">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># loaded_trainer = mlflow.pyfunc.load_model(model_uri)</span>
+<span class="n">loaded_trainer</span> <span class="o">=</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">pyfunc</span><span class="o">.</span><span class="n">load_model</span><span class="p">(</span><span class="s1">&#39;s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model&#39;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-20"># loaded_trainer = mlflow.pyfunc.load_model(model_uri)
+<div id="cell-20" class="clipboard-copy-txt"># loaded_trainer = mlflow.pyfunc.load_model(model_uri)
 loaded_trainer = mlflow.pyfunc.load_model('s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model')</div>
+    
+        </div>
+    </div>
+
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-</div>
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+
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+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
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+
+
+<div class="jp-RenderedText jp-OutputArea-output " data-mime-type="text/plain">
 <pre>Downloading artifacts:   0%|          | 0/61 [00:00&lt;?, ?it/s]</pre>
 </div>
+
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+
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+
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+
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">loaded_trainer</span><span class="o">.</span><span class="n">unwrap_python_model</span><span class="p">()</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[19]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">loaded_trainer</span><span class="o">.</span><span class="n">unwrap_python_model</span><span class="p">()</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-21">loaded_trainer.unwrap_python_model()</div>
-</div>
-</div>
+<div id="cell-21" class="clipboard-copy-txt">loaded_trainer.unwrap_python_model()</div>
+    
+        </div>
+    </div>
+
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+
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+
+<div class="jp-OutputArea-child">
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[19]:</div>
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+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>&lt;churn_pred.training.trainer.Trainer at 0x7f3e17f31060&gt;</pre>
 </div>
+
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+
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+
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+
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle')</span>
-<span class="n">mlflow</span><span class="o">.</span><span class="n">pyfunc</span><span class="o">.</span><span class="n">save_model</span><span class="p">(</span><span class="s2">"data/model"</span><span class="p">,</span> <span class="n">python_model</span><span class="o">=</span><span class="n">loaded_trainer</span><span class="o">.</span><span class="n">unwrap_python_model</span><span class="p">())</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[21]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># mlflow.statsmodels.save_model(loaded_trainer, &#39;data/model&#39;)#, serialization_format=&#39;pickle&#39;)</span>
+<span class="n">mlflow</span><span class="o">.</span><span class="n">pyfunc</span><span class="o">.</span><span class="n">save_model</span><span class="p">(</span><span class="s2">&quot;data/model&quot;</span><span class="p">,</span> <span class="n">python_model</span><span class="o">=</span><span class="n">loaded_trainer</span><span class="o">.</span><span class="n">unwrap_python_model</span><span class="p">())</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-22"># mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle')
+<div id="cell-22" class="clipboard-copy-txt"># mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle')
 mlflow.pyfunc.save_model("data/model", python_model=loaded_trainer.unwrap_python_model())</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: &lt;class 'pandas.core.frame.DataFrame'&gt;, skipping schema inference
-2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: &lt;class 'pandas.core.frame.DataFrame'&gt;, skipping schema inference
-2024/06/18 21:31:37 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: data/model, flavor: python_function). Fall back to return ['cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback. 
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: &lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;, skipping schema inference
+2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: &lt;class &#39;pandas.core.frame.DataFrame&#39;&gt;, skipping schema inference
+2024/06/18 21:31:37 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: data/model, flavor: python_function). Fall back to return [&#39;cloudpickle==2.2.1&#39;]. Set logging level to DEBUG to see the full traceback. 
 </pre>
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-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">loaded_trainer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"Exited"</span><span class="p">]))</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[22]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-23">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">loaded_trainer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Exited&quot;</span><span class="p">]))</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-23">loaded_trainer.predict(df_pd.iloc[:2].drop(columns=["Exited"]))</div>
-</div>
-</div>
+<div id="cell-23" class="clipboard-copy-txt">loaded_trainer.predict(df_pd.iloc[:2].drop(columns=["Exited"]))</div>
+    
+        </div>
+    </div>
+
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+
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[22]:</div>
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@@ -4105,118 +5536,153 @@ <h3 id="downloaded-trainer">Downloaded Trainer<a class="anchor-link" href="#down
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>Exited</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>0</th>
-<td>-9.958064</td>
-</tr>
-<tr>
-<th>1</th>
-<td>-12.560959</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>Exited</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>0</th>
+      <td>-9.958064</td>
+    </tr>
+    <tr>
+      <th>1</th>
+      <td>-12.560959</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
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+
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+
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+
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-<h3 id="downloadedserved-trainer">Downloaded/Served Trainer<a class="anchor-link" href="#downloadedserved-trainer">¶</a></h3><ul>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h3 id="downloadedserved-trainer">Downloaded/Served Trainer<a class="anchor-link" href="#downloadedserved-trainer">&#182;</a></h3><ul>
 <li>i.e. testing <a href="https://mlflow.org/docs/latest/deployment/deploy-model-to-kubernetes/tutorial.html?highlight=kserve#step-6-testing-model-serving-locally">model locally</a></li>
 </ul>
+
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+<div class="jp-Cell-inputWrapper">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [23]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">model_uri</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[23]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-24">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">model_uri</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-24">model_uri</div>
-</div>
-</div>
+<div id="cell-24" class="clipboard-copy-txt">model_uri</div>
+    
+        </div>
+    </div>
+
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+
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-<pre>'s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model'</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[23]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&#39;s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model&#39;</pre>
 </div>
+
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+
 </div>
+
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+
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+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="err">!</span> <span class="n">mlflow</span> <span class="n">models</span> <span class="n">serve</span> <span class="o">-</span><span class="n">m</span> <span class="s2">"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model"</span> <span class="o">--</span><span class="n">env</span><span class="o">-</span><span class="n">manager</span> <span class="n">local</span> <span class="o">-</span><span class="n">p</span> <span class="mi">5000</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[24]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-25">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="err">!</span> <span class="n">mlflow</span> <span class="n">models</span> <span class="n">serve</span> <span class="o">-</span><span class="n">m</span> <span class="s2">&quot;s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model&quot;</span> <span class="o">--</span><span class="n">env</span><span class="o">-</span><span class="n">manager</span> <span class="n">local</span> <span class="o">-</span><span class="n">p</span> <span class="mi">5000</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-25">! mlflow models serve -m "s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model" --env-manager local -p 5000</div>
-</div>
-</div>
+<div id="cell-25" class="clipboard-copy-txt">! mlflow models serve -m "s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model" --env-manager local -p 5000</div>
+    
+        </div>
+    </div>
+
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 </div>
+
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+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Downloading artifacts: 100%|███████████████████| 61/61 [00:00&lt;00:00, 691.42it/s]
-2024/06/18 21:32:14 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function'
+2024/06/18 21:32:14 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor &#39;python_function&#39;
 Downloading artifacts: 100%|███████████████████| 61/61 [00:00&lt;00:00, 304.59it/s]
-2024/06/18 21:32:15 INFO mlflow.pyfunc.backend: === Running command 'exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app'
+2024/06/18 21:32:15 INFO mlflow.pyfunc.backend: === Running command &#39;exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app&#39;
 [2024-06-18 21:32:15 +0000] [1380] [INFO] Starting gunicorn 22.0.0
 [2024-06-18 21:32:15 +0000] [1380] [INFO] Listening at: http://127.0.0.1:5000 (1380)
 [2024-06-18 21:32:15 +0000] [1380] [INFO] Using worker: sync
@@ -4227,95 +5693,102 @@ <h3 id="downloadedserved-trainer">Downloaded/Served Trainer<a class="anchor-link
 </pre>
 </div>
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+
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+
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+
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>output from terminal:</p>
+
 <pre><code>root@jupyter-5uperpalo:~/assignment/ecovadis_assignment# curl -X POST -H "Content-Type:application/json" --data '{"inputs": {"CustomerId": [15787619, 15770309], "CreditScore": [844, 656], "Country": ["France", "France"], "Gender": ["Male", "Male"], "Age": [18, 18], "Tenure": [2, 10], "Balance (EUR)": [160980.03, 151762.74], "NumberOfProducts": [1, 1], "HasCreditCard": ["0", "0"], "IsActiveMember": ["0", "1"], "EstimatedSalary": [145936.28, 127014.32], "CustomerFeedback_sentiment3": ["neutral", "neutral"], "CustomerFeedback_sentiment5": ["4 stars", "1 star"], "Surname_Country": ["Taiwan", "United States"], "Surname_Country_region": ["Asia", "Americas"], "Surname_Country_subregion": ["Eastern Asia", "Northern America"], "Country_region": ["Europe", "Europe"], "Country_subregion": ["Western Europe", "Western Europe"], "is_native": ["0", "0"], "Country_hemisphere": ["northern", "northern"], "Country_gdp_per_capita": [57594.03402, 57594.03402], "Country_IncomeGroup": ["High income", "High income"], "Surname_Country_gdp_per_capita": [32756.0, 76329.58227], "Surname_Country_IncomeGroup": ["None", "High income"], "working_class": ["working_age", "working_age"], "stage_of_life": ["teen", "teen"], "generation": ["gen_z", "gen_z"]}}' http://127.0.0.1:5000/invocations
-{"predictions": [{"Exited": -9.958063834023024}, {"Exited": -12.560959268044444}]}
-</code></pre>
+{"predictions": [{"Exited": -9.958063834023024}, {"Exited": -12.560959268044444}]}</code></pre>
+
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-<h1 id="model-deployment-using-kserve-knative-istio">Model deployment using Kserve + Knative + Istio<a class="anchor-link" href="#model-deployment-using-kserve-knative-istio">¶</a></h1><p><strong>reasons:</strong></p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="model-deployment-using-kserve-knative-istio">Model deployment using Kserve + Knative + Istio<a class="anchor-link" href="#model-deployment-using-kserve-knative-istio">&#182;</a></h1><p><strong>reasons:</strong></p>
 <ul>
 <li>Kserve is less mature than Seldon core but is primarly adopted by Kubeflow and MLflow</li>
 <li>"Serverless installation using Knative" is a primary installation option of Ksserve as <em>KServe Serverless installation enables autoscaling based on request volume and supports scale down to and from zero. It also supports revision management and canary rollout based on revisions.</em></li>
 <li>Istio is a primary choice of service routing for Knative</li>
-<li>Kserve uses MLserver from Seldoncore but according to "quick internet search" it is adopted more by community than Seldon-core as Seldon-core focuses more on enterprise version of its software</li>
+<li>Kserve uses MLserver from Seldoncore but according to "quick internet search" it is adopted more by community than Seldon-core as Seldon-core focuses more on enterprise version of its software </li>
 </ul>
 <p><strong>issues:</strong></p>
 <ul>
 <li>Kserve uses MLserver which ignores <code>requirements.txt</code> and needs prepackaged conda environment in tar.gz file</li>
 <li>Kserve uses old version of MLserver(1.3.2) resulting in incorrect usage of <code>conda-unpack</code></li>
-<li>related code has to be included with model and conda environment</li>
-<li>mlserver with correct version has to be defined in the conda env:</li>
+<li>related code has to be included with model and conda environment  </li>
+<li>mlserver with correct version has to be defined in the conda env:
+<pre><code>- mlserver==1.3.5
+- mlserver-mlflow==1.3.5</code></pre>
+</li>
 </ul>
-<pre><code>  - mlserver==1.3.5
-  - mlserver-mlflow==1.3.5
-</code></pre>
 <p><strong>related github issue discussions/troubleshooting:</strong></p>
 <ul>
 <li><a href="https://github.com/kserve/kserve/issues/3733#issuecomment-2168261646">https://github.com/kserve/kserve/issues/3733#issuecomment-2168261646</a></li>
 <li><a href="https://github.com/SeldonIO/MLServer/issues/1801">https://github.com/SeldonIO/MLServer/issues/1801</a></li>
 </ul>
+
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="create-conda-environment-targz-it-and-upload-it-to-minio">Create conda environment, tar.gz it and upload it to Minio<a class="anchor-link" href="#create-conda-environment-targz-it-and-upload-it-to-minio">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="create-conda-environment-targz-it-and-upload-it-to-minio">Create conda environment, tar.gz it and upload it to Minio<a class="anchor-link" href="#create-conda-environment-targz-it-and-upload-it-to-minio">&#182;</a></h2>
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-26">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-26">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">minio</span> <span class="kn">import</span> <span class="n">Minio</span>
 
 <span class="c1"># to download conda.yaml</span>
-<span class="n">client</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="s2">"10.152.183.156:9000"</span><span class="p">,</span> <span class="s2">"minioadmin"</span><span class="p">,</span> <span class="s2">"minioadmin"</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-<span class="n">bucket</span> <span class="o">=</span> <span class="s2">"mlflow"</span>
-<span class="n">object_name</span> <span class="o">=</span> <span class="s2">"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml"</span>
-<span class="n">file_path</span> <span class="o">=</span> <span class="s2">"notebooks/conda.yaml"</span>
+<span class="n">client</span> <span class="o">=</span> <span class="n">Minio</span><span class="p">(</span><span class="s2">&quot;10.152.183.156:9000&quot;</span><span class="p">,</span> <span class="s2">&quot;minioadmin&quot;</span><span class="p">,</span> <span class="s2">&quot;minioadmin&quot;</span><span class="p">,</span> <span class="n">secure</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">bucket</span> <span class="o">=</span> <span class="s2">&quot;mlflow&quot;</span>
+<span class="n">object_name</span> <span class="o">=</span> <span class="s2">&quot;6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml&quot;</span>
+<span class="n">file_path</span> <span class="o">=</span> <span class="s2">&quot;notebooks/conda.yaml&quot;</span>
 <span class="n">client</span><span class="o">.</span><span class="n">fget_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="o">=</span><span class="n">bucket</span><span class="p">,</span> <span class="n">object_name</span><span class="o">=</span><span class="n">object_name</span><span class="p">,</span> <span class="n">file_path</span><span class="o">=</span><span class="n">file_path</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-26">from minio import Minio
+<div id="cell-26" class="clipboard-copy-txt">from minio import Minio
 
 # to download conda.yaml
 client = Minio("10.152.183.156:9000", "minioadmin", "minioadmin", secure=False)
@@ -4323,91 +5796,127 @@ <h2 id="create-conda-environment-targz-it-and-upload-it-to-minio">Create conda e
 object_name = "6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml"
 file_path = "notebooks/conda.yaml"
 client.fget_object(bucket_name=bucket, object_name=object_name, file_path=file_path)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>&lt;minio.datatypes.Object at 0x730858235e70&gt;</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>following commands were run in terminal to create environment:</p>
+
 <pre><code>pip install conda_pack
 conda env create --force -f conda.yaml
-conda-pack --name mlflow-env --output environment.tar.gz --force --ignore-editable-packages --ignore-missing-files
-</code></pre>
+conda-pack --name mlflow-env --output environment.tar.gz --force --ignore-editable-packages --ignore-missing-files</code></pre>
+
 </div>
 </div>
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [28]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-27">
-<div>
-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># upload environmnet.tar.gz</span>
-<span class="n">object_name</span> <span class="o">=</span> <span class="s2">"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz"</span>
-<span class="n">file_path</span> <span class="o">=</span> <span class="s2">"notebooks/environment.tar.gz"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[28]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-27">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="c1"># upload environmnet.tar.gz</span>
+<span class="n">object_name</span> <span class="o">=</span> <span class="s2">&quot;6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz&quot;</span>
+<span class="n">file_path</span> <span class="o">=</span> <span class="s2">&quot;notebooks/environment.tar.gz&quot;</span>
 <span class="n">client</span><span class="o">.</span><span class="n">fput_object</span><span class="p">(</span><span class="n">bucket_name</span><span class="o">=</span><span class="n">bucket</span><span class="p">,</span> <span class="n">object_name</span><span class="o">=</span><span class="n">object_name</span><span class="p">,</span> <span class="n">file_path</span><span class="o">=</span><span class="n">file_path</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-27"># upload environmnet.tar.gz
+<div id="cell-27" class="clipboard-copy-txt"># upload environmnet.tar.gz
 object_name = "6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz"
 file_path = "notebooks/environment.tar.gz"
 client.fput_object(bucket_name=bucket, object_name=object_name, file_path=file_path)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[28]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[28]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
 <pre>&lt;minio.helpers.ObjectWriteResult at 0x7f409c5bbac0&gt;</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="create-kserver-inference-service-in-kubernetes">Create Kserver Inference Service in Kubernetes<a class="anchor-link" href="#create-kserver-inference-service-in-kubernetes">¶</a></h2><pre><code>apiVersion: "serving.kserve.io/v1beta1"
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="create-kserver-inference-service-in-kubernetes">Create Kserver Inference Service in Kubernetes<a class="anchor-link" href="#create-kserver-inference-service-in-kubernetes">&#182;</a></h2>
+<pre><code>apiVersion: "serving.kserve.io/v1beta1"
 kind: "InferenceService"
 metadata:
   name: "custom-ecovadis-model"
@@ -4419,29 +5928,30 @@ <h2 id="create-kserver-inference-service-in-kubernetes">Create Kserver Inference
       modelFormat:
         name: mlflow
       protocolVersion: v2
-      storageUri: "s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model"
-</code></pre>
+      storageUri: "s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model"</code></pre>
+
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="test-the-inference-service">Test the Inference service<a class="anchor-link" href="#test-the-inference-service">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="test-the-inference-service">Test the Inference service<a class="anchor-link" href="#test-the-inference-service">&#182;</a></h2>
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+
 <pre><code>export INGRESS_NAME=istio-ingressgateway
 export INGRESS_NS=istio-system
 export INGRESS_PORT=$(kubectl -n "${INGRESS_NS}" get service "${INGRESS_NAME}" -o jsonpath='{.spec.ports[?(@.name=="http2")].nodePort}')
@@ -4452,58 +5962,62 @@ <h2 id="test-the-inference-service">Test the Inference service<a class="anchor-l
   -H "Host: ${SERVICE_HOSTNAME}" \
   -H "Content-Type: application/json" \
   -d @./test.json \
-  http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-model/infer
-</code></pre>
+  http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-model/infer</code></pre>
+
 </div>
 </div>
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="summary">SUMMARY<a class="anchor-link" href="#summary">¶</a></h2><p>This unfortunately crashes due to the size of conda environment that is being downloaded from minio database by Kserve. COnda env has 3.36GB due to language models (libraries).</p>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="summary">SUMMARY<a class="anchor-link" href="#summary">&#182;</a></h2><p>This unfortunately crashes due to the size of conda environment that is being downloaded from minio database by Kserve. COnda env has 3.36GB due to language models (libraries).</p>
 <p><strong>Note:</strong> this approach was succesfully tested on a custom model that used small conda env ~360MB</p>
+
 </div>
 </div>
 </div>
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-</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></h2><ol>
-<li>save/serve(deploy) only the LightGBM model</li>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">&#182;</a></h2><ol>
+<li>save/serve(deploy) only the LightGBM model </li>
 <li>preprocess the data locally; this could be done in production using (i) <a href="https://kserve.github.io/website/master/modelserving/inference_graph/#concepts">Kserve InferenceGpraph</a> or (ii) <a href="https://www.kubeflow.org/docs/components/pipelines/v1/sdk/build-pipeline/">pipelines in Kubeflow</a></li>
 <li>send preprocessed data to API</li>
 </ol>
+
 </div>
 </div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">run_name</span> <span class="o">=</span> <span class="s2">"init_run"</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">run_name</span> <span class="o">=</span> <span class="s2">&quot;init_run&quot;</span>
 <span class="k">with</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">start_run</span><span class="p">(</span><span class="n">experiment_id</span><span class="o">=</span><span class="n">experiment_id</span><span class="p">,</span> <span class="n">run_name</span><span class="o">=</span><span class="n">run_name</span><span class="p">,</span> <span class="n">nested</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">run</span><span class="p">:</span>
     <span class="n">mlflow</span><span class="o">.</span><span class="n">log_params</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">best</span><span class="p">)</span>
     <span class="n">mlflow</span><span class="o">.</span><span class="n">log_metrics</span><span class="p">(</span><span class="n">metrics_dict_flattened</span><span class="p">)</span>
@@ -4511,17 +6025,17 @@ <h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></
     <span class="c1"># Log tags</span>
     <span class="n">mlflow</span><span class="o">.</span><span class="n">set_tags</span><span class="p">(</span>
         <span class="n">tags</span><span class="o">=</span><span class="p">{</span>
-            <span class="s2">"project"</span><span class="p">:</span> <span class="s2">"ecovadis"</span><span class="p">,</span>
-            <span class="s2">"optimizer_engine"</span><span class="p">:</span> <span class="s2">"optuna"</span><span class="p">,</span>
-            <span class="s2">"model_family"</span><span class="p">:</span> <span class="s2">"ligtgbm"</span><span class="p">,</span>
-            <span class="s2">"feature_set_version"</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+            <span class="s2">&quot;project&quot;</span><span class="p">:</span> <span class="s2">&quot;ecovadis&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;optimizer_engine&quot;</span><span class="p">:</span> <span class="s2">&quot;optuna&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;model_family&quot;</span><span class="p">:</span> <span class="s2">&quot;ligtgbm&quot;</span><span class="p">,</span>
+            <span class="s2">&quot;feature_set_version&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
         <span class="p">}</span>
     <span class="p">)</span>
     <span class="c1"># Log figure - for future fun</span>
-    <span class="c1"># mlflow.log_figure(figure=correlation_plot, artifact_file="correlation_plot.png")</span>
+    <span class="c1"># mlflow.log_figure(figure=correlation_plot, artifact_file=&quot;correlation_plot.png&quot;)</span>
 
-    <span class="n">artifact_path</span> <span class="o">=</span> <span class="s2">"ecovadis_raw_model"</span>
-    <span class="n">registered_model_name</span> <span class="o">=</span> <span class="s2">"ecovadis_raw_lgbm_model"</span>
+    <span class="n">artifact_path</span> <span class="o">=</span> <span class="s2">&quot;ecovadis_raw_model&quot;</span>
+    <span class="n">registered_model_name</span> <span class="o">=</span> <span class="s2">&quot;ecovadis_raw_lgbm_model&quot;</span>
     <span class="n">mlflow</span><span class="o">.</span><span class="n">lightgbm</span><span class="o">.</span><span class="n">log_model</span><span class="p">(</span>
             <span class="n">lgb_model</span><span class="o">=</span><span class="n">trainer</span><span class="o">.</span><span class="n">model</span><span class="p">,</span>
             <span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">,</span> 
@@ -4529,11 +6043,11 @@ <h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></
     <span class="c1"># mlflow.pyfunc.log_model(python_model=trainer, </span>
     <span class="c1">#                         artifact_path=artifact_path, </span>
     <span class="c1">#                         registered_model_name=registered_model_name,</span>
-    <span class="c1">#                         code_paths=["churn_pred"])  </span>
+    <span class="c1">#                         code_paths=[&quot;churn_pred&quot;])  </span>
     <span class="n">model_uri</span> <span class="o">=</span> <span class="n">mlflow</span><span class="o">.</span><span class="n">get_artifact_uri</span><span class="p">(</span><span class="n">artifact_path</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Run ID:</span><span class="se">\n</span><span class="si">{</span><span class="n">run</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">run_id</span><span class="si">}</span><span class="se">\n</span><span class="s2">Model uri:</span><span class="se">\n</span><span class="si">{</span><span class="n">model_uri</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Run ID:</span><span class="se">\n</span><span class="si">{</span><span class="n">run</span><span class="o">.</span><span class="n">info</span><span class="o">.</span><span class="n">run_id</span><span class="si">}</span><span class="se">\n</span><span class="s2">Model uri:</span><span class="se">\n</span><span class="si">{</span><span class="n">model_uri</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-28">run_name = "init_run"
+<div id="cell-28" class="clipboard-copy-txt">run_name = "init_run"
 with mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run:
     mlflow.log_params(trainer.optimizer.best)
     mlflow.log_metrics(metrics_dict_flattened)
@@ -4562,25 +6076,40 @@ <h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></
     #                         code_paths=["churn_pred"])  
     model_uri = mlflow.get_artifact_uri(artifact_path)
     print(f"Run ID:\n{run.info.run_id}\nModel uri:\n{model_uri}")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>Successfully registered model 'ecovadis_raw_lgbm_model'.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Successfully registered model &#39;ecovadis_raw_lgbm_model&#39;.
 2024/06/19 05:43:13 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: ecovadis_raw_lgbm_model, version 1
 </pre>
 </div>
 </div>
+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain">
 <pre>Run ID:
 9a74f8e9087044b9b9d4935a268c26d0
 Model uri:
@@ -4588,47 +6117,57 @@ <h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></
 </pre>
 </div>
 </div>
+
 <div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr" tabindex="0">
-<pre>Created version '1' of model 'ecovadis_raw_lgbm_model'.
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
+
+
+<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
+<pre>Created version &#39;1&#39; of model &#39;ecovadis_raw_lgbm_model&#39;.
 </pre>
 </div>
 </div>
+
 </div>
+
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+
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_infser_test</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[39]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_infser_test</span> <span class="o">=</span> <span class="n">df_pd</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
 <span class="k">for</span> <span class="n">prep</span> <span class="ow">in</span> <span class="n">trainer</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">:</span>
     <span class="n">df_infser_test</span> <span class="o">=</span> <span class="n">prep</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df_infser_test</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">trainer</span><span class="p">,</span> <span class="s2">"optimizer"</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">"best_to_drop"</span><span class="p">):</span>  <span class="c1"># type: ignore</span>
+    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">trainer</span><span class="p">,</span> <span class="s2">&quot;optimizer&quot;</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">optimizer</span><span class="p">,</span> <span class="s2">&quot;best_to_drop&quot;</span><span class="p">):</span>  <span class="c1"># type: ignore</span>
             <span class="n">df_infser_test</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span>
                 <span class="n">columns</span><span class="o">=</span><span class="n">trainer</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">best_to_drop</span><span class="p">,</span>  <span class="c1"># type: ignore</span>
                 <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
             <span class="p">)</span>
 <span class="n">df_infser_test</span> <span class="o">=</span> <span class="n">df_infser_test</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">trainer</span><span class="o">.</span><span class="n">id_cols</span> <span class="o">+</span> <span class="p">[</span><span class="n">trainer</span><span class="o">.</span><span class="n">target_col</span><span class="p">])</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-29">df_infser_test = df_pd.iloc[:2]
+<div id="cell-29" class="clipboard-copy-txt">df_infser_test = df_pd.iloc[:2]
 for prep in trainer.preprocessors:
     df_infser_test = prep.transform(df_infser_test)
     if hasattr(trainer, "optimizer"):
@@ -4638,80 +6177,102 @@ <h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></
                 inplace=True,
             )
 df_infser_test = df_infser_test.drop(columns=trainer.id_cols + [trainer.target_col])</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_infser_test</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">'index'</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[40]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
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+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_infser_test</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">&#39;index&#39;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-30">df_infser_test.transpose().to_dict(orient='index')</div>
-</div>
-</div>
+<div id="cell-30" class="clipboard-copy-txt">df_infser_test.transpose().to_dict(orient='index')</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 <div class="jp-Cell-outputWrapper">
 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
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+
 <div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[40]:</div>
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-<pre>{'is_native': {0: 1.0, 1: 1.0},
- 'generation': {0: 1.0, 1: 1.0},
- 'Surname_Country': {0: 1.0, 1: 2.0},
- 'Country_subregion': {0: 1.0, 1: 1.0},
- 'Surname_Country_subregion': {0: 1.0, 1: 2.0},
- 'Surname_Country_IncomeGroup': {0: 1.0, 1: 2.0},
- 'stage_of_life': {0: 1.0, 1: 1.0},
- 'IsActiveMember': {0: 1.0, 1: 2.0},
- 'Country': {0: 1.0, 1: 1.0},
- 'CustomerFeedback_sentiment5': {0: 1.0, 1: 2.0},
- 'CustomerFeedback_sentiment3': {0: 1.0, 1: 1.0},
- 'Gender': {0: 1.0, 1: 1.0},
- 'Surname_Country_region': {0: 1.0, 1: 2.0},
- 'working_class': {0: 1.0, 1: 1.0},
- 'HasCreditCard': {0: 1.0, 1: 1.0},
- 'EstimatedSalary': {0: 145936.28125, 1: 127014.3203125},
- 'Age': {0: 18.0, 1: 18.0},
- 'Tenure': {0: 2.0, 1: 10.0},
- 'NumberOfProducts': {0: 1.0, 1: 1.0},
- 'Country_gdp_per_capita': {0: 57594.03515625, 1: 57594.03515625},
- 'Surname_Country_gdp_per_capita': {0: 32756.0, 1: 76329.5859375},
- 'CreditScore': {0: 844.0, 1: 656.0},
- 'Balance (EUR)': {0: 160980.03125, 1: 151762.734375}}</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[40]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>{&#39;is_native&#39;: {0: 1.0, 1: 1.0},
+ &#39;generation&#39;: {0: 1.0, 1: 1.0},
+ &#39;Surname_Country&#39;: {0: 1.0, 1: 2.0},
+ &#39;Country_subregion&#39;: {0: 1.0, 1: 1.0},
+ &#39;Surname_Country_subregion&#39;: {0: 1.0, 1: 2.0},
+ &#39;Surname_Country_IncomeGroup&#39;: {0: 1.0, 1: 2.0},
+ &#39;stage_of_life&#39;: {0: 1.0, 1: 1.0},
+ &#39;IsActiveMember&#39;: {0: 1.0, 1: 2.0},
+ &#39;Country&#39;: {0: 1.0, 1: 1.0},
+ &#39;CustomerFeedback_sentiment5&#39;: {0: 1.0, 1: 2.0},
+ &#39;CustomerFeedback_sentiment3&#39;: {0: 1.0, 1: 1.0},
+ &#39;Gender&#39;: {0: 1.0, 1: 1.0},
+ &#39;Surname_Country_region&#39;: {0: 1.0, 1: 2.0},
+ &#39;working_class&#39;: {0: 1.0, 1: 1.0},
+ &#39;HasCreditCard&#39;: {0: 1.0, 1: 1.0},
+ &#39;EstimatedSalary&#39;: {0: 145936.28125, 1: 127014.3203125},
+ &#39;Age&#39;: {0: 18.0, 1: 18.0},
+ &#39;Tenure&#39;: {0: 2.0, 1: 10.0},
+ &#39;NumberOfProducts&#39;: {0: 1.0, 1: 1.0},
+ &#39;Country_gdp_per_capita&#39;: {0: 57594.03515625, 1: 57594.03515625},
+ &#39;Surname_Country_gdp_per_capita&#39;: {0: 32756.0, 1: 76329.5859375},
+ &#39;CreditScore&#39;: {0: 844.0, 1: 656.0},
+ &#39;Balance (EUR)&#39;: {0: 160980.03125, 1: 151762.734375}}</pre>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
 </div>
 </div>
 <div class="jp-Cell jp-MarkdownCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Cell-inputWrapper">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
 <p>create test.json:</p>
+
 <pre><code>cat test.json 
 {
   "parameters": {
@@ -4856,10 +6417,9 @@ <h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></
         "datatype": "FP64",
         "data": [160980.03125]
       }
-  ]
-
-</code></pre>
+  ]</code></pre>
 <p>test service:</p>
+
 <pre><code>export INGRESS_NAME=istio-ingressgateway
 export INGRESS_NS=istio-system
 export SERVICE_HOSTNAME=custom-ecovadis-raw-model.mlflow-kserve-success6g.example.com
@@ -4870,9 +6430,9 @@ <h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></
   -H "Host: ${SERVICE_HOSTNAME}" \
   -H "Content-Type: application/json" \
   -d @./test.json \
-  http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer
-</code></pre>
+  http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer</code></pre>
 <p>output:</p>
+
 <pre><code>{INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer
 *   Trying 10.1.24.50:32702...
 * Connected to 10.1.24.50 (10.1.24.50) port 32702 (#0)
@@ -4900,64 +6460,61 @@ <h2 id="workaround">WORKAROUND<a class="anchor-link" href="#workaround">¶</a></
 &lt; x-envoy-upstream-service-time: 712
 &lt; 
 * Connection #0 to host 10.1.24.50 left intact
-{"model_name":"custom-ecovadis-raw-model","id":"0cd6e630-3a2b-44de-8284-299a10cf5283","parameters":{"content_type":"np"},"outputs":[{"name":"output-1","shape":[1,1],"datatype":"FP64","parameters":{"content_type":"np"},"data":[-9.958064]}]}
-</code></pre>
+{"model_name":"custom-ecovadis-raw-model","id":"0cd6e630-3a2b-44de-8284-299a10cf5283","parameters":{"content_type":"np"},"outputs":[{"name":"output-1","shape":[1,1],"datatype":"FP64","parameters":{"content_type":"np"},"data":[-9.958064]}]}</code></pre>
+
+</div>
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 </div>
 </div>
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diff --git a/docs/site/notebooks/05_model_analysis.html b/docs/site/notebooks/05_model_analysis.html
index 336ea81..f69c830 100644
--- a/docs/site/notebooks/05_model_analysis.html
+++ b/docs/site/notebooks/05_model_analysis.html
@@ -8,34 +8,24 @@
       
       
       
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       <link rel="icon" href="../assets/images/favicon.png">
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+      <meta name="generator" content="mkdocs-1.3.1, mkdocs-material-8.3.9">
     
     
       
-        <title>05_model_analysis.ipynb - ecovadis_assignment</title>
+        <title>05_model_analysis.ipynb - churnpred</title>
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.66ac8b77.min.css">
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.cbb835fc.min.css">
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@@ -50,13 +40,12 @@
     
       <link rel="stylesheet" href="../stylesheets/extra.css">
     
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+    <script>__md_scope=new URL("..",location),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
       
 
     
     
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@@ -67,9 +56,12 @@
     
     
     
-    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal">
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink">
   
     
+    
+      <script>var palette=__md_get("__palette");if(palette&&"object"==typeof palette.color)for(var key of Object.keys(palette.color))document.body.setAttribute("data-md-color-"+key,palette.color[key])</script>
+    
     <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
     <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
     <label class="md-overlay" for="__drawer"></label>
@@ -90,22 +82,21 @@
 
   
 
-<header class="md-header md-header--shadow md-header--lifted" data-md-component="header">
+<header class="md-header md-header--lifted" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="ecovadis_assignment" class="md-header__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-header__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
     <label class="md-header__button md-icon" for="__drawer">
-      
       <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
     </label>
     <div class="md-header__title" data-md-component="header-title">
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            ecovadis_assignment
+            churnpred
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -118,39 +109,31 @@
       </div>
     </div>
     
-      
-        <form class="md-header__option" data-md-component="palette">
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_0">
-    
-      <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_1" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
-      </label>
-    
-  
-    
-    
-    
-    <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="green" data-md-color-accent="teal"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_1">
-    
-      <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_0" hidden>
-        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
-      </label>
-    
-  
-</form>
-      
-    
-    
-      <script>var media,input,key,value,palette=__md_get("__palette");if(palette&&palette.color){"(prefers-color-scheme)"===palette.color.media&&(media=matchMedia("(prefers-color-scheme: light)"),input=document.querySelector(media.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: dark)']"),palette.color.media=input.getAttribute("data-md-color-media"),palette.color.scheme=input.getAttribute("data-md-color-scheme"),palette.color.primary=input.getAttribute("data-md-color-primary"),palette.color.accent=input.getAttribute("data-md-color-accent"));for([key,value]of Object.entries(palette.color))document.body.setAttribute("data-md-color-"+key,value)}</script>
+      <form class="md-header__option" data-md-component="palette">
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="default" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to dark mode"  type="radio" name="__palette" id="__palette_1">
+          
+            <label class="md-header__button md-icon" title="Switch to dark mode" for="__palette_2" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a4 4 0 0 0-4 4 4 4 0 0 0 4 4 4 4 0 0 0 4-4 4 4 0 0 0-4-4m0 10a6 6 0 0 1-6-6 6 6 0 0 1 6-6 6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+          
+          
+          <input class="md-option" data-md-color-media="" data-md-color-scheme="slate" data-md-color-primary="purple" data-md-color-accent="pink"  aria-label="Switch to light mode"  type="radio" name="__palette" id="__palette_2">
+          
+            <label class="md-header__button md-icon" title="Switch to light mode" for="__palette_1" hidden>
+              <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 18c-.89 0-1.74-.2-2.5-.55C11.56 16.5 13 14.42 13 12c0-2.42-1.44-4.5-3.5-5.45C10.26 6.2 11.11 6 12 6a6 6 0 0 1 6 6 6 6 0 0 1-6 6m8-9.31V4h-4.69L12 .69 8.69 4H4v4.69L.69 12 4 15.31V20h4.69L12 23.31 15.31 20H20v-4.69L23.31 12 20 8.69Z"/></svg>
+            </label>
+          
+        
+      </form>
     
     
     
       <label class="md-header__button md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
       </label>
       <div class="md-search" data-md-component="search" role="dialog">
@@ -159,15 +142,12 @@
     <form class="md-search__form" name="search">
       <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
       <label class="md-search__icon md-icon" for="__search">
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
-        
         <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
       </label>
       <nav class="md-search__options" aria-label="Search">
         
-        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
-          
+        <button type="reset" class="md-search__icon md-icon" aria-label="Clear" tabindex="-1">
           <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
         </button>
       </nav>
@@ -179,7 +159,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -188,13 +168,13 @@
     
     
       <div class="md-header__source">
-        <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+        <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
       </div>
@@ -204,60 +184,46 @@
     
       
 <nav class="md-tabs" aria-label="Tabs" data-md-component="tabs">
-  <div class="md-grid">
+  <div class="md-tabs__inner md-grid">
     <ul class="md-tabs__list">
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../index.html" class="md-tabs__link">
-        
-  
-    
-  
-  home
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../index.html" class="md-tabs__link">
+      home
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../installation.html" class="md-tabs__link">
-        
-  
-    
-  
-  installation
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../installation.html" class="md-tabs__link">
+      installation
+    </a>
+  </li>
 
       
         
   
   
+
+
   
-    
-    
-      <li class="md-tabs__item">
-        <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
-          
   
-    
   
-  churn_pred
-
-        </a>
-      </li>
-    
+    <li class="md-tabs__item">
+      <a href="../churn_pred/code_profiling.html" class="md-tabs__link">
+        churn_pred
+      </a>
+    </li>
   
 
       
@@ -266,35 +232,14 @@
   
     
   
-  
-    
-    
-      <li class="md-tabs__item md-tabs__item--active">
-        <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link">
-          
-  
-    
-  
-  notebooks
 
-        </a>
-      </li>
-    
-  
 
-      
-        
   
   
   
     <li class="md-tabs__item">
-      <a href="../cicd.html" class="md-tabs__link">
-        
-  
-    
-  
-  cicd
-
+      <a href="00_auxiliary_features_surname_origin_country_classification.html" class="md-tabs__link md-tabs__link--active">
+        notebooks
       </a>
     </li>
   
@@ -303,35 +248,37 @@
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../code_profiling.html" class="md-tabs__link">
-        
-  
-    
-  
-  code profiling
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../cicd.html" class="md-tabs__link">
+      cicd
+    </a>
+  </li>
 
       
         
   
   
-  
-    <li class="md-tabs__item">
-      <a href="../contributing.html" class="md-tabs__link">
+
+
+  <li class="md-tabs__item">
+    <a href="../code_profiling.html" class="md-tabs__link">
+      code profiling
+    </a>
+  </li>
+
+      
         
   
-    
   
-  contributing
 
-      </a>
-    </li>
-  
+
+  <li class="md-tabs__item">
+    <a href="../contributing.html" class="md-tabs__link">
+      contributing
+    </a>
+  </li>
 
       
     </ul>
@@ -356,7 +303,6 @@
                   <div class="md-sidebar__inner">
                     
 
-
   
 
 
@@ -364,22 +310,22 @@
 
 <nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="ecovadis_assignment" class="md-nav__button md-logo" aria-label="ecovadis_assignment" data-md-component="logo">
+    <a href="../index.html" title="churnpred" class="md-nav__button md-logo" aria-label="churnpred" data-md-component="logo">
       
-  <img src="../assets/images/ecovadis_green_logo.png" alt="logo">
+  <img src="../assets/images/logo.png" alt="logo">
 
     </a>
-    ecovadis_assignment
+    churnpred
   </label>
   
     <div class="md-nav__source">
-      <a href="https://github.com/5uperpalo/ecovadis_assignment" title="Go to repository" class="md-source" data-md-component="source">
+      <a href="https://github.com/5uperpalo/churnpred" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2024 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.1.1 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
-    ecovadis_assignment
+    churnpred
   </div>
 </a>
     </div>
@@ -388,19 +334,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../index.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    home
-  </span>
-  
-
+        home
       </a>
     </li>
   
@@ -408,19 +349,14 @@
     
       
       
-  
+      
+
   
   
   
     <li class="md-nav__item">
       <a href="../installation.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    installation
-  </span>
-  
-
+        installation
       </a>
     </li>
   
@@ -428,365 +364,256 @@
     
       
       
+      
+
   
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--nested">
       
-        
-      
-        
-      
-        
-      
-        
       
-    
-    
+        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" data-md-toggle="__nav_3" type="checkbox" id="__nav_3" checked>
       
       
-    
-    
-    <li class="md-nav__item md-nav__item--nested">
       
         
+          
         
           
         
-        <input class="md-nav__toggle md-toggle md-toggle--indeterminate" type="checkbox" id="__nav_3" >
+          
         
           
-          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
-            
-  
-  <span class="md-ellipsis">
-    churn_pred
-  </span>
-  
-
-            <span class="md-nav__icon md-icon"></span>
-          </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_3">
-            <span class="md-nav__icon md-icon"></span>
-            churn_pred
-          </label>
-          <ul class="md-nav__list" data-md-scrollfix>
+      
+      
+        <label class="md-nav__link" for="__nav_3">
+          churn_pred
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="churn_pred" data-md-level="1">
+        <label class="md-nav__title" for="__nav_3">
+          <span class="md-nav__icon md-icon"></span>
+          churn_pred
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/code_profiling.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    code_profiling
-  </span>
-  
-
+        code_profiling
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/eda.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    eda
-  </span>
-  
-
+        eda
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/preprocessing.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    preprocessing
-  </span>
-  
-
+        preprocessing
       </a>
     </li>
   
 
-              
+            
+          
             
               
-                
-  
   
   
   
     <li class="md-nav__item">
       <a href="../churn_pred/training.html" class="md-nav__link">
-        
-  
-  <span class="md-ellipsis">
-    training
-  </span>
-  
-
+        training
       </a>
     </li>
   
 
-              
             
-          </ul>
-        </nav>
-      
+          
+        </ul>
+      </nav>
     </li>
   
 
     
       
       
+      
+
   
   
     
   
   
-  
-    
     
+    <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
-        
-      
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Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(0, 0, 0, 1);--jp-ui-font-color1: rgba(0, 0, 0, .87);--jp-ui-font-color2: rgba(0, 0, 0, .54);--jp-ui-font-color3: rgba(0, 0, 0, .38);--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);--jp-ui-inverse-font-color2: rgba(255, 255, 255, .7);--jp-ui-inverse-font-color3: rgba(255, 255, 255, .5);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}[data-md-color-scheme=slate] .jupyter-wrapper{--jp-shadow-base-lightness: 32;--jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .2 );--jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .14 );--jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), .12 );--jp-elevation-z0: none;--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color);--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color);--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color);--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color);--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color);--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color);--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color);--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color);--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color);--jp-border-width: 1px;--jp-border-color0: var(--md-grey-700);--jp-border-color1: var(--md-grey-700);--jp-border-color2: var(--md-grey-800);--jp-border-color3: var(--md-grey-900);--jp-inverse-border-color: var(--md-grey-600);--jp-border-radius: 2px;--jp-ui-font-scale-factor: 1.2;--jp-ui-font-size0: .83333em;--jp-ui-font-size1: 13px;--jp-ui-font-size2: 1.2em;--jp-ui-font-size3: 1.44em;--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-ui-font-color0: rgba(255, 255, 255, 1);--jp-ui-font-color1: rgba(255, 255, 255, .87);--jp-ui-font-color2: rgba(255, 255, 255, .54);--jp-ui-font-color3: rgba(255, 255, 255, .38);--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);--jp-ui-inverse-font-color1: rgba(0, 0, 0, .8);--jp-ui-inverse-font-color2: rgba(0, 0, 0, .5);--jp-ui-inverse-font-color3: rgba(0, 0, 0, .3);--jp-content-line-height: 1.6;--jp-content-font-scale-factor: 1.2;--jp-content-font-size0: .83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(255, 255, 255, 1);--jp-content-font-color1: rgba(255, 255, 255, 1);--jp-content-font-color2: rgba(255, 255, 255, .7);--jp-content-font-color3: rgba(255, 255, 255, .5);--jp-content-link-color: var(--md-blue-300);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: #111111;--jp-layout-color1: var(--md-grey-900);--jp-layout-color2: var(--md-grey-800);--jp-layout-color3: var(--md-grey-700);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: white;--jp-inverse-layout-color1: white;--jp-inverse-layout-color2: var(--md-grey-200);--jp-inverse-layout-color3: var(--md-grey-400);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-700);--jp-brand-color1: var(--md-blue-500);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-700);--jp-accent-color1: var(--md-green-500);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-700);--jp-warn-color1: var(--md-orange-500);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-700);--jp-error-color1: var(--md-red-500);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-700);--jp-success-color1: var(--md-green-500);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-700);--jp-info-color1: var(--md-cyan-500);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--jp-layout-color1);--jp-cell-editor-border-color: var(--md-grey-700);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: 1;--jp-cell-prompt-not-active-font-color: var(--md-grey-300);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: rgba(33, 150, 243, .24);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: rgba(244, 67, 54, .28);--jp-rendermime-table-row-background: var(--md-grey-900);--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, .2);--jp-dialog-background: rgba(0, 0, 0, .6);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color2);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .8);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--jp-layout-color0);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color0);--jp-input-hover-background: var(--jp-layout-color2);--jp-input-background: var(--md-grey-800);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: var(--jp-layout-color2);--jp-editor-selected-focused-background: rgba(33, 150, 243, .24);--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: var(--md-green-500);--jp-mirror-editor-atom-color: var(--md-blue-300);--jp-mirror-editor-number-color: var(--md-green-400);--jp-mirror-editor-def-color: var(--md-blue-600);--jp-mirror-editor-variable-color: var(--md-grey-300);--jp-mirror-editor-variable-2-color: var(--md-blue-400);--jp-mirror-editor-variable-3-color: var(--md-green-600);--jp-mirror-editor-punctuation-color: var(--md-blue-400);--jp-mirror-editor-property-color: var(--md-blue-400);--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ff7070;--jp-mirror-editor-string-2-color: var(--md-purple-300);--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: var(--md-green-600);--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: var(--md-green-700);--jp-mirror-editor-attribute-color: var(--md-blue-700);--jp-mirror-editor-header-color: var(--md-blue-500);--jp-mirror-editor-quote-color: var(--md-green-300);--jp-mirror-editor-link-color: var(--md-blue-700);--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ad4a00;--jp-collaborator-color2: #7b6a00;--jp-collaborator-color3: #007e00;--jp-collaborator-color4: #008772;--jp-collaborator-color5: #0079b9;--jp-collaborator-color6: #8b45c6;--jp-collaborator-color7: #be208b;--jp-vega-background: var(--md-grey-400);--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .6;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(255, 225, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( 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.83333em;--jp-content-font-size1: 14px;--jp-content-font-size2: 1.2em;--jp-content-font-size3: 1.44em;--jp-content-font-size4: 1.728em;--jp-content-font-size5: 2.0736em;--jp-content-presentation-font-size1: 17px;--jp-content-heading-line-height: 1;--jp-content-heading-margin-top: 1.2em;--jp-content-heading-margin-bottom: .8em;--jp-content-heading-font-weight: 500;--jp-content-font-color0: rgba(0, 0, 0, 1);--jp-content-font-color1: rgba(0, 0, 0, .87);--jp-content-font-color2: rgba(0, 0, 0, .54);--jp-content-font-color3: rgba(0, 0, 0, .38);--jp-content-link-color: var(--md-blue-700);--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";--jp-code-font-size: 13px;--jp-code-line-height: 1.3077;--jp-code-padding: 5px;--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono", monospace;--jp-code-font-family: var(--jp-code-font-family-default);--jp-code-presentation-font-size: 16px;--jp-code-cursor-width0: 1.4px;--jp-code-cursor-width1: 2px;--jp-code-cursor-width2: 4px;--jp-layout-color0: white;--jp-layout-color1: white;--jp-layout-color2: var(--md-grey-200);--jp-layout-color3: var(--md-grey-400);--jp-layout-color4: var(--md-grey-600);--jp-inverse-layout-color0: #111111;--jp-inverse-layout-color1: var(--md-grey-900);--jp-inverse-layout-color2: var(--md-grey-800);--jp-inverse-layout-color3: var(--md-grey-700);--jp-inverse-layout-color4: var(--md-grey-600);--jp-brand-color0: var(--md-blue-900);--jp-brand-color1: var(--md-blue-700);--jp-brand-color2: var(--md-blue-300);--jp-brand-color3: var(--md-blue-100);--jp-brand-color4: var(--md-blue-50);--jp-accent-color0: var(--md-green-900);--jp-accent-color1: var(--md-green-700);--jp-accent-color2: var(--md-green-300);--jp-accent-color3: var(--md-green-100);--jp-warn-color0: var(--md-orange-900);--jp-warn-color1: var(--md-orange-700);--jp-warn-color2: var(--md-orange-300);--jp-warn-color3: var(--md-orange-100);--jp-error-color0: var(--md-red-900);--jp-error-color1: var(--md-red-700);--jp-error-color2: var(--md-red-300);--jp-error-color3: var(--md-red-100);--jp-success-color0: var(--md-green-900);--jp-success-color1: var(--md-green-700);--jp-success-color2: var(--md-green-300);--jp-success-color3: var(--md-green-100);--jp-info-color0: var(--md-cyan-900);--jp-info-color1: var(--md-cyan-700);--jp-info-color2: var(--md-cyan-300);--jp-info-color3: var(--md-cyan-100);--jp-cell-padding: 5px;--jp-cell-collapser-width: 8px;--jp-cell-collapser-min-height: 20px;--jp-cell-collapser-not-active-hover-opacity: .6;--jp-cell-editor-background: var(--md-grey-100);--jp-cell-editor-border-color: var(--md-grey-300);--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-cell-editor-active-background: var(--jp-layout-color0);--jp-cell-editor-active-border-color: var(--jp-brand-color1);--jp-cell-prompt-width: 64px;--jp-cell-prompt-font-family: var(--jp-code-font-family-default);--jp-cell-prompt-letter-spacing: 0px;--jp-cell-prompt-opacity: 1;--jp-cell-prompt-not-active-opacity: .5;--jp-cell-prompt-not-active-font-color: var(--md-grey-700);--jp-cell-inprompt-font-color: #307fc1;--jp-cell-outprompt-font-color: #bf5b3d;--jp-notebook-padding: 10px;--jp-notebook-select-background: var(--jp-layout-color1);--jp-notebook-multiselected-color: var(--md-blue-50);--jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px );--jp-rendermime-error-background: #fdd;--jp-rendermime-table-row-background: var(--md-grey-100);--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);--jp-dialog-background: rgba(0, 0, 0, .25);--jp-console-padding: 10px;--jp-toolbar-border-color: var(--jp-border-color1);--jp-toolbar-micro-height: 8px;--jp-toolbar-background: var(--jp-layout-color1);--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, .24);--jp-toolbar-header-margin: 4px 4px 0px 4px;--jp-toolbar-active-background: var(--md-grey-300);--jp-statusbar-height: 24px;--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);--jp-input-active-background: var(--jp-layout-color1);--jp-input-hover-background: var(--jp-layout-color1);--jp-input-background: var(--md-grey-100);--jp-input-border-color: var(--jp-inverse-border-color);--jp-input-active-border-color: var(--jp-brand-color1);--jp-input-active-box-shadow-color: rgba(19, 124, 189, .3);--jp-editor-selected-background: #d9d9d9;--jp-editor-selected-focused-background: #d7d4f0;--jp-editor-cursor-color: var(--jp-ui-font-color0);--jp-mirror-editor-keyword-color: #008000;--jp-mirror-editor-atom-color: #88f;--jp-mirror-editor-number-color: #080;--jp-mirror-editor-def-color: #00f;--jp-mirror-editor-variable-color: var(--md-grey-900);--jp-mirror-editor-variable-2-color: #05a;--jp-mirror-editor-variable-3-color: #085;--jp-mirror-editor-punctuation-color: #05a;--jp-mirror-editor-property-color: #05a;--jp-mirror-editor-operator-color: #aa22ff;--jp-mirror-editor-comment-color: #408080;--jp-mirror-editor-string-color: #ba2121;--jp-mirror-editor-string-2-color: #708;--jp-mirror-editor-meta-color: #aa22ff;--jp-mirror-editor-qualifier-color: #555;--jp-mirror-editor-builtin-color: #008000;--jp-mirror-editor-bracket-color: #997;--jp-mirror-editor-tag-color: #170;--jp-mirror-editor-attribute-color: #00c;--jp-mirror-editor-header-color: blue;--jp-mirror-editor-quote-color: #090;--jp-mirror-editor-link-color: #00c;--jp-mirror-editor-error-color: #f00;--jp-mirror-editor-hr-color: #999;--jp-collaborator-color1: #ffad8e;--jp-collaborator-color2: #dac83d;--jp-collaborator-color3: #72dd76;--jp-collaborator-color4: #00e4d0;--jp-collaborator-color5: #45d4ff;--jp-collaborator-color6: #e2b1ff;--jp-collaborator-color7: #ff9de6;--jp-vega-background: white;--jp-sidebar-min-width: 250px;--jp-search-toggle-off-opacity: .5;--jp-search-toggle-hover-opacity: .8;--jp-search-toggle-on-opacity: 1;--jp-search-selected-match-background-color: rgb(245, 200, 0);--jp-search-selected-match-color: black;--jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 );--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);--jp-icon-contrast-color0: var(--md-purple-600);--jp-icon-contrast-color1: var(--md-green-600);--jp-icon-contrast-color2: var(--md-pink-600);--jp-icon-contrast-color3: var(--md-blue-600);--jp-jupyter-icon-color: #f37626;--jp-notebook-icon-color: #f37626;--jp-json-icon-color: var(--md-orange-700);--jp-console-icon-background-color: var(--md-blue-700);--jp-console-icon-color: white;--jp-terminal-icon-background-color: var(--md-grey-800);--jp-terminal-icon-color: var(--md-grey-200);--jp-text-editor-icon-color: var(--md-grey-700);--jp-inspector-icon-color: var(--md-grey-700);--jp-switch-color: var(--md-grey-400);--jp-switch-true-position-color: var(--md-orange-900)}
-</style>
+/*
+This file is taken from the built JupyterLab theme.css
+Found on share/nbconvert/templates/lab/static
+Some changes have been made and marked with CHANGE
+*/
+
+.jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    --jp-shadow-base-lightness: 0;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-400);
+    --jp-border-color1: var(--md-grey-400);
+    --jp-border-color2: var(--md-grey-300);
+    --jp-border-color3: var(--md-grey-200);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-ui-font-color3: rgba(0, 0, 0, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
+    --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(0, 0, 0, 1);
+    --jp-content-font-color1: rgba(0, 0, 0, 0.87);
+    --jp-content-font-color2: rgba(0, 0, 0, 0.54);
+    --jp-content-font-color3: rgba(0, 0, 0, 0.38);
+
+    --jp-content-link-color: var(--md-blue-700);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: white;
+    --jp-layout-color1: white;
+    --jp-layout-color2: var(--md-grey-200);
+    --jp-layout-color3: var(--md-grey-400);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: #111111;
+    --jp-inverse-layout-color1: var(--md-grey-900);
+    --jp-inverse-layout-color2: var(--md-grey-800);
+    --jp-inverse-layout-color3: var(--md-grey-700);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-900);
+    --jp-brand-color1: var(--md-blue-700);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-900);
+    --jp-accent-color1: var(--md-green-700);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-900);
+    --jp-warn-color1: var(--md-orange-700);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-900);
+    --jp-error-color1: var(--md-red-700);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-900);
+    --jp-success-color1: var(--md-green-700);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-900);
+    --jp-info-color1: var(--md-cyan-700);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--md-grey-100);
+    --jp-cell-editor-border-color: var(--md-grey-300);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 0.5;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-700);
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: var(--md-blue-50);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: #fdd;
+    --jp-rendermime-table-row-background: var(--md-grey-100);
+    --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.25);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color1);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--md-grey-300);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color1);
+    --jp-input-hover-background: var(--jp-layout-color1);
+    --jp-input-background: var(--md-grey-100);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: #d9d9d9;
+    --jp-editor-selected-focused-background: #d7d4f0;
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: #008000;
+    --jp-mirror-editor-atom-color: #88f;
+    --jp-mirror-editor-number-color: #080;
+    --jp-mirror-editor-def-color: #00f;
+    --jp-mirror-editor-variable-color: var(--md-grey-900);
+    --jp-mirror-editor-variable-2-color: #05a;
+    --jp-mirror-editor-variable-3-color: #085;
+    --jp-mirror-editor-punctuation-color: #05a;
+    --jp-mirror-editor-property-color: #05a;
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ba2121;
+    --jp-mirror-editor-string-2-color: #708;
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: #008000;
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: #170;
+    --jp-mirror-editor-attribute-color: #00c;
+    --jp-mirror-editor-header-color: blue;
+    --jp-mirror-editor-quote-color: #090;
+    --jp-mirror-editor-link-color: #00c;
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: white;
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.5;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(245, 200, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+[data-md-color-scheme="slate"] .jupyter-wrapper {
+    /* Elevation
+   *
+   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
+   *
+   * https://github.com/material-components/material-components-web
+   * https://material-components-web.appspot.com/elevation.html
+   */
+
+    /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
+   * colors used in the theme as well. */
+    --jp-shadow-base-lightness: 32;
+    --jp-shadow-umbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.2
+    );
+    --jp-shadow-penumbra-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.14
+    );
+    --jp-shadow-ambient-color: rgba(
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        var(--jp-shadow-base-lightness),
+        0.12
+    );
+    --jp-elevation-z0: none;
+    --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
+        0px 1px 1px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 3px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
+        0px 2px 2px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 5px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
+        0px 4px 5px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 10px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
+        0px 6px 10px 0px var(--jp-shadow-penumbra-color),
+        0px 1px 18px 0px var(--jp-shadow-ambient-color);
+    --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
+        0px 8px 10px 1px var(--jp-shadow-penumbra-color),
+        0px 3px 14px 2px var(--jp-shadow-ambient-color);
+    --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
+        0px 12px 17px 2px var(--jp-shadow-penumbra-color),
+        0px 5px 22px 4px var(--jp-shadow-ambient-color);
+    --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
+        0px 16px 24px 2px var(--jp-shadow-penumbra-color),
+        0px 6px 30px 5px var(--jp-shadow-ambient-color);
+    --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
+        0px 20px 31px 3px var(--jp-shadow-penumbra-color),
+        0px 8px 38px 7px var(--jp-shadow-ambient-color);
+    --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
+        0px 24px 38px 3px var(--jp-shadow-penumbra-color),
+        0px 9px 46px 8px var(--jp-shadow-ambient-color);
+
+    /* Borders
+   *
+   * The following variables, specify the visual styling of borders in JupyterLab.
+   */
+
+    --jp-border-width: 1px;
+    --jp-border-color0: var(--md-grey-700);
+    --jp-border-color1: var(--md-grey-700);
+    --jp-border-color2: var(--md-grey-800);
+    --jp-border-color3: var(--md-grey-900);
+    --jp-border-radius: 2px;
+
+    /* UI Fonts
+   *
+   * The UI font CSS variables are used for the typography all of the JupyterLab
+   * user interface elements that are not directly user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-ui-font-scale-factor: 1.2;
+    --jp-ui-font-size0: 0.83333em;
+    --jp-ui-font-size1: 13px; /* Base font size */
+    --jp-ui-font-size2: 1.2em;
+    --jp-ui-font-size3: 1.44em;
+
+    --jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Use these font colors against the corresponding main layout colors.
+   * In a light theme, these go from dark to light.
+   */
+
+    /* Defaults use Material Design specification */
+    --jp-ui-font-color0: rgba(255, 255, 255, 1);
+    --jp-ui-font-color1: rgba(255, 255, 255, 0.87);
+    --jp-ui-font-color2: rgba(255, 255, 255, 0.54);
+    --jp-ui-font-color3: rgba(255, 255, 255, 0.38);
+
+    /*
+   * Use these against the brand/accent/warn/error colors.
+   * These will typically go from light to darker, in both a dark and light theme.
+   */
+
+    --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
+    --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
+    --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
+    --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
+
+    /* Content Fonts
+   *
+   * Content font variables are used for typography of user generated content.
+   *
+   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
+   * is applied to a parent element. When children elements, such as headings, are sized
+   * in em all things will be computed relative to that body size.
+   */
+
+    --jp-content-line-height: 1.6;
+    --jp-content-font-scale-factor: 1.2;
+    --jp-content-font-size0: 0.83333em;
+    --jp-content-font-size1: 14px; /* Base font size */
+    --jp-content-font-size2: 1.2em;
+    --jp-content-font-size3: 1.44em;
+    --jp-content-font-size4: 1.728em;
+    --jp-content-font-size5: 2.0736em;
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-content-presentation-font-size1: 17px;
+
+    --jp-content-heading-line-height: 1;
+    --jp-content-heading-margin-top: 1.2em;
+    --jp-content-heading-margin-bottom: 0.8em;
+    --jp-content-heading-font-weight: 500;
+
+    /* Defaults use Material Design specification */
+    --jp-content-font-color0: rgba(255, 255, 255, 1);
+    --jp-content-font-color1: rgba(255, 255, 255, 1);
+    --jp-content-font-color2: rgba(255, 255, 255, 0.7);
+    --jp-content-font-color3: rgba(255, 255, 255, 0.5);
+
+    --jp-content-link-color: var(--md-blue-300);
+
+    --jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
+        Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
+        "Segoe UI Symbol";
+
+    /*
+   * Code Fonts
+   *
+   * Code font variables are used for typography of code and other monospaces content.
+   */
+
+    --jp-code-font-size: 13px;
+    --jp-code-line-height: 1.3077; /* 17px for 13px base */
+    --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
+    --jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
+        monospace;
+    --jp-code-font-family: var(--jp-code-font-family-default);
+
+    /* This gives a magnification of about 125% in presentation mode over normal. */
+    --jp-code-presentation-font-size: 16px;
+
+    /* may need to tweak cursor width if you change font size */
+    --jp-code-cursor-width0: 1.4px;
+    --jp-code-cursor-width1: 2px;
+    --jp-code-cursor-width2: 4px;
+
+    /* Layout
+   *
+   * The following are the main layout colors use in JupyterLab. In a light
+   * theme these would go from light to dark.
+   */
+
+    --jp-layout-color0: #111111;
+    --jp-layout-color1: var(--md-grey-900);
+    --jp-layout-color2: var(--md-grey-800);
+    --jp-layout-color3: var(--md-grey-700);
+    --jp-layout-color4: var(--md-grey-600);
+
+    /* Inverse Layout
+   *
+   * The following are the inverse layout colors use in JupyterLab. In a light
+   * theme these would go from dark to light.
+   */
+
+    --jp-inverse-layout-color0: white;
+    --jp-inverse-layout-color1: white;
+    --jp-inverse-layout-color2: var(--md-grey-200);
+    --jp-inverse-layout-color3: var(--md-grey-400);
+    --jp-inverse-layout-color4: var(--md-grey-600);
+
+    /* Brand/accent */
+
+    --jp-brand-color0: var(--md-blue-700);
+    --jp-brand-color1: var(--md-blue-500);
+    --jp-brand-color2: var(--md-blue-300);
+    --jp-brand-color3: var(--md-blue-100);
+    --jp-brand-color4: var(--md-blue-50);
+
+    --jp-accent-color0: var(--md-green-700);
+    --jp-accent-color1: var(--md-green-500);
+    --jp-accent-color2: var(--md-green-300);
+    --jp-accent-color3: var(--md-green-100);
+
+    /* State colors (warn, error, success, info) */
+
+    --jp-warn-color0: var(--md-orange-700);
+    --jp-warn-color1: var(--md-orange-500);
+    --jp-warn-color2: var(--md-orange-300);
+    --jp-warn-color3: var(--md-orange-100);
+
+    --jp-error-color0: var(--md-red-700);
+    --jp-error-color1: var(--md-red-500);
+    --jp-error-color2: var(--md-red-300);
+    --jp-error-color3: var(--md-red-100);
+
+    --jp-success-color0: var(--md-green-700);
+    --jp-success-color1: var(--md-green-500);
+    --jp-success-color2: var(--md-green-300);
+    --jp-success-color3: var(--md-green-100);
+
+    --jp-info-color0: var(--md-cyan-700);
+    --jp-info-color1: var(--md-cyan-500);
+    --jp-info-color2: var(--md-cyan-300);
+    --jp-info-color3: var(--md-cyan-100);
+
+    /* Cell specific styles */
+
+    --jp-cell-padding: 5px;
+
+    --jp-cell-collapser-width: 8px;
+    --jp-cell-collapser-min-height: 20px;
+    --jp-cell-collapser-not-active-hover-opacity: 0.6;
+
+    --jp-cell-editor-background: var(--jp-layout-color1);
+    --jp-cell-editor-border-color: var(--md-grey-700);
+    --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-cell-editor-active-background: var(--jp-layout-color0);
+    --jp-cell-editor-active-border-color: var(--jp-brand-color1);
+
+    --jp-cell-prompt-width: 64px;
+    --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
+    --jp-cell-prompt-letter-spacing: 0px;
+    --jp-cell-prompt-opacity: 1;
+    --jp-cell-prompt-not-active-opacity: 1;
+    --jp-cell-prompt-not-active-font-color: var(--md-grey-300);
+
+    /* A custom blend of MD grey and blue 600
+   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
+    --jp-cell-inprompt-font-color: #307fc1;
+    /* A custom blend of MD grey and orange 600
+   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
+    --jp-cell-outprompt-font-color: #bf5b3d;
+
+    /* Notebook specific styles */
+
+    --jp-notebook-padding: 10px;
+    --jp-notebook-select-background: var(--jp-layout-color1);
+    --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
+
+    /* The scroll padding is calculated to fill enough space at the bottom of the
+  notebook to show one single-line cell (with appropriate padding) at the top
+  when the notebook is scrolled all the way to the bottom. We also subtract one
+  pixel so that no scrollbar appears if we have just one single-line cell in the
+  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
+  */
+    --jp-notebook-scroll-padding: calc(
+        100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
+            var(--jp-code-padding) - var(--jp-cell-padding) - 1px
+    );
+
+    /* Rendermime styles */
+
+    --jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
+    --jp-rendermime-table-row-background: var(--md-grey-900);
+    --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
+
+    /* Dialog specific styles */
+
+    --jp-dialog-background: rgba(0, 0, 0, 0.6);
+
+    /* Console specific styles */
+
+    --jp-console-padding: 10px;
+
+    /* Toolbar specific styles */
+
+    --jp-toolbar-border-color: var(--jp-border-color2);
+    --jp-toolbar-micro-height: 8px;
+    --jp-toolbar-background: var(--jp-layout-color1);
+    --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
+    --jp-toolbar-header-margin: 4px 4px 0px 4px;
+    --jp-toolbar-active-background: var(--jp-layout-color0);
+
+    /* Statusbar specific styles */
+
+    --jp-statusbar-height: 24px;
+
+    /* Input field styles */
+
+    --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
+    --jp-input-active-background: var(--jp-layout-color0);
+    --jp-input-hover-background: var(--jp-layout-color2);
+    --jp-input-background: var(--md-grey-800);
+    --jp-input-border-color: var(--jp-border-color1);
+    --jp-input-active-border-color: var(--jp-brand-color1);
+    --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
+
+    /* General editor styles */
+
+    --jp-editor-selected-background: var(--jp-layout-color2);
+    --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
+    --jp-editor-cursor-color: var(--jp-ui-font-color0);
+
+    /* Code mirror specific styles */
+
+    --jp-mirror-editor-keyword-color: var(--md-green-500);
+    --jp-mirror-editor-atom-color: var(--md-blue-300);
+    --jp-mirror-editor-number-color: var(--md-green-400);
+    --jp-mirror-editor-def-color: var(--md-blue-600);
+    --jp-mirror-editor-variable-color: var(--md-grey-300);
+    --jp-mirror-editor-variable-2-color: var(--md-blue-400);
+    --jp-mirror-editor-variable-3-color: var(--md-green-600);
+    --jp-mirror-editor-punctuation-color: var(--md-blue-400);
+    --jp-mirror-editor-property-color: var(--md-blue-400);
+    --jp-mirror-editor-operator-color: #aa22ff;
+    --jp-mirror-editor-comment-color: #408080;
+    --jp-mirror-editor-string-color: #ff7070;
+    --jp-mirror-editor-string-2-color: var(--md-purple-300);
+    --jp-mirror-editor-meta-color: #aa22ff;
+    --jp-mirror-editor-qualifier-color: #555;
+    --jp-mirror-editor-builtin-color: var(--md-green-600);
+    --jp-mirror-editor-bracket-color: #997;
+    --jp-mirror-editor-tag-color: var(--md-green-700);
+    --jp-mirror-editor-attribute-color: var(--md-blue-700);
+    --jp-mirror-editor-header-color: var(--md-blue-500);
+    --jp-mirror-editor-quote-color: var(--md-green-300);
+    --jp-mirror-editor-link-color: var(--md-blue-700);
+    --jp-mirror-editor-error-color: #f00;
+    --jp-mirror-editor-hr-color: #999;
+
+    /* Vega extension styles */
+
+    --jp-vega-background: var(--md-grey-400);
+
+    /* Sidebar-related styles */
+
+    --jp-sidebar-min-width: 250px;
+
+    /* Search-related styles */
+
+    --jp-search-toggle-off-opacity: 0.6;
+    --jp-search-toggle-hover-opacity: 0.8;
+    --jp-search-toggle-on-opacity: 1;
+    --jp-search-selected-match-background-color: rgb(255, 225, 0);
+    --jp-search-selected-match-color: black;
+    --jp-search-unselected-match-background-color: var(
+        --jp-inverse-layout-color0
+    );
+    --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
+
+    /* scrollbar related styles. Supports every browser except Edge. */
+
+    /* colors based on JetBrain's Darcula theme */
+
+    --jp-scrollbar-background-color: #3f4244;
+    --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
+
+    --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
+
+    /* hacks for setting the thumb shape. These do nothing in Firefox */
+
+    --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
+    --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
+
+    /* Icon colors that work well with light or dark backgrounds */
+    --jp-icon-contrast-color0: var(--md-purple-600);
+    --jp-icon-contrast-color1: var(--md-green-600);
+    --jp-icon-contrast-color2: var(--md-pink-600);
+    --jp-icon-contrast-color3: var(--md-blue-600);
+}
+
+:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: 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+Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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+
+Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
+Licensed under the Apache License, Version 2.0.
+
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 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
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-<h1 id="model-analysis">Model analysis<a class="anchor-link" href="#model-analysis">¶</a></h1>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h1 id="model-analysis">Model analysis<a class="anchor-link" href="#model-analysis">&#182;</a></h1>
 </div>
 </div>
 </div>
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-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
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 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-1">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">sys</span>
 
 <span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">getcwd</span><span class="p">())</span>
-<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">".."</span><span class="p">)</span>
+<span class="n">os</span><span class="o">.</span><span class="n">chdir</span><span class="p">(</span><span class="s2">&quot;..&quot;</span><span class="p">)</span>
 
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
-<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_columns"</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
-<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"display.max_rows"</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_columns&quot;</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">&quot;display.max_rows&quot;</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-1">import os
+<div id="cell-1" class="clipboard-copy-txt">import os
 import sys
 
 sys.path.append(os.getcwd())
@@ -1401,31 +1805,36 @@ <h1 id="model-analysis">Model analysis<a class="anchor-link" href="#model-analys
 
 pd.set_option("display.max_columns", 200)
 pd.set_option("display.max_rows", 300)</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">churn_pred.utils</span> <span class="kn">import</span> <span class="n">dill_load</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[2]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-2">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">churn_pred.utils</span> <span class="kn">import</span> <span class="n">dill_load</span>
 <span class="kn">from</span> <span class="nn">churn_pred.training.utils</span> <span class="kn">import</span> <span class="n">get_feature_importance</span>
 <span class="kn">from</span> <span class="nn">churn_pred.eda.target.analysis</span> <span class="kn">import</span> <span class="n">correlation</span>
 <span class="kn">from</span> <span class="nn">churn_pred.eda.target.plotting</span> <span class="kn">import</span> <span class="n">prob_distrib_per_class</span>
@@ -1437,7 +1846,7 @@ <h1 id="model-analysis">Model analysis<a class="anchor-link" href="#model-analys
 <span class="kn">import</span> <span class="nn">warnings</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-2">from churn_pred.utils import dill_load
+<div id="cell-2" class="clipboard-copy-txt">from churn_pred.utils import dill_load
 from churn_pred.training.utils import get_feature_importance
 from churn_pred.eda.target.analysis import correlation
 from churn_pred.eda.target.plotting import prob_distrib_per_class
@@ -1448,77 +1857,87 @@ <h1 id="model-analysis">Model analysis<a class="anchor-link" href="#model-analys
 import shap
 import warnings
 import numpy as np</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">trainer</span> <span class="o">=</span> <span class="n">dill_load</span><span class="p">(</span><span class="s2">"data/model/lgbm_trainer.dill"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[3]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-3">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">trainer</span> <span class="o">=</span> <span class="n">dill_load</span><span class="p">(</span><span class="s2">&quot;data/model/lgbm_trainer.dill&quot;</span><span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-3">trainer = dill_load("data/model/lgbm_trainer.dill")</div>
-</div>
-</div>
+<div id="cell-3" class="clipboard-copy-txt">trainer = dill_load("data/model/lgbm_trainer.dill")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs  ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">"data/dataset_auxiliary_features_cleaned.parquet"</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[4]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-4">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_pd</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_parquet</span><span class="p">(</span><span class="s2">&quot;data/dataset_auxiliary_features_cleaned.parquet&quot;</span><span class="p">)</span>
 <span class="n">cat_cols</span> <span class="o">=</span> <span class="p">[</span>
-    <span class="s2">"Country"</span><span class="p">,</span>
-    <span class="s2">"Gender"</span><span class="p">,</span>
-    <span class="s2">"HasCreditCard"</span><span class="p">,</span>
-    <span class="s2">"IsActiveMember"</span><span class="p">,</span>
-    <span class="s2">"CustomerFeedback_sentiment3"</span><span class="p">,</span>
-    <span class="s2">"CustomerFeedback_sentiment5"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_region"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_subregion"</span><span class="p">,</span>
-    <span class="s2">"Country_region"</span><span class="p">,</span>
-    <span class="s2">"Country_subregion"</span><span class="p">,</span>
-    <span class="s2">"is_native"</span><span class="p">,</span>
-    <span class="s2">"Country_hemisphere"</span><span class="p">,</span>
-    <span class="s2">"Country_IncomeGroup"</span><span class="p">,</span>
-    <span class="s2">"Surname_Country_IncomeGroup"</span><span class="p">,</span>
-    <span class="s2">"working_class"</span><span class="p">,</span>
-    <span class="s2">"stage_of_life"</span><span class="p">,</span>
-    <span class="s2">"generation"</span><span class="p">,</span>
+    <span class="s2">&quot;Country&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Gender&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;HasCreditCard&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;IsActiveMember&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CustomerFeedback_sentiment3&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;CustomerFeedback_sentiment5&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_region&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_subregion&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_region&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_subregion&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;is_native&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_hemisphere&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Country_IncomeGroup&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;Surname_Country_IncomeGroup&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;working_class&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;stage_of_life&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;generation&quot;</span><span class="p">,</span>
 <span class="p">]</span>
 <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_pd</span><span class="p">[</span><span class="n">cat_cols</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
 
@@ -1539,7 +1958,7 @@ <h1 id="model-analysis">Model analysis<a class="anchor-link" href="#model-analys
     <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-4">df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
+<div id="cell-4" class="clipboard-copy-txt">df_pd = pd.read_parquet("data/dataset_auxiliary_features_cleaned.parquet")
 cat_cols = [
     "Country",
     "Gender",
@@ -1578,180 +1997,240 @@ <h1 id="model-analysis">Model analysis<a class="anchor-link" href="#model-analys
     stratify=df_valid[trainer.target_col],
     random_state=random_state,
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
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-</div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_predicted_proba</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_predicted_proba</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span>
     <span class="n">df</span><span class="o">=</span><span class="n">df_test</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">trainer</span><span class="o">.</span><span class="n">target_col</span><span class="p">),</span> <span class="n">binary2d</span><span class="o">=</span><span class="kc">False</span>
 <span class="p">)</span>
 <span class="n">df_predicted_cls</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">predict_cls</span><span class="p">(</span><span class="n">df</span><span class="o">=</span><span class="n">df_test</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">trainer</span><span class="o">.</span><span class="n">target_col</span><span class="p">))</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-5">df_predicted_proba = trainer.predict_proba(
+<div id="cell-5" class="clipboard-copy-txt">df_predicted_proba = trainer.predict_proba(
     df=df_test.drop(columns=trainer.target_col), binary2d=False
 )
 df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col))</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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-<h2 id="model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class">Model feature importance and "certainty" of the model in predicting classes - probability density per class<a class="anchor-link" href="#model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class">Model feature importance and "certainty" of the model in predicting classes - probability density per class<a class="anchor-link" href="#model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class">&#182;</a></h2>
 </div>
 </div>
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+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">lgb</span><span class="o">.</span><span class="n">plot_importance</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">importance_type</span><span class="o">=</span><span class="s2">"split"</span><span class="p">)</span>
-<span class="c1"># lgb.plot_importance(trainer.model, importance_type="gain")</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">lgb</span><span class="o">.</span><span class="n">plot_importance</span><span class="p">(</span><span class="n">trainer</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">importance_type</span><span class="o">=</span><span class="s2">&quot;split&quot;</span><span class="p">)</span>
+<span class="c1"># lgb.plot_importance(trainer.model, importance_type=&quot;gain&quot;)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-6">lgb.plot_importance(trainer.model, importance_type="split")
+<div id="cell-6" class="clipboard-copy-txt">lgb.plot_importance(trainer.model, importance_type="split")
 # lgb.plot_importance(trainer.model, importance_type="gain")</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
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-<pre>&lt;Axes: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'&gt;</pre>
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
+
+
+
+
+<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
+<pre>&lt;Axes: title={&#39;center&#39;: &#39;Feature importance&#39;}, xlabel=&#39;Feature importance&#39;, ylabel=&#39;Features&#39;&gt;</pre>
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+
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"/>
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">prob_distrib_per_class</span><span class="p">(</span>
     <span class="n">predicted_probs</span><span class="o">=</span><span class="n">df_predicted_proba</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
     <span class="n">actual</span><span class="o">=</span><span class="n">df_test</span><span class="p">[</span><span class="n">trainer</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">(),</span>
-    <span class="n">task</span><span class="o">=</span><span class="s2">"binary"</span><span class="p">,</span>
+    <span class="n">task</span><span class="o">=</span><span class="s2">&quot;binary&quot;</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-7">fig = prob_distrib_per_class(
+<div id="cell-7" class="clipboard-copy-txt">fig = prob_distrib_per_class(
     predicted_probs=df_predicted_proba.values.flatten(),
     actual=df_test[trainer.target_col].values.flatten(),
     task="binary",
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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+
+
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+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedImage jp-OutputArea-output" tabindex="0">
-<img alt="No description has been provided for this image" class="" 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-<h2 id="shap-analysis">SHAP analysis<a class="anchor-link" href="#shap-analysis">¶</a></h2>
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
+<h2 id="shap-analysis">SHAP analysis<a class="anchor-link" href="#shap-analysis">&#182;</a></h2>
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+        
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_test_prep</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">preprocessors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span>
     <span class="n">df_test</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="n">trainer</span><span class="o">.</span><span class="n">target_col</span><span class="p">])</span>
 <span class="p">)</span>
 <span class="n">df_test_prep</span><span class="p">[</span><span class="n">trainer</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_test</span><span class="p">[</span><span class="n">trainer</span><span class="o">.</span><span class="n">target_col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
@@ -1763,7 +2242,7 @@ <h2 id="shap-analysis">SHAP analysis<a class="anchor-link" href="#shap-analysis"
     <span class="n">id_cols</span><span class="o">=</span><span class="n">trainer</span><span class="o">.</span><span class="n">id_cols</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-8">df_test_prep = trainer.preprocessors[0].transform(
+<div id="cell-8" class="clipboard-copy-txt">df_test_prep = trainer.preprocessors[0].transform(
     df_test.drop(columns=[trainer.target_col])
 )
 df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int)
@@ -1774,120 +2253,162 @@ <h2 id="shap-analysis">SHAP analysis<a class="anchor-link" href="#shap-analysis"
     target_col=trainer.target_col,
     id_cols=trainer.id_cols,
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs">
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 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">base_value</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">lgb_test</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">pred_contrib</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[9]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
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+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">base_value</span> <span class="o">=</span> <span class="n">trainer</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">lgb_test</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">pred_contrib</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">shap_values</span> <span class="o">=</span> <span class="n">base_value</span><span class="p">[:,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
 <span class="n">base_value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">base_value</span><span class="p">[:,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-9">base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True)
+<div id="cell-9" class="clipboard-copy-txt">base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True)
 shap_values = base_value[:, :-1]
 base_value = np.mean(base_value[:, -1])</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
+
 </div>
-</div>
-</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell jp-CodeCell jp-Notebook-cell">
-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
 </div>
 <div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">shap</span><span class="o">.</span><span class="n">summary_plot</span><span class="p">(</span>
-    <span class="n">shap_values</span><span class="p">,</span> <span class="n">features</span><span class="o">=</span><span class="n">lgb_test</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">plot_type</span><span class="o">=</span><span class="s2">"dot"</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[10]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-10">
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+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">shap</span><span class="o">.</span><span class="n">summary_plot</span><span class="p">(</span>
+    <span class="n">shap_values</span><span class="p">,</span> <span class="n">features</span><span class="o">=</span><span class="n">lgb_test</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">plot_type</span><span class="o">=</span><span class="s2">&quot;dot&quot;</span><span class="p">,</span> <span class="n">sort</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-10">shap.summary_plot(
+<div id="cell-10" class="clipboard-copy-txt">shap.summary_plot(
     shap_values, features=lgb_test.data, plot_type="dot", sort=False, show=True
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
-</div>
+
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 <div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
 </div>
+
+
 <div class="jp-OutputArea jp-Cell-outputArea">
+
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-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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-<div class="jp-Cell-inputWrapper" tabindex="0">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell jp-CodeCell jp-Notebook-cell   ">
+<div class="jp-Cell-inputWrapper">
 <div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
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-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_test</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">28</span><span class="p">:</span><span class="mi">30</span><span class="p">]</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[12]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-12">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">df_test</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">28</span><span class="p">:</span><span class="mi">30</span><span class="p">]</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-12">df_test.iloc[28:30]</div>
-</div>
-</div>
+<div id="cell-12" class="clipboard-copy-txt">df_test.iloc[28:30]</div>
+    
+        </div>
+    </div>
+
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 </div>
+
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+
+
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-<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
+
+<div class="jp-OutputArea-child">
+
+    
+    <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[12]:</div>
+
+
+
+<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
 <div>
-<style scoped="">
+<style scoped>
     .dataframe tbody tr th:only-of-type {
         vertical-align: middle;
     }
@@ -1972,141 +2510,147 @@ <h2 id="shap-analysis">SHAP analysis<a class="anchor-link" href="#shap-analysis"
     }
 </style>
 <table border="1" class="dataframe">
-<thead>
-<tr style="text-align: right;">
-<th></th>
-<th>CustomerId</th>
-<th>CreditScore</th>
-<th>Country</th>
-<th>Gender</th>
-<th>Age</th>
-<th>Tenure</th>
-<th>Balance (EUR)</th>
-<th>NumberOfProducts</th>
-<th>HasCreditCard</th>
-<th>IsActiveMember</th>
-<th>EstimatedSalary</th>
-<th>Exited</th>
-<th>CustomerFeedback_sentiment3</th>
-<th>CustomerFeedback_sentiment5</th>
-<th>Surname_Country</th>
-<th>Surname_Country_region</th>
-<th>Surname_Country_subregion</th>
-<th>Country_region</th>
-<th>Country_subregion</th>
-<th>is_native</th>
-<th>Country_hemisphere</th>
-<th>Country_gdp_per_capita</th>
-<th>Country_IncomeGroup</th>
-<th>Surname_Country_gdp_per_capita</th>
-<th>Surname_Country_IncomeGroup</th>
-<th>working_class</th>
-<th>stage_of_life</th>
-<th>generation</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<th>9941</th>
-<td>15714240</td>
-<td>712</td>
-<td>Spain</td>
-<td>Male</td>
-<td>74</td>
-<td>5</td>
-<td>0.0</td>
-<td>2</td>
-<td>0</td>
-<td>0</td>
-<td>151425.82</td>
-<td>0</td>
-<td>neutral</td>
-<td>2 stars</td>
-<td>Russian Federation</td>
-<td>Europe</td>
-<td>Eastern Europe</td>
-<td>Europe</td>
-<td>Southern Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>48685.49631</td>
-<td>High income</td>
-<td>34637.76172</td>
-<td>Upper middle income</td>
-<td>elderly</td>
-<td>senior_adult</td>
-<td>boomers_1</td>
-</tr>
-<tr>
-<th>7444</th>
-<td>15658057</td>
-<td>812</td>
-<td>Spain</td>
-<td>Female</td>
-<td>44</td>
-<td>8</td>
-<td>0.0</td>
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-<td>1</td>
-<td>0</td>
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-<td>1</td>
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-<td>1 star</td>
-<td>Italy</td>
-<td>Europe</td>
-<td>Southern Europe</td>
-<td>Europe</td>
-<td>Southern Europe</td>
-<td>0</td>
-<td>northern</td>
-<td>48685.49631</td>
-<td>High income</td>
-<td>55442.07843</td>
-<td>High income</td>
-<td>working_age</td>
-<td>middle_age_adult</td>
-<td>millennials</td>
-</tr>
-</tbody>
+  <thead>
+    <tr style="text-align: right;">
+      <th></th>
+      <th>CustomerId</th>
+      <th>CreditScore</th>
+      <th>Country</th>
+      <th>Gender</th>
+      <th>Age</th>
+      <th>Tenure</th>
+      <th>Balance (EUR)</th>
+      <th>NumberOfProducts</th>
+      <th>HasCreditCard</th>
+      <th>IsActiveMember</th>
+      <th>EstimatedSalary</th>
+      <th>Exited</th>
+      <th>CustomerFeedback_sentiment3</th>
+      <th>CustomerFeedback_sentiment5</th>
+      <th>Surname_Country</th>
+      <th>Surname_Country_region</th>
+      <th>Surname_Country_subregion</th>
+      <th>Country_region</th>
+      <th>Country_subregion</th>
+      <th>is_native</th>
+      <th>Country_hemisphere</th>
+      <th>Country_gdp_per_capita</th>
+      <th>Country_IncomeGroup</th>
+      <th>Surname_Country_gdp_per_capita</th>
+      <th>Surname_Country_IncomeGroup</th>
+      <th>working_class</th>
+      <th>stage_of_life</th>
+      <th>generation</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <th>9941</th>
+      <td>15714240</td>
+      <td>712</td>
+      <td>Spain</td>
+      <td>Male</td>
+      <td>74</td>
+      <td>5</td>
+      <td>0.0</td>
+      <td>2</td>
+      <td>0</td>
+      <td>0</td>
+      <td>151425.82</td>
+      <td>0</td>
+      <td>neutral</td>
+      <td>2 stars</td>
+      <td>Russian Federation</td>
+      <td>Europe</td>
+      <td>Eastern Europe</td>
+      <td>Europe</td>
+      <td>Southern Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>48685.49631</td>
+      <td>High income</td>
+      <td>34637.76172</td>
+      <td>Upper middle income</td>
+      <td>elderly</td>
+      <td>senior_adult</td>
+      <td>boomers_1</td>
+    </tr>
+    <tr>
+      <th>7444</th>
+      <td>15658057</td>
+      <td>812</td>
+      <td>Spain</td>
+      <td>Female</td>
+      <td>44</td>
+      <td>8</td>
+      <td>0.0</td>
+      <td>3</td>
+      <td>1</td>
+      <td>0</td>
+      <td>66926.83</td>
+      <td>1</td>
+      <td>neutral</td>
+      <td>1 star</td>
+      <td>Italy</td>
+      <td>Europe</td>
+      <td>Southern Europe</td>
+      <td>Europe</td>
+      <td>Southern Europe</td>
+      <td>0</td>
+      <td>northern</td>
+      <td>48685.49631</td>
+      <td>High income</td>
+      <td>55442.07843</td>
+      <td>High income</td>
+      <td>working_age</td>
+      <td>middle_age_adult</td>
+      <td>millennials</td>
+    </tr>
+  </tbody>
 </table>
 </div>
 </div>
+
 </div>
+
 </div>
+
 </div>
+
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
-<div class="CodeMirror cm-s-jupyter">
-<div class="zeroclipboard-container">
-<clipboard-copy for="cell-13">
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-<span class="notice" hidden="">Copied!</span>
-<svg aria-hidden="true" class="clipboard-copy-icon" data-view-component="true" height="20" version="1.1" viewbox="0 0 16 16" width="20">
-<path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-<path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z" fill="currentColor" fill-rule="evenodd"></path>
-</svg>
-</div>
-</clipboard-copy>
-</div>
-<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">shap_decision_plot</span> <span class="o">=</span> <span class="n">shap</span><span class="o">.</span><span class="n">decision_plot</span><span class="p">(</span>
+<div class="jp-InputPrompt jp-InputArea-prompt">In&nbsp;[13]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
+        
+        
+        <div class="CodeMirror cm-s-jupyter">
+        <div class="zeroclipboard-container">
+            <clipboard-copy for="cell-13">
+            <div>
+                <span class="notice" hidden>Copied!</span>
+                <svg aria-hidden="true" width="20" height="20" viewBox="0 0 16 16" version="1.1"  data-view-component="true" class="clipboard-copy-icon">
+                    <path fill="currentColor" fill-rule="evenodd" d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 010 1.5h-1.5a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-1.5a.75.75 0 011.5 0v1.5A1.75 1.75 0 019.25 16h-7.5A1.75 1.75 0 010 14.25v-7.5z"></path>
+                    <path fill="currentColor" fill-rule="evenodd" d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0114.25 11h-7.5A1.75 1.75 0 015 9.25v-7.5zm1.75-.25a.25.25 0 00-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 00.25-.25v-7.5a.25.25 0 00-.25-.25h-7.5z"></path>
+                </svg>
+                </div>
+            </clipboard-copy>
+        </div>
+            <div class="highlight-ipynb hl-python"><pre><span></span><span class="n">shap_decision_plot</span> <span class="o">=</span> <span class="n">shap</span><span class="o">.</span><span class="n">decision_plot</span><span class="p">(</span>
     <span class="n">base_value</span><span class="o">=</span><span class="n">base_value</span><span class="p">,</span>
     <span class="n">shap_values</span><span class="o">=</span><span class="n">shap_values</span><span class="p">[</span><span class="mi">28</span><span class="p">:</span><span class="mi">30</span><span class="p">],</span>
     <span class="n">features</span><span class="o">=</span><span class="n">trainer</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">feature_name</span><span class="p">(),</span>
     <span class="n">feature_names</span><span class="o">=</span><span class="n">trainer</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">feature_name</span><span class="p">(),</span>
     <span class="c1"># legend_labels=legend_labels,</span>
-    <span class="n">link</span><span class="o">=</span><span class="s2">"logit"</span><span class="p">,</span>
+    <span class="n">link</span><span class="o">=</span><span class="s2">&quot;logit&quot;</span><span class="p">,</span>
     <span class="c1"># row_index=0,</span>
     <span class="c1"># feature_order=list(range(len(trainer.model.feature_name()))),</span>
 <span class="p">)</span>
 </pre></div>
-<div class="clipboard-copy-txt" id="cell-13">shap_decision_plot = shap.decision_plot(
+<div id="cell-13" class="clipboard-copy-txt">shap_decision_plot = shap.decision_plot(
     base_value=base_value,
     shap_values=shap_values[28:30],
     features=trainer.model.feature_name(),
@@ -2116,77 +2660,93 @@ <h2 id="shap-analysis">SHAP analysis<a class="anchor-link" href="#shap-analysis"
     # row_index=0,
     # feature_order=list(range(len(trainer.model.feature_name()))),
 )</div>
+    
+        </div>
+    </div>
+
 </div>
 </div>
-</div>
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+
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diff --git a/docs/site/objects.inv b/docs/site/objects.inv
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-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"index.html","title":"Churn prediction assignment for EcoVadis","text":"<p>Main purpose of this library is to provide solution for an asisgnemnt in the interview process of EcoVadis.</p> <pre><code>In this assignment you're tasked with developing a machine learning solution for churn prediction to identify which customers are likely to leave a service (column \"Exited\" in the attached dataset). This assignment is meant to assess\n\n- analytical skills and reasoning\n- design and modelling choices, e.g. choices with respect to measuring model performance\n- coding skills, e.g. modularity, readability, reproducibility, any other best practices in software development\n\nPlease note that multiple solutions may exist and we do not expect a production ready solution, though any reflections on how you may wish to productionalise your solution are welcome. You are free to choose the medium (e.g., notebooks, python scripts). \n\nPlease return your solution by May 2 (9am CET). Once returned, our colleagues with the HR department will schedule the final interview where you'll be asked to walk us through your solution and reflect on any decisions made.\n\nAdditional explanation of independent variables:\n\nNumberOfProducts - the number of accounts and bank-affiliated products \nHasCreditCard - whether a customer has a credit card\nCustomerFeedback - latest customer feedback, if available\n</code></pre>"},{"location":"index.html#solution","title":"Solution","text":"<p>Please see the Notebooks section. The notebooks are sorted from 0 to 5. Notebooks start with gathering auxiliary data that I could extract from the provided dataset, e.g. 'country origin of the surname'. This is followed by Exploratory Data Analysis of features and target in the notebooks 2, 3. In the notebook 4, I presented a <code>Trainer</code> object that handles training an hyperparameter search of the model. In the notebook 5 I made a quick analysis of the model and it's predictions using SHAP values.</p> <p>The final solution uses LightGBM, a GBM model of my choice. I chose GBM as 4 out of top 5 models in H2O AutoML were GBMs.</p>"},{"location":"index.html#additional-work-note-mentioning","title":"Additional work note mentioning","text":"<p>In notebook <code>00_auxiliary_features_surname_origin_country_classification.ipynb</code> I adjusted(copy/paste+adjust) a BERT model for surname origin prediction. Due to lack of time I could not gather additional data that would help with model training, but I left some ideas in the notebook.</p> <p>The solution was tested in a virtual machine, spawned from <code>jupyter/datascience-notebook:python-3.10</code> image in Zero-to-JupyterHub solution. As the bare metal server with GPU was down in the kubernetes, I had to do additional troubleshooting and fixing.</p> <p>The code is easily extendable to <code>multiclass</code>, <code>regression</code> and <code>quantile_regression</code> tasks.</p>"},{"location":"cicd.html","title":"CI/CD","text":"<p>This section includes some ideas for model developmnet and deployment within Azure services - i.e. EcoVadis cloud services vendor.</p> <ul> <li>MLFlow for experiment tracking and endpoint deployment</li> <li>https://mlflow.org/docs/1.25.1/python_api/mlflow.azureml.html</li> <li>https://mlflow.org/docs/latest/deployment/index.html</li> <li>https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2&amp;tabs=mlflow</li> <li>DVC for dataset versioning </li> <li>python api: https://dvc.org/doc/api-reference</li> <li>Dataset storage in Azure Blob Storage<ul> <li>https://dvc.org/doc/user-guide/data-management/remote-storage/azure-blob-storage#microsoft-azure-blob-storage </li> </ul> </li> <li>Dataset and predictions(dev) visualization from Azure Blob Storage<ul> <li>https://learn.microsoft.com/en-us/azure/data-explorer/azure-data-explorer-dashboards</li> <li>https://learn.microsoft.com/en-us/azure/data-explorer/create-event-grid-connection?tabs=portal-adx%2Cazure-blob-storage</li> </ul> </li> <li>Data handling in PROD</li> <li>databricks: https://learn.microsoft.com/en-us/azure/databricks/getting-started/data-pipeline-get-started</li> <li>create and share dashboards within organization: https://learn.microsoft.com/en-us/azure/databricks/sql/get-started/sample-dashboards</li> </ul>"},{"location":"code_profiling.html","title":"Code profiling","text":"<p>his section incldues some ideas for code profiling - i.e. CO2, RAM, CPU, GPU tracking.</p>"},{"location":"code_profiling.html#carbon-emission-monitoring","title":"Carbon emission monitoring:","text":"<ul> <li>https://mlco2.github.io/codecarbon/usage.html#</li> <li>https://github.com/sb-ai-lab/Eco2AI</li> <li>https://github.com/lfwa/carbontracker</li> </ul>"},{"location":"code_profiling.html#ram-usage-profiling-and-code-execution-timing","title":"RAM usage profiling and Code execution timing","text":""},{"location":"code_profiling.html#ram-usage-profiling","title":"RAM usage profiling","text":"Max RAM used  To get max RAM used during executition of the function import the decorator and put it above it function to log max RAM used into specified logging file, e.g.: **NOTE**: this applies only to functions and NOT classes or class methods. In case somebody figures out how to use it also on methods please let me know[Pavol Mulinka]. Posts relevant to the issue I was unable to debug: * https://stackoverflow.com/questions/75409483/why-do-i-get-missing-required-argument-self-when-using-a-decorator-written-as * https://stackoverflow.com/questions/16593246/how-to-use-memory-profiler-python-module-with-class-methods <pre><code>from churn_pred.code_profiling import ram_usage\n\n@ram_usage\ndef training(config: TrainingConfig, custom_params: CustomParameters):\n</code></pre> Per line RAM usage analysis To analyze RAM usage per line of the code import profile decorator, put it above the function you want to analyze:  <pre><code>from memory_profiler import profile\n\n@profile\ndef training(config: TrainingConfig, custom_params: CustomParameters):\n</code></pre>  , run the code using memory_profile command line util, and analyze the RAM usage, e.g.:  <pre><code>$ python -m memory_profiler local_run.py\n\nFilename: /XXX/main.py\n\nLine #    Mem usage    Increment  Occurrences   Line Contents\n=============================================================\n    53    230.8 MiB    230.8 MiB           1   @ram_usage\n    54                                         @profile\n    55                                         def training(config: TrainingConfig, custom_params: CustomParameters):\n    56    230.9 MiB      0.1 MiB           1       data_config = DataConfig.load(config.data_config)\n    57    230.9 MiB      0.0 MiB           1       hyperparameters = Hyperparameters.parse_obj(config.hyperparameters)\n    58                                         \n    59    687.5 MiB    456.6 MiB           1       queries, df_data = get_data(config, custom_params)\n    60    687.5 MiB      0.0 MiB           1       (\n</code></pre>"},{"location":"code_profiling.html#code-execution-timing","title":"Code Execution timing","text":"Timer decorator Import and prepend the time decorator to log time(into specified log file) that it takes to execute analyzed function/class, e.g.:  <pre><code>from churn_pred.code_profiling import timer\n\n@timer\ndef training(config: TrainingConfig, custom_params: CustomParameters):\n</code></pre>"},{"location":"contributing.html","title":"Code","text":"<p>Before making a pushing a code and making a pull request please run codestyle checks and tests</p> <pre><code>./code_style.sh\npytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov=eda tests/\n</code></pre>"},{"location":"contributing.html#documentation","title":"Documentation","text":"<p>Documentation is created using github pages and mkdocs, see</p> <pre><code># to build locally\ncd docs\npip install -r requirements.txt\nmkdocs build\n# to push to github pages\nmkdocs gh-deploy\n# if you want to run webserver locally\nmkdocs serve\n</code></pre>"},{"location":"installation.html","title":"Installation","text":""},{"location":"installation.html#install-using-pip-directly-from-github","title":"Install using pip directly from github:","text":"<pre><code>pip install git+https://github.com/5uperpalo/churn_pred.git\n</code></pre>"},{"location":"installation.html#locally","title":"Locally","text":"<pre><code>git clone https://github.com/5uperpalo/churn_pred.git\ncd churn_pred\npip install .\n</code></pre>"},{"location":"installation.html#additional-reqs-on-a-newly-spawned-machine","title":"Additional reqs on a newly spawned machine","text":"<p>H20 AutoML requires JRE and language models are faster on GPU.</p> <pre><code>sudo apt install nvidia-utils-535\nsudo apt install openjdk-17-jdk\n</code></pre>"},{"location":"churn_pred/code_profiling.html","title":"Code profiling","text":""},{"location":"churn_pred/code_profiling.html#churn_pred.code_profiling.ram_usage","title":"<code>ram_usage(func)</code>","text":"<p>Wrapper to monitor and log RAM usage during the function execution. NOTE: can be applied to function but not to method of a class. Parameters:     func: function</p> Source code in <code>churn_pred/code_profiling.py</code> <pre><code>def ram_usage(func):\n    \"\"\"Wrapper to monitor and log RAM usage during the function execution.\n    NOTE: can be applied to function but not to method of a class.\n    Parameters:\n        func: function\n    \"\"\"\n\n    @wraps(func)\n    def ram_usage_func(*args, **kwargs):\n        ram, value = memory_usage(\n            (func, args, *kwargs), interval=1.0, retval=True, max_usage=True\n        )\n        logging.info(\n            f\"Finished {func.__name__,}. Max RAM used {(ram / 1000):.4f} GB.\"\n        )  # noqa\n        return value\n\n    return ram_usage_func\n</code></pre>"},{"location":"churn_pred/code_profiling.html#churn_pred.code_profiling.timer","title":"<code>timer(func)</code>","text":"<p>Wrapper to time and log the function execution. Parameters:     func: function</p> Source code in <code>churn_pred/code_profiling.py</code> <pre><code>def timer(func):\n    \"\"\"Wrapper to time and log the function execution.\n    Parameters:\n        func: function\n    \"\"\"\n\n    @wraps(func)\n    def timer_func(*args, **kwargs):\n        start_time = time()\n        value = func(*args, **kwargs)\n        end_time = time()\n        logging.info(\n            f\"Finished {func.__name__} in {(end_time - start_time):.4f} seconds.\"  # noqa\n        )\n        return value\n\n    return timer_func\n</code></pre>"},{"location":"churn_pred/eda.html","title":"Preprocess data","text":""},{"location":"churn_pred/eda.html#churn_pred.eda.features.analysis.entropy","title":"<code>entropy(df, scale='linear', plot=False)</code>","text":"<p>Procedure to plot features sorted by their entropy.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>dataset</p> required <code>scale</code> <code>str</code> <p>y scale of the plot</p> <code>'linear'</code> <code>plot</code> <code>bool</code> <p>whether to output the plot</p> <code>False</code> <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>plot with sorted entropy of the features</p> Source code in <code>churn_pred/eda/features/analysis.py</code> <pre><code>def entropy(\n    df: pd.DataFrame, scale: Literal[\"log\", \"linear\"] = \"linear\", plot: bool = False\n) -&gt; Tuple[pd.DataFrame, Figure]:\n    \"\"\"Procedure to plot features sorted by their entropy.\n\n    Args:\n        df (DataFrame): dataset\n        scale (str): y scale of the plot\n        plot (bool): whether to output the plot\n\n    Returns:\n        fig (Figure): plot with sorted entropy of the features\n    \"\"\"\n    data = df.copy()\n\n    data = data.astype(str)\n    col_entropies = data.apply(entropy_calc, axis=0)\n    col_entropies.sort_values(ascending=False, inplace=True)\n\n    if plot:\n        fig, ax = plt.subplots(figsize=(10, 5))\n        bar_plot(\n            col_entropies.dropna(), ax, title=\"Features entropies (NA are dropped)\"\n        )\n    else:\n        fig = None\n    ax.set_yscale(scale)\n    return col_entropies, fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.features.analysis.init_check","title":"<code>init_check(df, identifier=None, cat_cols=None, cont_cols=None, verbose=False)</code>","text":"<p>Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>pandas dataframe</p> required <code>identifier</code> <code>str</code> <p>column which identifies unique user IDs</p> <code>None</code> <code>cat_cols</code> <code>list</code> <p>categorical features in the dataset</p> <code>None</code> <code>cont_cols</code> <code>list</code> <p>numerical features in the dataset</p> <code>None</code> <p>Returns:</p> Name Type Description <code>duplicated_ids</code> <code>int</code> <code>cont_cols_desc</code> <code>DataFrame</code> <code>cat_cols_desc</code> <code>DataFrame</code> Source code in <code>churn_pred/eda/features/analysis.py</code> <pre><code>def init_check(\n    df: pd.DataFrame,\n    identifier: Optional[str] = None,\n    cat_cols: Optional[list] = None,\n    cont_cols: Optional[list] = None,\n    verbose: bool = False,\n) -&gt; Tuple[int, pd.DataFrame, pd.DataFrame]:\n    \"\"\"Procedure to check:\n    * duplicated rows in teh dataset\n    * general stats of numerical features\n    * general stats of categorical features\n\n    Args:\n        df (DataFrame): pandas dataframe\n        identifier (str): column which identifies unique user IDs\n        cat_cols (list): categorical features in the dataset\n        cont_cols (list): numerical features in the dataset\n\n    Returns:\n        duplicated_ids (int):\n        cont_cols_desc (pd.DataFrame):\n        cat_cols_desc (pd.DataFrame):\n    \"\"\"\n    data = df.copy()\n\n    if identifier:\n        duplicated_ids = data[identifier].duplicated().sum()\n        if verbose:\n            print(\"[CHECK] Number of duplicated ids: {}\".format(duplicated_ids))\n\n    if cont_cols:\n        cont_cols_f = intsec(data.columns.values, cont_cols)\n        cont_cols_desc = data[cont_cols_f].describe().transpose()\n        if verbose:\n            print(\"[CHECK] Numerical columns\")\n            with pd.option_context(\"display.precision\", 2):\n                print(cont_cols_desc)\n\n    if cat_cols:\n        cat_cols_f = intsec(data.columns.values, cat_cols)\n        cat_cols_desc = data[cat_cols_f].describe().transpose()\n        if verbose:\n            print(\"[CHECK] Categorical columns\")\n            with pd.option_context(\"display.precision\", 2):\n                print(cat_cols_desc)\n    return duplicated_ids, cont_cols_desc, cat_cols_desc\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.features.analysis.missing","title":"<code>missing(df, scale='linear', plot=False)</code>","text":"<p>Procedure to check fraction of missing values in the dataset.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>pandas dataframe</p> required <code>scale</code> <code>str</code> <p>y scale of the plot</p> <code>'linear'</code> <code>plot</code> <code>bool</code> <p>whether to output the plot</p> <code>False</code> <p>Returns:</p> Name Type Description <code>missing_val_frac</code> <code>DataFrame</code> <p>sorted dataframe with fraction of missing values per feature</p> <code>fig</code> <code>Figure</code> <p>plot with sorted fractions of missing values in each column</p> Source code in <code>churn_pred/eda/features/analysis.py</code> <pre><code>def missing(\n    df: pd.DataFrame,\n    scale: Literal[\"log\", \"linear\"] = \"linear\",\n    plot: bool = False,\n) -&gt; Tuple[pd.DataFrame, Figure]:\n    \"\"\"Procedure to check fraction of missing values in the dataset.\n\n    Args:\n        df (DataFrame): pandas dataframe\n        scale (str): y scale of the plot\n        plot (bool): whether to output the plot\n\n    Returns:\n        missing_val_frac (DataFrame): sorted dataframe with fraction of missing values\n            per feature\n        fig (Figure): plot with sorted fractions of missing values in each column\n    \"\"\"\n    data = df.copy()\n    missing_val_frac = data.isna().sum() / len(data)\n    missing_val_frac.sort_values(ascending=False, inplace=True)\n\n    if plot:\n        fig, ax = plt.subplots(figsize=(10, 5))\n        bar_plot(\n            missing_val_frac,\n            ax,\n            title=\"Fraction of missing values in the dataset\",\n        )\n    else:\n        fig = None\n    ax.set_yscale(scale)\n    return missing_val_frac, fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.features.analysis.nunique","title":"<code>nunique(df, scale='linear', plot=False)</code>","text":"<p>Procedure to plot features sorted by their number of unique values.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>dataset</p> required <code>scale</code> <code>str</code> <p>y scale of the plot</p> <code>'linear'</code> <code>plot</code> <code>bool</code> <p>whether to output the plot</p> <code>False</code> <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>plot with sorted number of unique values in features</p> Source code in <code>churn_pred/eda/features/analysis.py</code> <pre><code>def nunique(\n    df: pd.DataFrame,\n    scale: Literal[\"log\", \"linear\"] = \"linear\",\n    plot: bool = False,\n) -&gt; Tuple[pd.DataFrame, Figure]:\n    \"\"\"Procedure to plot features sorted by their number of unique values.\n\n    Args:\n        df (DataFrame): dataset\n        scale (str): y scale of the plot\n        plot (bool): whether to output the plot\n\n    Returns:\n        fig (Figure): plot with sorted number of unique values in features\n    \"\"\"\n    data = df.copy()\n\n    data_nunique = data.nunique()\n    data_nunique.sort_values(ascending=True, inplace=True)\n\n    if plot:\n        fig, ax = plt.subplots(figsize=(10, 5))\n        bar_plot(\n            data_nunique.dropna(),\n            ax,\n            title=\"Features number of unique values (NA are dropped)\",\n        )\n    else:\n        fig = None\n    ax.set_yscale(scale)\n    return data_nunique, fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.features.analysis.std","title":"<code>std(df, scale='linear', plot=False)</code>","text":"<p>Procedure to plot features sorted by their variance.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>dataset</p> required <code>scale</code> <code>str</code> <p>y scale of the plot</p> <code>'linear'</code> <code>plot</code> <code>bool</code> <p>whether to output the plot</p> <code>False</code> <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>plot with sorted standard deviation of continuous features</p> Source code in <code>churn_pred/eda/features/analysis.py</code> <pre><code>def std(\n    df: pd.DataFrame,\n    scale: Literal[\"log\", \"linear\"] = \"linear\",\n    plot: bool = False,\n) -&gt; Tuple[pd.DataFrame, Figure]:\n    \"\"\"Procedure to plot features sorted by their variance.\n\n    Args:\n        df (DataFrame): dataset\n        scale (str): y scale of the plot\n        plot (bool): whether to output the plot\n\n    Returns:\n        fig (Figure): plot with sorted standard deviation of continuous features\n    \"\"\"\n    data = df.copy()\n\n    data_std = data.std()\n\n    data_std.sort_values(ascending=False, inplace=True)\n\n    if plot:\n        fig, ax = plt.subplots(figsize=(10, 5))\n        bar_plot(data_std.dropna(), ax, title=\"Features variance (NA are dropped)\")\n    else:\n        fig = None\n    ax.set_yscale(scale)\n    return data_std, fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.features.analysis.zero","title":"<code>zero(df, scale='linear', plot=False)</code>","text":"<p>Procedure to check fraction of zero values in the dataset.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>pandas dataframe</p> required <code>scale</code> <code>str</code> <p>y scale of the plot</p> <code>'linear'</code> <code>plot</code> <code>bool</code> <p>whether to output the plot</p> <code>False</code> <p>Returns:</p> Name Type Description <code>zero_val_frac</code> <code>DataFrame</code> <p>sorted dataframe with fraction of '0' values per feature</p> <code>fig</code> <code>Figure</code> <p>plot with sorted fractions of '0' values in each column</p> Source code in <code>churn_pred/eda/features/analysis.py</code> <pre><code>def zero(\n    df: pd.DataFrame,\n    scale: Literal[\"log\", \"linear\"] = \"linear\",\n    plot: bool = False,\n) -&gt; Tuple[pd.DataFrame, Figure]:\n    \"\"\"Procedure to check fraction of zero values in the dataset.\n\n    Args:\n        df (DataFrame): pandas dataframe\n        scale (str): y scale of the plot\n        plot (bool): whether to output the plot\n\n    Returns:\n        zero_val_frac (DataFrame): sorted dataframe with fraction of '0' values per\n            feature\n        fig (Figure): plot with sorted fractions of '0' values in each column\n    \"\"\"\n    data = df.copy()\n    zero_val_frac = data.isin([0]).sum() / len(data)\n    zero_val_frac.sort_values(ascending=False, inplace=True)\n\n    if plot:\n        fig, ax = plt.subplots(figsize=(10, 5))\n        bar_plot(zero_val_frac, ax, title=\"Fraction of zero values in the dataset\")\n    else:\n        fig = None\n    ax.set_yscale(scale)\n    return zero_val_frac, fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.features.plotting.cross_correlation","title":"<code>cross_correlation(df, n=10, verbose=False)</code>","text":"<p>Procedure to calculate and plot cross-correlation of features in the dataset.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>pandas dataframe</p> required <code>verbose</code> <code>bool</code> <p>show n most correlated features</p> <code>False</code> <code>n</code> <code>int</code> <p>number correlated features in verbose output</p> <code>10</code> <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>heatmap of continuous features cross correlations</p> Source code in <code>churn_pred/eda/features/plotting.py</code> <pre><code>def cross_correlation(\n    df: pd.DataFrame,\n    n: int = 10,\n    verbose: bool = False,\n) -&gt; Figure:\n    \"\"\"Procedure to calculate and plot cross-correlation of features\n    in the dataset.\n\n    Args:\n        df (DataFrame): pandas dataframe\n        verbose (bool): show n most correlated features\n        n (int): number correlated features in verbose output\n\n    Returns:\n        fig (Figure): heatmap of continuous features cross correlations\n    \"\"\"\n    data = df.copy()\n\n    # corrwith uses numpy which does not like pandas Float dtypes\n    corr_matrix = data.astype(float).corr()\n    upper_stacked_sorted = (\n        corr_matrix.abs()\n        .where(np.triu(np.ones(corr_matrix.shape), k=1).astype(\"bool\"))\n        .stack()\n        .sort_values(ascending=False)\n    )\n\n    fig, ax = plt.subplots(figsize=(10, 10))\n    ax.set_title(\"Cross-correlation matrix\", size=20)\n    sns.set(font_scale=1.0)\n    sns.heatmap(\n        corr_matrix,\n        ax=ax,\n        cbar=True,\n        square=True,\n        # linewidths=0.5,\n        fmt=\".2f\",\n    )\n    if verbose:\n        print(\"Top {} absolute correlations\".format(n))\n        print(upper_stacked_sorted[:n])\n    return fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.features.plotting.distributions","title":"<code>distributions(df, low_per_cut=0, high_per_cut=1, type='box')</code>","text":"<p>Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>pandas dataframe</p> required <code>low_per_cut</code> <code>float</code> <p>lower percentile where to cut the plot for better readability</p> <code>0</code> <code>high_per_cut</code> <code>float</code> <p>higher percentile where to cut the plot for better readability</p> <code>1</code> <code>type</code> <code>str</code> <p>type of distribution plot</p> <code>'box'</code> <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>ditribution plot per each feature</p> Source code in <code>churn_pred/eda/features/plotting.py</code> <pre><code>def distributions(\n    df: pd.DataFrame,\n    low_per_cut: float = 0,\n    high_per_cut: float = 1,\n    type: Literal[\"box\", \"violin\"] = \"box\",\n) -&gt; Figure:\n    \"\"\"Procedure to plot distributions of the features splitted\n    by column split_col using workaround for violinplots in seaborn:\n    * https://stackoverflow.com/a/64787568/8147433\n    DISCALIMER: for now the cont_cols NA vals are filled with 0\n\n    Args:\n        df (DataFrame): pandas dataframe\n        low_per_cut (float): lower percentile where to cut the plot for better\n            readability\n        high_per_cut (float): higher percentile where to cut the plot for better\n            readability\n        type (str): type of distribution plot\n\n    Returns:\n        fig (Figure): ditribution plot per each feature\n    \"\"\"\n    data = df.copy()\n    cols = data.columns.values\n    data = data.fillna(0).astype(float)\n    fig, ax = plt.subplots(len(cols), 1, figsize=(16, len(cols) * 2))\n    fig.suptitle(\"Distribution of feature values\", size=20)\n    for col, i in zip(cols, range(len(cols))):\n        if type == \"violin\":\n            sns.violinplot(\n                ax=ax[i],\n                x=col,\n                orient=\"h\",\n                # cut=0,\n                # showextrem=False,\n                data=data,\n            )\n        if type == \"box\":\n            sns.boxplot(\n                ax=ax[i],\n                x=col,\n                orient=\"h\",\n                data=data,\n            )\n        ax[i].set_xlim(\n            data[col].quantile(low_per_cut), data[col].quantile(high_per_cut)\n        )\n        ax[i].get_yaxis().set_visible(False)\n    plt.subplots_adjust(hspace=0.9)\n    return fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.target.analysis.correlation","title":"<code>correlation(df, target, scale='linear', plot=False)</code>","text":"<p>Procedure to plot most correlated numerical features with target column.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>pandas dataframe</p> required <code>target</code> <code>Series</code> <p>target values</p> required <code>scale</code> <code>str</code> <p>y scale of the plot</p> <code>'linear'</code> <code>plot</code> <code>bool</code> <p>whether to output the plot</p> <code>False</code> <p>Returns:</p> Name Type Description <code>sorted_corr_cols</code> <code>DataFrame</code> <p>sorted dataframe with feature and target value correlation</p> <code>fig</code> <code>Figure</code> <p>sorted bar plot with feature and target value correlation</p> Source code in <code>churn_pred/eda/target/analysis.py</code> <pre><code>def correlation(\n    df: pd.DataFrame,\n    target: pd.Series,\n    scale: Literal[\"log\", \"linear\"] = \"linear\",\n    plot: bool = False,\n) -&gt; Tuple[pd.DataFrame, Figure]:\n    \"\"\"Procedure to plot most correlated numerical features with\n    target column.\n\n    Args:\n        df (pd.DataFrame): pandas dataframe\n        target (pd.Series): target values\n        scale (str): y scale of the plot\n        plot (bool): whether to output the plot\n\n    Returns:\n        sorted_corr_cols (pd.DataFrame): sorted dataframe with feature and target value\n            correlation\n        fig (Figure): sorted bar plot with feature and target value correlation\n    \"\"\"\n    data = df.copy()\n    target_col = target.name\n    correlation_df = pd.concat([data, target], axis=1).corr()\n    sorted_corr_cols = correlation_df[target_col].drop(index=[target_col])\n    sorted_corr_cols.sort_values(ascending=False, inplace=True)\n\n    if plot:\n        fig, ax = plt.subplots(figsize=(10, 5))\n        bar_plot(\n            sorted_corr_cols,\n            ax,\n            title=f\"Sorted features by their correlation with {target_col}\",\n        )\n    else:\n        fig = None\n    ax.set_yscale(scale)\n    return sorted_corr_cols, fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.target.plotting.distributions_in_binary_cls","title":"<code>distributions_in_binary_cls(df, target, low_per_cut=0, high_per_cut=1)</code>","text":"<p>Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>pandas dataframe</p> required <code>target</code> <code>Series</code> <p>target values, i.e. binary classes</p> required <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>ditribution plot per each feature</p> Source code in <code>churn_pred/eda/target/plotting.py</code> <pre><code>def distributions_in_binary_cls(\n    df: pd.DataFrame,\n    target: pd.Series,\n    low_per_cut: float = 0,\n    high_per_cut: float = 1,\n) -&gt; Figure:\n    \"\"\"Procedure to plot distributions of the features splitted\n    by column split_col using workaround for violinplots in seaborn:\n    * https://stackoverflow.com/a/64787568/8147433\n    DISCALIMER: for now the cont_cols NA vals are filled with 0\n\n    Args:\n        df (DataFrame): pandas dataframe\n        target (pd.Series): target values, i.e. binary classes\n\n    Returns:\n        fig (Figure): ditribution plot per each feature\n    \"\"\"\n    data = pd.concat([df, target], axis=1)\n    cols = data.columns.values\n    data[\"dummy\"] = 0\n    data = data.fillna(0).astype(float)\n    fig, ax = plt.subplots(len(cols), 1, figsize=(16, len(cols) * 2))\n    fig.suptitle(f\"Distribution of feature values splitted by {target.name}\", size=20)\n    for col, i in zip(cols, range(len(cols))):\n        if col != target.name:\n            sns.violinplot(\n                ax=ax[i],\n                hue=target.name,\n                x=col,\n                y=\"dummy\",\n                orient=\"h\",\n                cut=0,\n                showextrem=False,\n                split=True,\n                data=data,\n            )\n            ax[i].get_legend().remove()\n            ax[i].set_xlim(\n                data[col].quantile(low_per_cut), data[col].quantile(high_per_cut)\n            )\n            ax[i].get_yaxis().set_visible(False)\n    plt.subplots_adjust(hspace=0.9, top=0.97)\n    return fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.target.plotting.prob_distrib_per_class","title":"<code>prob_distrib_per_class(predicted_probs, actual, task)</code>","text":"<p>Procedure to plot probability density distributions per class from LightGBM predictions.</p> <p>Parameters:</p> Name Type Description Default <code>predicted_probs</code> <code>ndarray</code> <p>predicted probs</p> required <code>actual</code> <code>ndarray</code> <p>ground truth classes</p> required <code>task</code> <code>str</code> <p>type of task</p> required <p>Returns:</p> Name Type Description <code>fig</code> <code>Figure</code> <p>probability density ditributions plot per each class</p> Source code in <code>churn_pred/eda/target/plotting.py</code> <pre><code>def prob_distrib_per_class(\n    predicted_probs: np.ndarray,\n    actual: np.ndarray,\n    task: Literal[\"binary\", \"multiclass\"],\n) -&gt; Figure:\n    \"\"\"Procedure to plot probability density distributions per class from LightGBM\n    predictions.\n\n    Args:\n        predicted_probs (ndarray): predicted probs\n        actual (ndarray): ground truth classes\n        task (str): type of task\n\n    Returns:\n        fig (Figure): probability density ditributions plot per each class\n    \"\"\"\n    if task == \"binary\":\n        temp = pd.DataFrame({\"predicted_proba\": predicted_probs, \"actual\": actual})\n\n        fig, ax = plt.subplots(figsize=(3, 2))\n        fig.suptitle(\"Predicted probability density per class\")\n        for label, alpha, color in zip([0, 1], [1, 0.7], [\"black\", \"red\"]):\n            ax.hist(\n                temp[temp[\"actual\"] == label][\"predicted_proba\"],\n                density=True,\n                bins=np.linspace(0, 1, 10),\n                alpha=alpha,\n                color=color,\n                ec=\"k\",\n            )\n        ax.set_xlim([0, 1])\n        ax.set_xlabel(\"probability\")\n        ax.set_ylabel(\"probability_density\")\n    elif task == \"multiclass\":\n        temp = pd.DataFrame(\n            {\n                \"predicted_proba\": np.take_along_axis(\n                    predicted_probs, np.vstack(actual), axis=1  # type: ignore\n                ).flatten(),\n                \"actual\": actual,\n            }\n        )\n\n        # Dictionary of color for each label\n        color_d = dict(\n            zip_longest(\n                temp[\"actual\"].unique(),\n                plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"],\n            )\n        )\n\n        n_classes = temp[\"actual\"].nunique()\n        fig, ax = plt.subplots(\n            ncols=n_classes, figsize=(n_classes * 3, 2), sharex=True, sharey=True\n        )\n        fig.suptitle(\"Predicted probability density per class\")\n        plt.subplots_adjust(wspace=0.3, top=0.75)\n\n        for i, (label, gp) in enumerate(temp.groupby(\"actual\")):\n            ax[i].hist(\n                gp[\"predicted_proba\"],\n                density=True,\n                bins=np.linspace(0, 1, 10),\n                color=color_d[label],\n                ec=\"k\",\n            )\n            ax[i].set_title(label)\n            ax[i].set_xlim([0, 1])\n            ax[i].set_xlabel(\"probability\")\n            ax[i].set_ylabel(\"probability_density\")\n    return fig\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.plotting.bar_plot","title":"<code>bar_plot(df, ax, title)</code>","text":"<p>Helper method for unified bar plot</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>dataframe to plot</p> required <code>ax</code> <code>Axes</code> <p>axes defining where to plot it</p> required <p>Returns:</p> Name Type Description <code>adjusted_plot</code> <code>Axes</code> <p>adjusted axes</p> Source code in <code>churn_pred/eda/plotting.py</code> <pre><code>def bar_plot(df: pd.DataFrame, ax: Axes, title: str) -&gt; Axes:\n    \"\"\"Helper method for unified bar plot\n\n    Args:\n        df (DataFrame): dataframe to plot\n        ax (Axes): axes defining where to plot it\n\n    Returns:\n        adjusted_plot (Axes): adjusted axes\n    \"\"\"\n    data = df.copy()\n    ax.set_title(title, size=20)\n    ax.tick_params(labelsize=8)\n    ax.set_xlabel(\"sorted features\", size=12)\n    return data.plot.bar(ax=ax)\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.general_utils.entropy_calc","title":"<code>entropy_calc(labels, base=np.e)</code>","text":"<p>Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Args:     labels (list, ndarray, Series): list of values Returns:     ent (float): entropy of the list of values</p> Source code in <code>churn_pred/eda/general_utils.py</code> <pre><code>def entropy_calc(labels: list, base: float = np.e) -&gt; float:\n    \"\"\"Computes entropy of both continuous and categorical features.\n    Shamelessly stolen from :\n    https://stackoverflow.com/a/45091961\n    Args:\n        labels (list, ndarray, Series): list of values\n    Returns:\n        ent (float): entropy of the list of values\n    \"\"\"\n    n_labels = len(labels)\n    value, counts = np.unique(labels, return_counts=True)\n    probs = counts / n_labels\n    n_classes = np.count_nonzero(probs)\n\n    ent = float(0)\n    if n_classes &gt; 1:\n        for i in probs:\n            ent -= i * math.log(i, base)\n    return ent\n</code></pre>"},{"location":"churn_pred/eda.html#churn_pred.eda.general_utils.intsec","title":"<code>intsec(list1, list2)</code>","text":"<p>Simple intesection of two lists. Args:     list1 (list): list1     list2 (list): list2 Returns:     list (list): intersection of lists</p> Source code in <code>churn_pred/eda/general_utils.py</code> <pre><code>def intsec(list1: list, list2: list) -&gt; list:\n    \"\"\"Simple intesection of two lists.\n    Args:\n        list1 (list): list1\n        list2 (list): list2\n    Returns:\n        list (list): intersection of lists\n    \"\"\"\n    return list(set.intersection(set(list1), set(list2)))\n</code></pre>"},{"location":"churn_pred/preprocessing.html","title":"Preprocess data","text":""},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.age_categories","title":"<code>age_categories(df, age_col)</code>","text":"<p>Returns string pd.DataFrame identifing different age classification categories according to the age: * working_class (https://ourworldindata.org/age-structure)   * children_and_adolescents: &lt;0, 15)   * working_age: &lt;15, 65)   * elderly: &lt;65, inf) * stage_of_life (https://integrishealth.org/resources/on-your-health/2015/october/stages-of-life-health-for-every-age)   * infant: &lt;0, 2)   * toddler: &lt;2, 5)   * child: &lt;5, 13)   * teen: &lt;13, 20)   * adult: &lt;20, 40)   * middle_age_adult: &lt;40, 60)   * senior_adult: &lt;60, inf) * generation (https://www.beresfordresearch.com/age-range-by-generation/)   * gen_z: &lt;12, 28)   * millennials: &lt;28, 44)   * gen_x: &lt;44, 60)   * boomers_2: &lt;60, 70)   * boomers_1: &lt;70, 79)   * post_war: &lt;79, 97)   * ww2: &lt;97, 102)   * vampire: &lt;102, inf)</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataset</p> required <code>age_col</code> <code>str</code> <p>age column</p> required Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def age_categories(df: pd.DataFrame, age_col: str) -&gt; pd.DataFrame:\n    \"\"\"Returns string pd.DataFrame identifing different age classification\n    categories according to the age:\n    * working_class (https://ourworldindata.org/age-structure)\n      * children_and_adolescents: &lt;0, 15)\n      * working_age: &lt;15, 65)\n      * elderly: &lt;65, inf)\n    * stage_of_life (https://integrishealth.org/resources/on-your-health/2015/october/stages-of-life-health-for-every-age)\n      * infant: &lt;0, 2)\n      * toddler: &lt;2, 5)\n      * child: &lt;5, 13)\n      * teen: &lt;13, 20)\n      * adult: &lt;20, 40)\n      * middle_age_adult: &lt;40, 60)\n      * senior_adult: &lt;60, inf)\n    * generation (https://www.beresfordresearch.com/age-range-by-generation/)\n      * gen_z: &lt;12, 28)\n      * millennials: &lt;28, 44)\n      * gen_x: &lt;44, 60)\n      * boomers_2: &lt;60, 70)\n      * boomers_1: &lt;70, 79)\n      * post_war: &lt;79, 97)\n      * ww2: &lt;97, 102)\n      * vampire: &lt;102, inf)\n\n    Args:\n        df (pd.DataFrame): input dataset\n        age_col (str): age column\n    \"\"\"\n    dfc = df.copy()\n    dfc[\"working_class\"] = pd.cut(\n        dfc[age_col],\n        bins=[0, 15, 65, np.inf],\n        labels=[\"children_and_adolescents\", \"working_age\", \"elderly\"],\n        include_lowest=True,\n    )\n    dfc[\"stage_of_life\"] = pd.cut(\n        dfc[age_col],\n        bins=[0, 2, 5, 13, 20, 40, 60, np.inf],\n        labels=[\n            \"infant\",\n            \"toddler\",\n            \"child\",\n            \"teen\",\n            \"adult\",\n            \"middle_age_adult\",\n            \"senior_adult\",\n        ],\n        include_lowest=True,\n    )\n    dfc[\"generation\"] = pd.cut(\n        dfc[age_col],\n        bins=[0, 28, 44, 60, 70, 79, 97, 102, np.inf],\n        labels=[\n            \"gen_z\",\n            \"millennials\",\n            \"gen_x\",\n            \"boomers_2\",\n            \"boomers_1\",\n            \"post_war\",\n            \"ww2\",\n            \"vampire\",\n        ],\n        include_lowest=True,\n    )\n    return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.big_mac_index","title":"<code>big_mac_index(df, country_name_col)</code>","text":"<p>Returns dataframe with Big Max Index corresponding to country codes. Downloaded on 26/4/2024.</p> Note <p>Unfortunatelly Spain, France, Germany (unique companies in the dataset) are not included, other index to explore: https://en.wikipedia.org/wiki/Big_Mac_Index</p> Based on Downloaded from <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataset</p> required <code>country_name_col</code> <code>str</code> <p>column name with country names</p> required Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def big_mac_index(df: pd.DataFrame, country_name_col: str) -&gt; pd.DataFrame:\n    \"\"\"\n    Returns dataframe with Big Max Index corresponding to country codes.\n    Downloaded on 26/4/2024.\n\n    Note:\n        Unfortunatelly Spain, France, Germany (unique companies in the dataset) are not included, other index to explore:\n        https://en.wikipedia.org/wiki/Big_Mac_Index\n\n    Based on:\n        https://www.economist.com/big-mac-index\n\n    Downloaded from:\n        https://github.com/TheEconomist/big-mac-data\n        https://raw.githubusercontent.com/TheEconomist/big-mac-data/master/output-data/big-mac-full-index.csv\n\n    Args:\n        df (pd.DataFrame): input dataset\n        country_name_col: column name with country names\n    \"\"\"\n    big_mac_index = pd.read_csv(BIG_MAC_INDEX)\n    # filter the last available price\n    big_mac_index[\"date\"] = pd.to_datetime(big_mac_index[\"date\"])\n    big_mac_index = (\n        big_mac_index.sort_values(\"date\").groupby(\"iso_a3\").tail(1).reset_index()\n    )\n    big_mac_index = big_mac_index[[\"iso_a3\", \"dollar_price\"]]\n    big_mac_index.rename(\n        columns={\"dollar_price\": \"big_mac_index_dollar_price\"}, inplace=True\n    )\n\n    big_mac_index = big_mac_index.dropna()\n\n    dfc = df.copy()\n    dfc[\"iso_a3\"] = get_iso_a3(df=dfc, country_name_col=country_name_col)\n    dfc = dfc.merge(big_mac_index, on=\"iso_a3\", how=\"left\")\n    dfc.drop(columns=[\"iso_a3\"], inplace=True)\n    return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.gdppc","title":"<code>gdppc(df, country_name_col)</code>","text":"<p>Returns dataframe with Gross Domestic Product Per Capita corresponding to country codes. Uses information from worldbank API downloaded on 26/4/2024. Last file update: 3/28/2024. Posprocessed file has initial 3 lines removed for easier processing using pandas df.</p> Downloaded from <ul> <li>https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD</li> <li>https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.PP.CD?downloadformat=csv</li> </ul> <p>Main file before preprocessing:     'data/gdpp/API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv' Other included files:     'data/gdpp/Metadata_Country_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'     'data/gdpp/Metadata_Indicator_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataset</p> required <code>country_name_col</code> <code>str</code> <p>column name with country names</p> required Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def gdppc(df: pd.DataFrame, country_name_col: str) -&gt; pd.DataFrame:\n    \"\"\"\n    Returns dataframe with Gross Domestic Product Per Capita corresponding to country\n    codes. Uses information from worldbank API downloaded on 26/4/2024.\n    Last file update: 3/28/2024.\n    Posprocessed file has initial 3 lines removed for easier processing using pandas df.\n\n    Downloaded from:\n        * https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD\n        * https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.PP.CD?downloadformat=csv\n    Main file before preprocessing:\n        'data/gdpp/API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'\n    Other included files:\n        'data/gdpp/Metadata_Country_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'\n        'data/gdpp/Metadata_Indicator_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv'\n\n    Args:\n        df (pd.DataFrame): input dataset\n        country_name_col: column name with country names\n    \"\"\"\n    gdp_per_capita_raw = pd.read_csv(GDP_PER_CAPITA)\n    gdp_per_capita_ic = pd.read_csv(GDP_PER_CAPITA_INCOMEGROUP)\n    # add last available year\n    gdp_per_capita = gdp_per_capita_raw[[\"Country Code\"]]\n    gdp_per_capita.rename(columns={\"Country Code\": \"iso_a3\"}, inplace=True)\n    gdp_per_capita[\"gdp_per_capita\"] = (\n        gdp_per_capita_raw.drop(\n            columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"]\n        )\n        .ffill(axis=1)\n        .iloc[:, -1]\n    )\n\n    gdp_per_capita_ic = gdp_per_capita_ic[[\"Country Code\", \"IncomeGroup\"]]\n    gdp_per_capita_ic.rename(columns={\"Country Code\": \"iso_a3\"}, inplace=True)\n\n    gdp_per_capita = gdp_per_capita.dropna()\n    gdp_per_capita_ic = gdp_per_capita_ic.dropna()\n\n    dfc = df.copy()\n    dfc[\"iso_a3\"] = get_iso_a3(df=dfc, country_name_col=country_name_col)\n\n    dfc = dfc.merge(gdp_per_capita, on=\"iso_a3\", how=\"left\")\n    dfc = dfc.merge(gdp_per_capita_ic, on=\"iso_a3\", how=\"left\")\n    dfc.drop(columns=[\"iso_a3\"], inplace=True)\n\n    return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.get_country_name","title":"<code>get_country_name(df, country_name_col)</code>","text":"<p>Returns pd.Series with mapped country iso_a2 to names.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>Series</code> <p>input country name pandas series</p> required Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def get_country_name(df: pd.DataFrame, country_name_col: str):\n    \"\"\"Returns pd.Series with mapped country iso_a2 to names.\n\n    Args:\n        df (pd.Series): input country name pandas series\n    \"\"\"\n    dfc = df.copy()\n\n    def _get_country_name_map(country_name: str) -&gt; str:\n        \"\"\"Helper function for get_country_name\"\"\"\n        try:\n            result = pycountry.countries.get(alpha_2=country_name)\n            name_str = result.name\n        except:\n            name_str = \"NA\"\n        return name_str\n\n    dfc[\"res\"] = \"NA\"\n    for country_name in dfc[country_name_col].unique():\n        dfc.loc[dfc[country_name_col] == country_name, [\"res\"]] = _get_country_name_map(\n            country_name\n        )\n    return dfc[\"res\"]\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.get_country_region_subregion","title":"<code>get_country_region_subregion(df, country_name_col)</code>","text":"<p>Returns pd.Series with mapped country iso_a2 to names.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>Series</code> <p>input country name pandas series</p> required Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def get_country_region_subregion(df: pd.DataFrame, country_name_col: str):\n    \"\"\"Returns pd.Series with mapped country iso_a2 to names.\n\n    Args:\n        df (pd.Series): input country name pandas series\n    \"\"\"\n    dfc = df.copy()\n\n    def _get_country_region_subregion_map(country_name: str) -&gt; Tuple[str, str]:\n        \"\"\"Helper function for get_country_name\"\"\"\n        try:\n            result = CountryInfo(country_name)\n            region_str = result.region()\n            subregion_str = result.subregion()\n        except:\n            region_str = \"NA\"\n            subregion_str = \"NA\"\n        return (region_str, subregion_str)\n\n    dfc[[country_name_col + \"_region\", country_name_col + \"_subregion\"]] = (\"NA\", \"NA\")\n    for country_name in dfc[country_name_col].unique():\n        dfc.loc[\n            dfc[country_name_col] == country_name,\n            [country_name_col + \"_region\", country_name_col + \"_subregion\"],\n        ] = _get_country_region_subregion_map(country_name)\n    return dfc[[country_name_col + \"_region\", country_name_col + \"_subregion\"]]\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.get_iso_a3","title":"<code>get_iso_a3(df, country_name_col)</code>","text":"<p>Returns pd.Series with mapped country names to iso_a3.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>Series</code> <p>input country name pandas series</p> required Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def get_iso_a3(df: pd.DataFrame, country_name_col: str):\n    \"\"\"Returns pd.Series with mapped country names to iso_a3.\n\n    Args:\n        df (pd.Series): input country name pandas series\n    \"\"\"\n    dfc = df.copy()\n\n    def _get_iso_a3_map(country_name: str) -&gt; str:\n        \"\"\"Helper function for get_iso_a3\"\"\"\n        try:\n            result = pycountry.countries.search_fuzzy(country_name)\n            iso_a3_str = result[0].alpha_3  # type: ignore\n        except:\n            iso_a3_str = \"NA\"\n        return iso_a3_str\n\n    dfc[\"res\"] = \"NA\"\n    for country_name in dfc[country_name_col].unique():\n        dfc.loc[dfc[country_name_col] == country_name, [\"res\"]] = _get_iso_a3_map(\n            country_name\n        )\n    return dfc[\"res\"]\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.hemisphere","title":"<code>hemisphere(df, cc_col, loc_db_df=None, city_col=None)</code>","text":"<p>Returns string pd.Series identifing hemisphere: \"northern\" or \"southern\".</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataframe</p> required <code>city_col</code> <code>str</code> <p>city name column</p> <code>None</code> <code>cc_col</code> <code>str</code> <p>country code column</p> required <code>loc_db_df</code> <code>DataFrame</code> <p>pandas dataframe with previously saved locations</p> <code>None</code> Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def hemisphere(\n    df: pd.DataFrame,\n    cc_col: str,\n    loc_db_df: Optional[pd.DataFrame] = None,\n    city_col: Optional[str] = None,\n) -&gt; pd.Series:\n    \"\"\"Returns string pd.Series identifing hemisphere: \"northern\" or \"southern\".\n\n    Args:\n        df (pd.DataFrame): input dataframe\n        city_col (str): city name column\n        cc_col (str): country code column\n        loc_db_df (pd.DataFrame): pandas dataframe with previously saved locations\n    \"\"\"\n    if city_col:\n        uq_locs = (df[city_col] + \",\" + df[cc_col]).unique()\n    else:\n        uq_locs = (\" ,\" + df[cc_col]).unique()\n    dfc = df.copy()\n    geolocator = Nominatim(user_agent=\"hemisphere_identification\")\n    dfc[\"hemisphere\"] = \"northern\"\n\n    for uq_loc in uq_locs:\n        if (loc_db_df is not None) and (uq_loc in loc_db_df[\"location\"].values):\n            location = loc_db_df[loc_db_df[\"location\"] == uq_loc][\n                [\"latitude\", \"longitude\"]\n            ]\n            latitude = float(location.latitude)\n        else:\n            latitude = geolocator.geocode(uq_loc).latitude\n\n        if latitude &lt; 0:\n            city, country = uq_loc.split(\",\")\n            dfc.loc[\n                (dfc[city_col] == city) &amp; (dfc[cc_col] == country), \"hemisphere\"\n            ] = \"southern\"\n\n    return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.surname_origin","title":"<code>surname_origin(df, surname_col)</code>","text":"<p>Surname classification that uses db of names leaked from FB, see: https://github.com/philipperemy/name-dataset</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataset</p> required <code>surname_col</code> <code>str</code> <p>column name with surnames</p> required Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def surname_origin(df: pd.DataFrame, surname_col: str) -&gt; pd.DataFrame:\n    \"\"\"\n    Surname classification that uses db of names leaked from FB, see:\n    https://github.com/philipperemy/name-dataset\n\n    Args:\n        df (pd.DataFrame): input dataset\n        surname_col (str): column name with surnames\n    \"\"\"\n    dfc = df.copy()\n    nd = NameDataset()\n\n    def _search_surname(surname):\n        try:\n            res = nd.search(surname)\n            origin_country = max(\n                res[\"last_name\"][\"country\"], key=res[\"last_name\"][\"country\"].get\n            )\n        except:\n            origin_country = \"NA\"\n        return origin_country\n\n    dfc[\"res\"] = \"NA\"\n    for surname in dfc[surname_col].unique():\n        dfc.loc[dfc[surname_col] == surname, [\"res\"]] = _search_surname(surname)\n\n    return dfc[\"res\"]\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data.surname_origin_bert","title":"<code>surname_origin_bert(df, surname_col)</code>","text":"<p>THIS IS WORK IN PROGRESS!!! Surname classification that uses adjusted script scripts/surname_classification_with_bert.py) from: https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert to train a bert model for surname classification</p> <p>additional data from: https://github.com/greenelab/wiki-nationality-estimate/tree/master</p> <p>and possibly data from: https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataset</p> required <code>surname_col</code> <code>str</code> <p>column name with surnames</p> required Source code in <code>churn_pred/preprocessing/auxiliary_data.py</code> <pre><code>def surname_origin_bert(df: pd.DataFrame, surname_col: str) -&gt; pd.DataFrame:\n    \"\"\"\n    THIS IS WORK IN PROGRESS!!!\n    Surname classification that uses adjusted script\n    scripts/surname_classification_with_bert.py) from:\n    https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert\n    to train a bert model for surname classification\n\n    additional data from:\n    https://github.com/greenelab/wiki-nationality-estimate/tree/master\n\n    and possibly data from:\n    https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset\n\n    Args:\n        df (pd.DataFrame): input dataset\n        surname_col (str): column name with surnames\n    \"\"\"\n    dfc = df.copy()\n    return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.label_encoder.LabelEncoder","title":"<code>LabelEncoder</code>","text":"<p>             Bases: <code>object</code></p> <p>Label Encode categorical values for multiple columns at once</p> <p> NOTE: Shamlessly copied from https://github.com/jrzaurin/pytorch-widedeep</p> <p> NOTE: LabelEncoder reserves 0 for <code>unseen</code> new categories. This is convenient when defining the embedding layers, since we can just set padding idx to 0.</p> <p>Parameters:</p> Name Type Description Default <code>columns_to_encode</code> <code>list, Optional, default = None</code> <p>List of strings containing the names of the columns to encode. If <code>None</code> all columns of type <code>object</code> in the dataframe will be label encoded.</p> <code>None</code> <p>Attributes:</p> Name Type Description <code>encoding_dict</code> <code>Dict</code> <p>Dictionary containing the encoding mappings in the format, e.g. :  <code>{'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...}</code>  # noqa</p> <code>inverse_encoding_dict(Dict)</code> <code>Dict</code> <p>Dictionary containing the inverse encoding mappings in the format, e.g. :  <code>{'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...}</code>  # noqa</p> Source code in <code>churn_pred/preprocessing/label_encoder.py</code> <pre><code>class LabelEncoder(object):\n    r\"\"\"Label Encode categorical values for multiple columns at once\n\n    :information_source: **NOTE**:\n    Shamlessly copied from https://github.com/jrzaurin/pytorch-widedeep\n\n    :information_source: **NOTE**:\n    LabelEncoder reserves 0 for `unseen` new categories. This is convenient\n    when defining the embedding layers, since we can just set padding idx to 0.\n\n    Parameters:\n        columns_to_encode (list, Optional, default = None): List of strings containing\n            the names of the columns to encode. If `None` all columns of type `object`\n            in the dataframe will be label encoded.\n\n    Attributes:\n        encoding_dict (Dict): Dictionary containing the encoding mappings in the format,\n            e.g. : &lt;br/&gt; `{'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...}`  # noqa\n        inverse_encoding_dict(Dict): Dictionary containing the inverse encoding mappings\n            in the format, e.g. : &lt;br/&gt; `{'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...}`  # noqa\n    \"\"\"\n\n    def __init__(\n        self,\n        columns_to_encode: Optional[List[str]] = None,\n    ):\n        self.columns_to_encode = columns_to_encode\n\n    def fit(self, df: pd.DataFrame) -&gt; \"LabelEncoder\":\n        \"\"\"Creates encoding attributes\n\n        Returns:\n            LabelEncoder: `LabelEncoder` fitted object\n        \"\"\"\n\n        df_inp = df.copy()\n\n        if self.columns_to_encode is None:\n            self.columns_to_encode = list(\n                df_inp.select_dtypes(include=[\"object\"]).columns\n            )\n        else:\n            # sanity check to make sure all categorical columns are in an adequate\n            # format\n            for col in self.columns_to_encode:\n                df_inp[col] = df_inp[col].astype(\"O\")\n\n        unique_column_vals = dict()\n        for c in self.columns_to_encode:\n            unique_column_vals[c] = df_inp[c].unique()\n\n        self.encoding_dict = dict()\n\n        # leave 0 for padding/\"unseen\" categories\n        idx = 1\n        for k, v in unique_column_vals.items():\n            self.encoding_dict[k] = {\n                o: i + idx for i, o in enumerate(unique_column_vals[k])\n            }\n            idx = 1\n\n        self.inverse_encoding_dict = dict()\n        for c in self.encoding_dict:\n            self.inverse_encoding_dict[c] = {\n                v: k for k, v in self.encoding_dict[c].items()\n            }\n            self.inverse_encoding_dict[c][0] = \"unseen\"\n\n        return self\n\n    def transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Label Encoded the categories in `columns_to_encode`\n\n        Returns:\n            pd.DataFrame: label-encoded dataframe\n        \"\"\"\n        try:\n            self.encoding_dict\n        except AttributeError:\n            raise NotFittedError(\n                \"This LabelEncoder instance is not fitted yet. \"\n                \"Call 'fit' with appropriate arguments before using this LabelEncoder.\"\n            )\n\n        df_inp = df.copy()\n        # sanity check to make sure all categorical columns are in an adequate\n        # format\n        for col in self.columns_to_encode:  # type: ignore\n            df_inp[col] = df_inp[col].astype(\"O\")\n\n        for k, v in self.encoding_dict.items():\n            df_inp[k] = df_inp[k].apply(lambda x: v[x] if x in v.keys() else 0)\n\n        return df_inp\n\n    def fit_transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Combines `fit` and `transform`\n\n        Returns:\n            pd.DataFrame: label-encoded dataframe\n\n        Examples:\n            &gt;&gt;&gt; import pandas as pd\n            &gt;&gt;&gt; from churn_pred.preprocessing.label_encoder import LabelEncoder\n            &gt;&gt;&gt; df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']})\n            &gt;&gt;&gt; columns_to_encode = ['col2']\n            &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)\n            &gt;&gt;&gt; encoder.fit_transform(df)\n               col1  col2\n            0     1     1\n            1     2     2\n            2     3     3\n            &gt;&gt;&gt; encoder.encoding_dict\n            {'col2': {'me': 1, 'you': 2, 'him': 3}}\n        \"\"\"\n        return self.fit(df).transform(df)\n\n    def inverse_transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Returns the original categories\n\n        Returns:\n            pd.DataFrame: label-encoded dataframe\n\n        Examples:\n            &gt;&gt;&gt; import pandas as pd\n            &gt;&gt;&gt; from churn_pred.preprocessing.label_encoder import LabelEncoder\n            &gt;&gt;&gt; df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']})\n            &gt;&gt;&gt; columns_to_encode = ['col2']\n            &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)\n            &gt;&gt;&gt; df_enc = encoder.fit_transform(df)\n            &gt;&gt;&gt; encoder.inverse_transform(df_enc)\n               col1 col2\n            0     1   me\n            1     2  you\n            2     3  him\n        \"\"\"\n        for k, v in self.inverse_encoding_dict.items():\n            df[k] = df[k].apply(lambda x: v[x])\n        return df\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.label_encoder.LabelEncoder.fit","title":"<code>fit(df)</code>","text":"<p>Creates encoding attributes</p> <p>Returns:</p> Name Type Description <code>LabelEncoder</code> <code>LabelEncoder</code> <p><code>LabelEncoder</code> fitted object</p> Source code in <code>churn_pred/preprocessing/label_encoder.py</code> <pre><code>def fit(self, df: pd.DataFrame) -&gt; \"LabelEncoder\":\n    \"\"\"Creates encoding attributes\n\n    Returns:\n        LabelEncoder: `LabelEncoder` fitted object\n    \"\"\"\n\n    df_inp = df.copy()\n\n    if self.columns_to_encode is None:\n        self.columns_to_encode = list(\n            df_inp.select_dtypes(include=[\"object\"]).columns\n        )\n    else:\n        # sanity check to make sure all categorical columns are in an adequate\n        # format\n        for col in self.columns_to_encode:\n            df_inp[col] = df_inp[col].astype(\"O\")\n\n    unique_column_vals = dict()\n    for c in self.columns_to_encode:\n        unique_column_vals[c] = df_inp[c].unique()\n\n    self.encoding_dict = dict()\n\n    # leave 0 for padding/\"unseen\" categories\n    idx = 1\n    for k, v in unique_column_vals.items():\n        self.encoding_dict[k] = {\n            o: i + idx for i, o in enumerate(unique_column_vals[k])\n        }\n        idx = 1\n\n    self.inverse_encoding_dict = dict()\n    for c in self.encoding_dict:\n        self.inverse_encoding_dict[c] = {\n            v: k for k, v in self.encoding_dict[c].items()\n        }\n        self.inverse_encoding_dict[c][0] = \"unseen\"\n\n    return self\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.label_encoder.LabelEncoder.fit_transform","title":"<code>fit_transform(df)</code>","text":"<p>Combines <code>fit</code> and <code>transform</code></p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: label-encoded dataframe</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; import pandas as pd\n&gt;&gt;&gt; from churn_pred.preprocessing.label_encoder import LabelEncoder\n&gt;&gt;&gt; df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']})\n&gt;&gt;&gt; columns_to_encode = ['col2']\n&gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)\n&gt;&gt;&gt; encoder.fit_transform(df)\n   col1  col2\n0     1     1\n1     2     2\n2     3     3\n&gt;&gt;&gt; encoder.encoding_dict\n{'col2': {'me': 1, 'you': 2, 'him': 3}}\n</code></pre> Source code in <code>churn_pred/preprocessing/label_encoder.py</code> <pre><code>def fit_transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Combines `fit` and `transform`\n\n    Returns:\n        pd.DataFrame: label-encoded dataframe\n\n    Examples:\n        &gt;&gt;&gt; import pandas as pd\n        &gt;&gt;&gt; from churn_pred.preprocessing.label_encoder import LabelEncoder\n        &gt;&gt;&gt; df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']})\n        &gt;&gt;&gt; columns_to_encode = ['col2']\n        &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)\n        &gt;&gt;&gt; encoder.fit_transform(df)\n           col1  col2\n        0     1     1\n        1     2     2\n        2     3     3\n        &gt;&gt;&gt; encoder.encoding_dict\n        {'col2': {'me': 1, 'you': 2, 'him': 3}}\n    \"\"\"\n    return self.fit(df).transform(df)\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.label_encoder.LabelEncoder.inverse_transform","title":"<code>inverse_transform(df)</code>","text":"<p>Returns the original categories</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: label-encoded dataframe</p> <p>Examples:</p> <pre><code>&gt;&gt;&gt; import pandas as pd\n&gt;&gt;&gt; from churn_pred.preprocessing.label_encoder import LabelEncoder\n&gt;&gt;&gt; df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']})\n&gt;&gt;&gt; columns_to_encode = ['col2']\n&gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)\n&gt;&gt;&gt; df_enc = encoder.fit_transform(df)\n&gt;&gt;&gt; encoder.inverse_transform(df_enc)\n   col1 col2\n0     1   me\n1     2  you\n2     3  him\n</code></pre> Source code in <code>churn_pred/preprocessing/label_encoder.py</code> <pre><code>def inverse_transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Returns the original categories\n\n    Returns:\n        pd.DataFrame: label-encoded dataframe\n\n    Examples:\n        &gt;&gt;&gt; import pandas as pd\n        &gt;&gt;&gt; from churn_pred.preprocessing.label_encoder import LabelEncoder\n        &gt;&gt;&gt; df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']})\n        &gt;&gt;&gt; columns_to_encode = ['col2']\n        &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)\n        &gt;&gt;&gt; df_enc = encoder.fit_transform(df)\n        &gt;&gt;&gt; encoder.inverse_transform(df_enc)\n           col1 col2\n        0     1   me\n        1     2  you\n        2     3  him\n    \"\"\"\n    for k, v in self.inverse_encoding_dict.items():\n        df[k] = df[k].apply(lambda x: v[x])\n    return df\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.label_encoder.LabelEncoder.transform","title":"<code>transform(df)</code>","text":"<p>Label Encoded the categories in <code>columns_to_encode</code></p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: label-encoded dataframe</p> Source code in <code>churn_pred/preprocessing/label_encoder.py</code> <pre><code>def transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Label Encoded the categories in `columns_to_encode`\n\n    Returns:\n        pd.DataFrame: label-encoded dataframe\n    \"\"\"\n    try:\n        self.encoding_dict\n    except AttributeError:\n        raise NotFittedError(\n            \"This LabelEncoder instance is not fitted yet. \"\n            \"Call 'fit' with appropriate arguments before using this LabelEncoder.\"\n        )\n\n    df_inp = df.copy()\n    # sanity check to make sure all categorical columns are in an adequate\n    # format\n    for col in self.columns_to_encode:  # type: ignore\n        df_inp[col] = df_inp[col].astype(\"O\")\n\n    for k, v in self.encoding_dict.items():\n        df_inp[k] = df_inp[k].apply(lambda x: v[x] if x in v.keys() else 0)\n\n    return df_inp\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.drop_constant_cols","title":"<code>drop_constant_cols(df, verbose=False)</code>","text":"<p>Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows.</p> Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def drop_constant_cols(df: pd.DataFrame, verbose: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Returns dataframe without constant columns, i.e. those with just 1 unique\n    value for all rows.\"\"\"\n    nunique_per_col = df.apply(lambda x: x.nunique(), axis=0)\n    const_cols = nunique_per_col[nunique_per_col == 1].index.values\n    df = df.drop(const_cols, axis=1)\n    if verbose:\n        print(\n            \"\"\"\n            Dropped {} constant columns.\n            Affected columns: {}\n            \"\"\".format(\n                len(const_cols), const_cols\n            )\n        )\n    return df\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.drop_high_nan_cols","title":"<code>drop_high_nan_cols(df, threshold=0.8, verbose=False)</code>","text":"<p>Returns dataframe without columns that have ratio of missingness above threshold.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataframe</p> required <code>threshold</code> <code>float = 0.8</code> <p>ratio of missingness applied per column</p> <code>0.8</code> <code>verbose</code> <code>bool</code> <p>whether the output should be verbose</p> <code>False</code> Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def drop_high_nan_cols(\n    df: pd.DataFrame, threshold: float = 0.8, verbose: bool = False\n) -&gt; pd.DataFrame:\n    \"\"\"Returns dataframe without columns that have ratio of missingness above threshold.\n\n    Args:\n        df (pd.DataFrame): input dataframe\n        threshold (float = 0.8): ratio of missingness applied per column\n        verbose (bool): whether the output should be verbose\n    \"\"\"\n    n_rows = df.shape[0]\n    nan_fraction_per_col = df.apply(lambda x: x.isna().sum() / n_rows, axis=0)\n    high_nan_percentage_cols = nan_fraction_per_col[\n        nan_fraction_per_col &gt; threshold\n    ].index.values\n    df = df.drop(high_nan_percentage_cols, axis=1)\n    if verbose:\n        print(\n            \"\"\"\n            Dropped {} columns with fraction of NaN above threshold = {}.\n            Affected columns: {}\n            \"\"\".format(\n                len(high_nan_percentage_cols), threshold, high_nan_percentage_cols\n            )\n        )\n    return df\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.drop_high_uq_cat_cols","title":"<code>drop_high_uq_cat_cols(df, cat_cols, uq_val_count, verbose=False)</code>","text":"<p>Returns dataframe without categorical columns that have too many unique values</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataframe</p> required <code>cat_cols</code> <code>list</code> <p>list of categorical columns</p> required <code>uq_val_count</code> <code>int</code> <p>unique value count</p> required <code>verbose</code> <code>bool</code> <p>whether the output should be verbose</p> <code>False</code> Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def drop_high_uq_cat_cols(\n    df: pd.DataFrame, cat_cols: list, uq_val_count: int, verbose: bool = False\n) -&gt; pd.DataFrame:\n    \"\"\"Returns dataframe without categorical columns that have too many unique values\n\n    Args:\n        df (pd.DataFrame): input dataframe\n        cat_cols (list): list of categorical columns\n        uq_val_count (int): unique value count\n        verbose (bool): whether the output should be verbose\n    \"\"\"\n    nunique_per_col = df[cat_cols].apply(lambda x: x.nunique(), axis=0)\n    high_uq_value_cat_cols = nunique_per_col[\n        nunique_per_col &gt; uq_val_count\n    ].index.values\n    df = df.drop(high_uq_value_cat_cols, axis=1)\n    if verbose:\n        print(\n            \"\"\"\n            Dropped {} high unique value columns.\n            Affected columns: {}\n            \"\"\".format(\n                len(high_uq_value_cat_cols), high_uq_value_cat_cols\n            )\n        )\n    return df\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.drop_highly_correlated_columns","title":"<code>drop_highly_correlated_columns(df, cont_cols, crosscorr_val=0.95, verbose=False)</code>","text":"<p>Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataframe</p> required <code>cont_cols</code> <code>list</code> <p>list of columns to evaluate correlation for</p> required <code>crosscorr_val</code> <code>float = 0.95</code> <p>threshold value of correlation</p> <code>0.95</code> <code>verbose</code> <code>bool</code> <p>whether the output should be verbose</p> <code>False</code> Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def drop_highly_correlated_columns(\n    df: pd.DataFrame,\n    cont_cols: list,\n    crosscorr_val: float = 0.95,\n    verbose: bool = False,\n) -&gt; pd.DataFrame:\n    \"\"\"Returns dataframe without highly correlated columns, cross correlation is\n    evaluated with crosscorr_val.\n\n    Args:\n        df (pd.DataFrame): input dataframe\n        cont_cols (list): list of columns to evaluate correlation for\n        crosscorr_val (float = 0.95): threshold value of correlation\n        verbose (bool): whether the output should be verbose\n    \"\"\"\n    corr_matrix = df[cont_cols].corr().abs()\n    upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(\"bool\"))\n    upper_above = upper.apply(lambda x: any(x &gt; crosscorr_val), axis=1)\n    to_drop = upper_above[upper_above].index.values\n    df = df.drop(to_drop, axis=1)\n    if verbose:\n        print(\n            \"\"\"\n            Dropped {} highly correlated columns.\n            Affected columns: {}\n            \"\"\".format(\n                len(to_drop), to_drop\n            )\n        )\n    return df\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.most_frequent_in_list_col","title":"<code>most_frequent_in_list_col(dfs)</code>","text":"<p>Returns pd.Series with the most frequent values in the lists.</p> <p>Parameters:</p> Name Type Description Default <code>dfs</code> <code>Series</code> <p>input pandas series containing string list values</p> required Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def most_frequent_in_list_col(dfs: pd.Series):\n    \"\"\"Returns pd.Series with the most frequent values in the lists.\n\n    Args:\n        dfs (pd.Series): input pandas series containing string list values\n    \"\"\"\n    dfsc = dfs.copy()\n    dfsc.apply(\n        lambda string: (\n            _most_frequent(list(string.replace(\"[\", \"\").replace(\"]\", \"\").split(\", \")))\n            if isinstance(string, str)\n            else string\n        )\n    )\n    return dfsc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.nan_with_number_imputer","title":"<code>nan_with_number_imputer(df, columns, fill_number=-1.0, verbose=False)</code>","text":"<p>Fills NAs with surrogate float, -1 is default value used, it can be customized.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataframe</p> required <code>columns</code> <code>List[str]</code> <p>list of columns that will be filled</p> required <code>fill_number</code> <code>float = -1</code> <p>number used to replace NAs. Defaults to</p> <code>-1.0</code> <code>verbose</code> <code>bool</code> <p>whether the output should be verbose</p> <code>False</code> Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def nan_with_number_imputer(\n    df: pd.DataFrame,\n    columns: List[str],\n    fill_number: float = -1.0,\n    verbose: bool = False,\n) -&gt; pd.DataFrame:\n    \"\"\"Fills NAs with surrogate float, -1 is default value used, it can be customized.\n\n    Args:\n        df (pd.DataFrame): input dataframe\n        columns (List[str]): list of columns that will be filled\n        fill_number (float = -1): number used to replace NAs. Defaults to\n        verbose (bool): whether the output should be verbose\n    \"\"\"\n    dfc = df.copy()\n    if verbose:\n        sum_nan_vals = df[columns].isna().sum()\n        nans_cols = []\n        for c in columns:\n            nans_cols.append((c, df[c].isna().sum()))\n    dfc = df.copy()\n    dfc[columns] = dfc[columns].apply(lambda x: x.astype(float).fillna(fill_number))\n    if verbose:\n        print(\n            \"\"\"\n            Imputed {} NaN values with {}.\n            Affected columns (col, num_NaNs): {}\n            \"\"\".format(\n                sum_nan_vals, fill_number, nans_cols\n            )\n        )\n    return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.nan_with_unknown_imputer","title":"<code>nan_with_unknown_imputer(df, columns, fill_token='unknown', verbose=False)</code>","text":"<p>Fills NAs with surrogate string, 'unknown' is default value used, it can be customized.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataframe</p> required <code>columns</code> <code>List[str]</code> <p>ist of columns that will be filled</p> required <code>fill_token</code> <code>str = \"unknown\"</code> <p>string used to replace NAs</p> <code>'unknown'</code> <code>verbose</code> <code>bool</code> <p>whether the output should be verbose</p> <code>False</code> Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def nan_with_unknown_imputer(\n    df: pd.DataFrame,\n    columns: List[str],\n    fill_token: str = \"unknown\",\n    verbose: bool = False,\n) -&gt; pd.DataFrame:\n    \"\"\"Fills NAs with surrogate string, 'unknown' is default value used, it can be customized.\n\n    Args:\n        df (pd.DataFrame): input dataframe\n        columns (List[str]): ist of columns that will be filled\n        fill_token (str = \"unknown\"): string used to replace NAs\n        verbose (bool): whether the output should be verbose\n    \"\"\"\n    df = df.copy()\n    if verbose:\n        sum_nan_vals = df[columns].isna().sum()\n        nans_cols = []\n        for c in columns:\n            nans_cols.append((c, df[c].isna().sum()))\n    for c in columns:\n        df[c] = df[c].astype(object).fillna(fill_token)\n\n    dfc = df.copy()\n    dfc[columns] = dfc[columns].apply(lambda x: x.astype(object).fillna(fill_token))\n    if verbose:\n        print(\n            \"\"\"\n            Imputed {} NaN values with {}.\n            Affected columns (col, num_NaNs): {}\n            \"\"\".format(\n                sum_nan_vals, fill_token, nans_cols\n            )\n        )\n    return df\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.nuq_in_list_col","title":"<code>nuq_in_list_col(dfs)</code>","text":"<p>Returns pd.Series with the number of unique values in the lists.</p> <p>Parameters:</p> Name Type Description Default <code>dfs</code> <code>Series</code> <p>input pandas series containing string list values</p> required Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def nuq_in_list_col(dfs: pd.Series):\n    \"\"\"Returns pd.Series with the number of unique values in the lists.\n\n    Args:\n        dfs (pd.Series): input pandas series containing string list values\n    \"\"\"\n    dfsc = dfs.copy()\n    dfsc.apply(\n        lambda string: (\n            _nunique(list(string.replace(\"[\", \"\").replace(\"]\", \"\").split(\", \")))\n            if isinstance(string, str)\n            else string\n        )\n    )\n    return dfsc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data.replace_rare_categories_with_str_other","title":"<code>replace_rare_categories_with_str_other(df, categorical_cols, quantile=0.05, surrogate_value='other', verbose=False)</code>","text":"<p>Replaces rare category value with surrogate string.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataframe</p> required <code>categorical_cols</code> <code>List[str]</code> <p>list of columns in dataframe to process.</p> required <code>quantile</code> <code>float = 0.05</code> <p>determines what values are considered as rare</p> <code>0.05</code> <code>surrogate_value</code> <code>str = \"other\"</code> <p>string used to replace rare values</p> <code>'other'</code> <p>Returns:</p> Type Description <code>Tuple[DataFrame, Dict]</code> <p>Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values.</p> Source code in <code>churn_pred/preprocessing/preprocess_data.py</code> <pre><code>def replace_rare_categories_with_str_other(\n    df: pd.DataFrame,\n    categorical_cols: List[str],\n    quantile: float = 0.05,\n    surrogate_value: str = \"other\",\n    verbose: bool = False,\n) -&gt; Tuple[pd.DataFrame, Dict]:\n    \"\"\"Replaces rare category value with surrogate string.\n\n    Args:\n        df (pd.DataFrame): input dataframe\n        categorical_cols (List[str]): list of columns in dataframe to process.\n        quantile (float = 0.05): determines what values are considered as rare\n        surrogate_value (str = \"other\"): string used to replace rare values\n\n    Returns:\n        Tuple[pd.DataFrame, Dict]:\n            New dataframe and a dict. with mapping between orig. and surrogate values.\n    \"\"\"\n    if surrogate_value in df[categorical_cols].values:\n        raise ValueError(\n            \"Surrogate string - \"\n            + surrogate_value\n            + \" - is already present, choose another one.\"\n        )\n\n    dfc = df.copy()\n    replace_dict_per_col = (\n        df[categorical_cols]\n        .apply(_replace_rare_dict, args=[surrogate_value, quantile])\n        .to_dict()\n    )\n    dfc = dfc.replace(replace_dict_per_col)\n    if verbose:\n        sum_rare_cats = sum(\n            [len(replace_dict_per_col[col]) for col in replace_dict_per_col]\n        )\n        rare_cats_per_col = [\n            (col, list(replace_dict_per_col[col].keys()))\n            for col in replace_dict_per_col\n        ]\n        print(\n            \"\"\"\n            Replaced {} rare categories (val_count &lt; {}) with {}.\n            Affected columns (List[(col, rare_cats)]): {}\n            \"\"\".format(\n                sum_rare_cats,\n                quantile,\n                surrogate_value,\n                rare_cats_per_col,\n            )\n        )\n    return dfc, replace_dict_per_col\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_text.language_detection","title":"<code>language_detection(df, text_col, model_type='fasttext')</code>","text":"Source code in <code>churn_pred/preprocessing/preprocess_text.py</code> <pre><code>def language_detection(\n    df: pd.DataFrame,\n    text_col: str,\n    model_type: Literal[\"roberta\", \"fasttext\"] = \"fasttext\",\n) -&gt; pd.DataFrame:\n    \"\"\"\n    https://huggingface.co/papluca/xlm-roberta-base-language-detection\n    https://spacy.io/universe/project/spacy_fastlang\n    \"\"\"\n    dfc = df.copy()\n\n    if model_type == \"fasttext\":\n\n        def _get_language(token: Doc) -&gt; str:\n            return token._.language\n\n        def _get_language_score(token: Doc) -&gt; str:\n            return token._.language_score\n\n        nlp = spacy.load(\"en_core_web_sm\")\n        nlp.add_pipe(\"language_detector\")\n        res = df[text_col].astype(str).apply(nlp)\n        dfc[text_col + \"_language\"] = res.apply(_get_language)\n        dfc[text_col + \"_language_score\"] = res.apply(_get_language_score)\n    elif model_type == \"roberta\":\n\n        def _get_language(token: Doc) -&gt; str:\n            return token._.language\n\n        def _get_language_score(token: Doc) -&gt; str:\n            return token._.language_score\n\n        pipe = pipeline(\n            \"text-classification\", model=\"papluca/xlm-roberta-base-language-detection\"\n        )\n        res = pipe(dfc[\"CustomerFeedback\"].astype(str).to_list())\n        dfc[text_col + \"_language\"] = pipe(dfc[text_col].astype(str))\n    else:\n        raise NotImplementedError\n    return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_text.sentiment_analysis","title":"<code>sentiment_analysis(df, text_col, sentiment_depth=3)</code>","text":"<p>Returns dataframe with new column that analysis sentintent in the text_col.</p> <p>initial idea: Inspired by https://www.nature.com/articles/s41598-024-60210-7 I also used the 3 most popular models with voting: 1. cardiffnlp/twitter-roberta-base-sentiment-latest 2. nlptown/bert-base-multilingual-uncased-sentiment 3. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis 4. lxyuan/distilbert-base-multilingual-cased-sentiments-student 5. finiteautomata/bertweet-base-sentiment-analysis</p> <p>issues: 1. 3(pos, neutral, neg) vs 5(1-5 stars) sentiments; models 1,3 vs 2 2. maximum sequence length models 4-5</p> <p>final idea: Either 3 sentimnets(model 1) or 5 stars (model 2)</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataset</p> required <code>text_col</code> <code>str</code> <p>column name with text</p> required <code>sentiment_depth</code> <code>[3, 5]</code> <p>depth of sentiment analysis</p> <code>3</code> Source code in <code>churn_pred/preprocessing/preprocess_text.py</code> <pre><code>def sentiment_analysis(\n    df: pd.DataFrame, text_col: str, sentiment_depth: Literal[3, 5] = 3\n) -&gt; pd.DataFrame:\n    \"\"\"\n    Returns dataframe with new column that analysis sentintent in the text_col.\n\n    initial idea:\n    Inspired by https://www.nature.com/articles/s41598-024-60210-7 I also used\n    the 3 most popular models with voting:\n    1. cardiffnlp/twitter-roberta-base-sentiment-latest\n    2. nlptown/bert-base-multilingual-uncased-sentiment\n    3. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis\n    4. lxyuan/distilbert-base-multilingual-cased-sentiments-student\n    5. finiteautomata/bertweet-base-sentiment-analysis\n\n    issues:\n    1. 3(pos, neutral, neg) vs 5(1-5 stars) sentiments; models 1,3 vs 2\n    2. maximum sequence length models 4-5\n\n    final idea:\n    Either 3 sentimnets(model 1) or 5 stars (model 2)\n\n    Args:\n        df (pd.DataFrame): input dataset\n        text_col (str): column name with text\n        sentiment_depth ([3, 5]): depth of sentiment analysis\n    \"\"\"\n    dfc = df.copy()\n    if sentiment_depth == 3:\n        pipe = pipeline(\n            \"text-classification\",\n            model=\"cardiffnlp/twitter-roberta-base-sentiment-latest\",\n            device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n        )\n    elif sentiment_depth == 5:\n        pipe = pipeline(\n            \"text-classification\",\n            model=\"nlptown/bert-base-multilingual-uncased-sentiment\",\n            device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n        )\n    else:\n        raise NotImplementedError\n    res = pipe(dfc[text_col].astype(str).to_list())\n    res = pd.DataFrame(res).rename(\n        columns={\n            \"label\": text_col + \"_sentiment\",\n            \"score\": text_col + \"_sentiment_score\",\n        }\n    )\n    return pd.concat([dfc, res], axis=1)\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_text.text_cleaning","title":"<code>text_cleaning(df, text_col)</code>","text":"<p>Returns dataframe with preprocessed/cleaned text column. Spacy-cleaner that uses spacy functionalities. https://spacy.io/universe/project/spacy-cleaner</p> Note <p>The spacy-cleaner library does not do much. Future task - develop own cleaner</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>input dataset</p> required <code>text_col</code> <code>str</code> <p>column name with text</p> required Source code in <code>churn_pred/preprocessing/preprocess_text.py</code> <pre><code>def text_cleaning(df: pd.DataFrame, text_col: str) -&gt; pd.DataFrame:\n    \"\"\"\n    Returns dataframe with preprocessed/cleaned text column.\n    Spacy-cleaner that uses spacy functionalities.\n    https://spacy.io/universe/project/spacy-cleaner\n\n    Note:\n        The spacy-cleaner library does not do much.\n        Future task - develop own cleaner\n\n    Args:\n        df (pd.DataFrame): input dataset\n        text_col: column name with text\n    \"\"\"\n    dfc = df.copy()\n    model = spacy.load(\"en_core_web_sm\")\n    cleaner = spacy_cleaner.Cleaner(\n        model,\n        removers.remove_stopword_token,\n        replacers.replace_punctuation_token,\n        mutators.mutate_lemma_token,\n    )\n    dfc[text_col] = cleaner.clean(dfc[text_col].astype(str))\n\n    return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess.PreprocessData","title":"<code>PreprocessData</code>","text":"<p>Object to preprocess the dataset. Args:     target_col (str): target column name     id_cols (List[str]): id columns     cat_cols (Optional[List[str]]): list of categorical column names     cont_cols (Optional[List[str]]): list of continuous column names</p> Source code in <code>churn_pred/preprocessing/preprocess.py</code> <pre><code>class PreprocessData:\n    \"\"\"Object to preprocess the dataset.\n    Args:\n        target_col (str): target column name\n        id_cols (List[str]): id columns\n        cat_cols (Optional[List[str]]): list of categorical column names\n        cont_cols (Optional[List[str]]): list of continuous column names\n    \"\"\"\n\n    def __init__(\n        self,\n        target_col: str,\n        id_cols: str,\n        cat_cols: Optional[List[str]] = None,\n        cont_cols: Optional[List[str]] = None,\n    ):\n        self.target_col = target_col\n        self.id_cols = id_cols\n        self.cat_cols = cat_cols\n        self.cont_cols = cont_cols\n\n        self.is_fitted = False\n\n    def fit_transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Fit peprocessor and transform dataset in training step.\"\"\"\n\n        dfc = df.drop(columns=self.id_cols).copy()\n\n        dfc = dfc.pipe(drop_constant_cols)\n\n        self.cat_cols = intsec(list(dfc), self.cat_cols)\n\n        if self.cat_cols is None:\n            self.cat_cols = self._infere_cat_cols(dfc)\n            dfc_cat_cols = drop_high_nan_cols(dfc[self.cat_cols])\n            dfc = dfc.drop(columns=self.cat_cols)\n            dfc = pd.concat([dfc, dfc_cat_cols], axis=1)\n            self.cat_cols = dfc_cat_cols.columns.values.tolist()\n\n        if self.cont_cols is None:\n            self.init_cont_cols = self._infere_cont_cols(dfc, self.cat_cols)\n            dfc_init_cont_cols = drop_high_nan_cols(dfc[self.init_cont_cols])\n            dfc = dfc.drop(columns=self.init_cont_cols)\n            dfc = pd.concat([dfc, dfc_init_cont_cols], axis=1)\n            self.init_cont_cols = dfc_init_cont_cols.columns.values.tolist()\n\n            dfc = nan_with_number_imputer(dfc, self.init_cont_cols, -9999.0)\n\n            (\n                dfc,\n                self.final_cont_cols,\n            ) = self._drop_highly_corr_cols_and_get_final_cont_cols(\n                dfc, self.init_cont_cols\n            )\n\n        else:\n            self.final_cont_cols = list(\n                set(self.cont_cols).intersection(set(dfc.columns.values))\n            )\n\n            dfc = nan_with_number_imputer(dfc, self.final_cont_cols, -9999.0)\n\n        dfc = nan_with_unknown_imputer(dfc, self.cat_cols)\n\n        dfc = dfc[self.cat_cols + self.final_cont_cols + [self.target_col]]\n\n        self.label_encoder = LabelEncoder(self.cat_cols)\n        dfc_le = self.label_encoder.fit_transform(dfc)\n\n        dfc_le = self._change_int_float_types(dfc_le)\n\n        dfc_le[self.id_cols] = df[self.id_cols]\n\n        self.is_fitted = True\n\n        return dfc_le\n\n    def transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Transform dataset in inference step.\"\"\"\n\n        if not self.is_fitted:\n            raise NotFittedError(\n                \"\"\"This instance of 'PreprocessData' has not been fitted yet.\n                Please, run 'fit' first\"\"\"\n            )\n\n        dfc = df.drop(columns=self.id_cols).copy()\n\n        try:\n            dfc = dfc[self.cat_cols + self.final_cont_cols + [self.target_col]]\n        except KeyError:\n            dfc = dfc[self.cat_cols + self.final_cont_cols]\n\n        # dfc = dfc.replace(self.replace_rare_categories_dict)\n\n        dfc = nan_with_unknown_imputer(dfc, self.cat_cols)\n\n        dfc = nan_with_number_imputer(\n            dfc,\n            list(set(self.final_cont_cols)),\n            -9999.0,  # noqa\n        )\n\n        dfc_le = self.label_encoder.transform(dfc)\n\n        dfc_le = self._change_int_float_types(dfc_le)\n\n        dfc_le[self.id_cols] = df[self.id_cols]\n\n        return dfc_le\n\n    def fit(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Just to keep familiar naming convention with sklearn.\"\"\"\n        return self.fit_transform(df)\n\n    def _drop_highly_corr_cols_and_get_final_cont_cols(\n        self, df: pd.DataFrame, cont_cols: List[str]\n    ) -&gt; Tuple[pd.DataFrame, List[str]]:\n        \"\"\"Drop highly correlated columns to decrease the size of the dataset\n        by dropping redundant information.\"\"\"\n\n        df = drop_highly_correlated_columns(df, cont_cols)\n\n        final_cont_cols = [\n            c for c in df.columns if c not in self.cat_cols + [self.target_col]\n        ]\n\n        return df, final_cont_cols\n\n    def _infere_cat_cols(self, df: pd.DataFrame):\n        \"\"\"Guess the categorical columns by excluding clearly continuous\n        columns - int, float\"\"\"\n\n        cat_cols = []\n        for col in df.columns:\n            if (\n                df[col].dtype not in [\"int32\", \"int64\"]\n                and df[col].dtype not in [\"float32\", \"float64\"]\n                and col not in [self.target_col]\n            ):\n                cat_cols.append(col)\n        return cat_cols\n\n    def _infere_cont_cols(\n        self, df: pd.DataFrame, cat_cols: Optional[List[str]]\n    ):  # noqa\n        \"\"\"Guess the continuous columns by excluding clearly categorical,\n        and target_col and including only int or float.\"\"\"\n\n        if cat_cols is not None:\n            cont_cols = [c for c in df.columns if c not in cat_cols + [self.target_col]]\n        else:\n            cont_cols = []\n            for col in df.columns:\n                if (df[col].dtype == \"int\" or df[col].dtype == \"float\") and col not in [\n                    self.target_col\n                ]:\n                    cont_cols.append(col)\n\n        return cont_cols\n\n    def _change_int_float_types(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Change int to float as int causes data type issues in some ml\n        methods, eg. lightgbm.\"\"\"\n        dfc = df.copy()\n        dfc = dfc.astype(\n            dict.fromkeys(dfc.select_dtypes(np.int64).columns, np.int32)\n        )  # noqa\n        dfc = dfc.astype(\n            dict.fromkeys(dfc.select_dtypes(np.float64).columns, np.float32)\n        )\n\n        return dfc\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess.PreprocessData.fit","title":"<code>fit(df)</code>","text":"<p>Just to keep familiar naming convention with sklearn.</p> Source code in <code>churn_pred/preprocessing/preprocess.py</code> <pre><code>def fit(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Just to keep familiar naming convention with sklearn.\"\"\"\n    return self.fit_transform(df)\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess.PreprocessData.fit_transform","title":"<code>fit_transform(df)</code>","text":"<p>Fit peprocessor and transform dataset in training step.</p> Source code in <code>churn_pred/preprocessing/preprocess.py</code> <pre><code>def fit_transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Fit peprocessor and transform dataset in training step.\"\"\"\n\n    dfc = df.drop(columns=self.id_cols).copy()\n\n    dfc = dfc.pipe(drop_constant_cols)\n\n    self.cat_cols = intsec(list(dfc), self.cat_cols)\n\n    if self.cat_cols is None:\n        self.cat_cols = self._infere_cat_cols(dfc)\n        dfc_cat_cols = drop_high_nan_cols(dfc[self.cat_cols])\n        dfc = dfc.drop(columns=self.cat_cols)\n        dfc = pd.concat([dfc, dfc_cat_cols], axis=1)\n        self.cat_cols = dfc_cat_cols.columns.values.tolist()\n\n    if self.cont_cols is None:\n        self.init_cont_cols = self._infere_cont_cols(dfc, self.cat_cols)\n        dfc_init_cont_cols = drop_high_nan_cols(dfc[self.init_cont_cols])\n        dfc = dfc.drop(columns=self.init_cont_cols)\n        dfc = pd.concat([dfc, dfc_init_cont_cols], axis=1)\n        self.init_cont_cols = dfc_init_cont_cols.columns.values.tolist()\n\n        dfc = nan_with_number_imputer(dfc, self.init_cont_cols, -9999.0)\n\n        (\n            dfc,\n            self.final_cont_cols,\n        ) = self._drop_highly_corr_cols_and_get_final_cont_cols(\n            dfc, self.init_cont_cols\n        )\n\n    else:\n        self.final_cont_cols = list(\n            set(self.cont_cols).intersection(set(dfc.columns.values))\n        )\n\n        dfc = nan_with_number_imputer(dfc, self.final_cont_cols, -9999.0)\n\n    dfc = nan_with_unknown_imputer(dfc, self.cat_cols)\n\n    dfc = dfc[self.cat_cols + self.final_cont_cols + [self.target_col]]\n\n    self.label_encoder = LabelEncoder(self.cat_cols)\n    dfc_le = self.label_encoder.fit_transform(dfc)\n\n    dfc_le = self._change_int_float_types(dfc_le)\n\n    dfc_le[self.id_cols] = df[self.id_cols]\n\n    self.is_fitted = True\n\n    return dfc_le\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess.PreprocessData.transform","title":"<code>transform(df)</code>","text":"<p>Transform dataset in inference step.</p> Source code in <code>churn_pred/preprocessing/preprocess.py</code> <pre><code>def transform(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Transform dataset in inference step.\"\"\"\n\n    if not self.is_fitted:\n        raise NotFittedError(\n            \"\"\"This instance of 'PreprocessData' has not been fitted yet.\n            Please, run 'fit' first\"\"\"\n        )\n\n    dfc = df.drop(columns=self.id_cols).copy()\n\n    try:\n        dfc = dfc[self.cat_cols + self.final_cont_cols + [self.target_col]]\n    except KeyError:\n        dfc = dfc[self.cat_cols + self.final_cont_cols]\n\n    # dfc = dfc.replace(self.replace_rare_categories_dict)\n\n    dfc = nan_with_unknown_imputer(dfc, self.cat_cols)\n\n    dfc = nan_with_number_imputer(\n        dfc,\n        list(set(self.final_cont_cols)),\n        -9999.0,  # noqa\n    )\n\n    dfc_le = self.label_encoder.transform(dfc)\n\n    dfc_le = self._change_int_float_types(dfc_le)\n\n    dfc_le[self.id_cols] = df[self.id_cols]\n\n    return dfc_le\n</code></pre>"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.scaler.scaler_mapper","title":"<code>scaler_mapper(cont_cols, cat_cols, id_cols, scaler_mapper_def=None)</code>","text":"<p>Function that maps scaler functions to appropriate columns.</p> <p>By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset.</p> <p>Parameters:</p> Name Type Description Default <code>cont_cols</code> <code>list</code> <p>list of continuous feature columns in the dataset</p> required <code>cat_cols</code> <code>list</code> <p>list of categorical feature columns in the dataset</p> required <code>id_cols</code> <code>list</code> <p>identifier columns</p> required <code>scaler_mapper_def</code> <code>dict</code> <p>optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library</p> <code>None</code> Source code in <code>churn_pred/preprocessing/scaler.py</code> <pre><code>def scaler_mapper(\n    cont_cols: List[str],\n    cat_cols: List[str],\n    id_cols: List[str],\n    scaler_mapper_def: Optional[dict] = None,\n) -&gt; DataFrameMapper:\n    \"\"\"Function that maps scaler functions to appropriate columns.\n\n    By default does not assign any scaler to continuous, categorical or\n    identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers.\n    Only columns defined in mapper object will be present in the transformed dataset.\n\n    Args:\n        cont_cols (list): list of continuous feature columns in the dataset\n        cat_cols (list): list of categorical feature columns in the dataset\n        id_cols (list): identifier columns\n        scaler_mapper_def (dict): optional dictionary that contains keys\n            ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding\n            scalers (defined by names, not instantiated) from sklearn library\n    Returns:\n        scaler (DataFrameMapper): scaler object mapping sklearn scalers to columns in\n            pandas dataframe\n    \"\"\"\n    if scaler_mapper_def:\n        cont_cols_def = gen_features(\n            columns=list(map(lambda x: [x], cont_cols)),\n            classes=[scaler_mapper_def[\"cont_cols\"]],\n        )\n\n        cat_cols_def = gen_features(\n            columns=list(map(lambda x: [x], cat_cols)),\n            classes=[scaler_mapper_def[\"cat_cols\"]],\n        )\n\n        id_cols_def = gen_features(\n            columns=list(map(lambda x: [x], id_cols)),\n            classes=[scaler_mapper_def[\"id_cols\"]],\n        )\n\n    else:\n        cont_cols_def = gen_features(\n            columns=list(map(lambda x: [x], cont_cols)), classes=[None]\n        )\n\n        cat_cols_def = gen_features(\n            columns=list(map(lambda x: [x], cat_cols)), classes=[None]\n        )\n\n        id_cols_def = gen_features(\n            columns=list(map(lambda x: [x], id_cols)), classes=[None]\n        )\n\n    scaler = DataFrameMapper(cont_cols_def + cat_cols_def + id_cols_def, df_out=True)\n    return scaler\n</code></pre>"},{"location":"churn_pred/training.html","title":"Training","text":""},{"location":"churn_pred/training.html#churn_pred.training.metrics.aiqc","title":"<code>aiqc(actual, high_quantile, low_quantile)</code>","text":"<p>Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset.</p> <p>Parameters:</p> Name Type Description Default <code>actual</code> <code>ndarray</code> <p>actual values</p> required <code>high_quantile</code> <code>ndarray</code> <p>high quantile predicted values</p> required <code>low_quantile</code> <code>ndarray</code> <p>low quantile predicted values</p> required Source code in <code>churn_pred/training/metrics.py</code> <pre><code>def aiqc(actual: np.ndarray, high_quantile: np.ndarray, low_quantile: np.ndarray):\n    \"\"\"Average InterQuantile Coverage for Quantile Regression.\n    Check if the interquantile coverage is close to the coverage of in the test dataset.\n\n    Args:\n        actual (np.ndarray): actual values\n        high_quantile (np.ndarray): high quantile predicted values\n        low_quantile (np.ndarray): low quantile predicted values\n    Returns:\n        aiqc (float): average interquantile coverage\n    \"\"\"\n    return np.sum((actual &lt; high_quantile) &amp; (actual &gt; low_quantile)) / len(actual)\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.metrics.nacil","title":"<code>nacil(actual, high_quantile_predicted, low_quantile_predicted)</code>","text":"<p>Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value.</p> <p>Parameters:</p> Name Type Description Default <code>actual</code> <code>ndarray</code> <p>actual values</p> required <code>high_quantile_predicted</code> <code>ndarray</code> <p>high quantile predicted values</p> required <code>low_quantile_predicted</code> <code>ndarray</code> <p>low quantile predicted values</p> required Source code in <code>churn_pred/training/metrics.py</code> <pre><code>def nacil(\n    actual: np.ndarray,\n    high_quantile_predicted: np.ndarray,\n    low_quantile_predicted: np.ndarray,\n):\n    \"\"\"Normalized Average Confidence Interval Length for Quantile Regression.\n    The value is normalized to actual(expected) value.\n\n    Args:\n        actual (np.ndarray): actual values\n        high_quantile_predicted (np.ndarray): high quantile predicted values\n        low_quantile_predicted (np.ndarray): low quantile predicted values\n    Returns:\n        nacil (float): normalized average confidence interval length\n    \"\"\"\n    return np.mean((high_quantile_predicted - low_quantile_predicted) / actual)\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.metrics.ndcg","title":"<code>ndcg(actual, predicted)</code>","text":"<p>Normalized Discounted Cumulative Gain</p> <p>Parameters:</p> Name Type Description Default <code>actual</code> <code>ndarray</code> <p>actual values</p> required <code>predicted</code> <code>ndarray</code> <p>predicted values</p> required Source code in <code>churn_pred/training/metrics.py</code> <pre><code>def ndcg(actual: np.ndarray, predicted: Union[np.ndarray, list]) -&gt; float:\n    \"\"\"Normalized Discounted Cumulative Gain\n\n    Args:\n        actual (np.ndarray): actual values\n        predicted (np.ndarray): predicted values\n    Return:\n        ndcg (float): normalized discounted cumulative gain\n    \"\"\"\n    return ndcg_score(\n        [rankdata(actual, method=\"ordinal\")],\n        [rankdata(predicted, method=\"ordinal\")],\n    )\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.metrics.rmse","title":"<code>rmse(actual, predicted)</code>","text":"<p>Root Mean Squared Error</p> <p>Parameters:</p> Name Type Description Default <code>actual</code> <code>ndarray</code> <p>actual values</p> required <code>predicted</code> <code>ndarray</code> <p>predicted values</p> required Source code in <code>churn_pred/training/metrics.py</code> <pre><code>def rmse(actual: np.ndarray, predicted: Union[np.ndarray, list]) -&gt; float:\n    \"\"\"Root Mean Squared Error\n\n    Args:\n        actual (np.ndarray): actual values\n        predicted (np.ndarray): predicted values\n    Returns:\n        rmse (float): root mean square error\n    \"\"\"\n    return mean_squared_error(actual, predicted, squared=False)\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.metrics.smape","title":"<code>smape(actual, predicted)</code>","text":"<p>Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/</p> <p>Parameters:</p> Name Type Description Default <code>actual</code> <code>ndarray</code> <p>actual values</p> required <code>predicted</code> <code>ndarray</code> <p>predicted values</p> required Source code in <code>churn_pred/training/metrics.py</code> <pre><code>def smape(actual: np.ndarray, predicted: Union[np.ndarray, list]) -&gt; float:\n    \"\"\"Symmetric Mean Absolute Percentage Error\n    https://vedexcel.com/how-to-calculate-smape-in-python/\n\n    Args:\n        actual (np.ndarray): actual values\n        predicted (np.ndarray): predicted values\n    Returns:\n        smape (float): symmetric mean absolute percentage error\n    \"\"\"\n    return (\n        100\n        / len(actual)\n        * np.sum(2 * np.abs(predicted - actual) / (np.abs(actual) + np.abs(predicted)))\n    )\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer","title":"<code>LGBOptunaOptimizer</code>","text":"<p>             Bases: <code>BaseOptimizer</code></p> Source code in <code>churn_pred/training/optuna_optimizer.py</code> <pre><code>class LGBOptunaOptimizer(BaseOptimizer):\n    def __init__(\n        self,\n        objective: Literal[\"binary\", \"multiclass\", \"regression\"],\n        n_class: Optional[int] = None,\n    ):\n        \"\"\"Fallback/backup Optuna optimizer. Development is focused on Raytune.\n        Kepping this code as backup.\n\n        Args:\n            objective (str): objective of the model\n            n_class (int): number of classes in the dataset\n        \"\"\"\n        # Optuna does not support original lighgbm implemenattion and with a workaroud it\n        # is possible to get the binarry classfier with focal_loss(any custom loss)\n        # running but the multiclass requires changes to Optuna code, eg. this post:\n        # https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm  # noqa\n        super(LGBOptunaOptimizer, self).__init__(objective, n_class)\n        self.params = self.base_params\n\n    def optimize(self, dtrain: lgbDataset, deval: lgbDataset):\n        \"\"\"Optimize LGBM model on provided datasets.\n\n        Args:\n            dtrain (lgbDataset): training lgb dataset\n            deval (lgbDataset): evaluation lgb dataset\n        \"\"\"\n        dtrain_copy = deepcopy(dtrain)\n        deval_copy = deepcopy(deval) if deval is not None else None\n\n        tuner = LightGBMTuner(\n            params=self.params,\n            train_set=dtrain_copy,\n            valid_sets=deval_copy,\n            num_boost_round=1000,\n            callbacks=[lgb.early_stopping(stopping_rounds=50)],\n            feval=self.feval,\n            # fobj=self.fobj,\n        )\n        tuner.run()\n\n        self.best = tuner.best_params\n        # since n_estimators is not among the params that Optuna optimizes we\n        # need to add it manually. We add a high value since it will be used\n        # with early_stopping_rounds\n        self.best[\"n_estimators\"] = 1000  # type: ignore\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.__init__","title":"<code>__init__(objective, n_class=None)</code>","text":"<p>Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup.</p> <p>Parameters:</p> Name Type Description Default <code>objective</code> <code>str</code> <p>objective of the model</p> required <code>n_class</code> <code>int</code> <p>number of classes in the dataset</p> <code>None</code> Source code in <code>churn_pred/training/optuna_optimizer.py</code> <pre><code>def __init__(\n    self,\n    objective: Literal[\"binary\", \"multiclass\", \"regression\"],\n    n_class: Optional[int] = None,\n):\n    \"\"\"Fallback/backup Optuna optimizer. Development is focused on Raytune.\n    Kepping this code as backup.\n\n    Args:\n        objective (str): objective of the model\n        n_class (int): number of classes in the dataset\n    \"\"\"\n    # Optuna does not support original lighgbm implemenattion and with a workaroud it\n    # is possible to get the binarry classfier with focal_loss(any custom loss)\n    # running but the multiclass requires changes to Optuna code, eg. this post:\n    # https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm  # noqa\n    super(LGBOptunaOptimizer, self).__init__(objective, n_class)\n    self.params = self.base_params\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.optuna_optimizer.LGBOptunaOptimizer.optimize","title":"<code>optimize(dtrain, deval)</code>","text":"<p>Optimize LGBM model on provided datasets.</p> <p>Parameters:</p> Name Type Description Default <code>dtrain</code> <code>Dataset</code> <p>training lgb dataset</p> required <code>deval</code> <code>Dataset</code> <p>evaluation lgb dataset</p> required Source code in <code>churn_pred/training/optuna_optimizer.py</code> <pre><code>def optimize(self, dtrain: lgbDataset, deval: lgbDataset):\n    \"\"\"Optimize LGBM model on provided datasets.\n\n    Args:\n        dtrain (lgbDataset): training lgb dataset\n        deval (lgbDataset): evaluation lgb dataset\n    \"\"\"\n    dtrain_copy = deepcopy(dtrain)\n    deval_copy = deepcopy(deval) if deval is not None else None\n\n    tuner = LightGBMTuner(\n        params=self.params,\n        train_set=dtrain_copy,\n        valid_sets=deval_copy,\n        num_boost_round=1000,\n        callbacks=[lgb.early_stopping(stopping_rounds=50)],\n        feval=self.feval,\n        # fobj=self.fobj,\n    )\n    tuner.run()\n\n    self.best = tuner.best_params\n    # since n_estimators is not among the params that Optuna optimizes we\n    # need to add it manually. We add a high value since it will be used\n    # with early_stopping_rounds\n    self.best[\"n_estimators\"] = 1000  # type: ignore\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.trainer.Trainer","title":"<code>Trainer</code>","text":"<p>             Bases: <code>BaseTrainer</code></p> Source code in <code>churn_pred/training/trainer.py</code> <pre><code>class Trainer(BaseTrainer):\n    def __init__(\n        self,\n        cat_cols: List[str],\n        target_col: str,\n        id_cols: List[str],\n        objective: Literal[\"binary\"],\n        optimizer: Union[Any, BaseOptimizer, None] = None,\n        n_class: Optional[int] = None,\n        preprocessors: Optional[List[Union[Any, PreprocessData]]] = None,\n    ):\n        \"\"\"Objects that governs training and parameter optimization of the lgbm model.\n\n        Args:\n            cat_cols (list): list of categorical feature column names\n            target_col (str): column name that represents target\n            id_cols (list): identification column names\n            objective (str): type of the task/objective\n            optimizer (BaseOptimizer): parameter optimizer object\n        \"\"\"\n        super(Trainer, self).__init__(\n            cat_cols=cat_cols,\n            target_col=target_col,\n            id_cols=id_cols,\n            objective=objective,\n            n_class=n_class,\n            preprocessors=preprocessors,\n        )\n        if optimizer is not None:\n            if not hasattr(optimizer, \"optimize\"):\n                raise AttributeError(\n                    \"{} optimizer must have {} method\".format(optimizer, \"optimize\")\n                )\n        self.optimizer = optimizer\n\n    def train(\n        self,\n        df_train: pd.DataFrame,\n        params: Optional[Dict] = None,\n        df_valid: Optional[pd.DataFrame] = None,\n    ):\n        \"\"\"Train the model with the parameters.\n\n        Args:\n            df_train (pd.DataFrame): training dataset\n            params (dict): model paramaters\n            df_valid (pd.DataFrame): optional validation dataset\n        Returns:\n            model (lgb.basic.Booster): trained model\n        \"\"\"\n        if self.preprocessors:\n            for prep in self.preprocessors:\n                df_train_prep = prep.transform(df_train.drop(columns=[self.target_col]))\n                df_valid_prep = (\n                    prep.transform(df_valid.drop(columns=[self.target_col]))\n                    if df_valid is not None\n                    else None\n                )\n            if self.objective in [\"binary\"]:\n                df_train_prep[self.target_col] = df_train[self.target_col].astype(int)\n                if df_valid is not None:\n                    df_valid_prep[self.target_col] = df_valid[self.target_col].astype(\n                        int\n                    )\n            else:\n                raise NotImplementedError\n        else:\n            df_train_prep = df_train.copy()\n            df_valid_prep = df_valid.copy() if df_valid is not None else None\n\n        cat_cols = self.cat_cols\n        if params:\n            config = params\n        elif (params is None) and (self.optimizer):\n            config = self.optimizer.best\n            if hasattr(self.optimizer, \"best_to_drop\"):\n                df_train_prep.drop(\n                    columns=self.optimizer.best_to_drop,  # type: ignore\n                    inplace=True,\n                )\n                if df_valid is not None:\n                    df_valid_prep.drop(\n                        columns=self.optimizer.best_to_drop,  # type: ignore\n                        inplace=True,\n                    )\n                cat_cols = intsec(self.cat_cols, df_train_prep.columns.values)\n\n        elif (params is None) and (self.optimizer is None):\n            config = self.base_params\n\n        lgb_train, lgb_valid = to_lgbdataset(\n            train=df_train_prep,\n            cat_cols=cat_cols,\n            target_col=self.target_col,\n            id_cols=self.id_cols,\n            valid=df_valid_prep,\n        )\n\n        self.model = lgb_train_function(\n            config=config,\n            lgbtrain=lgb_train,\n            lgbeval=lgb_valid,\n            feval=self.feval,\n        )\n\n    def fit(\n        self,\n        df_train: pd.DataFrame,\n        df_valid: pd.DataFrame,\n        df_test: pd.DataFrame,\n    ) -&gt; pd.DataFrame:\n        \"\"\"Train the model and optimize the parameters.\n\n        Args:\n            df_train (pd.DataFrame): training dataset\n            df_valid (pd.DataFrame): validation dataset\n            df_test (pd.DataFrame): testing dataset\n        Returns:\n            model (lgb.basic.Booster): trained model\n        \"\"\"\n        if self.preprocessors:\n            # this should be\n            # df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col]))\n            for prep in self.preprocessors:\n                df_train_prep = prep.transform(df_train.drop(columns=[self.target_col]))\n                df_valid_prep = prep.transform(df_valid.drop(columns=[self.target_col]))\n            if self.objective in [\"binary\"]:\n                df_train_prep[self.target_col] = df_train[self.target_col].astype(int)\n                df_valid_prep[self.target_col] = df_valid[self.target_col].astype(int)\n            else:\n                raise NotImplementedError\n        else:\n            df_train_prep = df_train.copy()\n            df_valid_prep = df_valid.copy()\n\n        lgb_train, lgb_valid = to_lgbdataset(\n            train=df_train_prep,\n            cat_cols=self.cat_cols,\n            target_col=self.target_col,\n            id_cols=self.id_cols,\n            valid=df_valid_prep,\n        )\n        self.optimizer.optimize(dtrain=lgb_train, deval=lgb_valid)\n\n        df_train_valid = pd.concat([df_train, df_valid], ignore_index=True)\n        df_train_valid = df_train_valid.reset_index(drop=True)\n        self.train(df_train=df_train_valid)\n        metrics_dict = self.compute_metrics(df_test)\n        return metrics_dict\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.trainer.Trainer.__init__","title":"<code>__init__(cat_cols, target_col, id_cols, objective, optimizer=None, n_class=None, preprocessors=None)</code>","text":"<p>Objects that governs training and parameter optimization of the lgbm model.</p> <p>Parameters:</p> Name Type Description Default <code>cat_cols</code> <code>list</code> <p>list of categorical feature column names</p> required <code>target_col</code> <code>str</code> <p>column name that represents target</p> required <code>id_cols</code> <code>list</code> <p>identification column names</p> required <code>objective</code> <code>str</code> <p>type of the task/objective</p> required <code>optimizer</code> <code>BaseOptimizer</code> <p>parameter optimizer object</p> <code>None</code> Source code in <code>churn_pred/training/trainer.py</code> <pre><code>def __init__(\n    self,\n    cat_cols: List[str],\n    target_col: str,\n    id_cols: List[str],\n    objective: Literal[\"binary\"],\n    optimizer: Union[Any, BaseOptimizer, None] = None,\n    n_class: Optional[int] = None,\n    preprocessors: Optional[List[Union[Any, PreprocessData]]] = None,\n):\n    \"\"\"Objects that governs training and parameter optimization of the lgbm model.\n\n    Args:\n        cat_cols (list): list of categorical feature column names\n        target_col (str): column name that represents target\n        id_cols (list): identification column names\n        objective (str): type of the task/objective\n        optimizer (BaseOptimizer): parameter optimizer object\n    \"\"\"\n    super(Trainer, self).__init__(\n        cat_cols=cat_cols,\n        target_col=target_col,\n        id_cols=id_cols,\n        objective=objective,\n        n_class=n_class,\n        preprocessors=preprocessors,\n    )\n    if optimizer is not None:\n        if not hasattr(optimizer, \"optimize\"):\n            raise AttributeError(\n                \"{} optimizer must have {} method\".format(optimizer, \"optimize\")\n            )\n    self.optimizer = optimizer\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.trainer.Trainer.fit","title":"<code>fit(df_train, df_valid, df_test)</code>","text":"<p>Train the model and optimize the parameters.</p> <p>Parameters:</p> Name Type Description Default <code>df_train</code> <code>DataFrame</code> <p>training dataset</p> required <code>df_valid</code> <code>DataFrame</code> <p>validation dataset</p> required <code>df_test</code> <code>DataFrame</code> <p>testing dataset</p> required Source code in <code>churn_pred/training/trainer.py</code> <pre><code>def fit(\n    self,\n    df_train: pd.DataFrame,\n    df_valid: pd.DataFrame,\n    df_test: pd.DataFrame,\n) -&gt; pd.DataFrame:\n    \"\"\"Train the model and optimize the parameters.\n\n    Args:\n        df_train (pd.DataFrame): training dataset\n        df_valid (pd.DataFrame): validation dataset\n        df_test (pd.DataFrame): testing dataset\n    Returns:\n        model (lgb.basic.Booster): trained model\n    \"\"\"\n    if self.preprocessors:\n        # this should be\n        # df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col]))\n        for prep in self.preprocessors:\n            df_train_prep = prep.transform(df_train.drop(columns=[self.target_col]))\n            df_valid_prep = prep.transform(df_valid.drop(columns=[self.target_col]))\n        if self.objective in [\"binary\"]:\n            df_train_prep[self.target_col] = df_train[self.target_col].astype(int)\n            df_valid_prep[self.target_col] = df_valid[self.target_col].astype(int)\n        else:\n            raise NotImplementedError\n    else:\n        df_train_prep = df_train.copy()\n        df_valid_prep = df_valid.copy()\n\n    lgb_train, lgb_valid = to_lgbdataset(\n        train=df_train_prep,\n        cat_cols=self.cat_cols,\n        target_col=self.target_col,\n        id_cols=self.id_cols,\n        valid=df_valid_prep,\n    )\n    self.optimizer.optimize(dtrain=lgb_train, deval=lgb_valid)\n\n    df_train_valid = pd.concat([df_train, df_valid], ignore_index=True)\n    df_train_valid = df_train_valid.reset_index(drop=True)\n    self.train(df_train=df_train_valid)\n    metrics_dict = self.compute_metrics(df_test)\n    return metrics_dict\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.trainer.Trainer.train","title":"<code>train(df_train, params=None, df_valid=None)</code>","text":"<p>Train the model with the parameters.</p> <p>Parameters:</p> Name Type Description Default <code>df_train</code> <code>DataFrame</code> <p>training dataset</p> required <code>params</code> <code>dict</code> <p>model paramaters</p> <code>None</code> <code>df_valid</code> <code>DataFrame</code> <p>optional validation dataset</p> <code>None</code> Source code in <code>churn_pred/training/trainer.py</code> <pre><code>def train(\n    self,\n    df_train: pd.DataFrame,\n    params: Optional[Dict] = None,\n    df_valid: Optional[pd.DataFrame] = None,\n):\n    \"\"\"Train the model with the parameters.\n\n    Args:\n        df_train (pd.DataFrame): training dataset\n        params (dict): model paramaters\n        df_valid (pd.DataFrame): optional validation dataset\n    Returns:\n        model (lgb.basic.Booster): trained model\n    \"\"\"\n    if self.preprocessors:\n        for prep in self.preprocessors:\n            df_train_prep = prep.transform(df_train.drop(columns=[self.target_col]))\n            df_valid_prep = (\n                prep.transform(df_valid.drop(columns=[self.target_col]))\n                if df_valid is not None\n                else None\n            )\n        if self.objective in [\"binary\"]:\n            df_train_prep[self.target_col] = df_train[self.target_col].astype(int)\n            if df_valid is not None:\n                df_valid_prep[self.target_col] = df_valid[self.target_col].astype(\n                    int\n                )\n        else:\n            raise NotImplementedError\n    else:\n        df_train_prep = df_train.copy()\n        df_valid_prep = df_valid.copy() if df_valid is not None else None\n\n    cat_cols = self.cat_cols\n    if params:\n        config = params\n    elif (params is None) and (self.optimizer):\n        config = self.optimizer.best\n        if hasattr(self.optimizer, \"best_to_drop\"):\n            df_train_prep.drop(\n                columns=self.optimizer.best_to_drop,  # type: ignore\n                inplace=True,\n            )\n            if df_valid is not None:\n                df_valid_prep.drop(\n                    columns=self.optimizer.best_to_drop,  # type: ignore\n                    inplace=True,\n                )\n            cat_cols = intsec(self.cat_cols, df_train_prep.columns.values)\n\n    elif (params is None) and (self.optimizer is None):\n        config = self.base_params\n\n    lgb_train, lgb_valid = to_lgbdataset(\n        train=df_train_prep,\n        cat_cols=cat_cols,\n        target_col=self.target_col,\n        id_cols=self.id_cols,\n        valid=df_valid_prep,\n    )\n\n    self.model = lgb_train_function(\n        config=config,\n        lgbtrain=lgb_train,\n        lgbeval=lgb_valid,\n        feval=self.feval,\n    )\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.utils.get_feature_importance","title":"<code>get_feature_importance(model)</code>","text":"<p>Extract model feature importances and return sorted dataframe.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>Booster</code> <p>LightGBM model</p> required <p>Returns:</p> Name Type Description <code>feature_imp</code> <code>DataFrame</code> <p>sorted dataframe with features and their importances</p> Source code in <code>churn_pred/training/utils.py</code> <pre><code>def get_feature_importance(model: lgb.basic.Booster) -&gt; pd.DataFrame:\n    \"\"\"Extract model feature importances and return sorted dataframe.\n\n    Args:\n        model (lgb.basic.Booster): LightGBM model\n\n    Returns:\n        feature_imp (pd.DataFrame): sorted dataframe with features and their\n            importances\n    \"\"\"\n    feature_imp = pd.DataFrame(\n        {\"value\": model.feature_importance(), \"feature\": model.feature_name()}\n    )\n    feature_imp.sort_values(by=\"value\", inplace=True)\n    feature_imp.reset_index(drop=True, inplace=True)\n    return feature_imp\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.utils.predict_cls_lgbm_from_raw","title":"<code>predict_cls_lgbm_from_raw(preds_raw, task)</code>","text":"<p>Helper function to convert raw margin predictions through a sigmoid to represent a probability.</p> <p>Parameters:</p> Name Type Description Default <code>preds_raw</code> <code>ndarray</code> <p>predictions</p> required <code>task</code> <code>Literal['binary', 'multiclass']</code> <p>task type</p> required Source code in <code>churn_pred/training/utils.py</code> <pre><code>def predict_cls_lgbm_from_raw(\n    preds_raw: np.ndarray, task: Literal[\"binary\", \"multiclass\"]\n) -&gt; np.ndarray:\n    \"\"\"Helper function to convert raw margin predictions through a\n    sigmoid to represent a probability.\n\n    Args:\n        preds_raw (ndarray):\n            predictions\n        task (Literal[\"binary\", \"multiclass\"]):\n            task type\n    Returns:\n        (y_true, preds):\n            tuple containg labels and predictions for further evaluation\n    \"\"\"\n    predicted_probs = predict_proba_lgbm_from_raw(preds_raw=preds_raw, task=task)\n    if task == \"binary\":\n        pred_cls = np.array([int(p &gt; 0.5) for p in predicted_probs])\n    elif task == \"multiclass\":\n        pred_cls = predicted_probs.argmax(axis=1)\n\n    return pred_cls\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.utils.predict_proba_lgbm_from_raw","title":"<code>predict_proba_lgbm_from_raw(preds_raw, task, binary2d=False)</code>","text":"<p>Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba().</p> <p>Parameters:</p> Name Type Description Default <code>preds_raw</code> <code>ndarray</code> <p>1D numpy array of arrays</p> required <code>task</code> <code>str</code> <p>type of task/objective</p> required <code>binary2d</code> <code>boolean</code> <p>wether the output of binary classification should be 1 or 2d vector</p> <code>False</code> Source code in <code>churn_pred/training/utils.py</code> <pre><code>def predict_proba_lgbm_from_raw(\n    preds_raw: np.ndarray,\n    task: Literal[\"binary\", \"multiclass\"],\n    binary2d: Optional[bool] = False,\n) -&gt; np.ndarray:\n    \"\"\"Apply softmax to array of arrays that is an output of lightgbm.predct().\n    This replaces predict_proba().\n\n    Args:\n        preds_raw (ndarray):\n            1D numpy array of arrays\n        task (str):\n            type of task/objective\n        binary2d (boolean):\n            wether the output of binary classification should be 1 or 2d vector\n    Returns:\n        predicted_probs (ndarray): array with predicted probabilities\n    \"\"\"\n    if task == \"binary\":\n        predicted_probs = _sigmoid(preds_raw)\n        if binary2d:\n            predicted_probs = np.apply_along_axis(\n                _binary_margin_to_prob, 1, np.vstack(tuple(predicted_probs))\n            )\n    elif task == \"multiclass\":\n        predicted_probs = np.apply_along_axis(_softmax, 1, np.stack(tuple(preds_raw)))\n    return predicted_probs\n</code></pre>"},{"location":"churn_pred/training.html#churn_pred.training.utils.to_lgbdataset","title":"<code>to_lgbdataset(train, cat_cols, target_col, id_cols=[], valid=None)</code>","text":"<p>Transform pandas dataframe to lgbm dataset, or datasets(eval).</p> <p>Parameters:</p> Name Type Description Default <code>train</code> <code>DataFrame</code> <p>training dataset</p> required <code>cat_cols</code> <code>list</code> <p>list of categorical columns</p> required <code>target_col</code> <code>str</code> <p>target column in the dataset</p> required <code>id_cols</code> <code>list</code> <p>list of identifier columns</p> <code>[]</code> <code>valid</code> <code>DataFrame</code> <p>validation dataset</p> <code>None</code> Source code in <code>churn_pred/training/utils.py</code> <pre><code>def to_lgbdataset(\n    train: pd.DataFrame,\n    cat_cols: List[str],\n    target_col: str,\n    id_cols: List[str] = [],\n    valid: Optional[pd.DataFrame] = None,\n) -&gt; Tuple[lgbDataset, Optional[lgbDataset]]:\n    \"\"\"Transform pandas dataframe to lgbm dataset, or datasets(eval).\n\n    Args:\n        train (pd.DataFrame):\n            training dataset\n        cat_cols (list):\n            list of categorical columns\n        target_col (str):\n            target column in the dataset\n        id_cols (list):\n            list of identifier columns\n        valid (pd.DataFrame):\n            validation dataset\n    Returns:\n        lgb_train (lgbDataset):\n            lgbm training dataset\n        lgb_valid (lgbDataset):\n            lgbm valid dataset\n    \"\"\"\n\n    lgb_train = lgbDataset(\n        train.drop(columns=[target_col] + id_cols),\n        train[target_col],\n        categorical_feature=cat_cols,\n        free_raw_data=False,\n    )\n\n    if valid is not None:\n        lgb_valid = lgbDataset(\n            valid.drop(columns=[target_col] + id_cols),\n            valid[target_col],\n            reference=lgb_train,\n            free_raw_data=False,\n        )\n    else:\n        lgb_valid = None\n\n    return lgb_train, lgb_valid\n</code></pre>"},{"location":"notebooks/00_auxiliary_features.html","title":"00_auxiliary_features","text":"In\u00a0[1]: Copied! <pre>import os\nimport sys\n\nsys.path.append(os.getcwd())\nos.chdir(\"../..\")\n\nimport pandas as pd\nfrom churn_pred.preprocessing import auxiliary_data, preprocess_text\nimport spacy_fastlang\n</pre> import os import sys  sys.path.append(os.getcwd()) os.chdir(\"../..\")  import pandas as pd from churn_pred.preprocessing import auxiliary_data, preprocess_text import spacy_fastlang In\u00a0[2]: Copied! <pre>df_pd = pd.read_csv(\"data/dataset.csv\")\ndf_pd.head()\n</pre> df_pd = pd.read_csv(\"data/dataset.csv\") df_pd.head() Out[2]: RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited 0 747 15787619 Hsieh 844 France Male 18 2 I can not link my card to any third-party plat... 160980.03 1 0 0 145936.28 0 1 1620 15770309 McDonald 656 France Male 18 10 Last week, I reached out to my bank's support ... 151762.74 1 0 1 127014.32 0 2 1679 15569178 Kharlamov 570 France Female 18 4 NaN 82767.42 1 1 0 71811.90 0 3 2022 15795519 Vasiliev 716 Germany Female 18 3 NaN 128743.80 1 0 0 197322.13 0 4 2137 15621893 Bellucci 727 France Male 18 4 Yo, I gotta give some love to this bank's cust... 133550.67 1 1 1 46941.41 0 In\u00a0[3]: Copied! <pre>df_pd = preprocess_text.text_cleaning(df=df_pd, text_col=\"CustomerFeedback\")\ndf_pd = preprocess_text.language_detection(\n    df=df_pd, text_col=\"CustomerFeedback\", model_type=\"fasttext\"\n)\n</pre> df_pd = preprocess_text.text_cleaning(df=df_pd, text_col=\"CustomerFeedback\") df_pd = preprocess_text.language_detection(     df=df_pd, text_col=\"CustomerFeedback\", model_type=\"fasttext\" ) <pre>Cleaning Progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 10000/10000 [00:34&lt;00:00, 287.95it/s]\nWarning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n</pre> In\u00a0[4]: Copied! <pre>df_pd.head()\n</pre> df_pd.head() Out[4]: RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 1 0 0 145936.28 0 en 0.786177 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 1 0 1 127014.32 0 en 0.911405 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 1 1 0 71811.90 0 ht 0.974604 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 1 0 0 197322.13 0 ht 0.974604 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 1 1 1 46941.41 0 en 0.758962 In\u00a0[5]: Copied! <pre>df_pd[df_pd[\"CustomerFeedback\"].astype(str) != \"nan\"][\n    \"CustomerFeedback_language\"\n].unique()\n</pre> df_pd[df_pd[\"CustomerFeedback\"].astype(str) != \"nan\"][     \"CustomerFeedback_language\" ].unique() Out[5]: <pre>array(['en'], dtype=object)</pre> In\u00a0[6]: Copied! <pre>df_pd[\n    (df_pd[\"CustomerFeedback\"].astype(str) != \"nan\")\n    &amp; (df_pd[\"CustomerFeedback_language_score\"] &lt; 0.5)\n]\n</pre> df_pd[     (df_pd[\"CustomerFeedback\"].astype(str) != \"nan\")     &amp; (df_pd[\"CustomerFeedback_language_score\"] &lt; 0.5) ] Out[6]: RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score 105 2866 15590228 Greenwalt 715 France Male 21 6 hsi 76467.16 1 1 1 173511.72 0 en 0.124504 6270 7107 15723989 Carroll 646 France Male 40 5 fund accessible anticipate date _IS_PUNCT_ _IS... 93680.43 2 1 1 179473.26 0 en 0.468742 In\u00a0[7]: Copied! <pre>df_pd.loc[105, [\"CustomerFeedback\"]] = \"nan\"\n</pre> df_pd.loc[105, [\"CustomerFeedback\"]] = \"nan\" In\u00a0[8]: Copied! <pre>df_pd[df_pd[\"CustomerFeedback\"].astype(str) != \"nan\"][\n    \"CustomerFeedback_language_score\"\n].plot.hist(bins=50)\n</pre> df_pd[df_pd[\"CustomerFeedback\"].astype(str) != \"nan\"][     \"CustomerFeedback_language_score\" ].plot.hist(bins=50) Out[8]: <pre>&lt;Axes: ylabel='Frequency'&gt;</pre> In\u00a0[9]: Copied! <pre>df_pd[[\"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment3_score\"]] = (\n    preprocess_text.sentiment_analysis(\n        df=df_pd, text_col=\"CustomerFeedback\", sentiment_depth=3\n    )[[\"CustomerFeedback_sentiment\", \"CustomerFeedback_sentiment_score\"]]\n)\ndf_pd.head()\n</pre> df_pd[[\"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment3_score\"]] = (     preprocess_text.sentiment_analysis(         df=df_pd, text_col=\"CustomerFeedback\", sentiment_depth=3     )[[\"CustomerFeedback_sentiment\", \"CustomerFeedback_sentiment_score\"]] ) df_pd.head() <pre>Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n</pre> Out[9]: RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 1 0 0 145936.28 0 en 0.786177 neutral 0.827256 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 1 0 1 127014.32 0 en 0.911405 neutral 0.671348 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 1 1 0 71811.90 0 ht 0.974604 neutral 0.680865 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 1 0 0 197322.13 0 ht 0.974604 neutral 0.680865 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 1 1 1 46941.41 0 en 0.758962 positive 0.878306 In\u00a0[10]: Copied! <pre>df_pd[[\"CustomerFeedback_sentiment5\", \"CustomerFeedback_sentiment5_score\"]] = (\n    preprocess_text.sentiment_analysis(\n        df=df_pd, text_col=\"CustomerFeedback\", sentiment_depth=5\n    )[[\"CustomerFeedback_sentiment\", \"CustomerFeedback_sentiment_score\"]]\n)\ndf_pd.head()\n</pre> df_pd[[\"CustomerFeedback_sentiment5\", \"CustomerFeedback_sentiment5_score\"]] = (     preprocess_text.sentiment_analysis(         df=df_pd, text_col=\"CustomerFeedback\", sentiment_depth=5     )[[\"CustomerFeedback_sentiment\", \"CustomerFeedback_sentiment_score\"]] ) df_pd.head() Out[10]: RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 ... 0 0 145936.28 0 en 0.786177 neutral 0.827256 4 stars 0.259499 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 ... 0 1 127014.32 0 en 0.911405 neutral 0.671348 1 star 0.359483 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 ... 1 0 71811.90 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 ... 0 0 197322.13 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 ... 1 1 46941.41 0 en 0.758962 positive 0.878306 1 star 0.291449 <p>5 rows \u00d7 21 columns</p> In\u00a0[11]: Copied! <pre># save intermediate results\ndf_pd.to_parquet(\"data/dataset_sentiment.parquet\")\n</pre> # save intermediate results df_pd.to_parquet(\"data/dataset_sentiment.parquet\") In\u00a0[3]: Copied! <pre># df_pd = pd.read_parquet(\"data/dataset_sentiment.parquet\")\n</pre> # df_pd = pd.read_parquet(\"data/dataset_sentiment.parquet\") In\u00a0[12]: Copied! <pre>df_pd[\"Surname\" + \"_Country\"] = auxiliary_data.surname_origin(\n    df=df_pd, surname_col=\"Surname\"\n)\ndf_pd.head()\n</pre> df_pd[\"Surname\" + \"_Country\"] = auxiliary_data.surname_origin(     df=df_pd, surname_col=\"Surname\" ) df_pd.head() Out[12]: RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score Surname_Country 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 ... 0 145936.28 0 en 0.786177 neutral 0.827256 4 stars 0.259499 Taiwan, Province of China 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 ... 1 127014.32 0 en 0.911405 neutral 0.671348 1 star 0.359483 United States 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 ... 0 71811.90 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 Russian Federation 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 ... 0 197322.13 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 Russian Federation 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 ... 1 46941.41 0 en 0.758962 positive 0.878306 1 star 0.291449 Italy <p>5 rows \u00d7 22 columns</p> In\u00a0[13]: Copied! <pre>df_pd[(df_pd[\"Surname_Country\"] == \"NA\")][\"Surname\"].nunique()\n</pre> df_pd[(df_pd[\"Surname_Country\"] == \"NA\")][\"Surname\"].nunique() Out[13]: <pre>496</pre> <p>less than 500 so I can use Namsor free trial to get data, I will use their online csv tool and not API function<code>auxiliary_data.surname_origin_namsor</code> to avoid wasting another email address for free trail</p> In\u00a0[6]: Copied! <pre>namsor_csv = pd.DataFrame(\n    {\"Surname\": df_pd[(df_pd[\"Surname_Country\"] == \"NA\")][\"Surname\"].unique()}\n)\nnamsor_csv[\"first_name\"] = \"unset\"\nnamsor_csv.to_csv(\"data/surnames/surnames_for_namsor.csv\", index=False)\n</pre> namsor_csv = pd.DataFrame(     {\"Surname\": df_pd[(df_pd[\"Surname_Country\"] == \"NA\")][\"Surname\"].unique()} ) namsor_csv[\"first_name\"] = \"unset\" namsor_csv.to_csv(\"data/surnames/surnames_for_namsor.csv\", index=False) <p>after namsor processing</p> In\u00a0[14]: Copied! <pre># keep_default_na=False as NA-Namibia is intrepreted as NaN\nnamsor_surnames = pd.read_csv(\n    \"data/surnames/namsor_name-origin_surnames_for_namsor.csv\",\n    keep_default_na=False,\n    na_values=[\"_\"],\n)\nnamsor_surnames = namsor_surnames[[\"lastName\", \"countryOrigin\"]].rename(\n    columns={\"lastName\": \"Surname\", \"countryOrigin\": \"Surname_Country_namsor\"}\n)\n</pre> # keep_default_na=False as NA-Namibia is intrepreted as NaN namsor_surnames = pd.read_csv(     \"data/surnames/namsor_name-origin_surnames_for_namsor.csv\",     keep_default_na=False,     na_values=[\"_\"], ) namsor_surnames = namsor_surnames[[\"lastName\", \"countryOrigin\"]].rename(     columns={\"lastName\": \"Surname\", \"countryOrigin\": \"Surname_Country_namsor\"} ) In\u00a0[15]: Copied! <pre>namsor_surnames[\"Surname_Country_namsor\"] = auxiliary_data.get_country_name(\n    df=namsor_surnames, country_name_col=\"Surname_Country_namsor\"\n)\nnamsor_surnames.head()\n</pre> namsor_surnames[\"Surname_Country_namsor\"] = auxiliary_data.get_country_name(     df=namsor_surnames, country_name_col=\"Surname_Country_namsor\" ) namsor_surnames.head() Out[15]: Surname Surname_Country_namsor 0 Vaguine France 1 Onyekaozulu Nigeria 2 Chiazagomekpele Nigeria 3 Fleetwood-Smith United Kingdom 4 Anayolisa Nigeria In\u00a0[16]: Copied! <pre>df_pd = df_pd.merge(namsor_surnames, on=\"Surname\", how=\"left\")\n</pre> df_pd = df_pd.merge(namsor_surnames, on=\"Surname\", how=\"left\") In\u00a0[17]: Copied! <pre>df_pd.loc[(df_pd[\"Surname_Country\"] == \"NA\"), \"Surname_Country\"] = df_pd.loc[\n    (df_pd[\"Surname_Country\"] == \"NA\"), \"Surname_Country_namsor\"\n]\n</pre> df_pd.loc[(df_pd[\"Surname_Country\"] == \"NA\"), \"Surname_Country\"] = df_pd.loc[     (df_pd[\"Surname_Country\"] == \"NA\"), \"Surname_Country_namsor\" ] In\u00a0[18]: Copied! <pre>df_pd = df_pd.drop(columns=[\"Surname_Country_namsor\"])\n</pre> df_pd = df_pd.drop(columns=[\"Surname_Country_namsor\"]) In\u00a0[19]: Copied! <pre>df_pd[\"Surname_Country\"].unique()\n</pre> df_pd[\"Surname_Country\"].unique() Out[19]: <pre>array(['Taiwan, Province of China', 'United States', 'Russian Federation',\n       'Italy', 'Macao', 'Ireland', 'United Kingdom', 'Nigeria', 'France',\n       'Algeria', 'Germany', 'Malaysia', 'Mexico', 'Hong Kong', 'China',\n       'Israel', 'Kazakhstan', 'Colombia', 'Lesotho', 'Saudi Arabia',\n       'Uruguay', 'Togo', 'Ethiopia', 'Spain', 'Egypt', 'Ukraine',\n       'Lebanon', 'Zimbabwe', 'India', 'Thailand', 'Brazil', 'Panama',\n       'T\u00fcrkiye', \"C\u00f4te d'Ivoire\", 'South Africa', 'Azerbaijan', 'Congo',\n       'Czechia', 'Burkina Faso', 'Uzbekistan', 'Morocco', 'Ghana',\n       'Portugal', 'Austria', 'Sri Lanka', 'Bulgaria', 'Norway', 'Greece',\n       'Estonia', 'Bangladesh', 'Viet Nam', 'Netherlands', 'Cameroon',\n       'Sweden', 'Costa Rica', 'Japan', 'Liberia', 'Myanmar',\n       'Brunei Darussalam', 'Canada', 'Moldova, Republic of', 'Denmark',\n       'Benin', 'Niger', 'Latvia', 'Finland', 'Chile', 'Poland',\n       'Switzerland', 'Mozambique', 'Indonesia', 'Rwanda', 'Argentina',\n       'Namibia', 'Belgium', 'Botswana', 'Romania', 'Mali', 'Belarus',\n       'Cyprus', 'Palestine, State of', 'Hungary', 'Mauritius',\n       'Pakistan', 'Senegal', 'Slovakia', 'Iran, Islamic Republic of',\n       'Malawi', 'Jamaica'], dtype=object)</pre> In\u00a0[20]: Copied! <pre>country_info_problematic_vals = {\n    \"Iran, Islamic Republic of\": \"Iran\",\n    \"Moldova, Republic of\": \"Moldova\",\n    \"Taiwan, Province of China\": \"Taiwan\",\n    \"Czechia\": \"Czech republic\",\n    \"C\u00f4te d'Ivoire\": \"Ivory Coast\",\n    \"Brunei Darussalam\": \"Brunei\",\n    \"Viet Nam\": \"Vietnam\",\n    \"Macao\": \"Macau\",\n    \"T\u00fcrkiye\": \"Turkey\",\n    \"Congo\": \"Democratic Republic of the Congo\",\n    \"Palestine, State of\": \"Palestine\",\n}\ndf_pd[\"Surname_Country\"] = df_pd[\"Surname_Country\"].replace(\n    country_info_problematic_vals\n)\n</pre> country_info_problematic_vals = {     \"Iran, Islamic Republic of\": \"Iran\",     \"Moldova, Republic of\": \"Moldova\",     \"Taiwan, Province of China\": \"Taiwan\",     \"Czechia\": \"Czech republic\",     \"C\u00f4te d'Ivoire\": \"Ivory Coast\",     \"Brunei Darussalam\": \"Brunei\",     \"Viet Nam\": \"Vietnam\",     \"Macao\": \"Macau\",     \"T\u00fcrkiye\": \"Turkey\",     \"Congo\": \"Democratic Republic of the Congo\",     \"Palestine, State of\": \"Palestine\", } df_pd[\"Surname_Country\"] = df_pd[\"Surname_Country\"].replace(     country_info_problematic_vals ) In\u00a0[21]: Copied! <pre>df_pd[[\"Surname_Country_region\", \"Surname_Country_subregion\"]] = (\n    auxiliary_data.get_country_region_subregion(\n        df=df_pd, country_name_col=\"Surname_Country\"\n    )\n)\n</pre> df_pd[[\"Surname_Country_region\", \"Surname_Country_subregion\"]] = (     auxiliary_data.get_country_region_subregion(         df=df_pd, country_name_col=\"Surname_Country\"     ) ) In\u00a0[22]: Copied! <pre>df_pd[\n    (df_pd[\"Surname_Country_region\"] == \"NA\")\n    | (df_pd[\"Surname_Country_subregion\"] == \"NA\")\n][[\"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\"]]\n</pre> df_pd[     (df_pd[\"Surname_Country_region\"] == \"NA\")     | (df_pd[\"Surname_Country_subregion\"] == \"NA\") ][[\"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\"]] Out[22]: Surname_Country Surname_Country_region Surname_Country_subregion 2024 Myanmar NA NA 3322 Myanmar NA NA 4237 Myanmar NA NA 4626 Myanmar NA NA 4892 Myanmar NA NA 5348 Palestine NA NA 6190 Myanmar NA NA 7064 Myanmar NA NA 7098 Myanmar NA NA 7693 Myanmar NA NA 7895 Myanmar NA NA 8299 Myanmar NA NA 8789 Myanmar NA NA 9600 Myanmar NA NA In\u00a0[23]: Copied! <pre># https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming\ndf_pd.loc[\n    df_pd[\"Surname_Country\"] == \"Palestine\",\n    [\"Surname_Country_region\", \"Surname_Country_subregion\"],\n] = (\"Asia\", \"Western Asia\")\n# https://en.wikipedia.org/wiki/Myanmar - 'South-Eastern Asia' to follow countryinfo naming\ndf_pd.loc[\n    df_pd[\"Surname_Country\"] == \"Myanmar\",\n    [\"Surname_Country_region\", \"Surname_Country_subregion\"],\n] = (\"Asia\", \"South-Eastern Asia\")\n\ndf_pd[\n    (df_pd[\"Surname_Country_region\"] == \"NA\")\n    | (df_pd[\"Surname_Country_subregion\"] == \"NA\")\n]\n</pre> # https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming df_pd.loc[     df_pd[\"Surname_Country\"] == \"Palestine\",     [\"Surname_Country_region\", \"Surname_Country_subregion\"], ] = (\"Asia\", \"Western Asia\") # https://en.wikipedia.org/wiki/Myanmar - 'South-Eastern Asia' to follow countryinfo naming df_pd.loc[     df_pd[\"Surname_Country\"] == \"Myanmar\",     [\"Surname_Country_region\", \"Surname_Country_subregion\"], ] = (\"Asia\", \"South-Eastern Asia\")  df_pd[     (df_pd[\"Surname_Country_region\"] == \"NA\")     | (df_pd[\"Surname_Country_subregion\"] == \"NA\") ] Out[23]: RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score Surname_Country Surname_Country_region Surname_Country_subregion <p>0 rows \u00d7 24 columns</p> In\u00a0[24]: Copied! <pre>df_pd[[\"Country_region\", \"Country_subregion\"]] = (\n    auxiliary_data.get_country_region_subregion(df=df_pd, country_name_col=\"Country\")\n)\n</pre> df_pd[[\"Country_region\", \"Country_subregion\"]] = (     auxiliary_data.get_country_region_subregion(df=df_pd, country_name_col=\"Country\") ) In\u00a0[25]: Copied! <pre>df_pd[(df_pd[\"Country_region\"] == \"NA\") | (df_pd[\"Country_subregion\"] == \"NA\")][\n    [\"Country\", \"Country_region\", \"Country_subregion\"]\n]\n</pre> df_pd[(df_pd[\"Country_region\"] == \"NA\") | (df_pd[\"Country_subregion\"] == \"NA\")][     [\"Country\", \"Country_region\", \"Country_subregion\"] ] Out[25]: Country Country_region Country_subregion In\u00a0[26]: Copied! <pre>df_pd[\"is_native\"] = (df_pd[\"Surname_Country\"] == df_pd[\"Country\"]).astype(int)\n</pre> df_pd[\"is_native\"] = (df_pd[\"Surname_Country\"] == df_pd[\"Country\"]).astype(int) In\u00a0[27]: Copied! <pre># no need to know the hemisphere of the surname origin country...\ndf_pd = auxiliary_data.hemisphere(df=df_pd, cc_col=\"Country\").rename(\n    columns={\"hemisphere\": \"Country_hemisphere\"}\n)\n</pre> # no need to know the hemisphere of the surname origin country... df_pd = auxiliary_data.hemisphere(df=df_pd, cc_col=\"Country\").rename(     columns={\"hemisphere\": \"Country_hemisphere\"} ) In\u00a0[28]: Copied! <pre>df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col=\"Country\").rename(\n    columns={\n        \"gdp_per_capita\": \"Country_gdp_per_capita\",\n        \"IncomeGroup\": \"Country_IncomeGroup\",\n    }\n)\ndf_pd = auxiliary_data.gdppc(df=df_pd, country_name_col=\"Surname_Country\").rename(\n    columns={\n        \"gdp_per_capita\": \"Surname_Country_gdp_per_capita\",\n        \"IncomeGroup\": \"Surname_Country_IncomeGroup\",\n    }\n)\n</pre> df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col=\"Country\").rename(     columns={         \"gdp_per_capita\": \"Country_gdp_per_capita\",         \"IncomeGroup\": \"Country_IncomeGroup\",     } ) df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col=\"Surname_Country\").rename(     columns={         \"gdp_per_capita\": \"Surname_Country_gdp_per_capita\",         \"IncomeGroup\": \"Surname_Country_IncomeGroup\",     } ) <pre>/home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:283: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  gdp_per_capita.rename(columns={\"Country Code\": \"iso_a3\"}, inplace=True)\n/home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:284: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  gdp_per_capita[\"gdp_per_capita\"] = gdp_per_capita_raw.drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"]).ffill(axis=1).iloc[:, -1]\n/home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:283: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  gdp_per_capita.rename(columns={\"Country Code\": \"iso_a3\"}, inplace=True)\n/home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:284: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  gdp_per_capita[\"gdp_per_capita\"] = gdp_per_capita_raw.drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"]).ffill(axis=1).iloc[:, -1]\n</pre> <pre>SubdivisionHierarchy(code='MX-MEX', country_code='MX', name='M\u00e9xico', parent_code=None, type='State')\nSubdivisionHierarchy(code='PA-8', country_code='PA', name='Panam\u00e1', parent_code=None, type='Province')\nSubdivisionHierarchy(code='NG-NI', country_code='NG', name='Niger', parent_code=None, type='State')\nSubdivisionHierarchy(code='GN-ML', country_code='GN', name='Mali', parent='L', parent_code='GN-L', type='Prefecture')\n</pre> In\u00a0[29]: Copied! <pre>df_pd = auxiliary_data.age_categories(df=df_pd, age_col=\"Age\")\n</pre> df_pd = auxiliary_data.age_categories(df=df_pd, age_col=\"Age\") In\u00a0[30]: Copied! <pre>df_pd[df_pd[\"Surname_Country_gdp_per_capita\"].isna()][\"Surname_Country\"].unique()\n</pre> df_pd[df_pd[\"Surname_Country_gdp_per_capita\"].isna()][\"Surname_Country\"].unique() Out[30]: <pre>array(['Taiwan', 'Macau', 'Turkey', 'Ivory Coast',\n       'Democratic Republic of the Congo'], dtype=object)</pre> In\u00a0[31]: Copied! <pre># Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita\ndf_pd.loc[df_pd[\"Surname_Country\"] == \"Taiwan\", [\"Surname_Country_gdp_per_capita\"]] = (\n    32756.0\n)\n# remaining countries have some naming issues - ToBeResolved; e.g. Macau -&gt; Macao, Turkey -&gt; Turkiye, etc\ndf_pd.loc[df_pd[\"Surname_Country\"] == \"Macau\", [\"Surname_Country_gdp_per_capita\"]] = (\n    61230.96247\n)\ndf_pd.loc[df_pd[\"Surname_Country\"] == \"Turkey\", [\"Surname_Country_gdp_per_capita\"]] = (\n    38355.15397\n)\ndf_pd.loc[\n    df_pd[\"Surname_Country\"] == \"Ivory Coast\", [\"Surname_Country_gdp_per_capita\"]\n] = 6540.462355\ndf_pd.loc[\n    df_pd[\"Surname_Country\"] == \"Democratic Republic of the Congo\",\n    [\"Surname_Country_gdp_per_capita\"],\n] = 1337.834149\n</pre> # Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita df_pd.loc[df_pd[\"Surname_Country\"] == \"Taiwan\", [\"Surname_Country_gdp_per_capita\"]] = (     32756.0 ) # remaining countries have some naming issues - ToBeResolved; e.g. Macau -&gt; Macao, Turkey -&gt; Turkiye, etc df_pd.loc[df_pd[\"Surname_Country\"] == \"Macau\", [\"Surname_Country_gdp_per_capita\"]] = (     61230.96247 ) df_pd.loc[df_pd[\"Surname_Country\"] == \"Turkey\", [\"Surname_Country_gdp_per_capita\"]] = (     38355.15397 ) df_pd.loc[     df_pd[\"Surname_Country\"] == \"Ivory Coast\", [\"Surname_Country_gdp_per_capita\"] ] = 6540.462355 df_pd.loc[     df_pd[\"Surname_Country\"] == \"Democratic Republic of the Congo\",     [\"Surname_Country_gdp_per_capita\"], ] = 1337.834149 In\u00a0[32]: Copied! <pre>df_pd.to_parquet(\"data/dataset_auxiliary_features_raw.parquet\")\n</pre> df_pd.to_parquet(\"data/dataset_auxiliary_features_raw.parquet\") In\u00a0[33]: Copied! <pre>df_pd.columns\n</pre> df_pd.columns Out[33]: <pre>Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Country',\n       'Gender', 'Age', 'Tenure', 'CustomerFeedback', 'Balance (EUR)',\n       'NumberOfProducts', 'HasCreditCard', 'IsActiveMember',\n       'EstimatedSalary', 'Exited', 'CustomerFeedback_language',\n       'CustomerFeedback_language_score', 'CustomerFeedback_sentiment3',\n       'CustomerFeedback_sentiment3_score', 'CustomerFeedback_sentiment5',\n       'CustomerFeedback_sentiment5_score', 'Surname_Country',\n       'Surname_Country_region', 'Surname_Country_subregion', 'Country_region',\n       'Country_subregion', 'is_native', 'Country_hemisphere',\n       'Country_gdp_per_capita', 'Country_IncomeGroup',\n       'Surname_Country_gdp_per_capita', 'Surname_Country_IncomeGroup',\n       'working_class', 'stage_of_life', 'generation'],\n      dtype='object')</pre> In\u00a0[34]: Copied! <pre>df_pd.drop(\n    columns=[\n        \"RowNumber\",\n        \"Surname\",\n        \"CustomerFeedback\",\n        \"CustomerFeedback_language\",\n        \"CustomerFeedback_language_score\",\n        \"CustomerFeedback_sentiment3_score\",\n        \"CustomerFeedback_sentiment5_score\",\n    ]\n).to_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\")\n</pre> df_pd.drop(     columns=[         \"RowNumber\",         \"Surname\",         \"CustomerFeedback\",         \"CustomerFeedback_language\",         \"CustomerFeedback_language_score\",         \"CustomerFeedback_sentiment3_score\",         \"CustomerFeedback_sentiment5_score\",     ] ).to_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\")"},{"location":"notebooks/00_auxiliary_features.html#add-auxiliary-features-to-the-dataset","title":"Add auxiliary features to the dataset\u00b6","text":"<p><code>sudo apt install nvidia-utils-535</code> to get GPU running</p>"},{"location":"notebooks/00_auxiliary_features.html#sentiment-analysis","title":"Sentiment analysis\u00b6","text":""},{"location":"notebooks/00_auxiliary_features.html#cleaning-and-langauge-detection","title":"Cleaning and langauge detection\u00b6","text":""},{"location":"notebooks/00_auxiliary_features.html#sentiment-analysis-prediction","title":"Sentiment analysis prediction\u00b6","text":""},{"location":"notebooks/00_auxiliary_features.html#surname-origin-country","title":"Surname origin country\u00b6","text":""},{"location":"notebooks/00_auxiliary_features.html#additional-country-info","title":"Additional country info\u00b6","text":""},{"location":"notebooks/00_auxiliary_features.html#country-region-and-subregion","title":"Country region and subregion\u00b6","text":""},{"location":"notebooks/00_auxiliary_features.html#native-or-not","title":"Native or not\u00b6","text":"<ul> <li>if the client is possibly an immigrant(from immigrant family?) or not</li> </ul>"},{"location":"notebooks/00_auxiliary_features.html#country-hemisphere","title":"Country hemisphere\u00b6","text":"<ul> <li>could be usefull with some timestamp combination to know the season(oposite season in hemispeheres) etc.; anything that might affect mood/behaviour</li> </ul>"},{"location":"notebooks/00_auxiliary_features.html#country-gdpp","title":"Country gdpp\u00b6","text":"<ul> <li>check 'wealthiness' of the current and origin country</li> </ul>"},{"location":"notebooks/00_auxiliary_features.html#additional-age-info","title":"Additional age info\u00b6","text":""},{"location":"notebooks/00_auxiliary_features.html#appendix-tbd","title":"Appendix [TBD]\u00b6","text":"<ul> <li>analyze score and probabilities of the sentiment and country detection methods</li> <li>make an object or procedure to do this automatically</li> </ul>"},{"location":"notebooks/00_auxiliary_features_surname_origin_country_classification.html","title":"TBD_00_auxiliary_features_surname_origin_country_classification.ipynb","text":"In\u00a0[1]: Copied! <pre>import os\nimport sys\n\nsys.path.append(os.getcwd())\nos.chdir(\"../../...\")\n\nimport pandas as pd\nimport torch\nfrom churn_pred.preprocessing import surname_classification\nfrom churn_pred import utils\nfrom torch.utils.data import DataLoader\nimport pickle\nfrom transformers import (\n    BertForSequenceClassification,\n    get_linear_schedule_with_warmup,\n    BertTokenizer,\n)\nfrom transformers import BertForSequenceClassification\n</pre> import os import sys  sys.path.append(os.getcwd()) os.chdir(\"../../...\")  import pandas as pd import torch from churn_pred.preprocessing import surname_classification from churn_pred import utils from torch.utils.data import DataLoader import pickle from transformers import (     BertForSequenceClassification,     get_linear_schedule_with_warmup,     BertTokenizer, ) from transformers import BertForSequenceClassification In\u00a0[2]: Copied! <pre>surname_data = pd.read_csv(\"data/surnames/surname-nationality.csv\")\nsplitted_data = pd.read_csv(\"data/surnames/surnames_with_splits.csv\")\n</pre> surname_data = pd.read_csv(\"data/surnames/surname-nationality.csv\") splitted_data = pd.read_csv(\"data/surnames/surnames_with_splits.csv\") In\u00a0[5]: Copied! <pre>splitted_data\n</pre> splitted_data Out[5]: nationality nationality_index split surname 0 Arabic 15 train Totah 1 Arabic 15 train Abboud 2 Arabic 15 train Fakhoury 3 Arabic 15 train Srour 4 Arabic 15 train Sayegh ... ... ... ... ... 10975 Vietnamese 11 test Dinh 10976 Vietnamese 11 test Phung 10977 Vietnamese 11 test Quang 10978 Vietnamese 11 test Vu 10979 Vietnamese 11 test Ha <p>10980 rows \u00d7 4 columns</p> In\u00a0[6]: Copied! <pre>surname_data.surname.nunique()\n</pre> surname_data.surname.nunique() Out[6]: <pre>30923</pre> In\u00a0[4]: Copied! <pre>splitted_data[\"nationality_index\"]\n</pre> splitted_data[\"nationality_index\"] Out[4]: <pre>0        15\n1        15\n2        15\n3        15\n4        15\n         ..\n10975    11\n10976    11\n10977    11\n10978    11\n10979    11\nName: nationality_index, Length: 10980, dtype: int64</pre> In\u00a0[3]: Copied! <pre>labeld_decoder = dict(\n    zip(splitted_data[\"nationality\"], splitted_data[\"nationality_index\"])\n)\ninverse_label_dict = {v: k for k, v in labeld_decoder.items()}\n</pre> labeld_decoder = dict(     zip(splitted_data[\"nationality\"], splitted_data[\"nationality_index\"]) ) inverse_label_dict = {v: k for k, v in labeld_decoder.items()} In\u00a0[4]: Copied! <pre># Split data into train, validation, and test sets\ntrain_df = splitted_data[splitted_data[\"split\"] == \"train\"]\nval_df = splitted_data[\n    splitted_data[\"split\"] == \"val\"\n]  # Assuming \"val\" is used for validation set\ntest_df = splitted_data[splitted_data[\"split\"] == \"test\"]\n</pre> # Split data into train, validation, and test sets train_df = splitted_data[splitted_data[\"split\"] == \"train\"] val_df = splitted_data[     splitted_data[\"split\"] == \"val\" ]  # Assuming \"val\" is used for validation set test_df = splitted_data[splitted_data[\"split\"] == \"test\"] In\u00a0[5]: Copied! <pre># Tokenize each dataset\ntrain_encodings = surname_classification.tokenize_data(train_df)\nval_encodings = surname_classification.tokenize_data(val_df)\ntest_encodings = surname_classification.tokenize_data(test_df)\n</pre> # Tokenize each dataset train_encodings = surname_classification.tokenize_data(train_df) val_encodings = surname_classification.tokenize_data(val_df) test_encodings = surname_classification.tokenize_data(test_df) In\u00a0[8]: Copied! <pre>batch_size = 32\n\ntrain_dataset = surname_classification.create_dataset(train_encodings, train_df)\nval_dataset = surname_classification.create_dataset(val_encodings, val_df)\ntest_dataset = surname_classification.create_dataset(test_encodings, test_df)\n\ntrain_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\nval_dataloader = DataLoader(val_dataset, batch_size=batch_size)\ntest_dataloader = DataLoader(test_dataset, batch_size=batch_size)\n</pre> batch_size = 32  train_dataset = surname_classification.create_dataset(train_encodings, train_df) val_dataset = surname_classification.create_dataset(val_encodings, val_df) test_dataset = surname_classification.create_dataset(test_encodings, test_df)  train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) val_dataloader = DataLoader(val_dataset, batch_size=batch_size) test_dataloader = DataLoader(test_dataset, batch_size=batch_size) In\u00a0[9]: Copied! <pre>num_labels = splitted_data[\"nationality_index\"].nunique()\nnum_epochs = 10\nmodel = BertForSequenceClassification.from_pretrained(\n    \"bert-base-cased\", num_labels=num_labels, hidden_dropout_prob=0.4\n)\n# freeze layers\nfor param in model.bert.parameters():\n    param.requires_grad = False\n\noptimizer = surname_classification.create_optimizer(model, learning_rate=5e-5, eps=1e-8)\ntotal_steps = len(train_dataloader) * num_epochs\n\nscheduler = get_linear_schedule_with_warmup(\n    optimizer,\n    num_warmup_steps=0.2,  # Usually a fraction of total_steps\n    num_training_steps=total_steps,\n)\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nmodel.to(device)\n</pre> num_labels = splitted_data[\"nationality_index\"].nunique() num_epochs = 10 model = BertForSequenceClassification.from_pretrained(     \"bert-base-cased\", num_labels=num_labels, hidden_dropout_prob=0.4 ) # freeze layers for param in model.bert.parameters():     param.requires_grad = False  optimizer = surname_classification.create_optimizer(model, learning_rate=5e-5, eps=1e-8) total_steps = len(train_dataloader) * num_epochs  scheduler = get_linear_schedule_with_warmup(     optimizer,     num_warmup_steps=0.2,  # Usually a fraction of total_steps     num_training_steps=total_steps, )  device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") model.to(device) <pre>Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n/mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n  warnings.warn(\n</pre> Out[9]: <pre>BertForSequenceClassification(\n  (bert): BertModel(\n    (embeddings): BertEmbeddings(\n      (word_embeddings): Embedding(28996, 768, padding_idx=0)\n      (position_embeddings): Embedding(512, 768)\n      (token_type_embeddings): Embedding(2, 768)\n      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n      (dropout): Dropout(p=0.4, inplace=False)\n    )\n    (encoder): BertEncoder(\n      (layer): ModuleList(\n        (0-11): 12 x BertLayer(\n          (attention): BertAttention(\n            (self): BertSelfAttention(\n              (query): Linear(in_features=768, out_features=768, bias=True)\n              (key): Linear(in_features=768, out_features=768, bias=True)\n              (value): Linear(in_features=768, out_features=768, bias=True)\n              (dropout): Dropout(p=0.1, inplace=False)\n            )\n            (output): BertSelfOutput(\n              (dense): Linear(in_features=768, out_features=768, bias=True)\n              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n              (dropout): Dropout(p=0.4, inplace=False)\n            )\n          )\n          (intermediate): BertIntermediate(\n            (dense): Linear(in_features=768, out_features=3072, bias=True)\n            (intermediate_act_fn): GELUActivation()\n          )\n          (output): BertOutput(\n            (dense): Linear(in_features=3072, out_features=768, bias=True)\n            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n            (dropout): Dropout(p=0.4, inplace=False)\n          )\n        )\n      )\n    )\n    (pooler): BertPooler(\n      (dense): Linear(in_features=768, out_features=768, bias=True)\n      (activation): Tanh()\n    )\n  )\n  (dropout): Dropout(p=0.4, inplace=False)\n  (classifier): Linear(in_features=768, out_features=18, bias=True)\n)</pre> In\u00a0[10]: Copied! <pre>trained_model = surname_classification.train_model(\n    model,\n    train_dataloader,\n    val_dataloader,\n    optimizer,\n    scheduler,\n    device,\n    num_epochs=num_epochs,\n)\n</pre> trained_model = surname_classification.train_model(     model,     train_dataloader,     val_dataloader,     optimizer,     scheduler,     device,     num_epochs=num_epochs, ) <p>log from running a script on a server</p> <pre><code>(ecovadis) pmulinka@supercom-wssonata:~/assignment/ecovadis_assignment$ python notebooks/eda/surname_classification_with_bert.py\n/home/pmulinka/assignment/ecovadis_assignment\nSome weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n/home/pmulinka/assignment/ecovadis/lib/python3.10/site-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n  warnings.warn(\n\nEpoch 1/10\nTraining Loss: 2.394\nValidation Loss: 2.256, Validation Accuracy: 0.267\n\nEpoch 2/10\nTraining Loss: 2.267\nValidation Loss: 2.251, Validation Accuracy: 0.267\n\nEpoch 3/10\nTraining Loss: 2.265\nValidation Loss: 2.243, Validation Accuracy: 0.306\n\nEpoch 4/10\nTraining Loss: 2.250\nValidation Loss: 2.237, Validation Accuracy: 0.385\n\nEpoch 5/10\nTraining Loss: 2.251\nValidation Loss: 2.227, Validation Accuracy: 0.317\n\nEpoch 6/10\nTraining Loss: 1.620\nValidation Loss: 0.982, Validation Accuracy: 0.735\n\nEpoch 7/10\nTraining Loss: 1.062\nValidation Loss: 0.826, Validation Accuracy: 0.772\n\nEpoch 8/10\nTraining Loss: 0.911\nValidation Loss: 0.799, Validation Accuracy: 0.785\n\nEpoch 9/10\nTraining Loss: 0.832\nValidation Loss: 0.775, Validation Accuracy: 0.791\n\nEpoch 10/10\nTraining Loss: 0.780\nValidation Loss: 0.768, Validation Accuracy: 0.790\nTraining complete\nEvaluation on test dataset yield\nLoss: 0.7684005849206677\nAccuracy: 0.7960164835164836\n</code></pre> In\u00a0[\u00a0]: Copied! <pre>loss, accuracy = surname_classification.evaluate_model(\n    trained_model, test_dataloader, device\n)\nprint(f\"Evaluation on test dataset yield\\nLoss: {loss}\\nAccuracy: {accuracy}\")\n</pre> loss, accuracy = surname_classification.evaluate_model(     trained_model, test_dataloader, device ) print(f\"Evaluation on test dataset yield\\nLoss: {loss}\\nAccuracy: {accuracy}\") In\u00a0[8]: Copied! <pre>inverse_label_dict\n</pre> inverse_label_dict Out[8]: <pre>{15: 'Arabic',\n 3: 'Chinese',\n 5: 'Czech',\n 2: 'Dutch',\n 12: 'English',\n 8: 'French',\n 9: 'German',\n 4: 'Greek',\n 1: 'Irish',\n 17: 'Italian',\n 7: 'Japanese',\n 16: 'Korean',\n 14: 'Polish',\n 0: 'Portuguese',\n 13: 'Russian',\n 10: 'Scottish',\n 6: 'Spanish',\n 11: 'Vietnamese'}</pre> In\u00a0[7]: Copied! <pre>utils.dill_dump(\n    file_loc=\"data/surnames/inverse_label_dict.dill\", content=inverse_label_dict\n)\ninverse_label_dict = utils.dill_load(file_loc=\"data/surnames/inverse_label_dict.dill\")\n</pre> utils.dill_dump(     file_loc=\"data/surnames/inverse_label_dict.dill\", content=inverse_label_dict ) inverse_label_dict = utils.dill_load(file_loc=\"data/surnames/inverse_label_dict.dill\") In\u00a0[\u00a0]: Copied! <pre># Save the model\ntorch.save(trained_model.state_dict(), \"surname_model_state_dict.pth\")\ntorch.save(trained_model, \"data/surnames/surname_model.pth\")\nutils.json_dump(\n    file_loc=\"data/surnames/inverse_label_dict.json\", content=inverse_label_dict\n)\n</pre> # Save the model torch.save(trained_model.state_dict(), \"surname_model_state_dict.pth\") torch.save(trained_model, \"data/surnames/surname_model.pth\") utils.json_dump(     file_loc=\"data/surnames/inverse_label_dict.json\", content=inverse_label_dict ) In\u00a0[10]: Copied! <pre># Load the model\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ntrained_model = torch.load(\"data/surnames/surname_model.pth\", map_location=device)\ninverse_label_dict = utils.dill_load(file_loc=\"data/surnames/inverse_label_dict.dill\")\n# model.eval()\n# trained_model.load_state_dict(torch.load(\"surname_model.pth\"))\n</pre> # Load the model device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") trained_model = torch.load(\"data/surnames/surname_model.pth\", map_location=device) inverse_label_dict = utils.dill_load(file_loc=\"data/surnames/inverse_label_dict.dill\") # model.eval() # trained_model.load_state_dict(torch.load(\"surname_model.pth\")) In\u00a0[15]: Copied! <pre>predicted_surnames\n</pre> predicted_surnames Out[15]: <pre>[('Hsieh', 'English'),\n ('McDonald', 'Spanish'),\n ('Kharlamov', 'Arabic'),\n ('Vasiliev', 'English'),\n ('Bellucci', 'English'),\n ('Wallace', 'English'),\n ('Chao', 'Chinese'),\n ('Boylan', 'English'),\n ('Burgess', 'Chinese'),\n ('Cattaneo', 'English')]</pre> In\u00a0[14]: Copied! <pre>df_pd = pd.read_csv(\"data/dataset.csv\")\nuq_surnames = df_pd.Surname.unique()\ntokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\npredicted_surnames = surname_classification.predict_nationality(\n    trained_model, uq_surnames[:10], tokenizer, inverse_label_dict, device\n)\n</pre> df_pd = pd.read_csv(\"data/dataset.csv\") uq_surnames = df_pd.Surname.unique() tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\") predicted_surnames = surname_classification.predict_nationality(     trained_model, uq_surnames[:10], tokenizer, inverse_label_dict, device ) <pre>Predicting Nationalities:   0%|          | 0/10 [00:00&lt;?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n/mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2674: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n  warnings.warn(\n                                                                         \r</pre> In\u00a0[\u00a0]: Copied! <pre>predicted_df = pd.DataFrame(predicted_surnames, columns=[\"Surname\", \"Nationality\"])\npredicted_df.to_csv(\"predicted_surnames_dataset.csv\")\npredicted_df.head()\n</pre> predicted_df = pd.DataFrame(predicted_surnames, columns=[\"Surname\", \"Nationality\"]) predicted_df.to_csv(\"predicted_surnames_dataset.csv\") predicted_df.head() In\u00a0[\u00a0]: Copied! <pre>\n</pre>"},{"location":"notebooks/00_auxiliary_features_surname_origin_country_classification.html#this-is-a-work-in-progress","title":"THIS IS A WORK IN PROGRESS\u00b6","text":""},{"location":"notebooks/00_auxiliary_features_surname_origin_country_classification.html#surname-origin-country-classification","title":"Surname origin country classification\u00b6","text":"<ul> <li>approach shamelessly copied from https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert</li> <li>NOTE from author:</li> </ul> <pre><code>Based on the discussion [here](https://www.kaggle.com/competitions/playground-series-s4e1/discussion/465517), I created a Bert based Surname classifier.\n</code></pre> <ul> <li>possible improvement of the base dataset by scraping wiki: https://github.com/greenelab/wiki-nationality-estimate</li> </ul>"},{"location":"notebooks/01_feature_analysis.html","title":"01_feature_analysis","text":"In\u00a0[1]: Copied! <pre>import os\nimport sys\n\nsys.path.append(os.getcwd())\nos.chdir(\"../..\")\n\nimport pandas as pd\n\n# import plotly.graph_objects as go\nfrom churn_pred.eda.features.analysis import (\n    missing,\n    zero,\n    nunique,\n    init_check,\n    std,\n    entropy,\n    nunique,\n)\n\npd.set_option(\"display.max_columns\", 100)\npd.set_option(\"display.max_rows\", 300)\nfrom churn_pred.eda.features.plotting import cross_correlation, distributions\n</pre> import os import sys  sys.path.append(os.getcwd()) os.chdir(\"../..\")  import pandas as pd  # import plotly.graph_objects as go from churn_pred.eda.features.analysis import (     missing,     zero,     nunique,     init_check,     std,     entropy,     nunique, )  pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from churn_pred.eda.features.plotting import cross_correlation, distributions In\u00a0[2]: Copied! <pre>df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\")\ndf_pd.head()\n</pre> df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[2]: CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In\u00a0[3]: Copied! <pre>target_col = \"Exited\"\nid_cols = [\"CustomerId\"]\ncat_cols = [\n    \"Country\",\n    \"Gender\",\n    \"HasCreditCard\",\n    \"IsActiveMember\",\n    \"CustomerFeedback_sentiment3\",\n    \"CustomerFeedback_sentiment5\",\n    \"Surname_Country\",\n    \"Surname_Country_region\",\n    \"Surname_Country_subregion\",\n    \"Country_region\",\n    \"Country_subregion\",\n    \"is_native\",\n    \"Country_hemisphere\",\n    \"Country_IncomeGroup\",\n    \"Surname_Country_IncomeGroup\",\n    \"working_class\",\n    \"stage_of_life\",\n    \"generation\",\n]\ncont_cols = df_pd.drop(\n    columns=id_cols + cat_cols + [target_col]\n).columns.values.tolist()\n</pre> target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [     \"Country\",     \"Gender\",     \"HasCreditCard\",     \"IsActiveMember\",     \"CustomerFeedback_sentiment3\",     \"CustomerFeedback_sentiment5\",     \"Surname_Country\",     \"Surname_Country_region\",     \"Surname_Country_subregion\",     \"Country_region\",     \"Country_subregion\",     \"is_native\",     \"Country_hemisphere\",     \"Country_IncomeGroup\",     \"Surname_Country_IncomeGroup\",     \"working_class\",     \"stage_of_life\",     \"generation\", ] cont_cols = df_pd.drop(     columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In\u00a0[4]: Copied! <pre>df_pd[cat_cols] = df_pd[cat_cols].astype(str)\n</pre> df_pd[cat_cols] = df_pd[cat_cols].astype(str) In\u00a0[5]: Copied! <pre>duplicated_ids, cont_cols_desc, cat_cols_desc = init_check(\n    df=df_pd,\n    identifier=id_cols,\n    cat_cols=cat_cols,\n    cont_cols=cont_cols,\n    verbose=True,\n)\n\n# duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head()\n</pre> duplicated_ids, cont_cols_desc, cat_cols_desc = init_check(     df=df_pd,     identifier=id_cols,     cat_cols=cat_cols,     cont_cols=cont_cols,     verbose=True, )  # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() <pre>[CHECK] Number of duplicated ids: 0\n[CHECK] Numerical columns\n                                  count       mean       std       min  \\\nAge                             10000.0      38.92     10.49     18.00   \nEstimatedSalary                 10000.0  100090.24  57510.49     11.58   \nBalance (EUR)                   10000.0   76485.89  62397.41      0.00   \nCreditScore                     10000.0     650.53     96.65    350.00   \nTenure                          10000.0       5.01      2.89      0.00   \nCountry_gdp_per_capita          10000.0   57651.01   6330.96  48685.50   \nNumberOfProducts                10000.0       1.53      0.58      1.00   \nSurname_Country_gdp_per_capita  10000.0   51228.52  25976.50   1337.83   \n\n                                     25%        50%        75%        max  \nAge                                32.00      37.00      44.00      92.00  \nEstimatedSalary                 51002.11  100193.91  149388.25  199992.48  \nBalance (EUR)                       0.00   97198.54  127644.24  250898.09  \nCreditScore                       584.00     652.00     718.00     850.00  \nTenure                              3.00       5.00       7.00      10.00  \nCountry_gdp_per_capita          57594.03   57594.03   66616.02   66616.02  \nNumberOfProducts                    1.00       1.00       2.00       4.00  \nSurname_Country_gdp_per_capita  33525.30   57460.51   76329.58  133822.76  \n[CHECK] Categorical columns\n                             count unique               top   freq\nGender                       10000      2              Male   5457\nSurname_Country_subregion    10000     18  Northern America   2410\nCountry_subregion            10000      2    Western Europe   7523\nCountry_hemisphere           10000      1          northern  10000\nCountry_region               10000      1            Europe  10000\nis_native                    10000      2                 0   9914\nSurname_Country              10000     89     United States   2401\nCustomerFeedback_sentiment3  10000      3           neutral   8352\nSurname_Country_region       10000      4            Europe   4392\nworking_class                10000      2       working_age   9736\nstage_of_life                10000      4             adult   6330\nIsActiveMember               10000      2                 1   5151\nCustomerFeedback_sentiment5  10000      5           2 stars   7121\nHasCreditCard                10000      2                 1   7055\ngeneration                   10000      6       millennials   6367\nCountry_IncomeGroup          10000      1       High income  10000\nSurname_Country_IncomeGroup  10000      5       High income   6686\nCountry                      10000      3            France   5014\n</pre> In\u00a0[6]: Copied! <pre>missing_val_frac, fig = missing(\n    df=df_pd.drop(columns=id_cols),\n    scale=\"linear\",\n    plot=True,\n)\n\nmissing_val_frac.head()\n</pre> missing_val_frac, fig = missing(     df=df_pd.drop(columns=id_cols),     scale=\"linear\",     plot=True, )  missing_val_frac.head() Out[6]: <pre>CreditScore                    0.0\nSurname_Country_region         0.0\nstage_of_life                  0.0\nworking_class                  0.0\nSurname_Country_IncomeGroup    0.0\ndtype: float64</pre> In\u00a0[7]: Copied! <pre>zero_val_frac, fig = zero(\n    df=df_pd.drop(columns=id_cols),\n    scale=\"linear\",\n    plot=True,\n)\n\nzero_val_frac.head()\n</pre> zero_val_frac, fig = zero(     df=df_pd.drop(columns=id_cols),     scale=\"linear\",     plot=True, )  zero_val_frac.head() Out[7]: <pre>Exited                       0.7963\nBalance (EUR)                0.3617\nTenure                       0.0413\nCreditScore                  0.0000\nSurname_Country_subregion    0.0000\ndtype: float64</pre> In\u00a0[8]: Copied! <pre>data_nunique, fig = nunique(\n    df=df_pd.drop(columns=id_cols),\n    scale=\"log\",\n    plot=True,\n)\n\ndata_nunique.head()\n</pre> data_nunique, fig = nunique(     df=df_pd.drop(columns=id_cols),     scale=\"log\",     plot=True, )  data_nunique.head() Out[8]: <pre>Country_IncomeGroup    1\nCountry_hemisphere     1\nCountry_region         1\nIsActiveMember         2\nis_native              2\ndtype: int64</pre> In\u00a0[9]: Copied! <pre>data_std, fig = std(\n    df=df_pd[cont_cols],\n    scale=\"log\",\n    plot=True,\n)\n\ndata_std.head()\n</pre> data_std, fig = std(     df=df_pd[cont_cols],     scale=\"log\",     plot=True, )  data_std.head() Out[9]: <pre>Balance (EUR)                     62397.405202\nEstimatedSalary                   57510.492818\nSurname_Country_gdp_per_capita    25976.502611\nCountry_gdp_per_capita             6330.960841\nCreditScore                          96.653299\ndtype: float64</pre> In\u00a0[10]: Copied! <pre>col_entropies, fig = entropy(\n    df=df_pd.drop(columns=id_cols),\n    scale=\"linear\",\n    plot=True,\n)\n\ncol_entropies.head()\n</pre> col_entropies, fig = entropy(     df=df_pd.drop(columns=id_cols),     scale=\"linear\",     plot=True, )  col_entropies.head() Out[10]: <pre>EstimatedSalary    9.210202\nBalance (EUR)      6.246510\nCreditScore        5.873574\nAge                3.677614\nSurname_Country    2.481289\ndtype: float64</pre> In\u00a0[11]: Copied! <pre>fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True)\n</pre> fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) <pre>Top 10 absolute correlations\nBalance (EUR)     Country_gdp_per_capita            0.329762\n                  NumberOfProducts                  0.304180\nAge               NumberOfProducts                  0.030680\n                  Country_gdp_per_capita            0.029999\n                  Balance (EUR)                     0.028308\nTenure            Surname_Country_gdp_per_capita    0.017138\nNumberOfProducts  EstimatedSalary                   0.014204\nTenure            NumberOfProducts                  0.013444\nBalance (EUR)     EstimatedSalary                   0.012797\nTenure            Balance (EUR)                     0.012254\ndtype: float64\n</pre> In\u00a0[12]: Copied! <pre>fig = distributions(\n    df=df_pd[cont_cols],\n    low_per_cut=0,\n    high_per_cut=1,\n    type=\"box\",\n)\n</pre> fig = distributions(     df=df_pd[cont_cols],     low_per_cut=0,     high_per_cut=1,     type=\"box\", )"},{"location":"notebooks/01_feature_analysis.html#feature-analysis","title":"Feature analysis\u00b6","text":"<p>Purpose:</p> <ul> <li>analyse dataset features without inclusion of target value<ul> <li>if it looks healthy<ul> <li>duplicate values</li> <li>NA values</li> <li>zero/0 values</li> </ul> </li> <li>if the values are \"too constant\", ie. most [robably bad features for classifcation/regression<ul> <li>small std values</li> <li>entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\"</li> <li>number of unique values in categorical features is too high - most probably they are unable to identify any group of targets</li> </ul> </li> <li>drop highly correlated values<ul> <li>to improve performance of the model training</li> <li>to decrease dataset size for further exploration</li> </ul> </li> </ul> </li> </ul>"},{"location":"notebooks/01_feature_analysis.html#imports","title":"Imports\u00b6","text":""},{"location":"notebooks/01_feature_analysis.html#dataset","title":"Dataset\u00b6","text":""},{"location":"notebooks/01_feature_analysis.html#features","title":"Features\u00b6","text":""},{"location":"notebooks/01_feature_analysis.html#analysis","title":"Analysis\u00b6","text":""},{"location":"notebooks/01_feature_analysis.html#plotting","title":"Plotting\u00b6","text":""},{"location":"notebooks/02_target_analysis.html","title":"02_target_analysis","text":"In\u00a0[1]: Copied! <pre>import os\nimport sys\n\nsys.path.append(os.getcwd())\nos.chdir(\"../..\")\n\nimport pandas as pd\nfrom churn_pred.eda.target.analysis import correlation\nfrom churn_pred.eda.target.plotting import (\n    distributions_in_binary_cls,\n)\n</pre> import os import sys  sys.path.append(os.getcwd()) os.chdir(\"../..\")  import pandas as pd from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import (     distributions_in_binary_cls, ) In\u00a0[2]: Copied! <pre>df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\")\ndf_pd.head()\n</pre> df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[2]: CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember ... Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 ... Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 ... Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 ... Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 ... Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 ... Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z <p>5 rows \u00d7 28 columns</p> In\u00a0[3]: Copied! <pre>target_col = \"Exited\"\nid_cols = [\"CustomerId\"]\ncat_cols = [\n    \"Country\",\n    \"Gender\",\n    \"HasCreditCard\",\n    \"IsActiveMember\",\n    \"CustomerFeedback_sentiment3\",\n    \"CustomerFeedback_sentiment5\",\n    \"Surname_Country\",\n    \"Surname_Country_region\",\n    \"Surname_Country_subregion\",\n    \"Country_region\",\n    \"Country_subregion\",\n    \"is_native\",\n    \"Country_hemisphere\",\n    \"Country_IncomeGroup\",\n    \"Surname_Country_IncomeGroup\",\n    \"working_class\",\n    \"stage_of_life\",\n    \"generation\",\n]\ncont_cols = df_pd.drop(\n    columns=id_cols + cat_cols + [target_col]\n).columns.values.tolist()\n</pre> target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [     \"Country\",     \"Gender\",     \"HasCreditCard\",     \"IsActiveMember\",     \"CustomerFeedback_sentiment3\",     \"CustomerFeedback_sentiment5\",     \"Surname_Country\",     \"Surname_Country_region\",     \"Surname_Country_subregion\",     \"Country_region\",     \"Country_subregion\",     \"is_native\",     \"Country_hemisphere\",     \"Country_IncomeGroup\",     \"Surname_Country_IncomeGroup\",     \"working_class\",     \"stage_of_life\",     \"generation\", ] cont_cols = df_pd.drop(     columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In\u00a0[4]: Copied! <pre>df_pd[cat_cols] = df_pd[cat_cols].astype(str)\n</pre> df_pd[cat_cols] = df_pd[cat_cols].astype(str) In\u00a0[5]: Copied! <pre>sorted_corr_cols, fig = correlation(\n    df=df_pd[cont_cols],\n    target=df_pd[target_col],\n    scale=\"linear\",\n    plot=True,\n)\n\nsorted_corr_cols.head()\n</pre> sorted_corr_cols, fig = correlation(     df=df_pd[cont_cols],     target=df_pd[target_col],     scale=\"linear\",     plot=True, )  sorted_corr_cols.head() Out[5]: <pre>Age                               0.285323\nCountry_gdp_per_capita            0.139180\nBalance (EUR)                     0.118533\nEstimatedSalary                   0.012097\nSurname_Country_gdp_per_capita    0.002470\nName: Exited, dtype: float64</pre> In\u00a0[6]: Copied! <pre>fig = distributions_in_binary_cls(\n    df=df_pd[cont_cols],\n    target=df_pd[target_col],\n    low_per_cut=0,\n    high_per_cut=1,\n)\n</pre> fig = distributions_in_binary_cls(     df=df_pd[cont_cols],     target=df_pd[target_col],     low_per_cut=0,     high_per_cut=1, )"},{"location":"notebooks/02_target_analysis.html#target-analysis","title":"Target analysis\u00b6","text":"<p>Purpose:</p> <ul> <li>put features into context with predicted or actual target values<ul> <li>analyse feature distribution per group in binary classification</li> <li>check correlation of features with target value</li> </ul> </li> <li>check 'certainty' of the model predictions by plotting <code>prob_distrib_per_class</code> to see how certain the model is in predicting each class</li> <li>visually check predicted vs actual(ground truth values)<ul> <li>by simple scatter plot</li> <li>by 'improved scatter plot' (<code>joint_dist</code>) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values</li> </ul> </li> </ul> <p>Intended use:</p> <ul> <li>check how your model is perfroming</li> <li>check the possible connection between feature and target values (eg. if the distributions of features are different for each class)</li> </ul>"},{"location":"notebooks/02_target_analysis.html#imports","title":"Imports\u00b6","text":""},{"location":"notebooks/02_target_analysis.html#dataset","title":"Dataset\u00b6","text":""},{"location":"notebooks/02_target_analysis.html#analysis","title":"Analysis\u00b6","text":""},{"location":"notebooks/02_target_analysis.html#plotting","title":"Plotting\u00b6","text":"<p>Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset</p>"},{"location":"notebooks/03_models.html","title":"03_models","text":"In\u00a0[1]: Copied! <pre>import os\nimport sys\n\nsys.path.append(os.getcwd())\nos.chdir(\"..\")\n\nimport pandas as pd\n\npd.set_option(\"display.max_columns\", 200)\npd.set_option(\"display.max_rows\", 300)\n</pre> import os import sys  sys.path.append(os.getcwd()) os.chdir(\"..\")  import pandas as pd  pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In\u00a0[2]: Copied! <pre>import dill\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport h2o\nfrom h2o.automl import H2OAutoML\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score\nfrom sklearn.linear_model import LogisticRegression\n\nimport lightgbm as lgbm\nfrom churn_pred.preprocessing.scaler import scaler_mapper\nfrom churn_pred.training.utils import (\n    predict_cls_lgbm_from_raw,\n    predict_proba_lgbm_from_raw,\n)\nfrom churn_pred.preprocessing.label_encoder import LabelEncoder\n\n# import tracemalloc\n# import warnings\n# from typing import Optional, Dict\n# tracemalloc.start()\n</pre> import dill import numpy as np import matplotlib.pyplot as plt  import h2o from h2o.automl import H2OAutoML  from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression  import lightgbm as lgbm from churn_pred.preprocessing.scaler import scaler_mapper from churn_pred.training.utils import (     predict_cls_lgbm_from_raw,     predict_proba_lgbm_from_raw, ) from churn_pred.preprocessing.label_encoder import LabelEncoder  # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() In\u00a0[3]: Copied! <pre>valid_size = 0.2\ntest_size = 0.5\nrandom_state = 1\ntest_n_valid_combined = True\n</pre> valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In\u00a0[4]: Copied! <pre>df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\")\ndf_pd.head()\n</pre> df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[4]: CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In\u00a0[5]: Copied! <pre>target_col = \"Exited\"\nid_cols = [\"CustomerId\"]\ncat_cols = [\n    \"Country\",\n    \"Gender\",\n    \"HasCreditCard\",\n    \"IsActiveMember\",\n    \"CustomerFeedback_sentiment3\",\n    \"CustomerFeedback_sentiment5\",\n    \"Surname_Country\",\n    \"Surname_Country_region\",\n    \"Surname_Country_subregion\",\n    \"Country_region\",\n    \"Country_subregion\",\n    \"is_native\",\n    \"Country_hemisphere\",\n    \"Country_IncomeGroup\",\n    \"Surname_Country_IncomeGroup\",\n    \"working_class\",\n    \"stage_of_life\",\n    \"generation\",\n]\ncont_cols = df_pd.drop(\n    columns=id_cols + cat_cols + [target_col]\n).columns.values.tolist()\n</pre> target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [     \"Country\",     \"Gender\",     \"HasCreditCard\",     \"IsActiveMember\",     \"CustomerFeedback_sentiment3\",     \"CustomerFeedback_sentiment5\",     \"Surname_Country\",     \"Surname_Country_region\",     \"Surname_Country_subregion\",     \"Country_region\",     \"Country_subregion\",     \"is_native\",     \"Country_hemisphere\",     \"Country_IncomeGroup\",     \"Surname_Country_IncomeGroup\",     \"working_class\",     \"stage_of_life\",     \"generation\", ] cont_cols = df_pd.drop(     columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In\u00a0[6]: Copied! <pre>df_pd[cat_cols] = df_pd[cat_cols].astype(str)\n</pre> df_pd[cat_cols] = df_pd[cat_cols].astype(str) <p>check possible class imbalance</p> In\u00a0[7]: Copied! <pre>df_pd[target_col].value_counts()\n</pre> df_pd[target_col].value_counts() Out[7]: <pre>Exited\n0    7963\n1    2037\nName: count, dtype: int64</pre> In\u00a0[8]: Copied! <pre>df_train, df_valid = train_test_split(\n    df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state\n)\ndf_valid, df_test = train_test_split(\n    df_valid,\n    test_size=test_size,\n    stratify=df_valid[target_col],\n    random_state=random_state,\n)\n\ndf_train.reset_index(inplace=True, drop=True)\ndf_valid.reset_index(inplace=True, drop=True)\ndf_test.reset_index(inplace=True, drop=True)\n\nscaler_mapper_def = {\n    \"cont_cols\": StandardScaler,\n    \"cat_cols\": None,\n    \"id_cols\": None,\n}\nscaler = scaler_mapper(\n    cont_cols=cont_cols,\n    cat_cols=cat_cols,\n    id_cols=[target_col] + id_cols,\n    scaler_mapper_def=scaler_mapper_def,\n)\n\ndf_train_scaled = scaler.fit_transform(df_train)\ndf_test_scaled = scaler.transform(df_test)\ndf_valid_scaled = scaler.transform(df_valid)\n</pre> df_train, df_valid = train_test_split(     df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split(     df_valid,     test_size=test_size,     stratify=df_valid[target_col],     random_state=random_state, )  df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True)  scaler_mapper_def = {     \"cont_cols\": StandardScaler,     \"cat_cols\": None,     \"id_cols\": None, } scaler = scaler_mapper(     cont_cols=cont_cols,     cat_cols=cat_cols,     id_cols=[target_col] + id_cols,     scaler_mapper_def=scaler_mapper_def, )  df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid) In\u00a0[9]: Copied! <pre>LR_clf = LogisticRegression(class_weight=\"balanced\")\nLR_clf.fit(\n    df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols),\n    df_train_scaled[target_col],\n)\nLR_predicted = LR_clf.predict(\n    df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols)\n)\n\nprint(\n    \"LR classification report :\\n\"\n    + str(classification_report(df_test_scaled[target_col], LR_predicted))\n)\n</pre> LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit(     df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols),     df_train_scaled[target_col], ) LR_predicted = LR_clf.predict(     df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )  print(     \"LR classification report :\\n\"     + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) <pre>LR classification report :\n              precision    recall  f1-score   support\n\n           0       0.91      0.71      0.79       796\n           1       0.38      0.71      0.50       204\n\n    accuracy                           0.71      1000\n   macro avg       0.64      0.71      0.65      1000\nweighted avg       0.80      0.71      0.73      1000\n\n</pre> In\u00a0[10]: Copied! <pre># which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders?\nfrom sklearn.metrics import f1_score\n\ncriterion = f1_score\n\nthreshold_score = []\nfor t in np.arange(0.2, 0.8, 0.01):\n    # preds_bin = [int(p &gt; t) for p in y_pred]\n    preds_bin = (\n        LR_clf.predict_proba(\n            df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols)\n        )[:, 1]\n        &gt;= t\n    ).astype(int)\n    threshold_score.append(\n        (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\"))\n    )\n\nthreshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True)\nbest_threshold, best_score = threshold_score[0][0], threshold_score[0][1]\n\nprint(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\")\n</pre> # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score  criterion = f1_score  threshold_score = [] for t in np.arange(0.2, 0.8, 0.01):     # preds_bin = [int(p &gt; t) for p in y_pred]     preds_bin = (         LR_clf.predict_proba(             df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols)         )[:, 1]         &gt;= t     ).astype(int)     threshold_score.append(         (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\"))     )  threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1]  print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") <pre>The best threshold\n0.6100000000000003\n, with score:\n0.7973245061612856\n</pre> In\u00a0[11]: Copied! <pre>y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values\ny_score = LR_clf.predict_proba(\n    df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols)\n)\n\nmicro_roc_auc_per_class = roc_auc_score(\n    y_onehot_test,\n    y_score,\n    average=None,\n)\n\nmicro_roc_auc_weighted = roc_auc_score(\n    y_onehot_test,\n    y_score,\n    average=\"weighted\",\n)\n</pre> y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba(     df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )  micro_roc_auc_per_class = roc_auc_score(     y_onehot_test,     y_score,     average=None, )  micro_roc_auc_weighted = roc_auc_score(     y_onehot_test,     y_score,     average=\"weighted\", ) In\u00a0[12]: Copied! <pre>print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\")\nprint(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\")\n</pre> print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") <pre>Per class AUC:\n[0.76928761 0.76928761]\nWeighted AUC:\n0.7692876145433049\n</pre> In\u00a0[13]: Copied! <pre># initialize H2O\nh2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\")\n\n# read as h2o file\nh2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols))\nh2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols))\nh2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols))\n\n# For binary classification, response should be a factor\nh2o_train[target_col] = h2o_train[target_col].asfactor()\nh2o_valid[target_col] = h2o_valid[target_col].asfactor()\nh2o_test[target_col] = h2o_test[target_col].asfactor()\n\n# Define AML task\naml = H2OAutoML(seed=random_state, max_runtime_secs=180)\n\n# over/under sample for classification tasks\naml.balance_classes = True\n\n# Run it\n_ = aml.train(\n    x=list(cont_cols + cat_cols),\n    y=target_col,\n    training_frame=h2o_train,\n    leaderboard_frame=h2o_valid,\n)\n\nm = aml.get_best_model()\n\n# Leaderboard, show and save\nlb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\")\nprint(lb)\nprint(m.model_performance(h2o_valid))\npredictions = m.predict(h2o_test)\n\n# save results and model\n# h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True)\n# MOJO is h2o version agnostic\n# m.save_mojo(\"h2o_logs/bestmodel.zip\")\n</pre> # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\")  # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols))  # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor()  # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180)  # over/under sample for classification tasks aml.balance_classes = True  # Run it _ = aml.train(     x=list(cont_cols + cat_cols),     y=target_col,     training_frame=h2o_train,     leaderboard_frame=h2o_valid, )  m = aml.get_best_model()  # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test)  # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") <pre>Checking whether there is an H2O instance running at http://localhost:54321..... not found.\nAttempting to start a local H2O server...\n  Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing)\n  Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar\n  Ice root: /tmp/tmpaylgzon8\n  JVM stdout: /tmp/tmpaylgzon8/h2o_unknownUser_started_from_python.out\n  JVM stderr: /tmp/tmpaylgzon8/h2o_unknownUser_started_from_python.err\n  Server is running at http://127.0.0.1:54321\nConnecting to H2O server at http://127.0.0.1:54321 ... successful.\n</pre> H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.1 H2O_cluster_version_age: 1 month and 18 days H2O_cluster_name: H2O_from_python_unknownUser_3enaho H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final <pre>Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100%\nParse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100%\nParse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100%\nAutoML progress: |\n21:14:36.227: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\u2588\u2588\n21:14:47.43: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\n21:14:50.191: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\n21:14:58.843: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\n21:14:59.823: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\n21:15:06.470: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\u2588\n21:15:14.629: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\n21:15:18.475: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\n21:15:22.46: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\n21:15:25.584: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\n21:15:26.398: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n21:15:27.398: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\n21:15:33.757: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\u2588\n21:15:41.23: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\n21:15:44.787: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\n21:15:51.131: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\n21:15:52.50: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\n21:17:30.497: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n21:17:31.185: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere]\n\n\u2588\u2588| (done) 100%\nmodel_id                                                      auc    logloss     aucpr    mean_per_class_error      rmse        mse    training_time_ms    predict_time_per_row_ms  algo\nStackedEnsemble_BestOfFamily_4_AutoML_1_20240501_211436  0.880101   0.321353  0.718049                0.19825   0.313431  0.0982391                 587                   0.08015   StackedEnsemble\nGBM_grid_1_AutoML_1_20240501_211436_model_7              0.880077   0.324523  0.715814                0.227877  0.315328  0.0994315                1275                   0.048422  GBM\nGBM_grid_1_AutoML_1_20240501_211436_model_2              0.879564   0.32755   0.700805                0.214585  0.317341  0.100705                 1284                   0.047805  GBM\nGBM_5_AutoML_1_20240501_211436                           0.879162   0.324106  0.720173                0.206754  0.31347   0.0982632                1324                   0.030257  GBM\nGBM_grid_1_AutoML_1_20240501_211436_model_6              0.876891   0.329434  0.696693                0.223137  0.318794  0.10163                  1338                   0.048563  GBM\nStackedEnsemble_AllModels_3_AutoML_1_20240501_211436     0.876439   0.326157  0.715138                0.201247  0.316194  0.0999788                1252                   0.067674  StackedEnsemble\nStackedEnsemble_BestOfFamily_3_AutoML_1_20240501_211436  0.875775   0.325977  0.719837                0.217583  0.315933  0.0998134                 814                   0.073439  StackedEnsemble\nGBM_3_AutoML_1_20240501_211436                           0.875664   0.333065  0.70305                 0.232269  0.318954  0.101732                 1345                   0.043399  GBM\nStackedEnsemble_BestOfFamily_2_AutoML_1_20240501_211436  0.875284   0.330522  0.690455                0.223718  0.318198  0.10125                   720                   0.060753  StackedEnsemble\nStackedEnsemble_AllModels_2_AutoML_1_20240501_211436     0.875203   0.326942  0.718431                0.225461  0.316482  0.100161                  847                   0.080788  StackedEnsemble\n[36 rows x 10 columns]\n\nModelMetricsBinomialGLM: stackedensemble\n** Reported on test data. **\n\nMSE: 0.09823909719810361\nRMSE: 0.31343116819822436\nLogLoss: 0.3213528582843091\nAUC: 0.8801014889579766\nAUCPR: 0.7180486621898251\nGini: 0.7602029779159531\nNull degrees of freedom: 999\nResidual degrees of freedom: 995\nNull deviance: 1009.0700428262837\nResidual deviance: 642.7057165686182\nAIC: 652.7057165686182\n\nConfusion Matrix (Act/Pred) for max f1 @ threshold = 0.28080823635190166\n       0    1    Error    Rate\n-----  ---  ---  -------  --------------\n0      693  104  0.1305   (104.0/797.0)\n1      54   149  0.266    (54.0/203.0)\nTotal  747  253  0.158    (158.0/1000.0)\n\nMaximum Metrics: Maximum metrics at their respective thresholds\nmetric                       threshold    value     idx\n---------------------------  -----------  --------  -----\nmax f1                       0.280808     0.653509  191\nmax f2                       0.160323     0.717949  247\nmax f0point5                 0.663482     0.710974  85\nmax accuracy                 0.499443     0.872     122\nmax precision                0.991398     1         0\nmax recall                   0.0175152    1         376\nmax specificity              0.991398     1         0\nmax absolute_mcc             0.449611     0.574975  137\nmax min_per_class_accuracy   0.187182     0.795483  232\nmax mean_per_class_accuracy  0.280808     0.80175   191\nmax tns                      0.991398     797       0\nmax fns                      0.991398     202       0\nmax fps                      0.0032163    797       399\nmax tps                      0.0175152    203       376\nmax tnr                      0.991398     1         0\nmax fnr                      0.991398     0.995074  0\nmax fpr                      0.0032163    1         399\nmax tpr                      0.0175152    1         376\n\nGains/Lift Table: Avg response rate: 20.30 %, avg score: 20.95 %\ngroup    cumulative_data_fraction    lower_threshold    lift       cumulative_lift    response_rate    score       cumulative_response_rate    cumulative_score    capture_rate    cumulative_capture_rate    gain      cumulative_gain    kolmogorov_smirnov\n-------  --------------------------  -----------------  ---------  -----------------  ---------------  ----------  --------------------------  ------------------  --------------  -------------------------  --------  -----------------  --------------------\n1        0.01                        0.940779           4.92611    4.92611            1                0.964295    1                           0.964295            0.0492611       0.0492611                  392.611   392.611            0.0492611\n2        0.02                        0.904438           3.94089    4.4335             0.8              0.92715     0.9                         0.945722            0.0394089       0.08867                    294.089   343.35             0.0861605\n3        0.03                        0.878852           4.92611    4.5977             1                0.893414    0.933333                    0.928286            0.0492611       0.137931                   392.611   359.77             0.135422\n4        0.04                        0.867626           4.92611    4.6798             1                0.871928    0.95                        0.914197            0.0492611       0.187192                   392.611   367.98             0.184683\n5        0.05                        0.834646           4.4335     4.63054            0.9              0.850263    0.94                        0.90141             0.044335        0.231527                   343.35    363.054            0.227763\n6        0.1                         0.687835           3.64532    4.13793            0.74             0.771377    0.84                        0.836393            0.182266        0.413793                   264.532   313.793            0.393718\n7        0.15                        0.508264           2.6601     3.64532            0.54             0.608741    0.74                        0.760509            0.133005        0.546798                   166.01    264.532            0.497865\n8        0.2                         0.388087           1.67488    3.15271            0.34             0.448371    0.64                        0.682474            0.0837438       0.630542                   67.4877   215.271            0.540203\n9        0.3                         0.207693           1.47783    2.59442            0.3              0.291717    0.526667                    0.552222            0.147783        0.778325                   47.7833   159.442            0.600157\n10       0.4                         0.130789           0.640394   2.10591            0.13             0.165365    0.4275                      0.455508            0.0640394       0.842365                   -35.9606  110.591            0.555037\n11       0.5                         0.0857897          0.640394   1.81281            0.13             0.104819    0.368                       0.38537             0.0640394       0.906404                   -35.9606  81.2808            0.509917\n12       0.6                         0.057346           0.44335    1.58456            0.09             0.0704711   0.321667                    0.332887            0.044335        0.950739                   -55.665   58.4565            0.440074\n13       0.7                         0.0338547          0.246305   1.39338            0.05             0.0452273   0.282857                    0.291793            0.0246305       0.975369                   -75.3695  39.3385            0.345507\n14       0.8                         0.0216684          0.147783   1.23768            0.03             0.027519    0.25125                     0.258759            0.0147783       0.990148                   -85.2217  23.7685            0.238579\n15       0.9                         0.0120087          0.0985222  1.11111            0.02             0.0167146   0.225556                    0.231865            0.00985222      1                          -90.1478  11.1111            0.125471\n16       1                           0.0032163          0          1                  0                0.00824799  0.203                       0.209503            0               1                          -100      0                  0\nstackedensemble prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100%\n</pre> In\u00a0[14]: Copied! <pre>y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values\ny_score = h2o.as_list(predictions.drop([\"predict\"])).values\n\nmicro_roc_auc_per_class = roc_auc_score(\n    y_onehot_test,\n    y_score,\n    average=None,\n)\n\nmicro_roc_auc_weighted = roc_auc_score(\n    y_onehot_test,\n    y_score,\n    average=\"weighted\",\n)\n</pre> y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values  micro_roc_auc_per_class = roc_auc_score(     y_onehot_test,     y_score,     average=None, )  micro_roc_auc_weighted = roc_auc_score(     y_onehot_test,     y_score,     average=\"weighted\", ) <pre>/opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using:\n\nwith h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True):\n    pandas_df = h2o_df.as_data_frame()\n\n  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n</pre> In\u00a0[15]: Copied! <pre>print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\")\nprint(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\")\n</pre> print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") <pre>Per class AUC:\n[0.90083383 0.90083383]\nWeighted AUC:\n0.9008338259927087\n</pre> In\u00a0[18]: Copied! <pre>fig, ax = plt.subplots(figsize=(6, 6))\nn_classes = 2\n\nfor class_id in range(n_classes):\n    RocCurveDisplay.from_predictions(\n        y_onehot_test[:, class_id],\n        y_score[:, class_id],\n        name=f\"ROC curve for class {class_id}\",\n        ax=ax,\n        # plot_chance_level=(class_id == 2),\n    )\n</pre> fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2  for class_id in range(n_classes):     RocCurveDisplay.from_predictions(         y_onehot_test[:, class_id],         y_score[:, class_id],         name=f\"ROC curve for class {class_id}\",         ax=ax,         # plot_chance_level=(class_id == 2),     ) In\u00a0[19]: Copied! <pre>print(\n    \"Classification report:\\n{}\".format(\n        classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"]))\n    )\n)\n</pre> print(     \"Classification report:\\n{}\".format(         classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"]))     ) ) <pre>Classification report:\n              precision    recall  f1-score   support\n\n           0       0.94      0.90      0.92       796\n           1       0.66      0.76      0.71       204\n\n    accuracy                           0.87      1000\n   macro avg       0.80      0.83      0.81      1000\nweighted avg       0.88      0.87      0.87      1000\n\n</pre> <pre>/opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using:\n\nwith h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True):\n    pandas_df = h2o_df.as_data_frame()\n\n  warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread.  For faster conversion using\"\n</pre> In\u00a0[20]: Copied! <pre>config = {}\nconfig[\"objective\"] = \"binary\"\n\ndf_train_scaled_enc = df_train_scaled.copy()\ndf_valid_scaled_enc = df_valid_scaled.copy()\ndf_test_scaled_enc = df_test_scaled.copy()\nlgb_cat_cols = cat_cols\n\nlabel_encoder = LabelEncoder(lgb_cat_cols)\ndf_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc)\ndf_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc)\ndf_test_scaled_enc = label_encoder.transform(df_test_scaled_enc)\n\nlgbtrain = lgbm.Dataset(\n    df_train_scaled_enc.drop(columns=[target_col] + id_cols),\n    df_train_scaled_enc[target_col],\n    categorical_feature=lgb_cat_cols,\n    free_raw_data=False,\n)\nlgbvalid = lgbm.Dataset(\n    df_valid_scaled_enc.drop(columns=[target_col] + id_cols),\n    df_valid_scaled_enc[target_col],\n    reference=lgbtrain,\n    free_raw_data=False,\n)\n# Final TRAIN/TEST\nftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True)\nflgbtrain = lgbm.Dataset(\n    ftrain.drop(columns=[target_col] + id_cols),\n    ftrain[target_col],\n    categorical_feature=lgb_cat_cols,\n    free_raw_data=False,\n)\nlgbtest = lgbm.Dataset(\n    df_test_scaled_enc.drop(columns=[target_col] + id_cols),\n    df_test_scaled_enc[target_col],\n    categorical_feature=lgb_cat_cols,\n    reference=flgbtrain,\n    free_raw_data=False,\n)\n</pre> config = {} config[\"objective\"] = \"binary\"  df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols  label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc)  lgbtrain = lgbm.Dataset(     df_train_scaled_enc.drop(columns=[target_col] + id_cols),     df_train_scaled_enc[target_col],     categorical_feature=lgb_cat_cols,     free_raw_data=False, ) lgbvalid = lgbm.Dataset(     df_valid_scaled_enc.drop(columns=[target_col] + id_cols),     df_valid_scaled_enc[target_col],     reference=lgbtrain,     free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset(     ftrain.drop(columns=[target_col] + id_cols),     ftrain[target_col],     categorical_feature=lgb_cat_cols,     free_raw_data=False, ) lgbtest = lgbm.Dataset(     df_test_scaled_enc.drop(columns=[target_col] + id_cols),     df_test_scaled_enc[target_col],     categorical_feature=lgb_cat_cols,     reference=flgbtrain,     free_raw_data=False, ) In\u00a0[21]: Copied! <pre>model = lgbm.train(\n    config,\n    lgbtrain,\n    valid_sets=[lgbvalid],\n    valid_names=[\"\"],\n    feval=None,\n)\n</pre> model = lgbm.train(     config,     lgbtrain,     valid_sets=[lgbvalid],     valid_names=[\"\"],     feval=None, ) <pre>[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n[LightGBM] [Info] Number of positive: 1630, number of negative: 6370\n[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002671 seconds.\nYou can set `force_row_wise=true` to remove the overhead.\nAnd if memory is not enough, you can set `force_col_wise=true`.\n[LightGBM] [Info] Total Bins 1048\n[LightGBM] [Info] Number of data points in the train set: 8000, number of used features: 23\n[LightGBM] [Warning] Found whitespace in feature_names, replace with underlines\n[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203750 -&gt; initscore=-1.363019\n[LightGBM] [Info] Start training from score -1.363019\n</pre> In\u00a0[22]: Copied! <pre>res = predict_cls_lgbm_from_raw(\n    preds_raw=model.predict(lgbtest.data, raw_score=True),\n    task=\"binary\",\n)\n\nresult = pd.DataFrame(\n    {\n        \"predicted\": res,\n        \"ground_truth\": df_test[target_col].values,\n    }\n)\n\nprint(\n    \"Classification report:\\n{}\".format(\n        classification_report(result[\"ground_truth\"], result[\"predicted\"])\n    )\n)\n</pre> res = predict_cls_lgbm_from_raw(     preds_raw=model.predict(lgbtest.data, raw_score=True),     task=\"binary\", )  result = pd.DataFrame(     {         \"predicted\": res,         \"ground_truth\": df_test[target_col].values,     } )  print(     \"Classification report:\\n{}\".format(         classification_report(result[\"ground_truth\"], result[\"predicted\"])     ) ) <pre>Classification report:\n              precision    recall  f1-score   support\n\n           0       0.90      0.95      0.92       796\n           1       0.76      0.58      0.66       204\n\n    accuracy                           0.88      1000\n   macro avg       0.83      0.77      0.79      1000\nweighted avg       0.87      0.88      0.87      1000\n\n</pre> In\u00a0[23]: Copied! <pre>y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values\ny_score = predict_proba_lgbm_from_raw(\n    preds_raw=model.predict(lgbtest.data),\n    task=\"binary\",\n    binary2d=True,\n)\n\nmicro_roc_auc_per_class = roc_auc_score(\n    y_onehot_test,\n    y_score,\n    average=None,\n)\n\nmicro_roc_auc_weighted = roc_auc_score(\n    y_onehot_test,\n    y_score,\n    average=\"weighted\",\n)\n\nprint(f\"Per class AUC:\\n{micro_roc_auc_per_class}\")\nprint(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\")\n</pre> y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw(     preds_raw=model.predict(lgbtest.data),     task=\"binary\",     binary2d=True, )  micro_roc_auc_per_class = roc_auc_score(     y_onehot_test,     y_score,     average=None, )  micro_roc_auc_weighted = roc_auc_score(     y_onehot_test,     y_score,     average=\"weighted\", )  print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") <pre>Per class AUC:\n[0.89585797 0.89585797]\nWeighted AUC:\n0.8958579663020987\n</pre>"},{"location":"notebooks/03_models.html#models","title":"Models\u00b6","text":""},{"location":"notebooks/03_models.html#imports","title":"Imports\u00b6","text":""},{"location":"notebooks/03_models.html#dataset","title":"Dataset\u00b6","text":""},{"location":"notebooks/03_models.html#preprocessing","title":"Preprocessing\u00b6","text":"<ul> <li>divide dataset into train, test, valid</li> <li>scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...)</li> </ul>"},{"location":"notebooks/03_models.html#logistic-regression","title":"Logistic Regression\u00b6","text":""},{"location":"notebooks/03_models.html#h2o-automl","title":"H2O AutoML\u00b6","text":"<p>NOTE:</p> <p>java is required: <code>sudo apt install openjdk-17-jdk</code></p>"},{"location":"notebooks/03_models.html#lightgbm","title":"LightGBM\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html","title":"04_model_training_pipeline","text":"In\u00a0[1]: Copied! <pre>import os\nimport sys\n\nsys.path.append(os.getcwd())\nos.chdir(\"..\")\n\nimport pandas as pd\nimport mlflow\n\npd.set_option(\"display.max_columns\", 200)\npd.set_option(\"display.max_rows\", 300)\n</pre> import os import sys  sys.path.append(os.getcwd()) os.chdir(\"..\")  import pandas as pd import mlflow  pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In\u00a0[2]: Copied! <pre>import os\nimport json\nfrom typing import Literal\nimport pandas as pd\n\nfrom churn_pred.preprocessing.preprocess import PreprocessData\nfrom churn_pred.training.trainer import Trainer\nfrom churn_pred.training.optuna_optimizer import LGBOptunaOptimizer\nfrom churn_pred.training.utils import flatten_dict, get_or_create_experiment\n\nimport dill\nimport numpy as np\nfrom churn_pred.utils import dill_dump, dill_load\nfrom sklearn.model_selection import train_test_split\n\nfrom pprint import pprint\n</pre> import os import json from typing import Literal import pandas as pd  from churn_pred.preprocessing.preprocess import PreprocessData from churn_pred.training.trainer import Trainer from churn_pred.training.optuna_optimizer import LGBOptunaOptimizer from churn_pred.training.utils import flatten_dict, get_or_create_experiment  import dill import numpy as np from churn_pred.utils import dill_dump, dill_load from sklearn.model_selection import train_test_split  from pprint import pprint In\u00a0[3]: Copied! <pre># 1. get the data\ndf_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\")\ndf_pd.head()\n</pre> # 1. get the data df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[3]: CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In\u00a0[4]: Copied! <pre>target_col = \"Exited\"\nid_cols = [\"CustomerId\"]\ncat_cols = [\"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\",\"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\"]\ncont_cols = df_pd.drop(columns=id_cols + cat_cols + [target_col]).columns.values.tolist()\n</pre> target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [\"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\",\"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\"] cont_cols = df_pd.drop(columns=id_cols + cat_cols + [target_col]).columns.values.tolist() In\u00a0[5]: Copied! <pre>df_pd[cat_cols] = df_pd[cat_cols].astype(str)\n</pre> df_pd[cat_cols] = df_pd[cat_cols].astype(str) In\u00a0[28]: Copied! <pre>valid_size = 0.2\ntest_size = 0.5\nrandom_state = 1\ndf_train, df_valid = train_test_split(df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state)\ndf_valid, df_test = train_test_split(df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state)\n</pre> valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split(df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state) df_valid, df_test = train_test_split(df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state) In\u00a0[7]: Copied! <pre>prepare_data = PreprocessData(\n        id_cols=id_cols,\n        target_col=target_col,\n        cat_cols=cat_cols,\n        cont_cols=cont_cols,\n    )\n# this should be fitted only on training data\n_ = prepare_data.fit(df=df_pd)\n</pre> prepare_data = PreprocessData(         id_cols=id_cols,         target_col=target_col,         cat_cols=cat_cols,         cont_cols=cont_cols,     ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In\u00a0[8]: Copied! <pre>optimizer = LGBOptunaOptimizer(\n        objective=\"binary\",\n        n_class=2,\n    )\n\ntrainer = Trainer(\n        cat_cols=prepare_data.cat_cols,\n        target_col=prepare_data.target_col,\n        id_cols=id_cols,\n        objective=\"binary\",\n        n_class=2,\n        optimizer=optimizer,\n        preprocessors=[prepare_data],\n)\n\nmetrics_dict = trainer.fit(\n    df_train=df_train,\n    df_valid=df_valid,\n    df_test=df_test,\n    )\n</pre> optimizer = LGBOptunaOptimizer(         objective=\"binary\",         n_class=2,     )  trainer = Trainer(         cat_cols=prepare_data.cat_cols,         target_col=prepare_data.target_col,         id_cols=id_cols,         objective=\"binary\",         n_class=2,         optimizer=optimizer,         preprocessors=[prepare_data], )  metrics_dict = trainer.fit(     df_train=df_train,     df_valid=df_valid,     df_test=df_test,     ) <pre>[I 2024-06-18 21:05:47,850] A new study created in memory with name: no-name-72ade7dc-1867-41c2-b5a4-df3b58765bc7\nfeature_fraction, val_score: inf:   0%|          | 0/7 [00:00&lt;?, ?it/s]</pre> <pre>Training until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction, val_score: 0.388064:  14%|#4        | 1/7 [00:08&lt;00:48,  8.11s/it][I 2024-06-18 21:05:55,971] Trial 0 finished with value: 0.38806351066689354 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 0.38806351066689354.\nfeature_fraction, val_score: 0.388064:  14%|#4        | 1/7 [00:08&lt;00:48,  8.11s/it]</pre> <pre>Early stopping, best iteration is:\n[241]\tvalid_0's binary_logloss: 0.388064\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction, val_score: 0.384552:  29%|##8       | 2/7 [00:15&lt;00:39,  7.81s/it][I 2024-06-18 21:06:03,568] Trial 1 finished with value: 0.38455247747199556 and parameters: {'feature_fraction': 0.5}. Best is trial 1 with value: 0.38455247747199556.\nfeature_fraction, val_score: 0.384552:  29%|##8       | 2/7 [00:15&lt;00:39,  7.81s/it]</pre> <pre>Early stopping, best iteration is:\n[184]\tvalid_0's binary_logloss: 0.384552\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction, val_score: 0.381327:  43%|####2     | 3/7 [00:20&lt;00:25,  6.48s/it][I 2024-06-18 21:06:08,471] Trial 2 finished with value: 0.3813271028603074 and parameters: {'feature_fraction': 0.8}. Best is trial 2 with value: 0.3813271028603074.\nfeature_fraction, val_score: 0.381327:  43%|####2     | 3/7 [00:20&lt;00:25,  6.48s/it]</pre> <pre>Early stopping, best iteration is:\n[154]\tvalid_0's binary_logloss: 0.381327\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction, val_score: 0.381327:  57%|#####7    | 4/7 [00:22&lt;00:13,  4.64s/it][I 2024-06-18 21:06:10,281] Trial 3 finished with value: 0.40303264639386804 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 2 with value: 0.3813271028603074.\nfeature_fraction, val_score: 0.381327:  57%|#####7    | 4/7 [00:22&lt;00:13,  4.64s/it]</pre> <pre>Early stopping, best iteration is:\n[9]\tvalid_0's binary_logloss: 0.403033\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction, val_score: 0.381327:  71%|#######1  | 5/7 [00:29&lt;00:10,  5.45s/it][I 2024-06-18 21:06:17,181] Trial 4 finished with value: 0.385808915879594 and parameters: {'feature_fraction': 1.0}. Best is trial 2 with value: 0.3813271028603074.\nfeature_fraction, val_score: 0.381327:  71%|#######1  | 5/7 [00:29&lt;00:10,  5.45s/it]</pre> <pre>Early stopping, best iteration is:\n[196]\tvalid_0's binary_logloss: 0.385809\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction, val_score: 0.381327:  86%|########5 | 6/7 [00:35&lt;00:05,  5.57s/it][I 2024-06-18 21:06:22,970] Trial 5 finished with value: 0.3905588715180784 and parameters: {'feature_fraction': 0.7}. Best is trial 2 with value: 0.3813271028603074.\nfeature_fraction, val_score: 0.381327:  86%|########5 | 6/7 [00:35&lt;00:05,  5.57s/it]</pre> <pre>Early stopping, best iteration is:\n[152]\tvalid_0's binary_logloss: 0.390559\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41&lt;00:00,  5.97s/it][I 2024-06-18 21:06:29,778] Trial 6 finished with value: 0.38245734022417816 and parameters: {'feature_fraction': 0.6}. Best is trial 2 with value: 0.3813271028603074.\nfeature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41&lt;00:00,  5.99s/it]\n</pre> <pre>Early stopping, best iteration is:\n[201]\tvalid_0's binary_logloss: 0.382457\n</pre> <pre>num_leaves, val_score: 0.381327:   0%|          | 0/20 [00:00&lt;?, ?it/s]</pre> <pre>Training until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.381327:   5%|5         | 1/20 [00:06&lt;02:12,  6.99s/it][I 2024-06-18 21:06:36,772] Trial 7 finished with value: 0.3867130065464061 and parameters: {'num_leaves': 28}. Best is trial 7 with value: 0.3867130065464061.\nnum_leaves, val_score: 0.381327:   5%|5         | 1/20 [00:06&lt;02:12,  6.99s/it]</pre> <pre>Early stopping, best iteration is:\n[225]\tvalid_0's binary_logloss: 0.386713\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.370474:  10%|#         | 2/20 [00:14&lt;02:15,  7.51s/it][I 2024-06-18 21:06:44,655] Trial 8 finished with value: 0.3704743259843756 and parameters: {'num_leaves': 92}. Best is trial 8 with value: 0.3704743259843756.\nnum_leaves, val_score: 0.370474:  10%|#         | 2/20 [00:14&lt;02:15,  7.51s/it]</pre> <pre>Early stopping, best iteration is:\n[64]\tvalid_0's binary_logloss: 0.370474\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.370474:  15%|#5        | 3/20 [00:26&lt;02:42,  9.53s/it][I 2024-06-18 21:06:56,583] Trial 9 finished with value: 0.3718260260505307 and parameters: {'num_leaves': 149}. Best is trial 8 with value: 0.3704743259843756.\nnum_leaves, val_score: 0.370474:  15%|#5        | 3/20 [00:26&lt;02:42,  9.53s/it]</pre> <pre>Early stopping, best iteration is:\n[49]\tvalid_0's binary_logloss: 0.371826\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.370474:  20%|##        | 4/20 [00:44&lt;03:21, 12.59s/it][I 2024-06-18 21:07:13,876] Trial 10 finished with value: 0.37095297012345396 and parameters: {'num_leaves': 248}. Best is trial 8 with value: 0.3704743259843756.\nnum_leaves, val_score: 0.370474:  20%|##        | 4/20 [00:44&lt;03:21, 12.59s/it]</pre> <pre>Early stopping, best iteration is:\n[41]\tvalid_0's binary_logloss: 0.370953\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.370474:  25%|##5       | 5/20 [00:59&lt;03:25, 13.71s/it][I 2024-06-18 21:07:29,563] Trial 11 finished with value: 0.3738263141664088 and parameters: {'num_leaves': 252}. Best is trial 8 with value: 0.3704743259843756.\nnum_leaves, val_score: 0.370474:  25%|##5       | 5/20 [00:59&lt;03:25, 13.71s/it]</pre> <pre>Early stopping, best iteration is:\n[46]\tvalid_0's binary_logloss: 0.373826\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.370474:  30%|###       | 6/20 [01:08&lt;02:48, 12.01s/it][I 2024-06-18 21:07:38,283] Trial 12 finished with value: 0.38055573216311783 and parameters: {'num_leaves': 96}. Best is trial 8 with value: 0.3704743259843756.\nnum_leaves, val_score: 0.370474:  30%|###       | 6/20 [01:08&lt;02:48, 12.01s/it]</pre> <pre>Early stopping, best iteration is:\n[52]\tvalid_0's binary_logloss: 0.380556\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  35%|###5      | 7/20 [01:25&lt;02:58, 13.73s/it][I 2024-06-18 21:07:55,556] Trial 13 finished with value: 0.36708245318754934 and parameters: {'num_leaves': 253}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  35%|###5      | 7/20 [01:25&lt;02:58, 13.73s/it]</pre> <pre>Early stopping, best iteration is:\n[39]\tvalid_0's binary_logloss: 0.367082\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  40%|####      | 8/20 [01:36&lt;02:33, 12.81s/it][I 2024-06-18 21:08:06,381] Trial 14 finished with value: 0.3760206407150123 and parameters: {'num_leaves': 132}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  40%|####      | 8/20 [01:36&lt;02:33, 12.81s/it]</pre> <pre>Early stopping, best iteration is:\n[51]\tvalid_0's binary_logloss: 0.376021\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  45%|####5     | 9/20 [01:44&lt;02:05, 11.43s/it][I 2024-06-18 21:08:14,782] Trial 15 finished with value: 0.378543416834411 and parameters: {'num_leaves': 64}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  45%|####5     | 9/20 [01:44&lt;02:05, 11.43s/it]</pre> <pre>Early stopping, best iteration is:\n[103]\tvalid_0's binary_logloss: 0.378543\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  50%|#####     | 10/20 [01:56&lt;01:55, 11.58s/it][I 2024-06-18 21:08:26,682] Trial 16 finished with value: 0.37346546696167743 and parameters: {'num_leaves': 184}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  50%|#####     | 10/20 [01:56&lt;01:55, 11.58s/it]</pre> <pre>Early stopping, best iteration is:\n[32]\tvalid_0's binary_logloss: 0.373465\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  55%|#####5    | 11/20 [02:08&lt;01:45, 11.67s/it][I 2024-06-18 21:08:38,573] Trial 17 finished with value: 0.3716035258824437 and parameters: {'num_leaves': 178}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  55%|#####5    | 11/20 [02:08&lt;01:45, 11.67s/it]</pre> <pre>Early stopping, best iteration is:\n[33]\tvalid_0's binary_logloss: 0.371604\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  60%|######    | 12/20 [02:10&lt;01:08,  8.58s/it][I 2024-06-18 21:08:40,079] Trial 18 finished with value: 0.4090573635738217 and parameters: {'num_leaves': 6}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  60%|######    | 12/20 [02:10&lt;01:08,  8.58s/it]</pre> <pre>Early stopping, best iteration is:\n[219]\tvalid_0's binary_logloss: 0.409057\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  65%|######5   | 13/20 [02:19&lt;01:00,  8.68s/it][I 2024-06-18 21:08:48,983] Trial 19 finished with value: 0.3704743259843756 and parameters: {'num_leaves': 92}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  65%|######5   | 13/20 [02:19&lt;01:00,  8.68s/it]</pre> <pre>Early stopping, best iteration is:\n[64]\tvalid_0's binary_logloss: 0.370474\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  70%|#######   | 14/20 [02:34&lt;01:03, 10.58s/it][I 2024-06-18 21:09:03,972] Trial 20 finished with value: 0.37479451809569353 and parameters: {'num_leaves': 211}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  70%|#######   | 14/20 [02:34&lt;01:03, 10.58s/it]</pre> <pre>Early stopping, best iteration is:\n[38]\tvalid_0's binary_logloss: 0.374795\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  75%|#######5  | 15/20 [02:43&lt;00:50, 10.14s/it][I 2024-06-18 21:09:13,084] Trial 21 finished with value: 0.3772431537086629 and parameters: {'num_leaves': 90}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  75%|#######5  | 15/20 [02:43&lt;00:50, 10.14s/it]</pre> <pre>Early stopping, best iteration is:\n[69]\tvalid_0's binary_logloss: 0.377243\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  80%|########  | 16/20 [02:53&lt;00:40, 10.13s/it][I 2024-06-18 21:09:23,178] Trial 22 finished with value: 0.3728508215083201 and parameters: {'num_leaves': 94}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  80%|########  | 16/20 [02:53&lt;00:40, 10.13s/it]</pre> <pre>Early stopping, best iteration is:\n[76]\tvalid_0's binary_logloss: 0.372851\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  85%|########5 | 17/20 [03:01&lt;00:28,  9.57s/it][I 2024-06-18 21:09:31,462] Trial 23 finished with value: 0.3741261012500593 and parameters: {'num_leaves': 65}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  85%|########5 | 17/20 [03:01&lt;00:28,  9.57s/it]</pre> <pre>Early stopping, best iteration is:\n[130]\tvalid_0's binary_logloss: 0.374126\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  90%|######### | 18/20 [03:11&lt;00:19,  9.61s/it][I 2024-06-18 21:09:41,151] Trial 24 finished with value: 0.3809976671401048 and parameters: {'num_leaves': 116}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  90%|######### | 18/20 [03:11&lt;00:19,  9.61s/it]</pre> <pre>Early stopping, best iteration is:\n[61]\tvalid_0's binary_logloss: 0.380998\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082:  95%|#########5| 19/20 [03:18&lt;00:08,  8.92s/it][I 2024-06-18 21:09:48,483] Trial 25 finished with value: 0.3773659816174925 and parameters: {'num_leaves': 45}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082:  95%|#########5| 19/20 [03:18&lt;00:08,  8.92s/it]</pre> <pre>Early stopping, best iteration is:\n[150]\tvalid_0's binary_logloss: 0.377366\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>num_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27&lt;00:00,  8.98s/it][I 2024-06-18 21:09:57,579] Trial 26 finished with value: 0.379797423356925 and parameters: {'num_leaves': 148}. Best is trial 13 with value: 0.36708245318754934.\nnum_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27&lt;00:00, 10.39s/it]\n</pre> <pre>Early stopping, best iteration is:\n[32]\tvalid_0's binary_logloss: 0.379797\n</pre> <pre>bagging, val_score: 0.367082:   0%|          | 0/10 [00:00&lt;?, ?it/s]</pre> <pre>Training until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  10%|#         | 1/10 [00:15&lt;02:17, 15.28s/it][I 2024-06-18 21:10:12,864] Trial 27 finished with value: 0.3754694447762392 and parameters: {'bagging_fraction': 0.6017118946353502, 'bagging_freq': 3}. Best is trial 27 with value: 0.3754694447762392.\nbagging, val_score: 0.367082:  10%|#         | 1/10 [00:15&lt;02:17, 15.28s/it]</pre> <pre>Early stopping, best iteration is:\n[54]\tvalid_0's binary_logloss: 0.375469\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  20%|##        | 2/10 [00:32&lt;02:10, 16.29s/it][I 2024-06-18 21:10:29,858] Trial 28 finished with value: 0.37140708223458174 and parameters: {'bagging_fraction': 0.9697760068067878, 'bagging_freq': 7}. Best is trial 28 with value: 0.37140708223458174.\nbagging, val_score: 0.367082:  20%|##        | 2/10 [00:32&lt;02:10, 16.29s/it]</pre> <pre>Early stopping, best iteration is:\n[51]\tvalid_0's binary_logloss: 0.371407\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  30%|###       | 3/10 [00:40&lt;01:27, 12.51s/it][I 2024-06-18 21:10:37,880] Trial 29 finished with value: 0.3763001794627212 and parameters: {'bagging_fraction': 0.4336686702739523, 'bagging_freq': 1}. Best is trial 28 with value: 0.37140708223458174.\nbagging, val_score: 0.367082:  30%|###       | 3/10 [00:40&lt;01:27, 12.51s/it]</pre> <pre>Early stopping, best iteration is:\n[35]\tvalid_0's binary_logloss: 0.3763\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  40%|####      | 4/10 [00:57&lt;01:25, 14.27s/it][I 2024-06-18 21:10:54,855] Trial 30 finished with value: 0.37399860180838584 and parameters: {'bagging_fraction': 0.9955847594220013, 'bagging_freq': 7}. Best is trial 28 with value: 0.37140708223458174.\nbagging, val_score: 0.367082:  40%|####      | 4/10 [00:57&lt;01:25, 14.27s/it]</pre> <pre>Early stopping, best iteration is:\n[46]\tvalid_0's binary_logloss: 0.373999\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  50%|#####     | 5/10 [01:13&lt;01:15, 15.04s/it][I 2024-06-18 21:11:11,265] Trial 31 finished with value: 0.36851832518824595 and parameters: {'bagging_fraction': 0.7559826069760041, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595.\nbagging, val_score: 0.367082:  50%|#####     | 5/10 [01:13&lt;01:15, 15.04s/it]</pre> <pre>Early stopping, best iteration is:\n[46]\tvalid_0's binary_logloss: 0.368518\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  60%|######    | 6/10 [01:29&lt;01:00, 15.17s/it][I 2024-06-18 21:11:26,675] Trial 32 finished with value: 0.3695574686675923 and parameters: {'bagging_fraction': 0.7373615298502163, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595.\nbagging, val_score: 0.367082:  60%|######    | 6/10 [01:29&lt;01:00, 15.17s/it]</pre> <pre>Early stopping, best iteration is:\n[51]\tvalid_0's binary_logloss: 0.369557\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  70%|#######   | 7/10 [01:44&lt;00:45, 15.17s/it][I 2024-06-18 21:11:41,852] Trial 33 finished with value: 0.37298644850012186 and parameters: {'bagging_fraction': 0.7616544959864899, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595.\nbagging, val_score: 0.367082:  70%|#######   | 7/10 [01:44&lt;00:45, 15.17s/it]</pre> <pre>Early stopping, best iteration is:\n[38]\tvalid_0's binary_logloss: 0.372986\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  80%|########  | 8/10 [01:58&lt;00:29, 14.94s/it][I 2024-06-18 21:11:56,282] Trial 34 finished with value: 0.3745164899611851 and parameters: {'bagging_fraction': 0.7510199039916521, 'bagging_freq': 5}. Best is trial 31 with value: 0.36851832518824595.\nbagging, val_score: 0.367082:  80%|########  | 8/10 [01:58&lt;00:29, 14.94s/it]</pre> <pre>Early stopping, best iteration is:\n[35]\tvalid_0's binary_logloss: 0.374516\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082:  90%|######### | 9/10 [02:13&lt;00:14, 14.95s/it][I 2024-06-18 21:12:11,256] Trial 35 finished with value: 0.3790678087224149 and parameters: {'bagging_fraction': 0.6376411296778922, 'bagging_freq': 3}. Best is trial 31 with value: 0.36851832518824595.\nbagging, val_score: 0.367082:  90%|######### | 9/10 [02:13&lt;00:14, 14.95s/it]</pre> <pre>Early stopping, best iteration is:\n[41]\tvalid_0's binary_logloss: 0.379068\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>bagging, val_score: 0.367082: 100%|##########| 10/10 [02:28&lt;00:00, 14.82s/it][I 2024-06-18 21:12:25,777] Trial 36 finished with value: 0.36759282443526115 and parameters: {'bagging_fraction': 0.8438639692641409, 'bagging_freq': 5}. Best is trial 36 with value: 0.36759282443526115.\nbagging, val_score: 0.367082: 100%|##########| 10/10 [02:28&lt;00:00, 14.82s/it]\n</pre> <pre>Early stopping, best iteration is:\n[30]\tvalid_0's binary_logloss: 0.367593\n</pre> <pre>feature_fraction_stage2, val_score: 0.367082:   0%|          | 0/6 [00:00&lt;?, ?it/s]</pre> <pre>Training until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction_stage2, val_score: 0.367082:  17%|#6        | 1/6 [00:14&lt;01:11, 14.28s/it][I 2024-06-18 21:12:40,067] Trial 37 finished with value: 0.3751194682458601 and parameters: {'feature_fraction': 0.8480000000000001}. Best is trial 37 with value: 0.3751194682458601.\nfeature_fraction_stage2, val_score: 0.367082:  17%|#6        | 1/6 [00:14&lt;01:11, 14.28s/it]</pre> <pre>Early stopping, best iteration is:\n[31]\tvalid_0's binary_logloss: 0.375119\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction_stage2, val_score: 0.367082:  33%|###3      | 2/6 [00:31&lt;01:03, 15.82s/it][I 2024-06-18 21:12:56,969] Trial 38 finished with value: 0.37511946824586 and parameters: {'feature_fraction': 0.88}. Best is trial 38 with value: 0.37511946824586.\nfeature_fraction_stage2, val_score: 0.367082:  33%|###3      | 2/6 [00:31&lt;01:03, 15.82s/it]</pre> <pre>Early stopping, best iteration is:\n[31]\tvalid_0's binary_logloss: 0.375119\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction_stage2, val_score: 0.367082:  50%|#####     | 3/6 [00:48&lt;00:50, 16.73s/it][I 2024-06-18 21:13:14,771] Trial 39 finished with value: 0.37230053743309227 and parameters: {'feature_fraction': 0.7520000000000001}. Best is trial 39 with value: 0.37230053743309227.\nfeature_fraction_stage2, val_score: 0.367082:  50%|#####     | 3/6 [00:48&lt;00:50, 16.73s/it]</pre> <pre>Early stopping, best iteration is:\n[46]\tvalid_0's binary_logloss: 0.372301\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction_stage2, val_score: 0.367082:  67%|######6   | 4/6 [01:05&lt;00:33, 16.76s/it][I 2024-06-18 21:13:31,580] Trial 40 finished with value: 0.37230053743309227 and parameters: {'feature_fraction': 0.7200000000000001}. Best is trial 39 with value: 0.37230053743309227.\nfeature_fraction_stage2, val_score: 0.367082:  67%|######6   | 4/6 [01:05&lt;00:33, 16.76s/it]</pre> <pre>Early stopping, best iteration is:\n[46]\tvalid_0's binary_logloss: 0.372301\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction_stage2, val_score: 0.366281:  83%|########3 | 5/6 [01:21&lt;00:16, 16.48s/it][I 2024-06-18 21:13:47,556] Trial 41 finished with value: 0.3662812971282284 and parameters: {'feature_fraction': 0.8160000000000001}. Best is trial 41 with value: 0.3662812971282284.\nfeature_fraction_stage2, val_score: 0.366281:  83%|########3 | 5/6 [01:21&lt;00:16, 16.48s/it]</pre> <pre>Early stopping, best iteration is:\n[30]\tvalid_0's binary_logloss: 0.366281\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>feature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38&lt;00:00, 16.66s/it][I 2024-06-18 21:14:04,578] Trial 42 finished with value: 0.3670824531875493 and parameters: {'feature_fraction': 0.784}. Best is trial 41 with value: 0.3662812971282284.\nfeature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38&lt;00:00, 16.47s/it]\n</pre> <pre>Early stopping, best iteration is:\n[39]\tvalid_0's binary_logloss: 0.367082\n</pre> <pre>regularization_factors, val_score: 0.366281:   0%|          | 0/20 [00:00&lt;?, ?it/s]</pre> <pre>Training until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.366281:   5%|5         | 1/20 [00:13&lt;04:12, 13.29s/it][I 2024-06-18 21:14:17,877] Trial 43 finished with value: 0.36913074695699283 and parameters: {'lambda_l1': 0.26740014043884963, 'lambda_l2': 0.001760870184331642}. Best is trial 43 with value: 0.36913074695699283.\nregularization_factors, val_score: 0.366281:   5%|5         | 1/20 [00:13&lt;04:12, 13.29s/it]</pre> <pre>Early stopping, best iteration is:\n[32]\tvalid_0's binary_logloss: 0.369131\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.366281:  10%|#         | 2/20 [00:28&lt;04:21, 14.52s/it][I 2024-06-18 21:14:33,256] Trial 44 finished with value: 0.3744642189562516 and parameters: {'lambda_l1': 1.1442585672872564e-08, 'lambda_l2': 4.849247272391423e-08}. Best is trial 43 with value: 0.36913074695699283.\nregularization_factors, val_score: 0.366281:  10%|#         | 2/20 [00:28&lt;04:21, 14.52s/it]</pre> <pre>Early stopping, best iteration is:\n[37]\tvalid_0's binary_logloss: 0.374464\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.366281:  15%|#5        | 3/20 [00:45&lt;04:23, 15.52s/it][I 2024-06-18 21:14:49,962] Trial 45 finished with value: 0.3703169935094016 and parameters: {'lambda_l1': 9.96832677827828e-07, 'lambda_l2': 6.3332349736895}. Best is trial 43 with value: 0.36913074695699283.\nregularization_factors, val_score: 0.366281:  15%|#5        | 3/20 [00:45&lt;04:23, 15.52s/it]</pre> <pre>Early stopping, best iteration is:\n[75]\tvalid_0's binary_logloss: 0.370317\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.366281:  20%|##        | 4/20 [00:58&lt;03:54, 14.69s/it][I 2024-06-18 21:15:03,375] Trial 46 finished with value: 0.3672993919740384 and parameters: {'lambda_l1': 1.5127153863704856, 'lambda_l2': 4.478393628964223e-08}. Best is trial 46 with value: 0.3672993919740384.\nregularization_factors, val_score: 0.366281:  20%|##        | 4/20 [00:58&lt;03:54, 14.69s/it]</pre> <pre>Early stopping, best iteration is:\n[49]\tvalid_0's binary_logloss: 0.367299\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.366281:  25%|##5       | 5/20 [01:12&lt;03:36, 14.40s/it][I 2024-06-18 21:15:17,268] Trial 47 finished with value: 0.3673221145565776 and parameters: {'lambda_l1': 2.3927205612539093, 'lambda_l2': 5.0167732343674086e-08}. Best is trial 46 with value: 0.3672993919740384.\nregularization_factors, val_score: 0.366281:  25%|##5       | 5/20 [01:12&lt;03:36, 14.40s/it]</pre> <pre>Early stopping, best iteration is:\n[73]\tvalid_0's binary_logloss: 0.367322\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.366281:  30%|###       | 6/20 [01:25&lt;03:15, 13.96s/it][I 2024-06-18 21:15:30,372] Trial 48 finished with value: 0.38611937264133733 and parameters: {'lambda_l1': 6.054665616489558, 'lambda_l2': 1.800720533496738e-08}. Best is trial 46 with value: 0.3672993919740384.\nregularization_factors, val_score: 0.366281:  30%|###       | 6/20 [01:25&lt;03:15, 13.96s/it]</pre> <pre>Early stopping, best iteration is:\n[156]\tvalid_0's binary_logloss: 0.386119\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  35%|###5      | 7/20 [01:41&lt;03:08, 14.53s/it][I 2024-06-18 21:15:46,069] Trial 49 finished with value: 0.36481873368079354 and parameters: {'lambda_l1': 0.018981605475779886, 'lambda_l2': 4.496278591408666e-06}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  35%|###5      | 7/20 [01:41&lt;03:08, 14.53s/it]</pre> <pre>Early stopping, best iteration is:\n[32]\tvalid_0's binary_logloss: 0.364819\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  40%|####      | 8/20 [01:58&lt;03:02, 15.22s/it][I 2024-06-18 21:16:02,773] Trial 50 finished with value: 0.3676098962913463 and parameters: {'lambda_l1': 0.005938438143650016, 'lambda_l2': 1.6486829620090715e-05}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  40%|####      | 8/20 [01:58&lt;03:02, 15.22s/it]</pre> <pre>Early stopping, best iteration is:\n[32]\tvalid_0's binary_logloss: 0.36761\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  45%|####5     | 9/20 [02:13&lt;02:46, 15.12s/it][I 2024-06-18 21:16:17,683] Trial 51 finished with value: 0.3693663505867317 and parameters: {'lambda_l1': 0.14870260759215176, 'lambda_l2': 8.827878170241735e-07}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  45%|####5     | 9/20 [02:13&lt;02:46, 15.12s/it]</pre> <pre>Early stopping, best iteration is:\n[32]\tvalid_0's binary_logloss: 0.369366\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  50%|#####     | 10/20 [02:21&lt;02:11, 13.17s/it][I 2024-06-18 21:16:26,468] Trial 52 finished with value: 0.3995526935417914 and parameters: {'lambda_l1': 9.914097223337537, 'lambda_l2': 2.507766754016921e-06}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  50%|#####     | 10/20 [02:21&lt;02:11, 13.17s/it]</pre> <pre>Early stopping, best iteration is:\n[108]\tvalid_0's binary_logloss: 0.399553\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  55%|#####5    | 11/20 [02:36&lt;02:02, 13.60s/it][I 2024-06-18 21:16:41,058] Trial 53 finished with value: 0.37135627608204047 and parameters: {'lambda_l1': 0.004317296224576123, 'lambda_l2': 1.0694628305152963e-08}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  55%|#####5    | 11/20 [02:36&lt;02:02, 13.60s/it]</pre> <pre>Early stopping, best iteration is:\n[32]\tvalid_0's binary_logloss: 0.371356\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  60%|######    | 12/20 [02:50&lt;01:50, 13.82s/it][I 2024-06-18 21:16:55,362] Trial 54 finished with value: 0.3656757773203038 and parameters: {'lambda_l1': 0.1528681952118711, 'lambda_l2': 0.0003078435151438368}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  60%|######    | 12/20 [02:50&lt;01:50, 13.82s/it]</pre> <pre>Early stopping, best iteration is:\n[38]\tvalid_0's binary_logloss: 0.365676\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  65%|######5   | 13/20 [03:03&lt;01:34, 13.48s/it][I 2024-06-18 21:17:08,074] Trial 55 finished with value: 0.3683612678266087 and parameters: {'lambda_l1': 0.026844520069898837, 'lambda_l2': 0.0010041201106681954}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  65%|######5   | 13/20 [03:03&lt;01:34, 13.48s/it]</pre> <pre>Early stopping, best iteration is:\n[30]\tvalid_0's binary_logloss: 0.368361\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  70%|#######   | 14/20 [03:18&lt;01:24, 14.03s/it][I 2024-06-18 21:17:23,369] Trial 56 finished with value: 0.367596997615459 and parameters: {'lambda_l1': 0.0003242973314637712, 'lambda_l2': 5.561136449228501e-05}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  70%|#######   | 14/20 [03:18&lt;01:24, 14.03s/it]</pre> <pre>Early stopping, best iteration is:\n[30]\tvalid_0's binary_logloss: 0.367597\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  75%|#######5  | 15/20 [03:31&lt;01:07, 13.57s/it][I 2024-06-18 21:17:35,879] Trial 57 finished with value: 0.36589916181880006 and parameters: {'lambda_l1': 0.2057156258629452, 'lambda_l2': 0.024885518547422405}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  75%|#######5  | 15/20 [03:31&lt;01:07, 13.57s/it]</pre> <pre>Early stopping, best iteration is:\n[31]\tvalid_0's binary_logloss: 0.365899\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  80%|########  | 16/20 [03:45&lt;00:55, 13.85s/it][I 2024-06-18 21:17:50,372] Trial 58 finished with value: 0.36736185428940016 and parameters: {'lambda_l1': 0.0002150419853087141, 'lambda_l2': 0.034107683119902095}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  80%|########  | 16/20 [03:45&lt;00:55, 13.85s/it]</pre> <pre>Early stopping, best iteration is:\n[34]\tvalid_0's binary_logloss: 0.367362\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  85%|########5 | 17/20 [04:00&lt;00:42, 14.20s/it][I 2024-06-18 21:18:05,384] Trial 59 finished with value: 0.3702898033719683 and parameters: {'lambda_l1': 0.0583471784381045, 'lambda_l2': 0.3274496387110711}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  85%|########5 | 17/20 [04:00&lt;00:42, 14.20s/it]</pre> <pre>Early stopping, best iteration is:\n[40]\tvalid_0's binary_logloss: 0.37029\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  90%|######### | 18/20 [04:18&lt;00:30, 15.10s/it][I 2024-06-18 21:18:22,586] Trial 60 finished with value: 0.3651493917986752 and parameters: {'lambda_l1': 0.001458069822509784, 'lambda_l2': 0.008248248782706902}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  90%|######### | 18/20 [04:18&lt;00:30, 15.10s/it]</pre> <pre>Early stopping, best iteration is:\n[43]\tvalid_0's binary_logloss: 0.365149\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819:  95%|#########5| 19/20 [04:33&lt;00:15, 15.10s/it][I 2024-06-18 21:18:37,676] Trial 61 finished with value: 0.3696068718370661 and parameters: {'lambda_l1': 0.0006748569060520587, 'lambda_l2': 0.01603839211588336}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819:  95%|#########5| 19/20 [04:33&lt;00:15, 15.10s/it]</pre> <pre>Early stopping, best iteration is:\n[41]\tvalid_0's binary_logloss: 0.369607\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>regularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48&lt;00:00, 15.16s/it][I 2024-06-18 21:18:52,985] Trial 62 finished with value: 0.3700319935632785 and parameters: {'lambda_l1': 0.004313680126647664, 'lambda_l2': 0.008355812202217454}. Best is trial 49 with value: 0.36481873368079354.\nregularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48&lt;00:00, 14.42s/it]\n</pre> <pre>Early stopping, best iteration is:\n[38]\tvalid_0's binary_logloss: 0.370032\n</pre> <pre>min_child_samples, val_score: 0.364819:   0%|          | 0/5 [00:00&lt;?, ?it/s]</pre> <pre>Training until validation scores don't improve for 50 rounds\n</pre> <pre>min_child_samples, val_score: 0.364819:  20%|##        | 1/5 [00:10&lt;00:40, 10.19s/it][I 2024-06-18 21:19:03,252] Trial 63 finished with value: 0.3897736671401978 and parameters: {'min_child_samples': 50}. Best is trial 63 with value: 0.3897736671401978.\nmin_child_samples, val_score: 0.364819:  20%|##        | 1/5 [00:10&lt;00:40, 10.19s/it]</pre> <pre>Early stopping, best iteration is:\n[51]\tvalid_0's binary_logloss: 0.389774\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>min_child_samples, val_score: 0.364819:  40%|####      | 2/5 [00:29&lt;00:46, 15.39s/it][I 2024-06-18 21:19:22,277] Trial 64 finished with value: 0.3683263542462511 and parameters: {'min_child_samples': 5}. Best is trial 64 with value: 0.3683263542462511.\nmin_child_samples, val_score: 0.364819:  40%|####      | 2/5 [00:29&lt;00:46, 15.39s/it]</pre> <pre>Early stopping, best iteration is:\n[43]\tvalid_0's binary_logloss: 0.368326\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>min_child_samples, val_score: 0.364819:  60%|######    | 3/5 [00:36&lt;00:23, 11.88s/it][I 2024-06-18 21:19:29,977] Trial 65 finished with value: 0.3938280500430312 and parameters: {'min_child_samples': 100}. Best is trial 64 with value: 0.3683263542462511.\nmin_child_samples, val_score: 0.364819:  60%|######    | 3/5 [00:36&lt;00:23, 11.88s/it]</pre> <pre>Early stopping, best iteration is:\n[113]\tvalid_0's binary_logloss: 0.393828\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>min_child_samples, val_score: 0.364819:  80%|########  | 4/5 [00:53&lt;00:13, 13.86s/it][I 2024-06-18 21:19:46,881] Trial 66 finished with value: 0.36712449039705003 and parameters: {'min_child_samples': 10}. Best is trial 66 with value: 0.36712449039705003.\nmin_child_samples, val_score: 0.364819:  80%|########  | 4/5 [00:53&lt;00:13, 13.86s/it]</pre> <pre>Early stopping, best iteration is:\n[32]\tvalid_0's binary_logloss: 0.367124\nTraining until validation scores don't improve for 50 rounds\n</pre> <pre>min_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07&lt;00:00, 13.80s/it][I 2024-06-18 21:20:00,561] Trial 67 finished with value: 0.3719291335673048 and parameters: {'min_child_samples': 25}. Best is trial 66 with value: 0.36712449039705003.\nmin_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07&lt;00:00, 13.50s/it]</pre> <pre>Early stopping, best iteration is:\n[38]\tvalid_0's binary_logloss: 0.371929\n</pre> <pre>\n</pre> In\u00a0[8]: Copied! <pre># save/load trainer\n# dill_dump(\"lgbm_trainer.dill\", trainer)\ntrainer = dill_load(\"lgbm_trainer.dill\")\n</pre> # save/load trainer # dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load(\"lgbm_trainer.dill\") In\u00a0[11]: Copied! <pre>pprint(metrics_dict)\n</pre> pprint(metrics_dict) <pre>{'cls_report': {'0': {'f1-score': 0.9244823386114495,\n                      'precision': 0.8971631205673759,\n                      'recall': 0.9535175879396985,\n                      'support': 796},\n                '1': {'f1-score': 0.6536312849162011,\n                      'precision': 0.7597402597402597,\n                      'recall': 0.5735294117647058,\n                      'support': 204},\n                'accuracy': 0.876,\n                'macro avg': {'f1-score': 0.7890568117638252,\n                              'precision': 0.8284516901538178,\n                              'recall': 0.7635234998522022,\n                              'support': 1000},\n                'weighted avg': {'f1-score': 0.8692287236576187,\n                                 'precision': 0.8691288569586443,\n                                 'recall': 0.876,\n                                 'support': 1000}},\n 'cm': [[759, 37], [87, 117]],\n 'prec_rec_curve': [[0.204, 0.7597402597402597, 1.0],\n                    [1.0, 0.5735294117647058, 0.0],\n                    [0.0, 1.0]]}\n</pre> In\u00a0[32]: Copied! <pre>pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True))\n</pre> pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True)) <pre>{'cls_report': {'0': {'f1-score': 0.9244823386114495,\n                      'precision': 0.8971631205673759,\n                      'recall': 0.9535175879396985,\n                      'support': 796},\n                '1': {'f1-score': 0.6536312849162011,\n                      'precision': 0.7597402597402597,\n                      'recall': 0.5735294117647058,\n                      'support': 204},\n                'accuracy': 0.876,\n                'macro avg': {'f1-score': 0.7890568117638252,\n                              'precision': 0.8284516901538178,\n                              'recall': 0.7635234998522022,\n                              'support': 1000},\n                'weighted avg': {'f1-score': 0.8692287236576187,\n                                 'precision': 0.8691288569586443,\n                                 'recall': 0.876,\n                                 'support': 1000}},\n 'cm': [[759, 37], [87, 117]],\n 'prec_rec_curve': [[0.204, 0.7597402597402597, 1.0],\n                    [1.0, 0.5735294117647058, 0.0],\n                    [0.0, 1.0]]}\n</pre> In\u00a0[12]: Copied! <pre>mlflow_host = \"10.152.183.54\"\nmlflow_host_url = \"mlflow.mlflow.svc.cluster.local\"\nmlflow_port = \"5000\"\nos.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\"\nos.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\"\nos.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\"\n\nmlflow.set_tracking_uri(\"http://\" + mlflow_host + \":\" + mlflow_port)\nexperiment_id = get_or_create_experiment(\"ecovadis\")\nmlflow.set_experiment(experiment_id=experiment_id)\n</pre> mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\"  mlflow.set_tracking_uri(\"http://\" + mlflow_host + \":\" + mlflow_port) experiment_id = get_or_create_experiment(\"ecovadis\") mlflow.set_experiment(experiment_id=experiment_id) Out[12]: <pre>&lt;Experiment: artifact_location='s3://mlflow/6', creation_time=1718745770093, experiment_id='6', last_update_time=1718745770093, lifecycle_stage='active', name='ecovadis', tags={}&gt;</pre> In\u00a0[13]: Copied! <pre>metrics_dict_flattened = flatten_dict(metrics_dict)\n</pre> metrics_dict_flattened = flatten_dict(metrics_dict) In\u00a0[14]: Copied! <pre># mlflow metrics can be only int, float not list\ndel metrics_dict_flattened[\"cm\"]\ndel metrics_dict_flattened[\"prec_rec_curve\"]\n</pre> # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In\u00a0[15]: Copied! <pre>run_name = \"init_run\"\nwith mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run:\n    mlflow.log_params(trainer.optimizer.best)\n    mlflow.log_metrics(metrics_dict_flattened)\n\n    # Log tags\n    mlflow.set_tags(\n        tags={\n            \"project\": \"ecovadis\",\n            \"optimizer_engine\": \"optuna\",\n            \"model_family\": \"ligtgbm\",\n            \"feature_set_version\": 1,\n        }\n    )\n    # Log figure - for future fun\n    # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\")\n\n    artifact_path = \"ecovadis_model\"\n    registered_model_name = \"ecovadis_lgbm_model\"\n    mlflow.pyfunc.log_model(python_model=trainer, \n                            artifact_path=artifact_path, \n                            registered_model_name=registered_model_name,\n                            code_paths=[\"churn_pred\"])  \n    model_uri = mlflow.get_artifact_uri(artifact_path)\n    print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\")\n</pre> run_name = \"init_run\" with mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run:     mlflow.log_params(trainer.optimizer.best)     mlflow.log_metrics(metrics_dict_flattened)      # Log tags     mlflow.set_tags(         tags={             \"project\": \"ecovadis\",             \"optimizer_engine\": \"optuna\",             \"model_family\": \"ligtgbm\",             \"feature_set_version\": 1,         }     )     # Log figure - for future fun     # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\")      artifact_path = \"ecovadis_model\"     registered_model_name = \"ecovadis_lgbm_model\"     mlflow.pyfunc.log_model(python_model=trainer,                              artifact_path=artifact_path,                              registered_model_name=registered_model_name,                             code_paths=[\"churn_pred\"])       model_uri = mlflow.get_artifact_uri(artifact_path)     print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") <pre>2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: &lt;class 'pandas.core.frame.DataFrame'&gt;, skipping schema inference\n2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: &lt;class 'pandas.core.frame.DataFrame'&gt;, skipping schema inference\n2024/06/18 21:23:58 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpfsv2c_xg/model, flavor: python_function). Fall back to return ['cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback. \nSuccessfully registered model 'ecovadis_lgbm_model'.\n2024/06/18 21:27:30 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: ecovadis_lgbm_model, version 1\n</pre> <pre>Run ID:\n761906b692e94248940c6dbf6b12c23c\nModel uri:\ns3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\n</pre> <pre>Created version '1' of model 'ecovadis_lgbm_model'.\n</pre> In\u00a0[16]: Copied! <pre>df_pd.iloc[:2]\n</pre> df_pd.iloc[:2] Out[16]: CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z In\u00a0[16]: Copied! <pre>df_pd.iloc[:2].transpose().to_dict(orient='index')\n</pre> df_pd.iloc[:2].transpose().to_dict(orient='index') Out[16]: <pre>{'CustomerId': {0: 15787619, 1: 15770309},\n 'CreditScore': {0: 844, 1: 656},\n 'Country': {0: 'France', 1: 'France'},\n 'Gender': {0: 'Male', 1: 'Male'},\n 'Age': {0: 18, 1: 18},\n 'Tenure': {0: 2, 1: 10},\n 'Balance (EUR)': {0: 160980.03, 1: 151762.74},\n 'NumberOfProducts': {0: 1, 1: 1},\n 'HasCreditCard': {0: '0', 1: '0'},\n 'IsActiveMember': {0: '0', 1: '1'},\n 'EstimatedSalary': {0: 145936.28, 1: 127014.32},\n 'Exited': {0: 0, 1: 0},\n 'CustomerFeedback_sentiment3': {0: 'neutral', 1: 'neutral'},\n 'CustomerFeedback_sentiment5': {0: '4 stars', 1: '1 star'},\n 'Surname_Country': {0: 'Taiwan', 1: 'United States'},\n 'Surname_Country_region': {0: 'Asia', 1: 'Americas'},\n 'Surname_Country_subregion': {0: 'Eastern Asia', 1: 'Northern America'},\n 'Country_region': {0: 'Europe', 1: 'Europe'},\n 'Country_subregion': {0: 'Western Europe', 1: 'Western Europe'},\n 'is_native': {0: '0', 1: '0'},\n 'Country_hemisphere': {0: 'northern', 1: 'northern'},\n 'Country_gdp_per_capita': {0: 57594.03402, 1: 57594.03402},\n 'Country_IncomeGroup': {0: 'High income', 1: 'High income'},\n 'Surname_Country_gdp_per_capita': {0: 32756.0, 1: 76329.58227},\n 'Surname_Country_IncomeGroup': {0: 'None', 1: 'High income'},\n 'working_class': {0: 'working_age', 1: 'working_age'},\n 'stage_of_life': {0: 'teen', 1: 'teen'},\n 'generation': {0: 'gen_z', 1: 'gen_z'}}</pre> <p>after some notepad magic:</p> <pre><code>{\n    \"CustomerId\": [15787619, 15770309],\n    \"CreditScore\": [844, 656],\n    \"Country\": [\"France\", \"France\"],\n    \"Gender\": [\"Male\", \"Male\"],\n    \"Age\": [18, 18],\n    \"Tenure\": [2, 10],\n    \"Balance (EUR)\": [160980.03, 151762.74],\n    \"NumberOfProducts\": [1, 1],\n    \"HasCreditCard\": [\"0\", \"0\"],\n    \"IsActiveMember\": [\"0\", \"1\"],\n    \"EstimatedSalary\": [145936.28, 127014.32],\n    \"Exited\": [0, 0],\n    \"CustomerFeedback_sentiment3\": [\"neutral\", \"neutral\"],\n    \"CustomerFeedback_sentiment5\": [\"4 stars\", \"1 star\"],\n    \"Surname_Country\": [\"Taiwan\", \"United States\"],\n    \"Surname_Country_region\": [\"Asia\", \"Americas\"],\n    \"Surname_Country_subregion\": [\"Eastern Asia\", \"Northern America\"],\n    \"Country_region\": [\"Europe\", \"Europe\"],\n    \"Country_subregion\": [\"Western Europe\", \"Western Europe\"],\n    \"is_native\": [\"0\", \"0\"],\n    \"Country_hemisphere\": [\"northern\", \"northern\"],\n    \"Country_gdp_per_capita\": [57594.03402, 57594.03402],\n    \"Country_IncomeGroup\": [\"High income\", \"High income\"],\n    \"Surname_Country_gdp_per_capita\": [32756.0, 76329.58227],\n    \"Surname_Country_IncomeGroup\": [\"None\", \"High income\"],\n    \"working_class\": [\"working_age\", \"working_age\"],\n    \"stage_of_life\": [\"teen\", \"teen\"],\n    \"generation\": [\"gen_z\", \"gen_z\"],\n}\n</code></pre> In\u00a0[16]: Copied! <pre>trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"Exited\"]), context={})\n</pre> trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"Exited\"]), context={}) Out[16]: Exited 0 -9.958064 1 -12.560959 In\u00a0[17]: Copied! <pre>model_uri\n</pre> model_uri Out[17]: <pre>'s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model'</pre> In\u00a0[18]: Copied! <pre># loaded_trainer = mlflow.pyfunc.load_model(model_uri)\nloaded_trainer = mlflow.pyfunc.load_model('s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model')\n</pre> # loaded_trainer = mlflow.pyfunc.load_model(model_uri) loaded_trainer = mlflow.pyfunc.load_model('s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model') <pre>Downloading artifacts:   0%|          | 0/61 [00:00&lt;?, ?it/s]</pre> In\u00a0[19]: Copied! <pre>loaded_trainer.unwrap_python_model()\n</pre> loaded_trainer.unwrap_python_model() Out[19]: <pre>&lt;churn_pred.training.trainer.Trainer at 0x7f3e17f31060&gt;</pre> In\u00a0[21]: Copied! <pre># mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle')\nmlflow.pyfunc.save_model(\"data/model\", python_model=loaded_trainer.unwrap_python_model())\n</pre> # mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle') mlflow.pyfunc.save_model(\"data/model\", python_model=loaded_trainer.unwrap_python_model()) <pre>2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: &lt;class 'pandas.core.frame.DataFrame'&gt;, skipping schema inference\n2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: &lt;class 'pandas.core.frame.DataFrame'&gt;, skipping schema inference\n2024/06/18 21:31:37 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: data/model, flavor: python_function). Fall back to return ['cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback. \n</pre> In\u00a0[22]: Copied! <pre>loaded_trainer.predict(df_pd.iloc[:2].drop(columns=[\"Exited\"]))\n</pre> loaded_trainer.predict(df_pd.iloc[:2].drop(columns=[\"Exited\"])) Out[22]: Exited 0 -9.958064 1 -12.560959 In\u00a0[23]: Copied! <pre>model_uri\n</pre> model_uri Out[23]: <pre>'s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model'</pre> In\u00a0[24]: Copied! <pre>! mlflow models serve -m \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\" --env-manager local -p 5000\n</pre> ! mlflow models serve -m \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\" --env-manager local -p 5000 <pre>Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 61/61 [00:00&lt;00:00, 691.42it/s]\n2024/06/18 21:32:14 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function'\nDownloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 61/61 [00:00&lt;00:00, 304.59it/s]\n2024/06/18 21:32:15 INFO mlflow.pyfunc.backend: === Running command 'exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app'\n[2024-06-18 21:32:15 +0000] [1380] [INFO] Starting gunicorn 22.0.0\n[2024-06-18 21:32:15 +0000] [1380] [INFO] Listening at: http://127.0.0.1:5000 (1380)\n[2024-06-18 21:32:15 +0000] [1380] [INFO] Using worker: sync\n[2024-06-18 21:32:15 +0000] [1381] [INFO] Booting worker with pid: 1381\n^C\n[2024-06-18 21:36:45 +0000] [1380] [INFO] Handling signal: int\n[2024-06-18 21:36:45 +0000] [1381] [INFO] Worker exiting (pid: 1381)\n</pre> <p>output from terminal:</p> <pre><code>root@jupyter-5uperpalo:~/assignment/ecovadis_assignment# curl -X POST -H \"Content-Type:application/json\" --data '{\"inputs\": {\"CustomerId\": [15787619, 15770309], \"CreditScore\": [844, 656], \"Country\": [\"France\", \"France\"], \"Gender\": [\"Male\", \"Male\"], \"Age\": [18, 18], \"Tenure\": [2, 10], \"Balance (EUR)\": [160980.03, 151762.74], \"NumberOfProducts\": [1, 1], \"HasCreditCard\": [\"0\", \"0\"], \"IsActiveMember\": [\"0\", \"1\"], \"EstimatedSalary\": [145936.28, 127014.32], \"CustomerFeedback_sentiment3\": [\"neutral\", \"neutral\"], \"CustomerFeedback_sentiment5\": [\"4 stars\", \"1 star\"], \"Surname_Country\": [\"Taiwan\", \"United States\"], \"Surname_Country_region\": [\"Asia\", \"Americas\"], \"Surname_Country_subregion\": [\"Eastern Asia\", \"Northern America\"], \"Country_region\": [\"Europe\", \"Europe\"], \"Country_subregion\": [\"Western Europe\", \"Western Europe\"], \"is_native\": [\"0\", \"0\"], \"Country_hemisphere\": [\"northern\", \"northern\"], \"Country_gdp_per_capita\": [57594.03402, 57594.03402], \"Country_IncomeGroup\": [\"High income\", \"High income\"], \"Surname_Country_gdp_per_capita\": [32756.0, 76329.58227], \"Surname_Country_IncomeGroup\": [\"None\", \"High income\"], \"working_class\": [\"working_age\", \"working_age\"], \"stage_of_life\": [\"teen\", \"teen\"], \"generation\": [\"gen_z\", \"gen_z\"]}}' http://127.0.0.1:5000/invocations\n{\"predictions\": [{\"Exited\": -9.958063834023024}, {\"Exited\": -12.560959268044444}]}\n</code></pre> In\u00a0[7]: Copied! <pre>from minio import Minio\n\n# to download conda.yaml\nclient = Minio(\"10.152.183.156:9000\", \"minioadmin\", \"minioadmin\", secure=False)\nbucket = \"mlflow\"\nobject_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml\"\nfile_path = \"notebooks/conda.yaml\"\nclient.fget_object(bucket_name=bucket, object_name=object_name, file_path=file_path)\n</pre> from minio import Minio  # to download conda.yaml client = Minio(\"10.152.183.156:9000\", \"minioadmin\", \"minioadmin\", secure=False) bucket = \"mlflow\" object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml\" file_path = \"notebooks/conda.yaml\" client.fget_object(bucket_name=bucket, object_name=object_name, file_path=file_path) Out[7]: <pre>&lt;minio.datatypes.Object at 0x730858235e70&gt;</pre> <p>following commands were run in terminal to create environment:</p> <pre><code>pip install conda_pack\nconda env create --force -f conda.yaml\nconda-pack --name mlflow-env --output environment.tar.gz --force --ignore-editable-packages --ignore-missing-files\n</code></pre> In\u00a0[28]: Copied! <pre># upload environmnet.tar.gz\nobject_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz\"\nfile_path = \"notebooks/environment.tar.gz\"\nclient.fput_object(bucket_name=bucket, object_name=object_name, file_path=file_path)\n</pre> # upload environmnet.tar.gz object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz\" file_path = \"notebooks/environment.tar.gz\" client.fput_object(bucket_name=bucket, object_name=object_name, file_path=file_path) Out[28]: <pre>&lt;minio.helpers.ObjectWriteResult at 0x7f409c5bbac0&gt;</pre> <pre><code>export INGRESS_NAME=istio-ingressgateway\nexport INGRESS_NS=istio-system\nexport INGRESS_PORT=$(kubectl -n \"${INGRESS_NS}\" get service \"${INGRESS_NAME}\" -o jsonpath='{.spec.ports[?(@.name==\"http2\")].nodePort}')\nexport INGRESS_HOST=$(kubectl get po -l istio=ingressgateway -n \"${INGRESS_NS}\" -o jsonpath='{.items[0].status.hostIP}')\nexport SERVICE_HOSTNAME=$(kubectl get inferenceservice custom-ecovadis-model -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3)\n\ncurl -v \\\n  -H \"Host: ${SERVICE_HOSTNAME}\" \\\n  -H \"Content-Type: application/json\" \\\n  -d @./test.json \\\n  http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-model/infer\n</code></pre> In\u00a0[15]: Copied! <pre>run_name = \"init_run\"\nwith mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run:\n    mlflow.log_params(trainer.optimizer.best)\n    mlflow.log_metrics(metrics_dict_flattened)\n\n    # Log tags\n    mlflow.set_tags(\n        tags={\n            \"project\": \"ecovadis\",\n            \"optimizer_engine\": \"optuna\",\n            \"model_family\": \"ligtgbm\",\n            \"feature_set_version\": 1,\n        }\n    )\n    # Log figure - for future fun\n    # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\")\n\n    artifact_path = \"ecovadis_raw_model\"\n    registered_model_name = \"ecovadis_raw_lgbm_model\"\n    mlflow.lightgbm.log_model(\n            lgb_model=trainer.model,\n            artifact_path=artifact_path, \n            registered_model_name=registered_model_name)\n    # mlflow.pyfunc.log_model(python_model=trainer, \n    #                         artifact_path=artifact_path, \n    #                         registered_model_name=registered_model_name,\n    #                         code_paths=[\"churn_pred\"])  \n    model_uri = mlflow.get_artifact_uri(artifact_path)\n    print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\")\n</pre> run_name = \"init_run\" with mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run:     mlflow.log_params(trainer.optimizer.best)     mlflow.log_metrics(metrics_dict_flattened)      # Log tags     mlflow.set_tags(         tags={             \"project\": \"ecovadis\",             \"optimizer_engine\": \"optuna\",             \"model_family\": \"ligtgbm\",             \"feature_set_version\": 1,         }     )     # Log figure - for future fun     # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\")      artifact_path = \"ecovadis_raw_model\"     registered_model_name = \"ecovadis_raw_lgbm_model\"     mlflow.lightgbm.log_model(             lgb_model=trainer.model,             artifact_path=artifact_path,              registered_model_name=registered_model_name)     # mlflow.pyfunc.log_model(python_model=trainer,      #                         artifact_path=artifact_path,      #                         registered_model_name=registered_model_name,     #                         code_paths=[\"churn_pred\"])       model_uri = mlflow.get_artifact_uri(artifact_path)     print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") <pre>Successfully registered model 'ecovadis_raw_lgbm_model'.\n2024/06/19 05:43:13 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: ecovadis_raw_lgbm_model, version 1\n</pre> <pre>Run ID:\n9a74f8e9087044b9b9d4935a268c26d0\nModel uri:\ns3://mlflow/6/9a74f8e9087044b9b9d4935a268c26d0/artifacts/ecovadis_raw_model\n</pre> <pre>Created version '1' of model 'ecovadis_raw_lgbm_model'.\n</pre> In\u00a0[39]: Copied! <pre>df_infser_test = df_pd.iloc[:2]\nfor prep in trainer.preprocessors:\n    df_infser_test = prep.transform(df_infser_test)\n    if hasattr(trainer, \"optimizer\"):\n        if hasattr(trainer.optimizer, \"best_to_drop\"):  # type: ignore\n            df_infser_test.drop(\n                columns=trainer.optimizer.best_to_drop,  # type: ignore\n                inplace=True,\n            )\ndf_infser_test = df_infser_test.drop(columns=trainer.id_cols + [trainer.target_col])\n</pre> df_infser_test = df_pd.iloc[:2] for prep in trainer.preprocessors:     df_infser_test = prep.transform(df_infser_test)     if hasattr(trainer, \"optimizer\"):         if hasattr(trainer.optimizer, \"best_to_drop\"):  # type: ignore             df_infser_test.drop(                 columns=trainer.optimizer.best_to_drop,  # type: ignore                 inplace=True,             ) df_infser_test = df_infser_test.drop(columns=trainer.id_cols + [trainer.target_col]) In\u00a0[40]: Copied! <pre>df_infser_test.transpose().to_dict(orient='index')\n</pre> df_infser_test.transpose().to_dict(orient='index') Out[40]: <pre>{'is_native': {0: 1.0, 1: 1.0},\n 'generation': {0: 1.0, 1: 1.0},\n 'Surname_Country': {0: 1.0, 1: 2.0},\n 'Country_subregion': {0: 1.0, 1: 1.0},\n 'Surname_Country_subregion': {0: 1.0, 1: 2.0},\n 'Surname_Country_IncomeGroup': {0: 1.0, 1: 2.0},\n 'stage_of_life': {0: 1.0, 1: 1.0},\n 'IsActiveMember': {0: 1.0, 1: 2.0},\n 'Country': {0: 1.0, 1: 1.0},\n 'CustomerFeedback_sentiment5': {0: 1.0, 1: 2.0},\n 'CustomerFeedback_sentiment3': {0: 1.0, 1: 1.0},\n 'Gender': {0: 1.0, 1: 1.0},\n 'Surname_Country_region': {0: 1.0, 1: 2.0},\n 'working_class': {0: 1.0, 1: 1.0},\n 'HasCreditCard': {0: 1.0, 1: 1.0},\n 'EstimatedSalary': {0: 145936.28125, 1: 127014.3203125},\n 'Age': {0: 18.0, 1: 18.0},\n 'Tenure': {0: 2.0, 1: 10.0},\n 'NumberOfProducts': {0: 1.0, 1: 1.0},\n 'Country_gdp_per_capita': {0: 57594.03515625, 1: 57594.03515625},\n 'Surname_Country_gdp_per_capita': {0: 32756.0, 1: 76329.5859375},\n 'CreditScore': {0: 844.0, 1: 656.0},\n 'Balance (EUR)': {0: 160980.03125, 1: 151762.734375}}</pre> <p>create test.json:</p> <pre><code>cat test.json \n{\n  \"parameters\": {\n    \"content_type\": \"pd\"\n  },\n  \"inputs\": [    \n      {\n        \"name\": \"is_native\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"generation\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"Surname_Country\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"Country_subregion\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"Surname_Country_subregion\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"Surname_Country_IncomeGroup\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"stage_of_life\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"IsActiveMember\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"Country\",\n        \"shape\": [1],\n        \"datatype\": \"BYTES\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"CustomerFeedback_sentiment5\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"CustomerFeedback_sentiment3\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"Gender\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"Surname_Country_region\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"working_class\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"HasCreditCard\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"EstimatedSalary\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [145936.28]\n      },\n      {\n        \"name\": \"Age\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [18]\n      },\n      {\n        \"name\": \"Tenure\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [2]\n      },\n      {\n        \"name\": \"NumberOfProducts\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [1.0]\n      },\n      {\n        \"name\": \"Country_gdp_per_capita\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [57594.03402]\n      },\n      {\n        \"name\": \"Surname_Country_gdp_per_capita\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [32756.0]\n      },\n      {\n        \"name\": \"CreditScore\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [844.0]\n      },\n      {\n        \"name\": \"Balance (EUR)\",\n        \"shape\": [1],\n        \"datatype\": \"FP64\",\n        \"data\": [160980.03125]\n      }\n  ]\n\n</code></pre> <p>test service:</p> <pre><code>export INGRESS_NAME=istio-ingressgateway\nexport INGRESS_NS=istio-system\nexport SERVICE_HOSTNAME=custom-ecovadis-raw-model.mlflow-kserve-success6g.example.com\nexport INGRESS_PORT=$(kubectl -n \"${INGRESS_NS}\" get service \"${INGRESS_NAME}\" -o jsonpath='{.spec.ports[?(@.name==\"http2\")].nodePort}')\nexport SERVICE_HOSTNAME=$(kubectl get inferenceservice custom-ecovadis-raw-model -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3)\n\ncurl -v \\\n  -H \"Host: ${SERVICE_HOSTNAME}\" \\\n  -H \"Content-Type: application/json\" \\\n  -d @./test.json \\\n  http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer\n</code></pre> <p>output:</p> <pre><code>{INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer\n*   Trying 10.1.24.50:32702...\n* Connected to 10.1.24.50 (10.1.24.50) port 32702 (#0)\n&gt; POST /v2/models/custom-ecovadis-raw-model/infer HTTP/1.1\n&gt; Host: custom-ecovadis-raw-model.mlflow-kserve-success6g.example.com\n&gt; User-Agent: curl/8.1.1\n&gt; Accept: */*\n&gt; Content-Type: application/json\n&gt; Content-Length: 2823\n&gt; \n&lt; HTTP/1.1 200 OK\n&lt; ce-endpoint: custom-ecovadis-raw-model\n&lt; ce-id: 0cd6e630-3a2b-44de-8284-299a10cf5283\n&lt; ce-inferenceservicename: mlserver\n&lt; ce-modelid: custom-ecovadis-raw-model\n&lt; ce-namespace: mlflow-kserve-success6g\n&lt; ce-requestid: 0cd6e630-3a2b-44de-8284-299a10cf5283\n&lt; ce-source: io.seldon.serving.deployment.mlserver.mlflow-kserve-success6g\n&lt; ce-specversion: 0.3\n&lt; ce-type: io.seldon.serving.inference.response\n&lt; content-length: 251\n&lt; content-type: application/json\n&lt; date: Wed, 19 Jun 2024 06:49:46 GMT\n&lt; server: istio-envoy\n&lt; x-envoy-upstream-service-time: 712\n&lt; \n* Connection #0 to host 10.1.24.50 left intact\n{\"model_name\":\"custom-ecovadis-raw-model\",\"id\":\"0cd6e630-3a2b-44de-8284-299a10cf5283\",\"parameters\":{\"content_type\":\"np\"},\"outputs\":[{\"name\":\"output-1\",\"shape\":[1,1],\"datatype\":\"FP64\",\"parameters\":{\"content_type\":\"np\"},\"data\":[-9.958064]}]}\n</code></pre>"},{"location":"notebooks/04_model_training_pipeline.html#example-usage-notebook","title":"Example usage notebook\u00b6","text":"<p>Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation</p>"},{"location":"notebooks/04_model_training_pipeline.html#imports","title":"Imports\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html#train-a-testing-model","title":"Train a testing model\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html#mlflow","title":"Mlflow\u00b6","text":"<ul> <li>tracking</li> <li>model registration in Minio</li> </ul>"},{"location":"notebooks/04_model_training_pipeline.html#screenshots-from-mlflow-gui-nodeport-service-in-kubernetes","title":"Screenshots from MLflow GUI (Nodeport service in Kubernetes)\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html#predictions-testing","title":"Predictions testing\u00b6","text":"<p>IMPORTANT: the predictions are in raw_score format(for testing to see if I get same predictions), i.e. strange numbers and not classes in the output</p>"},{"location":"notebooks/04_model_training_pipeline.html#example-data","title":"Example data\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html#trainer","title":"Trainer\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html#downloaded-trainer","title":"Downloaded Trainer\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html#downloadedserved-trainer","title":"Downloaded/Served Trainer\u00b6","text":"<ul> <li>i.e. testing model locally</li> </ul>"},{"location":"notebooks/04_model_training_pipeline.html#model-deployment-using-kserve-knative-istio","title":"Model deployment using Kserve + Knative + Istio\u00b6","text":"<p>reasons:</p> <ul> <li>Kserve is less mature than Seldon core but is primarly adopted by Kubeflow and MLflow</li> <li>\"Serverless installation using Knative\" is a primary installation option of Ksserve as KServe Serverless installation enables autoscaling based on request volume and supports scale down to and from zero. It also supports revision management and canary rollout based on revisions.</li> <li>Istio is a primary choice of service routing for Knative</li> <li>Kserve uses MLserver from Seldoncore but according to \"quick internet search\" it is adopted more by community than Seldon-core as Seldon-core focuses more on enterprise version of its software</li> </ul> <p>issues:</p> <ul> <li>Kserve uses MLserver which ignores <code>requirements.txt</code> and needs prepackaged conda environment in tar.gz file</li> <li>Kserve uses old version of MLserver(1.3.2) resulting in incorrect usage of <code>conda-unpack</code></li> <li>related code has to be included with model and conda environment</li> <li>mlserver with correct version has to be defined in the conda env:</li> </ul> <pre><code>  - mlserver==1.3.5\n  - mlserver-mlflow==1.3.5\n</code></pre> <p>related github issue discussions/troubleshooting:</p> <ul> <li>https://github.com/kserve/kserve/issues/3733#issuecomment-2168261646</li> <li>https://github.com/SeldonIO/MLServer/issues/1801</li> </ul>"},{"location":"notebooks/04_model_training_pipeline.html#create-conda-environment-targz-it-and-upload-it-to-minio","title":"Create conda environment, tar.gz it and upload it to Minio\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html#create-kserver-inference-service-in-kubernetes","title":"Create Kserver Inference Service in Kubernetes\u00b6","text":"<pre><code>apiVersion: \"serving.kserve.io/v1beta1\"\nkind: \"InferenceService\"\nmetadata:\n  name: \"custom-ecovadis-model\"\n  namespace: \"mlflow-kserve-success6g\"\nspec:\n  predictor:\n    serviceAccountName: success6g\n    model:\n      modelFormat:\n        name: mlflow\n      protocolVersion: v2\n      storageUri: \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\"\n</code></pre>"},{"location":"notebooks/04_model_training_pipeline.html#test-the-inference-service","title":"Test the Inference service\u00b6","text":""},{"location":"notebooks/04_model_training_pipeline.html#summary","title":"SUMMARY\u00b6","text":"<p>This unfortunately crashes due to the size of conda environment that is being downloaded from minio database by Kserve. COnda env has 3.36GB due to language models (libraries).</p> <p>Note: this approach was succesfully tested on a custom model that used small conda env ~360MB</p>"},{"location":"notebooks/04_model_training_pipeline.html#workaround","title":"WORKAROUND\u00b6","text":"<ol> <li>save/serve(deploy) only the LightGBM model</li> <li>preprocess the data locally; this could be done in production using (i) Kserve InferenceGpraph or (ii) pipelines in Kubeflow</li> <li>send preprocessed data to API</li> </ol>"},{"location":"notebooks/05_model_analysis.html","title":"05_model_analysis.ipynb","text":"In\u00a0[1]: Copied! <pre>import os\nimport sys\n\nsys.path.append(os.getcwd())\nos.chdir(\"..\")\n\nimport pandas as pd\n\npd.set_option(\"display.max_columns\", 200)\npd.set_option(\"display.max_rows\", 300)\n</pre> import os import sys  sys.path.append(os.getcwd()) os.chdir(\"..\")  import pandas as pd  pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In\u00a0[2]: Copied! <pre>from churn_pred.utils import dill_load\nfrom churn_pred.training.utils import get_feature_importance\nfrom churn_pred.eda.target.analysis import correlation\nfrom churn_pred.eda.target.plotting import prob_distrib_per_class\nfrom sklearn.model_selection import train_test_split\nfrom pprint import pprint\nimport lightgbm as lgb\nfrom churn_pred.training.utils import to_lgbdataset\nimport shap\nimport warnings\nimport numpy as np\n</pre> from churn_pred.utils import dill_load from churn_pred.training.utils import get_feature_importance from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from churn_pred.training.utils import to_lgbdataset import shap import warnings import numpy as np In\u00a0[3]: Copied! <pre>trainer = dill_load(\"data/model/lgbm_trainer.dill\")\n</pre> trainer = dill_load(\"data/model/lgbm_trainer.dill\") In\u00a0[4]: Copied! <pre>df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\")\ncat_cols = [\n    \"Country\",\n    \"Gender\",\n    \"HasCreditCard\",\n    \"IsActiveMember\",\n    \"CustomerFeedback_sentiment3\",\n    \"CustomerFeedback_sentiment5\",\n    \"Surname_Country\",\n    \"Surname_Country_region\",\n    \"Surname_Country_subregion\",\n    \"Country_region\",\n    \"Country_subregion\",\n    \"is_native\",\n    \"Country_hemisphere\",\n    \"Country_IncomeGroup\",\n    \"Surname_Country_IncomeGroup\",\n    \"working_class\",\n    \"stage_of_life\",\n    \"generation\",\n]\ndf_pd[cat_cols] = df_pd[cat_cols].astype(str)\n\nvalid_size = 0.2\ntest_size = 0.5\n\nrandom_state = 1\ndf_train, df_valid = train_test_split(\n    df_pd,\n    test_size=valid_size,\n    stratify=df_pd[trainer.target_col],\n    random_state=random_state,\n)\ndf_valid, df_test = train_test_split(\n    df_valid,\n    test_size=test_size,\n    stratify=df_valid[trainer.target_col],\n    random_state=random_state,\n)\n</pre> df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") cat_cols = [     \"Country\",     \"Gender\",     \"HasCreditCard\",     \"IsActiveMember\",     \"CustomerFeedback_sentiment3\",     \"CustomerFeedback_sentiment5\",     \"Surname_Country\",     \"Surname_Country_region\",     \"Surname_Country_subregion\",     \"Country_region\",     \"Country_subregion\",     \"is_native\",     \"Country_hemisphere\",     \"Country_IncomeGroup\",     \"Surname_Country_IncomeGroup\",     \"working_class\",     \"stage_of_life\",     \"generation\", ] df_pd[cat_cols] = df_pd[cat_cols].astype(str)  valid_size = 0.2 test_size = 0.5  random_state = 1 df_train, df_valid = train_test_split(     df_pd,     test_size=valid_size,     stratify=df_pd[trainer.target_col],     random_state=random_state, ) df_valid, df_test = train_test_split(     df_valid,     test_size=test_size,     stratify=df_valid[trainer.target_col],     random_state=random_state, ) In\u00a0[5]: Copied! <pre>df_predicted_proba = trainer.predict_proba(\n    df=df_test.drop(columns=trainer.target_col), binary2d=False\n)\ndf_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col))\n</pre> df_predicted_proba = trainer.predict_proba(     df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col)) In\u00a0[6]: Copied! <pre>lgb.plot_importance(trainer.model, importance_type=\"split\")\n# lgb.plot_importance(trainer.model, importance_type=\"gain\")\n</pre> lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[6]: <pre>&lt;Axes: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'&gt;</pre> In\u00a0[7]: Copied! <pre>fig = prob_distrib_per_class(\n    predicted_probs=df_predicted_proba.values.flatten(),\n    actual=df_test[trainer.target_col].values.flatten(),\n    task=\"binary\",\n)\n</pre> fig = prob_distrib_per_class(     predicted_probs=df_predicted_proba.values.flatten(),     actual=df_test[trainer.target_col].values.flatten(),     task=\"binary\", ) In\u00a0[8]: Copied! <pre>df_test_prep = trainer.preprocessors[0].transform(\n    df_test.drop(columns=[trainer.target_col])\n)\ndf_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int)\n\nlgb_test, _ = to_lgbdataset(\n    train=df_test_prep,\n    cat_cols=trainer.cat_cols,\n    target_col=trainer.target_col,\n    id_cols=trainer.id_cols,\n)\n</pre> df_test_prep = trainer.preprocessors[0].transform(     df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int)  lgb_test, _ = to_lgbdataset(     train=df_test_prep,     cat_cols=trainer.cat_cols,     target_col=trainer.target_col,     id_cols=trainer.id_cols, ) In\u00a0[9]: Copied! <pre>base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True)\nshap_values = base_value[:, :-1]\nbase_value = np.mean(base_value[:, -1])\n</pre> base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In\u00a0[10]: Copied! <pre>shap.summary_plot(\n    shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True\n)\n</pre> shap.summary_plot(     shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In\u00a0[11]: Copied! <pre>df_predicted_cls[28:30]\n</pre> df_predicted_cls[28:30] Out[11]: Exited 28 0 29 1 In\u00a0[12]: Copied! <pre>df_test.iloc[28:30]\n</pre> df_test.iloc[28:30] Out[12]: CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 9941 15714240 712 Spain Male 74 5 0.0 2 0 0 151425.82 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Southern Europe 0 northern 48685.49631 High income 34637.76172 Upper middle income elderly senior_adult boomers_1 7444 15658057 812 Spain Female 44 8 0.0 3 1 0 66926.83 1 neutral 1 star Italy Europe Southern Europe Europe Southern Europe 0 northern 48685.49631 High income 55442.07843 High income working_age middle_age_adult millennials In\u00a0[13]: Copied! <pre>shap_decision_plot = shap.decision_plot(\n    base_value=base_value,\n    shap_values=shap_values[28:30],\n    features=trainer.model.feature_name(),\n    feature_names=trainer.model.feature_name(),\n    # legend_labels=legend_labels,\n    link=\"logit\",\n    # row_index=0,\n    # feature_order=list(range(len(trainer.model.feature_name()))),\n)\n</pre> shap_decision_plot = shap.decision_plot(     base_value=base_value,     shap_values=shap_values[28:30],     features=trainer.model.feature_name(),     feature_names=trainer.model.feature_name(),     # legend_labels=legend_labels,     link=\"logit\",     # row_index=0,     # feature_order=list(range(len(trainer.model.feature_name()))), )"},{"location":"notebooks/05_model_analysis.html#model-analysis","title":"Model analysis\u00b6","text":""},{"location":"notebooks/05_model_analysis.html#model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class","title":"Model feature importance and \"certainty\" of the model in predicting classes - probability density per class\u00b6","text":""},{"location":"notebooks/05_model_analysis.html#shap-analysis","title":"SHAP analysis\u00b6","text":""}]}
\ No newline at end of file
+{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"index.html","text":"Churn predictor \u00b6 Main purpose of this library is to provide solution for an asisgnemnt below: In this assignment you're tasked with developing a machine learning solution for churn prediction to identify which customers are likely to leave a service (column \"Exited\" in the attached dataset). This assignment is meant to assess - analytical skills and reasoning - design and modelling choices, e.g. choices with respect to measuring model performance - coding skills, e.g. modularity, readability, reproducibility, any other best practices in software development Please note that multiple solutions may exist and we do not expect a production ready solution, though any reflections on how you may wish to productionalise your solution are welcome. You are free to choose the medium (e.g., notebooks, python scripts). Additional explanation of independent variables: NumberOfProducts - the number of accounts and bank-affiliated products HasCreditCard - whether a customer has a credit card CustomerFeedback - latest customer feedback, if available Solution \u00b6 Please see the Notebooks section. The notebooks are sorted from 0 to 5. Notebooks start with gathering auxiliary data that I could extract from the provided dataset, e.g. 'country origin of the surname'. This is followed by Exploratory Data Analysis of features and target in the notebooks 2, 3. In the notebook 4, I presented a Trainer object that handles training an hyperparameter search of the model. In the notebook 5 I made a quick analysis of the model and it's predictions using SHAP values. The final solution uses LightGBM, a GBM model of my choice. I chose GBM as 4 out of top 5 models in H2O AutoML were GBMs. Additional work note mentioning \u00b6 In notebook 00_auxiliary_features_surname_origin_country_classification.ipynb I adjusted(copy/paste+adjust) a BERT model for surname origin prediction. Due to lack of time I could not gather additional data that would help with model training, but I left some ideas in the notebook. The solution was tested in a virtual machine, spawned from jupyter/datascience-notebook:python-3.10 image in Zero-to-JupyterHub solution. As the bare metal server with GPU was down in the kubernetes, I had to do additional troubleshooting and fixing. The code is easily extendable to multiclass , regression and quantile_regression tasks.","title":"home"},{"location":"index.html#churn-predictor","text":"Main purpose of this library is to provide solution for an asisgnemnt below: In this assignment you're tasked with developing a machine learning solution for churn prediction to identify which customers are likely to leave a service (column \"Exited\" in the attached dataset). This assignment is meant to assess - analytical skills and reasoning - design and modelling choices, e.g. choices with respect to measuring model performance - coding skills, e.g. modularity, readability, reproducibility, any other best practices in software development Please note that multiple solutions may exist and we do not expect a production ready solution, though any reflections on how you may wish to productionalise your solution are welcome. You are free to choose the medium (e.g., notebooks, python scripts). Additional explanation of independent variables: NumberOfProducts - the number of accounts and bank-affiliated products HasCreditCard - whether a customer has a credit card CustomerFeedback - latest customer feedback, if available","title":"Churn predictor"},{"location":"index.html#solution","text":"Please see the Notebooks section. The notebooks are sorted from 0 to 5. Notebooks start with gathering auxiliary data that I could extract from the provided dataset, e.g. 'country origin of the surname'. This is followed by Exploratory Data Analysis of features and target in the notebooks 2, 3. In the notebook 4, I presented a Trainer object that handles training an hyperparameter search of the model. In the notebook 5 I made a quick analysis of the model and it's predictions using SHAP values. The final solution uses LightGBM, a GBM model of my choice. I chose GBM as 4 out of top 5 models in H2O AutoML were GBMs.","title":"Solution"},{"location":"cicd.html","text":"CI/CD \u00b6 This section includes some ideas for model developmnet and deployment within Azure services - i.e. EcoVadis cloud services vendor. MLFlow for experiment tracking and endpoint deployment https://mlflow.org/docs/1.25.1/python_api/mlflow.azureml.html https://mlflow.org/docs/latest/deployment/index.html https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2&tabs=mlflow DVC for dataset versioning python api: https://dvc.org/doc/api-reference Dataset storage in Azure Blob Storage https://dvc.org/doc/user-guide/data-management/remote-storage/azure-blob-storage#microsoft-azure-blob-storage Dataset and predictions(dev) visualization from Azure Blob Storage https://learn.microsoft.com/en-us/azure/data-explorer/azure-data-explorer-dashboards https://learn.microsoft.com/en-us/azure/data-explorer/create-event-grid-connection?tabs=portal-adx%2Cazure-blob-storage Data handling in PROD databricks: https://learn.microsoft.com/en-us/azure/databricks/getting-started/data-pipeline-get-started create and share dashboards within organization: https://learn.microsoft.com/en-us/azure/databricks/sql/get-started/sample-dashboards","title":"cicd"},{"location":"cicd.html#cicd","text":"This section includes some ideas for model developmnet and deployment within Azure services - i.e. EcoVadis cloud services vendor. MLFlow for experiment tracking and endpoint deployment https://mlflow.org/docs/1.25.1/python_api/mlflow.azureml.html https://mlflow.org/docs/latest/deployment/index.html https://learn.microsoft.com/en-us/azure/machine-learning/how-to-deploy-mlflow-models-online-endpoints?view=azureml-api-2&tabs=mlflow DVC for dataset versioning python api: https://dvc.org/doc/api-reference Dataset storage in Azure Blob Storage https://dvc.org/doc/user-guide/data-management/remote-storage/azure-blob-storage#microsoft-azure-blob-storage Dataset and predictions(dev) visualization from Azure Blob Storage https://learn.microsoft.com/en-us/azure/data-explorer/azure-data-explorer-dashboards https://learn.microsoft.com/en-us/azure/data-explorer/create-event-grid-connection?tabs=portal-adx%2Cazure-blob-storage Data handling in PROD databricks: https://learn.microsoft.com/en-us/azure/databricks/getting-started/data-pipeline-get-started create and share dashboards within organization: https://learn.microsoft.com/en-us/azure/databricks/sql/get-started/sample-dashboards","title":"CI/CD"},{"location":"code_profiling.html","text":"Code profiling \u00b6 his section incldues some ideas for code profiling - i.e. CO2, RAM, CPU, GPU tracking. Carbon emission monitoring: \u00b6 https://mlco2.github.io/codecarbon/usage.html# https://github.com/sb-ai-lab/Eco2AI https://github.com/lfwa/carbontracker RAM usage profiling and Code execution timing \u00b6 RAM usage profiling \u00b6 Max RAM used To get max RAM used during executition of the function import the decorator and put it above it function to log max RAM used into specified logging file, e.g.: **NOTE**: this applies only to functions and NOT classes or class methods. In case somebody figures out how to use it also on methods please let me know[Pavol Mulinka]. Posts relevant to the issue I was unable to debug: * https://stackoverflow.com/questions/75409483/why-do-i-get-missing-required-argument-self-when-using-a-decorator-written-as * https://stackoverflow.com/questions/16593246/how-to-use-memory-profiler-python-module-with-class-methods from churn_pred.code_profiling import ram_usage @ram_usage def training(config: TrainingConfig, custom_params: CustomParameters): Per line RAM usage analysis To analyze RAM usage per line of the code import profile decorator, put it above the function you want to analyze: from memory_profiler import profile @profile def training(config: TrainingConfig, custom_params: CustomParameters): , run the code using memory_profile command line util, and analyze the RAM usage, e.g.: $ python -m memory_profiler local_run.py Filename: /XXX/main.py Line # Mem usage Increment Occurrences Line Contents ============================================================= 53 230.8 MiB 230.8 MiB 1 @ram_usage 54 @profile 55 def training(config: TrainingConfig, custom_params: CustomParameters): 56 230.9 MiB 0.1 MiB 1 data_config = DataConfig.load(config.data_config) 57 230.9 MiB 0.0 MiB 1 hyperparameters = Hyperparameters.parse_obj(config.hyperparameters) 58 59 687.5 MiB 456.6 MiB 1 queries, df_data = get_data(config, custom_params) 60 687.5 MiB 0.0 MiB 1 ( Code Execution timing \u00b6 Timer decorator Import and prepend the time decorator to log time(into specified log file) that it takes to execute analyzed function/class, e.g.: from churn_pred.code_profiling import timer @timer def training(config: TrainingConfig, custom_params: CustomParameters):","title":"code profiling"},{"location":"code_profiling.html#code-profiling","text":"his section incldues some ideas for code profiling - i.e. CO2, RAM, CPU, GPU tracking.","title":"Code profiling"},{"location":"code_profiling.html#carbon-emission-monitoring","text":"https://mlco2.github.io/codecarbon/usage.html# https://github.com/sb-ai-lab/Eco2AI https://github.com/lfwa/carbontracker","title":"Carbon emission monitoring:"},{"location":"code_profiling.html#ram-usage-profiling-and-code-execution-timing","text":"","title":"RAM usage profiling and Code execution timing"},{"location":"contributing.html","text":"Code \u00b6 Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/ Documentation \u00b6 Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"contributing"},{"location":"contributing.html#code","text":"Before making a pushing a code and making a pull request please run codestyle checks and tests ./code_style.sh pytest --doctest-modules churn_pred --cov-report xml --cov-report term --disable-pytest-warnings --cov = eda tests/","title":"Code"},{"location":"contributing.html#documentation","text":"Documentation is created using github pages and mkdocs, see # to build locally cd docs pip install -r requirements.txt mkdocs build # to push to github pages mkdocs gh-deploy # if you want to run webserver locally mkdocs serve","title":"Documentation"},{"location":"installation.html","text":"Installation \u00b6 Install using pip directly from github: \u00b6 pip install git+https://github.com/5uperpalo/churn_pred.git Locally \u00b6 git clone https://github.com/5uperpalo/churn_pred.git cd churn_pred pip install . Additional reqs on a newly spawned machine \u00b6 H20 AutoML requires JRE and language models are faster on GPU. sudo apt install nvidia-utils-535 sudo apt install openjdk-17-jdk","title":"installation"},{"location":"installation.html#installation","text":"","title":"Installation"},{"location":"installation.html#install-using-pip-directly-from-github","text":"pip install git+https://github.com/5uperpalo/churn_pred.git","title":"Install using pip directly from github:"},{"location":"installation.html#locally","text":"git clone https://github.com/5uperpalo/churn_pred.git cd churn_pred pip install .","title":"Locally"},{"location":"installation.html#additional-reqs-on-a-newly-spawned-machine","text":"H20 AutoML requires JRE and language models are faster on GPU. sudo apt install nvidia-utils-535 sudo apt install openjdk-17-jdk","title":"Additional reqs on a newly spawned machine"},{"location":"churn_pred/code_profiling.html","text":"Code profiling \u00b6 code_profiling \u00b6 timer \u00b6 timer ( func ) Wrapper to time and log the function execution. Parameters: func \u2013 function Source code in churn_pred/code_profiling.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 def timer ( func ): \"\"\"Wrapper to time and log the function execution. Parameters: func: function \"\"\" @wraps ( func ) def timer_func ( * args , ** kwargs ): start_time = time () value = func ( * args , ** kwargs ) end_time = time () logging . info ( f \"Finished { func . __name__ } in { ( end_time - start_time ) : .4f } seconds.\" # noqa ) return value return timer_func ram_usage \u00b6 ram_usage ( func ) Wrapper to monitor and log RAM usage during the function execution. NOTE: can be applied to function but not to method of a class. Parameters: func \u2013 function Source code in churn_pred/code_profiling.py 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 def ram_usage ( func ): \"\"\"Wrapper to monitor and log RAM usage during the function execution. NOTE: can be applied to function but not to method of a class. Parameters: func: function \"\"\" @wraps ( func ) def ram_usage_func ( * args , ** kwargs ): ram , value = memory_usage ( ( func , args , * kwargs ), interval = 1.0 , retval = True , max_usage = True ) logging . info ( f \"Finished { func . __name__ , } . Max RAM used { ( ram / 1000 ) : .4f } GB.\" ) # noqa return value return ram_usage_func","title":"code_profiling"},{"location":"churn_pred/code_profiling.html#code-profiling","text":"","title":"Code profiling"},{"location":"churn_pred/code_profiling.html#churn_pred.code_profiling","text":"","title":"code_profiling"},{"location":"churn_pred/eda.html","text":"Preprocess data \u00b6 analysis \u00b6 init_check \u00b6 init_check ( df , identifier = None , cat_cols = None , cont_cols = None , verbose = False , ) Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Parameters: df ( DataFrame ) \u2013 pandas dataframe identifier ( str ) \u2013 column which identifies unique user IDs cat_cols ( list ) \u2013 categorical features in the dataset cont_cols ( list ) \u2013 numerical features in the dataset Returns: duplicated_ids ( int ) \u2013 cont_cols_desc ( pd . DataFrame ) \u2013 cat_cols_desc ( pd . DataFrame ) \u2013 Source code in churn_pred/eda/features/analysis.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 def init_check ( df : pd . DataFrame , identifier : Optional [ str ] = None , cat_cols : Optional [ list ] = None , cont_cols : Optional [ list ] = None , verbose : bool = False , ) -> Tuple [ int , pd . DataFrame , pd . DataFrame ]: \"\"\"Procedure to check: * duplicated rows in teh dataset * general stats of numerical features * general stats of categorical features Args: df (DataFrame): pandas dataframe identifier (str): column which identifies unique user IDs cat_cols (list): categorical features in the dataset cont_cols (list): numerical features in the dataset Returns: duplicated_ids (int): cont_cols_desc (pd.DataFrame): cat_cols_desc (pd.DataFrame): \"\"\" data = df . copy () if identifier : duplicated_ids = data [ identifier ] . duplicated () . sum () if verbose : print ( \"[CHECK] Number of duplicated ids: {} \" . format ( duplicated_ids )) if cont_cols : cont_cols_f = intsec ( data . columns . values , cont_cols ) cont_cols_desc = data [ cont_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Numerical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cont_cols_desc ) if cat_cols : cat_cols_f = intsec ( data . columns . values , cat_cols ) cat_cols_desc = data [ cat_cols_f ] . describe () . transpose () if verbose : print ( \"[CHECK] Categorical columns\" ) with pd . option_context ( \"display.precision\" , 2 ): print ( cat_cols_desc ) return duplicated_ids , cont_cols_desc , cat_cols_desc missing \u00b6 missing ( df , scale = 'linear' , plot = False ) Procedure to check fraction of missing values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: missing_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of missing values per feature fig ( Figure ) \u2013 plot with sorted fractions of missing values in each column Source code in churn_pred/eda/features/analysis.py 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 def missing ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of missing values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: missing_val_frac (DataFrame): sorted dataframe with fraction of missing values per feature fig (Figure): plot with sorted fractions of missing values in each column \"\"\" data = df . copy () missing_val_frac = data . isna () . sum () / len ( data ) missing_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( missing_val_frac , ax , title = \"Fraction of missing values in the dataset\" , ) else : fig = None ax . set_yscale ( scale ) return missing_val_frac , fig zero \u00b6 zero ( df , scale = 'linear' , plot = False ) Procedure to check fraction of zero values in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: zero_val_frac ( DataFrame ) \u2013 sorted dataframe with fraction of '0' values per feature fig ( Figure ) \u2013 plot with sorted fractions of '0' values in each column Source code in churn_pred/eda/features/analysis.py 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 def zero ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to check fraction of zero values in the dataset. Args: df (DataFrame): pandas dataframe scale (str): y scale of the plot plot (bool): whether to output the plot Returns: zero_val_frac (DataFrame): sorted dataframe with fraction of '0' values per feature fig (Figure): plot with sorted fractions of '0' values in each column \"\"\" data = df . copy () zero_val_frac = data . isin ([ 0 ]) . sum () / len ( data ) zero_val_frac . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( zero_val_frac , ax , title = \"Fraction of zero values in the dataset\" ) else : fig = None ax . set_yscale ( scale ) return zero_val_frac , fig nunique \u00b6 nunique ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their number of unique values. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted number of unique values in features Source code in churn_pred/eda/features/analysis.py 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 def nunique ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their number of unique values. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted number of unique values in features \"\"\" data = df . copy () data_nunique = data . nunique () data_nunique . sort_values ( ascending = True , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_nunique . dropna (), ax , title = \"Features number of unique values (NA are dropped)\" , ) else : fig = None ax . set_yscale ( scale ) return data_nunique , fig std \u00b6 std ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their variance. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted standard deviation of continuous features Source code in churn_pred/eda/features/analysis.py 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 def std ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their variance. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted standard deviation of continuous features \"\"\" data = df . copy () data_std = data . std () data_std . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( data_std . dropna (), ax , title = \"Features variance (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return data_std , fig entropy \u00b6 entropy ( df , scale = 'linear' , plot = False ) Procedure to plot features sorted by their entropy. Parameters: df ( DataFrame ) \u2013 dataset scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: fig ( Figure ) \u2013 plot with sorted entropy of the features Source code in churn_pred/eda/features/analysis.py 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 def entropy ( df : pd . DataFrame , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot features sorted by their entropy. Args: df (DataFrame): dataset scale (str): y scale of the plot plot (bool): whether to output the plot Returns: fig (Figure): plot with sorted entropy of the features \"\"\" data = df . copy () data = data . astype ( str ) col_entropies = data . apply ( entropy_calc , axis = 0 ) col_entropies . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( col_entropies . dropna (), ax , title = \"Features entropies (NA are dropped)\" ) else : fig = None ax . set_yscale ( scale ) return col_entropies , fig plotting \u00b6 cross_correlation \u00b6 cross_correlation ( df , n = 10 , verbose = False ) Procedure to calculate and plot cross-correlation of features in the dataset. Parameters: df ( DataFrame ) \u2013 pandas dataframe verbose ( bool ) \u2013 show n most correlated features n ( int ) \u2013 number correlated features in verbose output Returns: fig ( Figure ) \u2013 heatmap of continuous features cross correlations Source code in churn_pred/eda/features/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 def cross_correlation ( df : pd . DataFrame , n : int = 10 , verbose : bool = False , ) -> Figure : \"\"\"Procedure to calculate and plot cross-correlation of features in the dataset. Args: df (DataFrame): pandas dataframe verbose (bool): show n most correlated features n (int): number correlated features in verbose output Returns: fig (Figure): heatmap of continuous features cross correlations \"\"\" data = df . copy () # corrwith uses numpy which does not like pandas Float dtypes corr_matrix = data . astype ( float ) . corr () upper_stacked_sorted = ( corr_matrix . abs () . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) . stack () . sort_values ( ascending = False ) ) fig , ax = plt . subplots ( figsize = ( 10 , 10 )) ax . set_title ( \"Cross-correlation matrix\" , size = 20 ) sns . set ( font_scale = 1.0 ) sns . heatmap ( corr_matrix , ax = ax , cbar = True , square = True , # linewidths=0.5, fmt = \".2f\" , ) if verbose : print ( \"Top {} absolute correlations\" . format ( n )) print ( upper_stacked_sorted [: n ]) return fig distributions \u00b6 distributions ( df , low_per_cut = 0 , high_per_cut = 1 , type = \"box\" ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe low_per_cut ( float ) \u2013 lower percentile where to cut the plot for better readability high_per_cut ( float ) \u2013 higher percentile where to cut the plot for better readability type ( str ) \u2013 type of distribution plot Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in churn_pred/eda/features/plotting.py 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 def distributions ( df : pd . DataFrame , low_per_cut : float = 0 , high_per_cut : float = 1 , type : Literal [ \"box\" , \"violin\" ] = \"box\" , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe low_per_cut (float): lower percentile where to cut the plot for better readability high_per_cut (float): higher percentile where to cut the plot for better readability type (str): type of distribution plot Returns: fig (Figure): ditribution plot per each feature \"\"\" data = df . copy () cols = data . columns . values data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( \"Distribution of feature values\" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if type == \"violin\" : sns . violinplot ( ax = ax [ i ], x = col , orient = \"h\" , # cut=0, # showextrem=False, data = data , ) if type == \"box\" : sns . boxplot ( ax = ax [ i ], x = col , orient = \"h\" , data = data , ) ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 ) return fig analysis \u00b6 correlation \u00b6 correlation ( df , target , scale = 'linear' , plot = False ) Procedure to plot most correlated numerical features with target column. Parameters: df ( pd . DataFrame ) \u2013 pandas dataframe target ( pd . Series ) \u2013 target values scale ( str ) \u2013 y scale of the plot plot ( bool ) \u2013 whether to output the plot Returns: sorted_corr_cols ( pd . DataFrame ) \u2013 sorted dataframe with feature and target value correlation fig ( Figure ) \u2013 sorted bar plot with feature and target value correlation Source code in churn_pred/eda/target/analysis.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def correlation ( df : pd . DataFrame , target : pd . Series , scale : Literal [ \"log\" , \"linear\" ] = \"linear\" , plot : bool = False , ) -> Tuple [ pd . DataFrame , Figure ]: \"\"\"Procedure to plot most correlated numerical features with target column. Args: df (pd.DataFrame): pandas dataframe target (pd.Series): target values scale (str): y scale of the plot plot (bool): whether to output the plot Returns: sorted_corr_cols (pd.DataFrame): sorted dataframe with feature and target value correlation fig (Figure): sorted bar plot with feature and target value correlation \"\"\" data = df . copy () target_col = target . name correlation_df = pd . concat ([ data , target ], axis = 1 ) . corr () sorted_corr_cols = correlation_df [ target_col ] . drop ( index = [ target_col ]) sorted_corr_cols . sort_values ( ascending = False , inplace = True ) if plot : fig , ax = plt . subplots ( figsize = ( 10 , 5 )) bar_plot ( sorted_corr_cols , ax , title = f \"Sorted features by their correlation with { target_col } \" , ) else : fig = None ax . set_yscale ( scale ) return sorted_corr_cols , fig plotting \u00b6 prob_distrib_per_class \u00b6 prob_distrib_per_class ( predicted_probs , actual , task ) Procedure to plot probability density distributions per class from LightGBM predictions. Parameters: predicted_probs ( ndarray ) \u2013 predicted probs actual ( ndarray ) \u2013 ground truth classes task ( str ) \u2013 type of task Returns: fig ( Figure ) \u2013 probability density ditributions plot per each class Source code in churn_pred/eda/target/plotting.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def prob_distrib_per_class ( predicted_probs : np . ndarray , actual : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], ) -> Figure : \"\"\"Procedure to plot probability density distributions per class from LightGBM predictions. Args: predicted_probs (ndarray): predicted probs actual (ndarray): ground truth classes task (str): type of task Returns: fig (Figure): probability density ditributions plot per each class \"\"\" if task == \"binary\" : temp = pd . DataFrame ({ \"predicted_proba\" : predicted_probs , \"actual\" : actual }) fig , ax = plt . subplots ( figsize = ( 3 , 2 )) fig . suptitle ( \"Predicted probability density per class\" ) for label , alpha , color in zip ([ 0 , 1 ], [ 1 , 0.7 ], [ \"black\" , \"red\" ]): ax . hist ( temp [ temp [ \"actual\" ] == label ][ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), alpha = alpha , color = color , ec = \"k\" , ) ax . set_xlim ([ 0 , 1 ]) ax . set_xlabel ( \"probability\" ) ax . set_ylabel ( \"probability_density\" ) elif task == \"multiclass\" : temp = pd . DataFrame ( { \"predicted_proba\" : np . take_along_axis ( predicted_probs , np . vstack ( actual ), axis = 1 # type: ignore ) . flatten (), \"actual\" : actual , } ) # Dictionary of color for each label color_d = dict ( zip_longest ( temp [ \"actual\" ] . unique (), plt . rcParams [ \"axes.prop_cycle\" ] . by_key ()[ \"color\" ], ) ) n_classes = temp [ \"actual\" ] . nunique () fig , ax = plt . subplots ( ncols = n_classes , figsize = ( n_classes * 3 , 2 ), sharex = True , sharey = True ) fig . suptitle ( \"Predicted probability density per class\" ) plt . subplots_adjust ( wspace = 0.3 , top = 0.75 ) for i , ( label , gp ) in enumerate ( temp . groupby ( \"actual\" )): ax [ i ] . hist ( gp [ \"predicted_proba\" ], density = True , bins = np . linspace ( 0 , 1 , 10 ), color = color_d [ label ], ec = \"k\" , ) ax [ i ] . set_title ( label ) ax [ i ] . set_xlim ([ 0 , 1 ]) ax [ i ] . set_xlabel ( \"probability\" ) ax [ i ] . set_ylabel ( \"probability_density\" ) return fig distributions_in_binary_cls \u00b6 distributions_in_binary_cls ( df , target , low_per_cut = 0 , high_per_cut = 1 ) Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Parameters: df ( DataFrame ) \u2013 pandas dataframe target ( pd . Series ) \u2013 target values, i.e. binary classes Returns: fig ( Figure ) \u2013 ditribution plot per each feature Source code in churn_pred/eda/target/plotting.py 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 def distributions_in_binary_cls ( df : pd . DataFrame , target : pd . Series , low_per_cut : float = 0 , high_per_cut : float = 1 , ) -> Figure : \"\"\"Procedure to plot distributions of the features splitted by column split_col using workaround for violinplots in seaborn: * https://stackoverflow.com/a/64787568/8147433 DISCALIMER: for now the cont_cols NA vals are filled with 0 Args: df (DataFrame): pandas dataframe target (pd.Series): target values, i.e. binary classes Returns: fig (Figure): ditribution plot per each feature \"\"\" data = pd . concat ([ df , target ], axis = 1 ) cols = data . columns . values data [ \"dummy\" ] = 0 data = data . fillna ( 0 ) . astype ( float ) fig , ax = plt . subplots ( len ( cols ), 1 , figsize = ( 16 , len ( cols ) * 2 )) fig . suptitle ( f \"Distribution of feature values splitted by { target . name } \" , size = 20 ) for col , i in zip ( cols , range ( len ( cols ))): if col != target . name : sns . violinplot ( ax = ax [ i ], hue = target . name , x = col , y = \"dummy\" , orient = \"h\" , cut = 0 , showextrem = False , split = True , data = data , ) ax [ i ] . get_legend () . remove () ax [ i ] . set_xlim ( data [ col ] . quantile ( low_per_cut ), data [ col ] . quantile ( high_per_cut ) ) ax [ i ] . get_yaxis () . set_visible ( False ) plt . subplots_adjust ( hspace = 0.9 , top = 0.97 ) return fig plotting \u00b6 bar_plot \u00b6 bar_plot ( df , ax , title ) Helper method for unified bar plot Parameters: df ( DataFrame ) \u2013 dataframe to plot ax ( Axes ) \u2013 axes defining where to plot it Returns: adjusted_plot ( Axes ) \u2013 adjusted axes Source code in churn_pred/eda/plotting.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def bar_plot ( df : pd . DataFrame , ax : Axes , title : str ) -> Axes : \"\"\"Helper method for unified bar plot Args: df (DataFrame): dataframe to plot ax (Axes): axes defining where to plot it Returns: adjusted_plot (Axes): adjusted axes \"\"\" data = df . copy () ax . set_title ( title , size = 20 ) ax . tick_params ( labelsize = 8 ) ax . set_xlabel ( \"sorted features\" , size = 12 ) return data . plot . bar ( ax = ax ) general_utils \u00b6 intsec \u00b6 intsec ( list1 , list2 ) Simple intesection of two lists. Parameters: list1 ( list ) \u2013 list1 list2 ( list ) \u2013 list2 Returns: list ( list ) \u2013 intersection of lists Source code in churn_pred/eda/general_utils.py 6 7 8 9 10 11 12 13 14 def intsec ( list1 : list , list2 : list ) -> list : \"\"\"Simple intesection of two lists. Args: list1 (list): list1 list2 (list): list2 Returns: list (list): intersection of lists \"\"\" return list ( set . intersection ( set ( list1 ), set ( list2 ))) entropy_calc \u00b6 entropy_calc ( labels , base = np . e ) Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Parameters: labels ( list , ndarray , Series ) \u2013 list of values Returns: ent ( float ) \u2013 entropy of the list of values Source code in churn_pred/eda/general_utils.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def entropy_calc ( labels : list , base : float = np . e ) -> float : \"\"\"Computes entropy of both continuous and categorical features. Shamelessly stolen from : https://stackoverflow.com/a/45091961 Args: labels (list, ndarray, Series): list of values Returns: ent (float): entropy of the list of values \"\"\" n_labels = len ( labels ) value , counts = np . unique ( labels , return_counts = True ) probs = counts / n_labels n_classes = np . count_nonzero ( probs ) ent = float ( 0 ) if n_classes > 1 : for i in probs : ent -= i * math . log ( i , base ) return ent","title":"eda"},{"location":"churn_pred/eda.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"churn_pred/eda.html#churn_pred.eda.features.analysis","text":"","title":"analysis"},{"location":"churn_pred/eda.html#churn_pred.eda.features.plotting","text":"","title":"plotting"},{"location":"churn_pred/eda.html#churn_pred.eda.target.analysis","text":"","title":"analysis"},{"location":"churn_pred/eda.html#churn_pred.eda.target.plotting","text":"","title":"plotting"},{"location":"churn_pred/eda.html#churn_pred.eda.plotting","text":"","title":"plotting"},{"location":"churn_pred/eda.html#churn_pred.eda.general_utils","text":"","title":"general_utils"},{"location":"churn_pred/preprocessing.html","text":"Preprocess data \u00b6 auxiliary_data \u00b6 surname_origin_bert \u00b6 surname_origin_bert ( df , surname_col ) THIS IS WORK IN PROGRESS!!! Surname classification that uses adjusted script scripts/surname_classification_with_bert.py) from: https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert to train a bert model for surname classification additional data from: https://github.com/greenelab/wiki-nationality-estimate/tree/master and possibly data from: https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset Parameters: df ( pd . DataFrame ) \u2013 input dataset surname_col ( str ) \u2013 column name with surnames Source code in churn_pred/preprocessing/auxiliary_data.py 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 def surname_origin_bert ( df : pd . DataFrame , surname_col : str ) -> pd . DataFrame : \"\"\" THIS IS WORK IN PROGRESS!!! Surname classification that uses adjusted script scripts/surname_classification_with_bert.py) from: https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert to train a bert model for surname classification additional data from: https://github.com/greenelab/wiki-nationality-estimate/tree/master and possibly data from: https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset Args: df (pd.DataFrame): input dataset surname_col (str): column name with surnames \"\"\" dfc = df . copy () return dfc surname_origin \u00b6 surname_origin ( df , surname_col ) Surname classification that uses db of names leaked from FB, see: https://github.com/philipperemy/name-dataset Parameters: df ( pd . DataFrame ) \u2013 input dataset surname_col ( str ) \u2013 column name with surnames Source code in churn_pred/preprocessing/auxiliary_data.py 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 def surname_origin ( df : pd . DataFrame , surname_col : str ) -> pd . DataFrame : \"\"\" Surname classification that uses db of names leaked from FB, see: https://github.com/philipperemy/name-dataset Args: df (pd.DataFrame): input dataset surname_col (str): column name with surnames \"\"\" dfc = df . copy () nd = NameDataset () def _search_surname ( surname ): try : res = nd . search ( surname ) origin_country = max ( res [ \"last_name\" ][ \"country\" ], key = res [ \"last_name\" ][ \"country\" ] . get ) except : origin_country = \"NA\" return origin_country dfc [ \"res\" ] = \"NA\" for surname in dfc [ surname_col ] . unique (): dfc . loc [ dfc [ surname_col ] == surname , [ \"res\" ]] = _search_surname ( surname ) return dfc [ \"res\" ] hemisphere \u00b6 hemisphere ( df , cc_col , loc_db_df = None , city_col = None ) Returns string pd.Series identifing hemisphere: \"northern\" or \"southern\". Parameters: df ( pd . DataFrame ) \u2013 input dataframe city_col ( str ) \u2013 city name column cc_col ( str ) \u2013 country code column loc_db_df ( pd . DataFrame ) \u2013 pandas dataframe with previously saved locations Source code in churn_pred/preprocessing/auxiliary_data.py 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 def hemisphere ( df : pd . DataFrame , cc_col : str , loc_db_df : Optional [ pd . DataFrame ] = None , city_col : Optional [ str ] = None , ) -> pd . Series : \"\"\"Returns string pd.Series identifing hemisphere: \"northern\" or \"southern\". Args: df (pd.DataFrame): input dataframe city_col (str): city name column cc_col (str): country code column loc_db_df (pd.DataFrame): pandas dataframe with previously saved locations \"\"\" if city_col : uq_locs = ( df [ city_col ] + \",\" + df [ cc_col ]) . unique () else : uq_locs = ( \" ,\" + df [ cc_col ]) . unique () dfc = df . copy () geolocator = Nominatim ( user_agent = \"hemisphere_identification\" ) dfc [ \"hemisphere\" ] = \"northern\" for uq_loc in uq_locs : if ( loc_db_df is not None ) and ( uq_loc in loc_db_df [ \"location\" ] . values ): location = loc_db_df [ loc_db_df [ \"location\" ] == uq_loc ][ [ \"latitude\" , \"longitude\" ] ] latitude = float ( location . latitude ) else : latitude = geolocator . geocode ( uq_loc ) . latitude if latitude < 0 : city , country = uq_loc . split ( \",\" ) dfc . loc [ ( dfc [ city_col ] == city ) & ( dfc [ cc_col ] == country ), \"hemisphere\" ] = \"southern\" return dfc age_categories \u00b6 age_categories ( df , age_col ) Returns string pd.DataFrame identifing different age classification categories according to the age: * working_class ( https://ourworldindata.org/age-structure ) * children_and_adolescents: <0, 15) * working_age: <15, 65) * elderly: <65, inf) * stage_of_life ( https://integrishealth.org/resources/on-your-health/2015/october/stages-of-life-health-for-every-age ) * infant: <0, 2) * toddler: <2, 5) * child: <5, 13) * teen: <13, 20) * adult: <20, 40) * middle_age_adult: <40, 60) * senior_adult: <60, inf) * generation ( https://www.beresfordresearch.com/age-range-by-generation/ ) * gen_z: <12, 28) * millennials: <28, 44) * gen_x: <44, 60) * boomers_2: <60, 70) * boomers_1: <70, 79) * post_war: <79, 97) * ww2: <97, 102) * vampire: <102, inf) Parameters: df ( pd . DataFrame ) \u2013 input dataset age_col ( str ) \u2013 age column Source code in churn_pred/preprocessing/auxiliary_data.py 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 def age_categories ( df : pd . DataFrame , age_col : str ) -> pd . DataFrame : \"\"\"Returns string pd.DataFrame identifing different age classification categories according to the age: * working_class (https://ourworldindata.org/age-structure) * children_and_adolescents: <0, 15) * working_age: <15, 65) * elderly: <65, inf) * stage_of_life (https://integrishealth.org/resources/on-your-health/2015/october/stages-of-life-health-for-every-age) * infant: <0, 2) * toddler: <2, 5) * child: <5, 13) * teen: <13, 20) * adult: <20, 40) * middle_age_adult: <40, 60) * senior_adult: <60, inf) * generation (https://www.beresfordresearch.com/age-range-by-generation/) * gen_z: <12, 28) * millennials: <28, 44) * gen_x: <44, 60) * boomers_2: <60, 70) * boomers_1: <70, 79) * post_war: <79, 97) * ww2: <97, 102) * vampire: <102, inf) Args: df (pd.DataFrame): input dataset age_col (str): age column \"\"\" dfc = df . copy () dfc [ \"working_class\" ] = pd . cut ( dfc [ age_col ], bins = [ 0 , 15 , 65 , np . inf ], labels = [ \"children_and_adolescents\" , \"working_age\" , \"elderly\" ], include_lowest = True , ) dfc [ \"stage_of_life\" ] = pd . cut ( dfc [ age_col ], bins = [ 0 , 2 , 5 , 13 , 20 , 40 , 60 , np . inf ], labels = [ \"infant\" , \"toddler\" , \"child\" , \"teen\" , \"adult\" , \"middle_age_adult\" , \"senior_adult\" , ], include_lowest = True , ) dfc [ \"generation\" ] = pd . cut ( dfc [ age_col ], bins = [ 0 , 28 , 44 , 60 , 70 , 79 , 97 , 102 , np . inf ], labels = [ \"gen_z\" , \"millennials\" , \"gen_x\" , \"boomers_2\" , \"boomers_1\" , \"post_war\" , \"ww2\" , \"vampire\" , ], include_lowest = True , ) return dfc big_mac_index \u00b6 big_mac_index ( df , country_name_col ) Returns dataframe with Big Max Index corresponding to country codes. Downloaded on 26/4/2024. Note Unfortunatelly Spain, France, Germany (unique companies in the dataset) are not included, other index to explore: https://en.wikipedia.org/wiki/Big_Mac_Index Based on Downloaded from Parameters: df ( pd . DataFrame ) \u2013 input dataset country_name_col ( str ) \u2013 column name with country names Source code in churn_pred/preprocessing/auxiliary_data.py 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 def big_mac_index ( df : pd . DataFrame , country_name_col : str ) -> pd . DataFrame : \"\"\" Returns dataframe with Big Max Index corresponding to country codes. Downloaded on 26/4/2024. Note: Unfortunatelly Spain, France, Germany (unique companies in the dataset) are not included, other index to explore: https://en.wikipedia.org/wiki/Big_Mac_Index Based on: https://www.economist.com/big-mac-index Downloaded from: https://github.com/TheEconomist/big-mac-data https://raw.githubusercontent.com/TheEconomist/big-mac-data/master/output-data/big-mac-full-index.csv Args: df (pd.DataFrame): input dataset country_name_col: column name with country names \"\"\" big_mac_index = pd . read_csv ( BIG_MAC_INDEX ) # filter the last available price big_mac_index [ \"date\" ] = pd . to_datetime ( big_mac_index [ \"date\" ]) big_mac_index = ( big_mac_index . sort_values ( \"date\" ) . groupby ( \"iso_a3\" ) . tail ( 1 ) . reset_index () ) big_mac_index = big_mac_index [[ \"iso_a3\" , \"dollar_price\" ]] big_mac_index . rename ( columns = { \"dollar_price\" : \"big_mac_index_dollar_price\" }, inplace = True ) big_mac_index = big_mac_index . dropna () dfc = df . copy () dfc [ \"iso_a3\" ] = get_iso_a3 ( df = dfc , country_name_col = country_name_col ) dfc = dfc . merge ( big_mac_index , on = \"iso_a3\" , how = \"left\" ) dfc . drop ( columns = [ \"iso_a3\" ], inplace = True ) return dfc gdppc \u00b6 gdppc ( df , country_name_col ) Returns dataframe with Gross Domestic Product Per Capita corresponding to country codes. Uses information from worldbank API downloaded on 26/4/2024. Last file update: 3/28/2024. Posprocessed file has initial 3 lines removed for easier processing using pandas df. Downloaded from https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.PP.CD?downloadformat=csv Main file before preprocessing 'data/gdpp/API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv' Other included files 'data/gdpp/Metadata_Country_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv' 'data/gdpp/Metadata_Indicator_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv' Parameters: df ( pd . DataFrame ) \u2013 input dataset country_name_col ( str ) \u2013 column name with country names Source code in churn_pred/preprocessing/auxiliary_data.py 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 def gdppc ( df : pd . DataFrame , country_name_col : str ) -> pd . DataFrame : \"\"\" Returns dataframe with Gross Domestic Product Per Capita corresponding to country codes. Uses information from worldbank API downloaded on 26/4/2024. Last file update: 3/28/2024. Posprocessed file has initial 3 lines removed for easier processing using pandas df. Downloaded from: * https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD * https://api.worldbank.org/v2/en/indicator/NY.GDP.PCAP.PP.CD?downloadformat=csv Main file before preprocessing: 'data/gdpp/API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv' Other included files: 'data/gdpp/Metadata_Country_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv' 'data/gdpp/Metadata_Indicator_API_NY.GDP.PCAP.PP.CD_DS2_en_csv_v2_213153.csv' Args: df (pd.DataFrame): input dataset country_name_col: column name with country names \"\"\" gdp_per_capita_raw = pd . read_csv ( GDP_PER_CAPITA ) gdp_per_capita_ic = pd . read_csv ( GDP_PER_CAPITA_INCOMEGROUP ) # add last available year gdp_per_capita = gdp_per_capita_raw [[ \"Country Code\" ]] gdp_per_capita . rename ( columns = { \"Country Code\" : \"iso_a3\" }, inplace = True ) gdp_per_capita [ \"gdp_per_capita\" ] = ( gdp_per_capita_raw . drop ( columns = [ \"Country Name\" , \"Country Code\" , \"Indicator Name\" , \"Indicator Code\" ] ) . ffill ( axis = 1 ) . iloc [:, - 1 ] ) gdp_per_capita_ic = gdp_per_capita_ic [[ \"Country Code\" , \"IncomeGroup\" ]] gdp_per_capita_ic . rename ( columns = { \"Country Code\" : \"iso_a3\" }, inplace = True ) gdp_per_capita = gdp_per_capita . dropna () gdp_per_capita_ic = gdp_per_capita_ic . dropna () dfc = df . copy () dfc [ \"iso_a3\" ] = get_iso_a3 ( df = dfc , country_name_col = country_name_col ) dfc = dfc . merge ( gdp_per_capita , on = \"iso_a3\" , how = \"left\" ) dfc = dfc . merge ( gdp_per_capita_ic , on = \"iso_a3\" , how = \"left\" ) dfc . drop ( columns = [ \"iso_a3\" ], inplace = True ) return dfc get_iso_a3 \u00b6 get_iso_a3 ( df , country_name_col ) Returns pd.Series with mapped country names to iso_a3. Parameters: df ( pd . Series ) \u2013 input country name pandas series Source code in churn_pred/preprocessing/auxiliary_data.py 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 def get_iso_a3 ( df : pd . DataFrame , country_name_col : str ): \"\"\"Returns pd.Series with mapped country names to iso_a3. Args: df (pd.Series): input country name pandas series \"\"\" dfc = df . copy () def _get_iso_a3_map ( country_name : str ) -> str : \"\"\"Helper function for get_iso_a3\"\"\" try : result = pycountry . countries . search_fuzzy ( country_name ) iso_a3_str = result [ 0 ] . alpha_3 # type: ignore except : iso_a3_str = \"NA\" return iso_a3_str dfc [ \"res\" ] = \"NA\" for country_name in dfc [ country_name_col ] . unique (): dfc . loc [ dfc [ country_name_col ] == country_name , [ \"res\" ]] = _get_iso_a3_map ( country_name ) return dfc [ \"res\" ] get_country_name \u00b6 get_country_name ( df , country_name_col ) Returns pd.Series with mapped country iso_a2 to names. Parameters: df ( pd . Series ) \u2013 input country name pandas series Source code in churn_pred/preprocessing/auxiliary_data.py 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 def get_country_name ( df : pd . DataFrame , country_name_col : str ): \"\"\"Returns pd.Series with mapped country iso_a2 to names. Args: df (pd.Series): input country name pandas series \"\"\" dfc = df . copy () def _get_country_name_map ( country_name : str ) -> str : \"\"\"Helper function for get_country_name\"\"\" try : result = pycountry . countries . get ( alpha_2 = country_name ) name_str = result . name except : name_str = \"NA\" return name_str dfc [ \"res\" ] = \"NA\" for country_name in dfc [ country_name_col ] . unique (): dfc . loc [ dfc [ country_name_col ] == country_name , [ \"res\" ]] = _get_country_name_map ( country_name ) return dfc [ \"res\" ] get_country_region_subregion \u00b6 get_country_region_subregion ( df , country_name_col ) Returns pd.Series with mapped country iso_a2 to names. Parameters: df ( pd . Series ) \u2013 input country name pandas series Source code in churn_pred/preprocessing/auxiliary_data.py 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 def get_country_region_subregion ( df : pd . DataFrame , country_name_col : str ): \"\"\"Returns pd.Series with mapped country iso_a2 to names. Args: df (pd.Series): input country name pandas series \"\"\" dfc = df . copy () def _get_country_region_subregion_map ( country_name : str ) -> Tuple [ str , str ]: \"\"\"Helper function for get_country_name\"\"\" try : result = CountryInfo ( country_name ) region_str = result . region () subregion_str = result . subregion () except : region_str = \"NA\" subregion_str = \"NA\" return ( region_str , subregion_str ) dfc [[ country_name_col + \"_region\" , country_name_col + \"_subregion\" ]] = ( \"NA\" , \"NA\" ) for country_name in dfc [ country_name_col ] . unique (): dfc . loc [ dfc [ country_name_col ] == country_name , [ country_name_col + \"_region\" , country_name_col + \"_subregion\" ], ] = _get_country_region_subregion_map ( country_name ) return dfc [[ country_name_col + \"_region\" , country_name_col + \"_subregion\" ]] label_encoder \u00b6 LabelEncoder \u00b6 LabelEncoder ( columns_to_encode = None ) Bases: object Label Encode categorical values for multiple columns at once NOTE : Shamlessly copied from https://github.com/jrzaurin/pytorch-widedeep NOTE : LabelEncoder reserves 0 for unseen new categories. This is convenient when defining the embedding layers, since we can just set padding idx to 0. Parameters: columns_to_encode ( list, Optional, default = None ) \u2013 List of strings containing the names of the columns to encode. If None all columns of type object in the dataframe will be label encoded. Attributes: encoding_dict ( Dict ) \u2013 Dictionary containing the encoding mappings in the format, e.g. : {'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...} # noqa inverse_encoding_dict(Dict) ( Dict ) \u2013 Dictionary containing the inverse encoding mappings in the format, e.g. : {'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...} # noqa Source code in churn_pred/preprocessing/label_encoder.py 29 30 31 32 33 def __init__ ( self , columns_to_encode : Optional [ List [ str ]] = None , ): self . columns_to_encode = columns_to_encode fit \u00b6 fit ( df ) Creates encoding attributes Returns: LabelEncoder ( LabelEncoder ) \u2013 LabelEncoder fitted object Source code in churn_pred/preprocessing/label_encoder.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 def fit ( self , df : pd . DataFrame ) -> \"LabelEncoder\" : \"\"\"Creates encoding attributes Returns: LabelEncoder: `LabelEncoder` fitted object \"\"\" df_inp = df . copy () if self . columns_to_encode is None : self . columns_to_encode = list ( df_inp . select_dtypes ( include = [ \"object\" ]) . columns ) else : # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) unique_column_vals = dict () for c in self . columns_to_encode : unique_column_vals [ c ] = df_inp [ c ] . unique () self . encoding_dict = dict () # leave 0 for padding/\"unseen\" categories idx = 1 for k , v in unique_column_vals . items (): self . encoding_dict [ k ] = { o : i + idx for i , o in enumerate ( unique_column_vals [ k ]) } idx = 1 self . inverse_encoding_dict = dict () for c in self . encoding_dict : self . inverse_encoding_dict [ c ] = { v : k for k , v in self . encoding_dict [ c ] . items () } self . inverse_encoding_dict [ c ][ 0 ] = \"unseen\" return self transform \u00b6 transform ( df ) Label Encoded the categories in columns_to_encode Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Source code in churn_pred/preprocessing/label_encoder.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Label Encoded the categories in `columns_to_encode` Returns: pd.DataFrame: label-encoded dataframe \"\"\" try : self . encoding_dict except AttributeError : raise NotFittedError ( \"This LabelEncoder instance is not fitted yet. \" \"Call 'fit' with appropriate arguments before using this LabelEncoder.\" ) df_inp = df . copy () # sanity check to make sure all categorical columns are in an adequate # format for col in self . columns_to_encode : # type: ignore df_inp [ col ] = df_inp [ col ] . astype ( \"O\" ) for k , v in self . encoding_dict . items (): df_inp [ k ] = df_inp [ k ] . apply ( lambda x : v [ x ] if x in v . keys () else 0 ) return df_inp fit_transform \u00b6 fit_transform ( df ) Combines fit and transform Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from churn_pred.preprocessing.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> encoder . fit_transform ( df ) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder . encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} Source code in churn_pred/preprocessing/label_encoder.py 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Combines `fit` and `transform` Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from churn_pred.preprocessing.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> encoder.fit_transform(df) col1 col2 0 1 1 1 2 2 2 3 3 >>> encoder.encoding_dict {'col2': {'me': 1, 'you': 2, 'him': 3}} \"\"\" return self . fit ( df ) . transform ( df ) inverse_transform \u00b6 inverse_transform ( df ) Returns the original categories Returns: pd . DataFrame \u2013 pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from churn_pred.preprocessing.label_encoder import LabelEncoder >>> df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'me' , 'you' , 'him' ]}) >>> columns_to_encode = [ 'col2' ] >>> encoder = LabelEncoder ( columns_to_encode ) >>> df_enc = encoder . fit_transform ( df ) >>> encoder . inverse_transform ( df_enc ) col1 col2 0 1 me 1 2 you 2 3 him Source code in churn_pred/preprocessing/label_encoder.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def inverse_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Returns the original categories Returns: pd.DataFrame: label-encoded dataframe Examples: >>> import pandas as pd >>> from churn_pred.preprocessing.label_encoder import LabelEncoder >>> df = pd.DataFrame({'col1': [1,2,3], 'col2': ['me', 'you', 'him']}) >>> columns_to_encode = ['col2'] >>> encoder = LabelEncoder(columns_to_encode) >>> df_enc = encoder.fit_transform(df) >>> encoder.inverse_transform(df_enc) col1 col2 0 1 me 1 2 you 2 3 him \"\"\" for k , v in self . inverse_encoding_dict . items (): df [ k ] = df [ k ] . apply ( lambda x : v [ x ]) return df preprocess_data \u00b6 drop_high_nan_cols \u00b6 drop_high_nan_cols ( df , threshold = 0.8 , verbose = False ) Returns dataframe without columns that have ratio of missingness above threshold. Parameters: df ( pd . DataFrame ) \u2013 input dataframe threshold ( float = 0.8 ) \u2013 ratio of missingness applied per column verbose ( bool ) \u2013 whether the output should be verbose Source code in churn_pred/preprocessing/preprocess_data.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 def drop_high_nan_cols ( df : pd . DataFrame , threshold : float = 0.8 , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without columns that have ratio of missingness above threshold. Args: df (pd.DataFrame): input dataframe threshold (float = 0.8): ratio of missingness applied per column verbose (bool): whether the output should be verbose \"\"\" n_rows = df . shape [ 0 ] nan_fraction_per_col = df . apply ( lambda x : x . isna () . sum () / n_rows , axis = 0 ) high_nan_percentage_cols = nan_fraction_per_col [ nan_fraction_per_col > threshold ] . index . values df = df . drop ( high_nan_percentage_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} columns with fraction of NaN above threshold = {}. Affected columns: {} \"\"\" . format ( len ( high_nan_percentage_cols ), threshold , high_nan_percentage_cols ) ) return df drop_constant_cols \u00b6 drop_constant_cols ( df , verbose = False ) Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows. Source code in churn_pred/preprocessing/preprocess_data.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 def drop_constant_cols ( df : pd . DataFrame , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without constant columns, i.e. those with just 1 unique value for all rows.\"\"\" nunique_per_col = df . apply ( lambda x : x . nunique (), axis = 0 ) const_cols = nunique_per_col [ nunique_per_col == 1 ] . index . values df = df . drop ( const_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} constant columns. Affected columns: {} \"\"\" . format ( len ( const_cols ), const_cols ) ) return df drop_high_uq_cat_cols \u00b6 drop_high_uq_cat_cols ( df , cat_cols , uq_val_count , verbose = False ) Returns dataframe without categorical columns that have too many unique values Parameters: df ( pd . DataFrame ) \u2013 input dataframe cat_cols ( list ) \u2013 list of categorical columns uq_val_count ( int ) \u2013 unique value count verbose ( bool ) \u2013 whether the output should be verbose Source code in churn_pred/preprocessing/preprocess_data.py 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 def drop_high_uq_cat_cols ( df : pd . DataFrame , cat_cols : list , uq_val_count : int , verbose : bool = False ) -> pd . DataFrame : \"\"\"Returns dataframe without categorical columns that have too many unique values Args: df (pd.DataFrame): input dataframe cat_cols (list): list of categorical columns uq_val_count (int): unique value count verbose (bool): whether the output should be verbose \"\"\" nunique_per_col = df [ cat_cols ] . apply ( lambda x : x . nunique (), axis = 0 ) high_uq_value_cat_cols = nunique_per_col [ nunique_per_col > uq_val_count ] . index . values df = df . drop ( high_uq_value_cat_cols , axis = 1 ) if verbose : print ( \"\"\" Dropped {} high unique value columns. Affected columns: {} \"\"\" . format ( len ( high_uq_value_cat_cols ), high_uq_value_cat_cols ) ) return df drop_highly_correlated_columns \u00b6 drop_highly_correlated_columns ( df , cont_cols , crosscorr_val = 0.95 , verbose = False ) Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Parameters: df ( pd . DataFrame ) \u2013 input dataframe cont_cols ( list ) \u2013 list of columns to evaluate correlation for crosscorr_val ( float = 0.95 ) \u2013 threshold value of correlation verbose ( bool ) \u2013 whether the output should be verbose Source code in churn_pred/preprocessing/preprocess_data.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 def drop_highly_correlated_columns ( df : pd . DataFrame , cont_cols : list , crosscorr_val : float = 0.95 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Returns dataframe without highly correlated columns, cross correlation is evaluated with crosscorr_val. Args: df (pd.DataFrame): input dataframe cont_cols (list): list of columns to evaluate correlation for crosscorr_val (float = 0.95): threshold value of correlation verbose (bool): whether the output should be verbose \"\"\" corr_matrix = df [ cont_cols ] . corr () . abs () upper = corr_matrix . where ( np . triu ( np . ones ( corr_matrix . shape ), k = 1 ) . astype ( \"bool\" )) upper_above = upper . apply ( lambda x : any ( x > crosscorr_val ), axis = 1 ) to_drop = upper_above [ upper_above ] . index . values df = df . drop ( to_drop , axis = 1 ) if verbose : print ( \"\"\" Dropped {} highly correlated columns. Affected columns: {} \"\"\" . format ( len ( to_drop ), to_drop ) ) return df nan_with_unknown_imputer \u00b6 nan_with_unknown_imputer ( df , columns , fill_token = \"unknown\" , verbose = False ) Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 ist of columns that will be filled fill_token ( str = \"unknown\" ) \u2013 string used to replace NAs verbose ( bool ) \u2013 whether the output should be verbose Source code in churn_pred/preprocessing/preprocess_data.py 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 def nan_with_unknown_imputer ( df : pd . DataFrame , columns : List [ str ], fill_token : str = \"unknown\" , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate string, 'unknown' is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): ist of columns that will be filled fill_token (str = \"unknown\"): string used to replace NAs verbose (bool): whether the output should be verbose \"\"\" df = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) for c in columns : df [ c ] = df [ c ] . astype ( object ) . fillna ( fill_token ) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( object ) . fillna ( fill_token )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_token , nans_cols ) ) return df nan_with_number_imputer \u00b6 nan_with_number_imputer ( df , columns , fill_number =- 1.0 , verbose = False ) Fills NAs with surrogate float, -1 is default value used, it can be customized. Parameters: df ( pd . DataFrame ) \u2013 input dataframe columns ( List [ str ] ) \u2013 list of columns that will be filled fill_number ( float = -1 ) \u2013 number used to replace NAs. Defaults to verbose ( bool ) \u2013 whether the output should be verbose Source code in churn_pred/preprocessing/preprocess_data.py 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 def nan_with_number_imputer ( df : pd . DataFrame , columns : List [ str ], fill_number : float = - 1.0 , verbose : bool = False , ) -> pd . DataFrame : \"\"\"Fills NAs with surrogate float, -1 is default value used, it can be customized. Args: df (pd.DataFrame): input dataframe columns (List[str]): list of columns that will be filled fill_number (float = -1): number used to replace NAs. Defaults to verbose (bool): whether the output should be verbose \"\"\" dfc = df . copy () if verbose : sum_nan_vals = df [ columns ] . isna () . sum () nans_cols = [] for c in columns : nans_cols . append (( c , df [ c ] . isna () . sum ())) dfc = df . copy () dfc [ columns ] = dfc [ columns ] . apply ( lambda x : x . astype ( float ) . fillna ( fill_number )) if verbose : print ( \"\"\" Imputed {} NaN values with {}. Affected columns (col, num_NaNs): {} \"\"\" . format ( sum_nan_vals , fill_number , nans_cols ) ) return dfc nuq_in_list_col \u00b6 nuq_in_list_col ( dfs ) Returns pd.Series with the number of unique values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in churn_pred/preprocessing/preprocess_data.py 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def nuq_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the number of unique values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : ( _nunique ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" ))) if isinstance ( string , str ) else string ) ) return dfsc most_frequent_in_list_col \u00b6 most_frequent_in_list_col ( dfs ) Returns pd.Series with the most frequent values in the lists. Parameters: dfs ( pd . Series ) \u2013 input pandas series containing string list values Source code in churn_pred/preprocessing/preprocess_data.py 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 def most_frequent_in_list_col ( dfs : pd . Series ): \"\"\"Returns pd.Series with the most frequent values in the lists. Args: dfs (pd.Series): input pandas series containing string list values \"\"\" dfsc = dfs . copy () dfsc . apply ( lambda string : ( _most_frequent ( list ( string . replace ( \"[\" , \"\" ) . replace ( \"]\" , \"\" ) . split ( \", \" ))) if isinstance ( string , str ) else string ) ) return dfsc replace_rare_categories_with_str_other \u00b6 replace_rare_categories_with_str_other ( df , categorical_cols , quantile = 0.05 , surrogate_value = \"other\" , verbose = False , ) Replaces rare category value with surrogate string. Parameters: df ( pd . DataFrame ) \u2013 input dataframe categorical_cols ( List [ str ] ) \u2013 list of columns in dataframe to process. quantile ( float = 0.05 ) \u2013 determines what values are considered as rare surrogate_value ( str = \"other\" ) \u2013 string used to replace rare values Returns: Tuple [ pd . DataFrame , Dict ] \u2013 Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. Source code in churn_pred/preprocessing/preprocess_data.py 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 def replace_rare_categories_with_str_other ( df : pd . DataFrame , categorical_cols : List [ str ], quantile : float = 0.05 , surrogate_value : str = \"other\" , verbose : bool = False , ) -> Tuple [ pd . DataFrame , Dict ]: \"\"\"Replaces rare category value with surrogate string. Args: df (pd.DataFrame): input dataframe categorical_cols (List[str]): list of columns in dataframe to process. quantile (float = 0.05): determines what values are considered as rare surrogate_value (str = \"other\"): string used to replace rare values Returns: Tuple[pd.DataFrame, Dict]: New dataframe and a dict. with mapping between orig. and surrogate values. \"\"\" if surrogate_value in df [ categorical_cols ] . values : raise ValueError ( \"Surrogate string - \" + surrogate_value + \" - is already present, choose another one.\" ) dfc = df . copy () replace_dict_per_col = ( df [ categorical_cols ] . apply ( _replace_rare_dict , args = [ surrogate_value , quantile ]) . to_dict () ) dfc = dfc . replace ( replace_dict_per_col ) if verbose : sum_rare_cats = sum ( [ len ( replace_dict_per_col [ col ]) for col in replace_dict_per_col ] ) rare_cats_per_col = [ ( col , list ( replace_dict_per_col [ col ] . keys ())) for col in replace_dict_per_col ] print ( \"\"\" Replaced {} rare categories (val_count < {}) with {}. Affected columns (List[(col, rare_cats)]): {} \"\"\" . format ( sum_rare_cats , quantile , surrogate_value , rare_cats_per_col , ) ) return dfc , replace_dict_per_col preprocess_text \u00b6 language_detection \u00b6 language_detection ( df , text_col , model_type = 'fasttext' ) Source code in churn_pred/preprocessing/preprocess_text.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 def language_detection ( df : pd . DataFrame , text_col : str , model_type : Literal [ \"roberta\" , \"fasttext\" ] = \"fasttext\" , ) -> pd . DataFrame : \"\"\" https://huggingface.co/papluca/xlm-roberta-base-language-detection https://spacy.io/universe/project/spacy_fastlang \"\"\" dfc = df . copy () if model_type == \"fasttext\" : def _get_language ( token : Doc ) -> str : return token . _ . language def _get_language_score ( token : Doc ) -> str : return token . _ . language_score nlp = spacy . load ( \"en_core_web_sm\" ) nlp . add_pipe ( \"language_detector\" ) res = df [ text_col ] . astype ( str ) . apply ( nlp ) dfc [ text_col + \"_language\" ] = res . apply ( _get_language ) dfc [ text_col + \"_language_score\" ] = res . apply ( _get_language_score ) elif model_type == \"roberta\" : def _get_language ( token : Doc ) -> str : return token . _ . language def _get_language_score ( token : Doc ) -> str : return token . _ . language_score pipe = pipeline ( \"text-classification\" , model = \"papluca/xlm-roberta-base-language-detection\" ) res = pipe ( dfc [ \"CustomerFeedback\" ] . astype ( str ) . to_list ()) dfc [ text_col + \"_language\" ] = pipe ( dfc [ text_col ] . astype ( str )) else : raise NotImplementedError return dfc text_cleaning \u00b6 text_cleaning ( df , text_col ) Returns dataframe with preprocessed/cleaned text column. Spacy-cleaner that uses spacy functionalities. https://spacy.io/universe/project/spacy-cleaner Note The spacy-cleaner library does not do much. Future task - develop own cleaner Parameters: df ( pd . DataFrame ) \u2013 input dataset text_col ( str ) \u2013 column name with text Source code in churn_pred/preprocessing/preprocess_text.py 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 def text_cleaning ( df : pd . DataFrame , text_col : str ) -> pd . DataFrame : \"\"\" Returns dataframe with preprocessed/cleaned text column. Spacy-cleaner that uses spacy functionalities. https://spacy.io/universe/project/spacy-cleaner Note: The spacy-cleaner library does not do much. Future task - develop own cleaner Args: df (pd.DataFrame): input dataset text_col: column name with text \"\"\" dfc = df . copy () model = spacy . load ( \"en_core_web_sm\" ) cleaner = spacy_cleaner . Cleaner ( model , removers . remove_stopword_token , replacers . replace_punctuation_token , mutators . mutate_lemma_token , ) dfc [ text_col ] = cleaner . clean ( dfc [ text_col ] . astype ( str )) return dfc sentiment_analysis \u00b6 sentiment_analysis ( df , text_col , sentiment_depth = 3 ) Returns dataframe with new column that analysis sentintent in the text_col. initial idea: Inspired by https://www.nature.com/articles/s41598-024-60210-7 I also used the 3 most popular models with voting: 1. cardiffnlp/twitter-roberta-base-sentiment-latest 2. nlptown/bert-base-multilingual-uncased-sentiment 3. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis 4. lxyuan/distilbert-base-multilingual-cased-sentiments-student 5. finiteautomata/bertweet-base-sentiment-analysis issues: 1. 3(pos, neutral, neg) vs 5(1-5 stars) sentiments; models 1,3 vs 2 2. maximum sequence length models 4-5 final idea: Either 3 sentimnets(model 1) or 5 stars (model 2) Parameters: df ( pd . DataFrame ) \u2013 input dataset text_col ( str ) \u2013 column name with text sentiment_depth ( [3, 5] ) \u2013 depth of sentiment analysis Source code in churn_pred/preprocessing/preprocess_text.py 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 def sentiment_analysis ( df : pd . DataFrame , text_col : str , sentiment_depth : Literal [ 3 , 5 ] = 3 ) -> pd . DataFrame : \"\"\" Returns dataframe with new column that analysis sentintent in the text_col. initial idea: Inspired by https://www.nature.com/articles/s41598-024-60210-7 I also used the 3 most popular models with voting: 1. cardiffnlp/twitter-roberta-base-sentiment-latest 2. nlptown/bert-base-multilingual-uncased-sentiment 3. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis 4. lxyuan/distilbert-base-multilingual-cased-sentiments-student 5. finiteautomata/bertweet-base-sentiment-analysis issues: 1. 3(pos, neutral, neg) vs 5(1-5 stars) sentiments; models 1,3 vs 2 2. maximum sequence length models 4-5 final idea: Either 3 sentimnets(model 1) or 5 stars (model 2) Args: df (pd.DataFrame): input dataset text_col (str): column name with text sentiment_depth ([3, 5]): depth of sentiment analysis \"\"\" dfc = df . copy () if sentiment_depth == 3 : pipe = pipeline ( \"text-classification\" , model = \"cardiffnlp/twitter-roberta-base-sentiment-latest\" , device = torch . device ( \"cuda\" if torch . cuda . is_available () else \"cpu\" ), ) elif sentiment_depth == 5 : pipe = pipeline ( \"text-classification\" , model = \"nlptown/bert-base-multilingual-uncased-sentiment\" , device = torch . device ( \"cuda\" if torch . cuda . is_available () else \"cpu\" ), ) else : raise NotImplementedError res = pipe ( dfc [ text_col ] . astype ( str ) . to_list ()) res = pd . DataFrame ( res ) . rename ( columns = { \"label\" : text_col + \"_sentiment\" , \"score\" : text_col + \"_sentiment_score\" , } ) return pd . concat ([ dfc , res ], axis = 1 ) preprocess \u00b6 PreprocessData \u00b6 PreprocessData ( target_col , id_cols , cat_cols = None , cont_cols = None ) Object to preprocess the dataset. Parameters: target_col ( str ) \u2013 target column name id_cols ( List [ str ] ) \u2013 id columns cat_cols ( Optional [ List [ str ]] ) \u2013 list of categorical column names cont_cols ( Optional [ List [ str ]] ) \u2013 list of continuous column names Source code in churn_pred/preprocessing/preprocess.py 28 29 30 31 32 33 34 35 36 37 38 39 40 def __init__ ( self , target_col : str , id_cols : str , cat_cols : Optional [ List [ str ]] = None , cont_cols : Optional [ List [ str ]] = None , ): self . target_col = target_col self . id_cols = id_cols self . cat_cols = cat_cols self . cont_cols = cont_cols self . is_fitted = False fit_transform \u00b6 fit_transform ( df ) Fit peprocessor and transform dataset in training step. Source code in churn_pred/preprocessing/preprocess.py 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def fit_transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Fit peprocessor and transform dataset in training step.\"\"\" dfc = df . drop ( columns = self . id_cols ) . copy () dfc = dfc . pipe ( drop_constant_cols ) self . cat_cols = intsec ( list ( dfc ), self . cat_cols ) if self . cat_cols is None : self . cat_cols = self . _infere_cat_cols ( dfc ) dfc_cat_cols = drop_high_nan_cols ( dfc [ self . cat_cols ]) dfc = dfc . drop ( columns = self . cat_cols ) dfc = pd . concat ([ dfc , dfc_cat_cols ], axis = 1 ) self . cat_cols = dfc_cat_cols . columns . values . tolist () if self . cont_cols is None : self . init_cont_cols = self . _infere_cont_cols ( dfc , self . cat_cols ) dfc_init_cont_cols = drop_high_nan_cols ( dfc [ self . init_cont_cols ]) dfc = dfc . drop ( columns = self . init_cont_cols ) dfc = pd . concat ([ dfc , dfc_init_cont_cols ], axis = 1 ) self . init_cont_cols = dfc_init_cont_cols . columns . values . tolist () dfc = nan_with_number_imputer ( dfc , self . init_cont_cols , - 9999.0 ) ( dfc , self . final_cont_cols , ) = self . _drop_highly_corr_cols_and_get_final_cont_cols ( dfc , self . init_cont_cols ) else : self . final_cont_cols = list ( set ( self . cont_cols ) . intersection ( set ( dfc . columns . values )) ) dfc = nan_with_number_imputer ( dfc , self . final_cont_cols , - 9999.0 ) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] self . label_encoder = LabelEncoder ( self . cat_cols ) dfc_le = self . label_encoder . fit_transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] self . is_fitted = True return dfc_le transform \u00b6 transform ( df ) Transform dataset in inference step. Source code in churn_pred/preprocessing/preprocess.py 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 def transform ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Transform dataset in inference step.\"\"\" if not self . is_fitted : raise NotFittedError ( \"\"\"This instance of 'PreprocessData' has not been fitted yet. Please, run 'fit' first\"\"\" ) dfc = df . drop ( columns = self . id_cols ) . copy () # added as mlflow inference is receiving all the data as objects dfc [ self . cat_cols ] = dfc [ self . cat_cols ] . astype ( str ) dfc [ self . final_cont_cols ] = dfc [ self . final_cont_cols ] . astype ( float ) try : dfc = dfc [ self . cat_cols + self . final_cont_cols + [ self . target_col ]] except KeyError : dfc = dfc [ self . cat_cols + self . final_cont_cols ] # dfc = dfc.replace(self.replace_rare_categories_dict) dfc = nan_with_unknown_imputer ( dfc , self . cat_cols ) dfc = nan_with_number_imputer ( dfc , list ( set ( self . final_cont_cols )), - 9999.0 , # noqa ) dfc_le = self . label_encoder . transform ( dfc ) dfc_le = self . _change_int_float_types ( dfc_le ) dfc_le [ self . id_cols ] = df [ self . id_cols ] return dfc_le fit \u00b6 fit ( df ) Just to keep familiar naming convention with sklearn. Source code in churn_pred/preprocessing/preprocess.py 134 135 136 def fit ( self , df : pd . DataFrame ) -> pd . DataFrame : \"\"\"Just to keep familiar naming convention with sklearn.\"\"\" return self . fit_transform ( df ) scaler \u00b6 scaler_mapper \u00b6 scaler_mapper ( cont_cols , cat_cols , id_cols , scaler_mapper_def = None ) Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Parameters: cont_cols ( list ) \u2013 list of continuous feature columns in the dataset cat_cols ( list ) \u2013 list of categorical feature columns in the dataset id_cols ( list ) \u2013 identifier columns scaler_mapper_def ( dict ) \u2013 optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler ( DataFrameMapper ) \u2013 scaler object mapping sklearn scalers to columns in pandas dataframe Source code in churn_pred/preprocessing/scaler.py 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 def scaler_mapper ( cont_cols : List [ str ], cat_cols : List [ str ], id_cols : List [ str ], scaler_mapper_def : Optional [ dict ] = None , ) -> DataFrameMapper : \"\"\"Function that maps scaler functions to appropriate columns. By default does not assign any scaler to continuous, categorical or identifier columns. The scalers must be set in scaler_mapper_def. Use sklearn scalers. Only columns defined in mapper object will be present in the transformed dataset. Args: cont_cols (list): list of continuous feature columns in the dataset cat_cols (list): list of categorical feature columns in the dataset id_cols (list): identifier columns scaler_mapper_def (dict): optional dictionary that contains keys ['cont_cols', 'cat_cols', 'id_cols'] with their corresponding scalers (defined by names, not instantiated) from sklearn library Returns: scaler (DataFrameMapper): scaler object mapping sklearn scalers to columns in pandas dataframe \"\"\" if scaler_mapper_def : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ scaler_mapper_def [ \"cont_cols\" ]], ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ scaler_mapper_def [ \"cat_cols\" ]], ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ scaler_mapper_def [ \"id_cols\" ]], ) else : cont_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cont_cols )), classes = [ None ] ) cat_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], cat_cols )), classes = [ None ] ) id_cols_def = gen_features ( columns = list ( map ( lambda x : [ x ], id_cols )), classes = [ None ] ) scaler = DataFrameMapper ( cont_cols_def + cat_cols_def + id_cols_def , df_out = True ) return scaler","title":"preprocessing"},{"location":"churn_pred/preprocessing.html#preprocess-data","text":"","title":"Preprocess data"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.auxiliary_data","text":"","title":"auxiliary_data"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.label_encoder","text":"","title":"label_encoder"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_data","text":"","title":"preprocess_data"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess_text","text":"","title":"preprocess_text"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.preprocess","text":"","title":"preprocess"},{"location":"churn_pred/preprocessing.html#churn_pred.preprocessing.scaler","text":"","title":"scaler"},{"location":"churn_pred/training.html","text":"Training \u00b6 metrics \u00b6 smape \u00b6 smape ( actual , predicted ) Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: smape ( float ) \u2013 symmetric mean absolute percentage error Source code in churn_pred/training/metrics.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def smape ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Symmetric Mean Absolute Percentage Error https://vedexcel.com/how-to-calculate-smape-in-python/ Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: smape (float): symmetric mean absolute percentage error \"\"\" return ( 100 / len ( actual ) * np . sum ( 2 * np . abs ( predicted - actual ) / ( np . abs ( actual ) + np . abs ( predicted ))) ) ndcg \u00b6 ndcg ( actual , predicted ) Normalized Discounted Cumulative Gain Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Return ndcg (float): normalized discounted cumulative gain Source code in churn_pred/training/metrics.py 32 33 34 35 36 37 38 39 40 41 42 43 44 def ndcg ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Normalized Discounted Cumulative Gain Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Return: ndcg (float): normalized discounted cumulative gain \"\"\" return ndcg_score ( [ rankdata ( actual , method = \"ordinal\" )], [ rankdata ( predicted , method = \"ordinal\" )], ) rmse \u00b6 rmse ( actual , predicted ) Root Mean Squared Error Parameters: actual ( np . ndarray ) \u2013 actual values predicted ( np . ndarray ) \u2013 predicted values Returns: rmse ( float ) \u2013 root mean square error Source code in churn_pred/training/metrics.py 47 48 49 50 51 52 53 54 55 56 def rmse ( actual : np . ndarray , predicted : Union [ np . ndarray , list ]) -> float : \"\"\"Root Mean Squared Error Args: actual (np.ndarray): actual values predicted (np.ndarray): predicted values Returns: rmse (float): root mean square error \"\"\" return mean_squared_error ( actual , predicted , squared = False ) nacil \u00b6 nacil ( actual , high_quantile_predicted , low_quantile_predicted ) Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile_predicted ( np . ndarray ) \u2013 high quantile predicted values low_quantile_predicted ( np . ndarray ) \u2013 low quantile predicted values Returns: nacil ( float ) \u2013 normalized average confidence interval length Source code in churn_pred/training/metrics.py 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def nacil ( actual : np . ndarray , high_quantile_predicted : np . ndarray , low_quantile_predicted : np . ndarray , ): \"\"\"Normalized Average Confidence Interval Length for Quantile Regression. The value is normalized to actual(expected) value. Args: actual (np.ndarray): actual values high_quantile_predicted (np.ndarray): high quantile predicted values low_quantile_predicted (np.ndarray): low quantile predicted values Returns: nacil (float): normalized average confidence interval length \"\"\" return np . mean (( high_quantile_predicted - low_quantile_predicted ) / actual ) aiqc \u00b6 aiqc ( actual , high_quantile , low_quantile ) Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Parameters: actual ( np . ndarray ) \u2013 actual values high_quantile ( np . ndarray ) \u2013 high quantile predicted values low_quantile ( np . ndarray ) \u2013 low quantile predicted values Returns: aiqc ( float ) \u2013 average interquantile coverage Source code in churn_pred/training/metrics.py 77 78 79 80 81 82 83 84 85 86 87 88 def aiqc ( actual : np . ndarray , high_quantile : np . ndarray , low_quantile : np . ndarray ): \"\"\"Average InterQuantile Coverage for Quantile Regression. Check if the interquantile coverage is close to the coverage of in the test dataset. Args: actual (np.ndarray): actual values high_quantile (np.ndarray): high quantile predicted values low_quantile (np.ndarray): low quantile predicted values Returns: aiqc (float): average interquantile coverage \"\"\" return np . sum (( actual < high_quantile ) & ( actual > low_quantile )) / len ( actual ) optuna_optimizer \u00b6 LGBOptunaOptimizer \u00b6 LGBOptunaOptimizer ( objective , n_class = None ) Bases: BaseOptimizer Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Parameters: objective ( str ) \u2013 objective of the model n_class ( int ) \u2013 number of classes in the dataset Source code in churn_pred/training/optuna_optimizer.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def __init__ ( self , objective : Literal [ \"binary\" , \"multiclass\" , \"regression\" ], n_class : Optional [ int ] = None , ): \"\"\"Fallback/backup Optuna optimizer. Development is focused on Raytune. Kepping this code as backup. Args: objective (str): objective of the model n_class (int): number of classes in the dataset \"\"\" # Optuna does not support original lighgbm implemenattion and with a workaroud it # is possible to get the binarry classfier with focal_loss(any custom loss) # running but the multiclass requires changes to Optuna code, eg. this post: # https://lightrun.com/answers/optuna-optuna-error-when-using-custom-metrics-in-optunaintegrationlightgbm # noqa super ( LGBOptunaOptimizer , self ) . __init__ ( objective , n_class ) self . params = self . base_params optimize \u00b6 optimize ( dtrain , deval ) Optimize LGBM model on provided datasets. Parameters: dtrain ( lgbDataset ) \u2013 training lgb dataset deval ( lgbDataset ) \u2013 evaluation lgb dataset Source code in churn_pred/training/optuna_optimizer.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def optimize ( self , dtrain : lgbDataset , deval : lgbDataset ): \"\"\"Optimize LGBM model on provided datasets. Args: dtrain (lgbDataset): training lgb dataset deval (lgbDataset): evaluation lgb dataset \"\"\" dtrain_copy = deepcopy ( dtrain ) deval_copy = deepcopy ( deval ) if deval is not None else None tuner = LightGBMTuner ( params = self . params , train_set = dtrain_copy , valid_sets = deval_copy , num_boost_round = 1000 , callbacks = [ lgb . early_stopping ( stopping_rounds = 50 )], feval = self . feval , # fobj=self.fobj, ) tuner . run () self . best = tuner . best_params # since n_estimators is not among the params that Optuna optimizes we # need to add it manually. We add a high value since it will be used # with early_stopping_rounds self . best [ \"n_estimators\" ] = 1000 # type: ignore trainer \u00b6 Trainer \u00b6 Trainer ( cat_cols , target_col , id_cols , objective , optimizer = None , n_class = None , preprocessors = None , ) Bases: BaseTrainer Objects that governs training and parameter optimization of the lgbm model. Parameters: cat_cols ( list ) \u2013 list of categorical feature column names target_col ( str ) \u2013 column name that represents target id_cols ( list ) \u2013 identification column names objective ( str ) \u2013 type of the task/objective optimizer ( BaseOptimizer ) \u2013 parameter optimizer object Source code in churn_pred/training/trainer.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def __init__ ( self , cat_cols : List [ str ], target_col : str , id_cols : List [ str ], objective : Literal [ \"binary\" ], optimizer : Union [ Any , BaseOptimizer , None ] = None , n_class : Optional [ int ] = None , preprocessors : Optional [ List [ Union [ Any , PreprocessData ]]] = None , ): \"\"\"Objects that governs training and parameter optimization of the lgbm model. Args: cat_cols (list): list of categorical feature column names target_col (str): column name that represents target id_cols (list): identification column names objective (str): type of the task/objective optimizer (BaseOptimizer): parameter optimizer object \"\"\" super ( Trainer , self ) . __init__ ( cat_cols = cat_cols , target_col = target_col , id_cols = id_cols , objective = objective , n_class = n_class , preprocessors = preprocessors , ) if optimizer is not None : if not hasattr ( optimizer , \"optimize\" ): raise AttributeError ( \" {} optimizer must have {} method\" . format ( optimizer , \"optimize\" ) ) self . optimizer = optimizer train \u00b6 train ( df_train , params = None , df_valid = None ) Train the model with the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset params ( dict ) \u2013 model paramaters df_valid ( pd . DataFrame ) \u2013 optional validation dataset Returns: model ( lgb . basic . Booster ) \u2013 trained model Source code in churn_pred/training/trainer.py 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 def train ( self , df_train : pd . DataFrame , params : Optional [ Dict ] = None , df_valid : Optional [ pd . DataFrame ] = None , ): \"\"\"Train the model with the parameters. Args: df_train (pd.DataFrame): training dataset params (dict): model paramaters df_valid (pd.DataFrame): optional validation dataset Returns: model (lgb.basic.Booster): trained model \"\"\" if self . preprocessors : for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = ( prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if df_valid is not None else None ) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) if df_valid is not None : df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () if df_valid is not None else None cat_cols = self . cat_cols if params : config = params elif ( params is None ) and ( self . optimizer ): config = self . optimizer . best if hasattr ( self . optimizer , \"best_to_drop\" ): df_train_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) if df_valid is not None : df_valid_prep . drop ( columns = self . optimizer . best_to_drop , # type: ignore inplace = True , ) cat_cols = intsec ( self . cat_cols , df_train_prep . columns . values ) elif ( params is None ) and ( self . optimizer is None ): config = self . base_params lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . model = lgb_train_function ( config = config , lgbtrain = lgb_train , lgbeval = lgb_valid , feval = self . feval , ) fit \u00b6 fit ( df_train , df_valid , df_test ) Train the model and optimize the parameters. Parameters: df_train ( pd . DataFrame ) \u2013 training dataset df_valid ( pd . DataFrame ) \u2013 validation dataset df_test ( pd . DataFrame ) \u2013 testing dataset Returns: model ( lgb . basic . Booster ) \u2013 trained model Source code in churn_pred/training/trainer.py 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def fit ( self , df_train : pd . DataFrame , df_valid : pd . DataFrame , df_test : pd . DataFrame , ) -> pd . DataFrame : \"\"\"Train the model and optimize the parameters. Args: df_train (pd.DataFrame): training dataset df_valid (pd.DataFrame): validation dataset df_test (pd.DataFrame): testing dataset Returns: model (lgb.basic.Booster): trained model \"\"\" if self . preprocessors : # this should be # df_train_prep = prep.transform(df_train_prep.drop(columns=[self.target_col])) for prep in self . preprocessors : df_train_prep = prep . transform ( df_train . drop ( columns = [ self . target_col ])) df_valid_prep = prep . transform ( df_valid . drop ( columns = [ self . target_col ])) if self . objective in [ \"binary\" ]: df_train_prep [ self . target_col ] = df_train [ self . target_col ] . astype ( int ) df_valid_prep [ self . target_col ] = df_valid [ self . target_col ] . astype ( int ) else : raise NotImplementedError else : df_train_prep = df_train . copy () df_valid_prep = df_valid . copy () lgb_train , lgb_valid = to_lgbdataset ( train = df_train_prep , cat_cols = self . cat_cols , target_col = self . target_col , id_cols = self . id_cols , valid = df_valid_prep , ) self . optimizer . optimize ( dtrain = lgb_train , deval = lgb_valid ) df_train_valid = pd . concat ([ df_train , df_valid ], ignore_index = True ) df_train_valid = df_train_valid . reset_index ( drop = True ) self . train ( df_train = df_train_valid ) metrics_dict = self . compute_metrics ( df_test ) return metrics_dict utils \u00b6 flatten_dict \u00b6 flatten_dict ( d , parent_key = '' , sep = '_' ) fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ Source code in churn_pred/training/utils.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 def flatten_dict ( d : MutableMapping , parent_key : str = \"\" , sep : str = \"_\" ) -> MutableMapping : \"\"\" fastest according to https://www.freecodecamp.org/news/how-to-flatten-a-dictionary-in-python-in-4-different-ways/ \"\"\" items = [] for k , v in d . items (): new_key = parent_key + sep + k if parent_key else k if isinstance ( v , MutableMapping ): items . extend ( flatten_dict ( v , new_key , sep = sep ) . items ()) else : items . append (( new_key , v )) return dict ( items ) get_or_create_experiment \u00b6 get_or_create_experiment ( experiment_name ) Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. experiment_name (str): Name of the MLflow experiment. str: ID of the existing or newly created MLflow experiment. Source code in churn_pred/training/utils.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 def get_or_create_experiment ( experiment_name ): \"\"\" Retrieve the ID of an existing MLflow experiment or create a new one if it doesn't exist. This function checks if an experiment with the given name exists within MLflow. If it does, the function returns its ID. If not, it creates a new experiment with the provided name and returns its ID. Parameters: - experiment_name (str): Name of the MLflow experiment. Returns: - str: ID of the existing or newly created MLflow experiment. \"\"\" if experiment := mlflow . get_experiment_by_name ( experiment_name ): return experiment . experiment_id else : return mlflow . create_experiment ( experiment_name ) to_lgbdataset \u00b6 to_lgbdataset ( train , cat_cols , target_col , id_cols = [], valid = None ) Transform pandas dataframe to lgbm dataset, or datasets(eval). Parameters: train ( pd . DataFrame ) \u2013 training dataset cat_cols ( list ) \u2013 list of categorical columns target_col ( str ) \u2013 target column in the dataset id_cols ( list ) \u2013 list of identifier columns valid ( pd . DataFrame ) \u2013 validation dataset Returns: lgb_train ( lgbDataset ) \u2013 lgbm training dataset lgb_valid ( lgbDataset ) \u2013 lgbm valid dataset Source code in churn_pred/training/utils.py 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 def to_lgbdataset ( train : pd . DataFrame , cat_cols : List [ str ], target_col : str , id_cols : List [ str ] = [], valid : Optional [ pd . DataFrame ] = None , ) -> Tuple [ lgbDataset , Optional [ lgbDataset ]]: \"\"\"Transform pandas dataframe to lgbm dataset, or datasets(eval). Args: train (pd.DataFrame): training dataset cat_cols (list): list of categorical columns target_col (str): target column in the dataset id_cols (list): list of identifier columns valid (pd.DataFrame): validation dataset Returns: lgb_train (lgbDataset): lgbm training dataset lgb_valid (lgbDataset): lgbm valid dataset \"\"\" lgb_train = lgbDataset ( train . drop ( columns = [ target_col ] + id_cols ), train [ target_col ], categorical_feature = cat_cols , free_raw_data = False , ) if valid is not None : lgb_valid = lgbDataset ( valid . drop ( columns = [ target_col ] + id_cols ), valid [ target_col ], reference = lgb_train , free_raw_data = False , ) else : lgb_valid = None return lgb_train , lgb_valid predict_proba_lgbm_from_raw \u00b6 predict_proba_lgbm_from_raw ( preds_raw , task , binary2d = False ) Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Parameters: preds_raw ( ndarray ) \u2013 1D numpy array of arrays task ( str ) \u2013 type of task/objective binary2d ( boolean ) \u2013 wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs ( ndarray ) \u2013 array with predicted probabilities Source code in churn_pred/training/utils.py 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 def predict_proba_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ], binary2d : Optional [ bool ] = False , ) -> np . ndarray : \"\"\"Apply softmax to array of arrays that is an output of lightgbm.predct(). This replaces predict_proba(). Args: preds_raw (ndarray): 1D numpy array of arrays task (str): type of task/objective binary2d (boolean): wether the output of binary classification should be 1 or 2d vector Returns: predicted_probs (ndarray): array with predicted probabilities \"\"\" if task == \"binary\" : predicted_probs = _sigmoid ( preds_raw ) if binary2d : predicted_probs = np . apply_along_axis ( _binary_margin_to_prob , 1 , np . vstack ( tuple ( predicted_probs )) ) elif task == \"multiclass\" : predicted_probs = np . apply_along_axis ( _softmax , 1 , np . stack ( tuple ( preds_raw ))) return predicted_probs predict_cls_lgbm_from_raw \u00b6 predict_cls_lgbm_from_raw ( preds_raw , task ) Helper function to convert raw margin predictions through a sigmoid to represent a probability. Parameters: preds_raw ( ndarray ) \u2013 predictions task ( Literal ['binary', 'multiclass'] ) \u2013 task type Returns: y_true , preds \u2013 tuple containg labels and predictions for further evaluation Source code in churn_pred/training/utils.py 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 def predict_cls_lgbm_from_raw ( preds_raw : np . ndarray , task : Literal [ \"binary\" , \"multiclass\" ] ) -> np . ndarray : \"\"\"Helper function to convert raw margin predictions through a sigmoid to represent a probability. Args: preds_raw (ndarray): predictions task (Literal[\"binary\", \"multiclass\"]): task type Returns: (y_true, preds): tuple containg labels and predictions for further evaluation \"\"\" predicted_probs = predict_proba_lgbm_from_raw ( preds_raw = preds_raw , task = task ) if task == \"binary\" : pred_cls = np . array ([ int ( p > 0.5 ) for p in predicted_probs ]) elif task == \"multiclass\" : pred_cls = predicted_probs . argmax ( axis = 1 ) return pred_cls get_feature_importance \u00b6 get_feature_importance ( model ) Extract model feature importances and return sorted dataframe. Parameters: model ( lgb . basic . Booster ) \u2013 LightGBM model Returns: feature_imp ( pd . DataFrame ) \u2013 sorted dataframe with features and their importances Source code in churn_pred/training/utils.py 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 def get_feature_importance ( model : lgb . basic . Booster ) -> pd . DataFrame : \"\"\"Extract model feature importances and return sorted dataframe. Args: model (lgb.basic.Booster): LightGBM model Returns: feature_imp (pd.DataFrame): sorted dataframe with features and their importances \"\"\" feature_imp = pd . DataFrame ( { \"value\" : model . feature_importance (), \"feature\" : model . feature_name ()} ) feature_imp . sort_values ( by = \"value\" , inplace = True ) feature_imp . reset_index ( drop = True , inplace = True ) return feature_imp","title":"training"},{"location":"churn_pred/training.html#training","text":"","title":"Training"},{"location":"churn_pred/training.html#churn_pred.training.metrics","text":"","title":"metrics"},{"location":"churn_pred/training.html#churn_pred.training.optuna_optimizer","text":"","title":"optuna_optimizer"},{"location":"churn_pred/training.html#churn_pred.training.trainer","text":"","title":"trainer"},{"location":"churn_pred/training.html#churn_pred.training.utils","text":"","title":"utils"},{"location":"notebooks/00_auxiliary_features.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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} else { return copyNode(content); } } function clicked(event) { const button = event.currentTarget; if (button instanceof HTMLElement) { copy(button); } } function keydown(event) { if (event.key === ' ' || event.key === 'Enter') { const button = event.currentTarget; if (button instanceof HTMLElement) { event.preventDefault(); copy(button); } } } function focused(event) { event.currentTarget.addEventListener('keydown', keydown); } function blurred(event) { event.currentTarget.removeEventListener('keydown', keydown); } class ClipboardCopyElement extends HTMLElement { constructor() { super(); this.addEventListener('click', clicked); this.addEventListener('focus', focused); this.addEventListener('blur', blurred); } connectedCallback() { if (!this.hasAttribute('tabindex')) { this.setAttribute('tabindex', '0'); } if (!this.hasAttribute('role')) { this.setAttribute('role', 'button'); } } get value() { return this.getAttribute('value') || ''; } set value(text) { this.setAttribute('value', text); } } if (!window.customElements.get('clipboard-copy')) { window.ClipboardCopyElement = ClipboardCopyElement; window.customElements.define('clipboard-copy', ClipboardCopyElement); } return ClipboardCopyElement; })); document.addEventListener('clipboard-copy', function(event) { const notice = event.target.querySelector('.notice') notice.hidden = false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Add auxiliary features to the dataset \u00b6 sudo apt install nvidia-utils-535 to get GPU running In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from churn_pred.preprocessing import auxiliary_data , preprocess_text import spacy_fastlang import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from churn_pred.preprocessing import auxiliary_data, preprocess_text import spacy_fastlang In [2]: Copied! df_pd = pd . read_csv ( \"data/dataset.csv\" ) df_pd . head () df_pd = pd.read_csv(\"data/dataset.csv\") df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited 0 747 15787619 Hsieh 844 France Male 18 2 I can not link my card to any third-party plat... 160980.03 1 0 0 145936.28 0 1 1620 15770309 McDonald 656 France Male 18 10 Last week, I reached out to my bank's support ... 151762.74 1 0 1 127014.32 0 2 1679 15569178 Kharlamov 570 France Female 18 4 NaN 82767.42 1 1 0 71811.90 0 3 2022 15795519 Vasiliev 716 Germany Female 18 3 NaN 128743.80 1 0 0 197322.13 0 4 2137 15621893 Bellucci 727 France Male 18 4 Yo, I gotta give some love to this bank's cust... 133550.67 1 1 1 46941.41 0 Sentiment analysis \u00b6 Cleaning and langauge detection \u00b6 In [3]: Copied! df_pd = preprocess_text . text_cleaning ( df = df_pd , text_col = \"CustomerFeedback\" ) df_pd = preprocess_text . language_detection ( df = df_pd , text_col = \"CustomerFeedback\" , model_type = \"fasttext\" ) df_pd = preprocess_text.text_cleaning(df=df_pd, text_col=\"CustomerFeedback\") df_pd = preprocess_text.language_detection( df=df_pd, text_col=\"CustomerFeedback\", model_type=\"fasttext\" ) Cleaning Progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 10000/10000 [00:34<00:00, 287.95it/s] Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar. In [4]: Copied! df_pd . head () df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 1 0 0 145936.28 0 en 0.786177 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 1 0 1 127014.32 0 en 0.911405 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 1 1 0 71811.90 0 ht 0.974604 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 1 0 0 197322.13 0 ht 0.974604 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 1 1 1 46941.41 0 en 0.758962 In [5]: Copied! df_pd [ df_pd [ \"CustomerFeedback\" ] . astype ( str ) != \"nan\" ][ \"CustomerFeedback_language\" ] . unique () df_pd[df_pd[\"CustomerFeedback\"].astype(str) != \"nan\"][ \"CustomerFeedback_language\" ].unique() Out[5]: array(['en'], dtype=object) In [6]: Copied! df_pd [ ( df_pd [ \"CustomerFeedback\" ] . astype ( str ) != \"nan\" ) & ( df_pd [ \"CustomerFeedback_language_score\" ] < 0.5 ) ] df_pd[ (df_pd[\"CustomerFeedback\"].astype(str) != \"nan\") & (df_pd[\"CustomerFeedback_language_score\"] < 0.5) ] Out[6]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score 105 2866 15590228 Greenwalt 715 France Male 21 6 hsi 76467.16 1 1 1 173511.72 0 en 0.124504 6270 7107 15723989 Carroll 646 France Male 40 5 fund accessible anticipate date _IS_PUNCT_ _IS... 93680.43 2 1 1 179473.26 0 en 0.468742 In [7]: Copied! df_pd . loc [ 105 , [ \"CustomerFeedback\" ]] = \"nan\" df_pd.loc[105, [\"CustomerFeedback\"]] = \"nan\" In [8]: Copied! df_pd [ df_pd [ \"CustomerFeedback\" ] . astype ( str ) != \"nan\" ][ \"CustomerFeedback_language_score\" ] . plot . hist ( bins = 50 ) df_pd[df_pd[\"CustomerFeedback\"].astype(str) != \"nan\"][ \"CustomerFeedback_language_score\" ].plot.hist(bins=50) Out[8]: <Axes: ylabel='Frequency'> Sentiment analysis prediction \u00b6 In [9]: Copied! df_pd [[ \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment3_score\" ]] = ( preprocess_text . sentiment_analysis ( df = df_pd , text_col = \"CustomerFeedback\" , sentiment_depth = 3 )[[ \"CustomerFeedback_sentiment\" , \"CustomerFeedback_sentiment_score\" ]] ) df_pd . head () df_pd[[\"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment3_score\"]] = ( preprocess_text.sentiment_analysis( df=df_pd, text_col=\"CustomerFeedback\", sentiment_depth=3 )[[\"CustomerFeedback_sentiment\", \"CustomerFeedback_sentiment_score\"]] ) df_pd.head() Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight'] - This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Out[9]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 1 0 0 145936.28 0 en 0.786177 neutral 0.827256 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 1 0 1 127014.32 0 en 0.911405 neutral 0.671348 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 1 1 0 71811.90 0 ht 0.974604 neutral 0.680865 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 1 0 0 197322.13 0 ht 0.974604 neutral 0.680865 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 1 1 1 46941.41 0 en 0.758962 positive 0.878306 In [10]: Copied! df_pd [[ \"CustomerFeedback_sentiment5\" , \"CustomerFeedback_sentiment5_score\" ]] = ( preprocess_text . sentiment_analysis ( df = df_pd , text_col = \"CustomerFeedback\" , sentiment_depth = 5 )[[ \"CustomerFeedback_sentiment\" , \"CustomerFeedback_sentiment_score\" ]] ) df_pd . head () df_pd[[\"CustomerFeedback_sentiment5\", \"CustomerFeedback_sentiment5_score\"]] = ( preprocess_text.sentiment_analysis( df=df_pd, text_col=\"CustomerFeedback\", sentiment_depth=5 )[[\"CustomerFeedback_sentiment\", \"CustomerFeedback_sentiment_score\"]] ) df_pd.head() Out[10]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 ... 0 0 145936.28 0 en 0.786177 neutral 0.827256 4 stars 0.259499 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 ... 0 1 127014.32 0 en 0.911405 neutral 0.671348 1 star 0.359483 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 ... 1 0 71811.90 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 ... 0 0 197322.13 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 ... 1 1 46941.41 0 en 0.758962 positive 0.878306 1 star 0.291449 5 rows \u00d7 21 columns In [11]: Copied! # save intermediate results df_pd . to_parquet ( \"data/dataset_sentiment.parquet\" ) # save intermediate results df_pd.to_parquet(\"data/dataset_sentiment.parquet\") Surname origin country \u00b6 In [3]: Copied! # df_pd = pd.read_parquet(\"data/dataset_sentiment.parquet\") # df_pd = pd.read_parquet(\"data/dataset_sentiment.parquet\") In [12]: Copied! df_pd [ \"Surname\" + \"_Country\" ] = auxiliary_data . surname_origin ( df = df_pd , surname_col = \"Surname\" ) df_pd . head () df_pd[\"Surname\" + \"_Country\"] = auxiliary_data.surname_origin( df=df_pd, surname_col=\"Surname\" ) df_pd.head() Out[12]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score Surname_Country 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 ... 0 145936.28 0 en 0.786177 neutral 0.827256 4 stars 0.259499 Taiwan, Province of China 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 ... 1 127014.32 0 en 0.911405 neutral 0.671348 1 star 0.359483 United States 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 ... 0 71811.90 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 Russian Federation 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 ... 0 197322.13 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 Russian Federation 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 ... 1 46941.41 0 en 0.758962 positive 0.878306 1 star 0.291449 Italy 5 rows \u00d7 22 columns In [13]: Copied! df_pd [( df_pd [ \"Surname_Country\" ] == \"NA\" )][ \"Surname\" ] . nunique () df_pd[(df_pd[\"Surname_Country\"] == \"NA\")][\"Surname\"].nunique() Out[13]: 496 less than 500 so I can use Namsor free trial to get data, I will use their online csv tool and not API function auxiliary_data.surname_origin_namsor to avoid wasting another email address for free trail In [6]: Copied! namsor_csv = pd . DataFrame ( { \"Surname\" : df_pd [( df_pd [ \"Surname_Country\" ] == \"NA\" )][ \"Surname\" ] . unique ()} ) namsor_csv [ \"first_name\" ] = \"unset\" namsor_csv . to_csv ( \"data/surnames/surnames_for_namsor.csv\" , index = False ) namsor_csv = pd.DataFrame( {\"Surname\": df_pd[(df_pd[\"Surname_Country\"] == \"NA\")][\"Surname\"].unique()} ) namsor_csv[\"first_name\"] = \"unset\" namsor_csv.to_csv(\"data/surnames/surnames_for_namsor.csv\", index=False) after namsor processing In [14]: Copied! # keep_default_na=False as NA-Namibia is intrepreted as NaN namsor_surnames = pd . read_csv ( \"data/surnames/namsor_name-origin_surnames_for_namsor.csv\" , keep_default_na = False , na_values = [ \"_\" ], ) namsor_surnames = namsor_surnames [[ \"lastName\" , \"countryOrigin\" ]] . rename ( columns = { \"lastName\" : \"Surname\" , \"countryOrigin\" : \"Surname_Country_namsor\" } ) # keep_default_na=False as NA-Namibia is intrepreted as NaN namsor_surnames = pd.read_csv( \"data/surnames/namsor_name-origin_surnames_for_namsor.csv\", keep_default_na=False, na_values=[\"_\"], ) namsor_surnames = namsor_surnames[[\"lastName\", \"countryOrigin\"]].rename( columns={\"lastName\": \"Surname\", \"countryOrigin\": \"Surname_Country_namsor\"} ) In [15]: Copied! namsor_surnames [ \"Surname_Country_namsor\" ] = auxiliary_data . get_country_name ( df = namsor_surnames , country_name_col = \"Surname_Country_namsor\" ) namsor_surnames . head () namsor_surnames[\"Surname_Country_namsor\"] = auxiliary_data.get_country_name( df=namsor_surnames, country_name_col=\"Surname_Country_namsor\" ) namsor_surnames.head() Out[15]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Surname Surname_Country_namsor 0 Vaguine France 1 Onyekaozulu Nigeria 2 Chiazagomekpele Nigeria 3 Fleetwood-Smith United Kingdom 4 Anayolisa Nigeria In [16]: Copied! df_pd = df_pd . merge ( namsor_surnames , on = \"Surname\" , how = \"left\" ) df_pd = df_pd.merge(namsor_surnames, on=\"Surname\", how=\"left\") In [17]: Copied! df_pd . loc [( df_pd [ \"Surname_Country\" ] == \"NA\" ), \"Surname_Country\" ] = df_pd . loc [ ( df_pd [ \"Surname_Country\" ] == \"NA\" ), \"Surname_Country_namsor\" ] df_pd.loc[(df_pd[\"Surname_Country\"] == \"NA\"), \"Surname_Country\"] = df_pd.loc[ (df_pd[\"Surname_Country\"] == \"NA\"), \"Surname_Country_namsor\" ] In [18]: Copied! df_pd = df_pd . drop ( columns = [ \"Surname_Country_namsor\" ]) df_pd = df_pd.drop(columns=[\"Surname_Country_namsor\"]) In [19]: Copied! df_pd [ \"Surname_Country\" ] . unique () df_pd[\"Surname_Country\"].unique() Out[19]: array(['Taiwan, Province of China', 'United States', 'Russian Federation', 'Italy', 'Macao', 'Ireland', 'United Kingdom', 'Nigeria', 'France', 'Algeria', 'Germany', 'Malaysia', 'Mexico', 'Hong Kong', 'China', 'Israel', 'Kazakhstan', 'Colombia', 'Lesotho', 'Saudi Arabia', 'Uruguay', 'Togo', 'Ethiopia', 'Spain', 'Egypt', 'Ukraine', 'Lebanon', 'Zimbabwe', 'India', 'Thailand', 'Brazil', 'Panama', 'T\u00fcrkiye', \"C\u00f4te d'Ivoire\", 'South Africa', 'Azerbaijan', 'Congo', 'Czechia', 'Burkina Faso', 'Uzbekistan', 'Morocco', 'Ghana', 'Portugal', 'Austria', 'Sri Lanka', 'Bulgaria', 'Norway', 'Greece', 'Estonia', 'Bangladesh', 'Viet Nam', 'Netherlands', 'Cameroon', 'Sweden', 'Costa Rica', 'Japan', 'Liberia', 'Myanmar', 'Brunei Darussalam', 'Canada', 'Moldova, Republic of', 'Denmark', 'Benin', 'Niger', 'Latvia', 'Finland', 'Chile', 'Poland', 'Switzerland', 'Mozambique', 'Indonesia', 'Rwanda', 'Argentina', 'Namibia', 'Belgium', 'Botswana', 'Romania', 'Mali', 'Belarus', 'Cyprus', 'Palestine, State of', 'Hungary', 'Mauritius', 'Pakistan', 'Senegal', 'Slovakia', 'Iran, Islamic Republic of', 'Malawi', 'Jamaica'], dtype=object) In [20]: Copied! country_info_problematic_vals = { \"Iran, Islamic Republic of\" : \"Iran\" , \"Moldova, Republic of\" : \"Moldova\" , \"Taiwan, Province of China\" : \"Taiwan\" , \"Czechia\" : \"Czech republic\" , \"C\u00f4te d'Ivoire\" : \"Ivory Coast\" , \"Brunei Darussalam\" : \"Brunei\" , \"Viet Nam\" : \"Vietnam\" , \"Macao\" : \"Macau\" , \"T\u00fcrkiye\" : \"Turkey\" , \"Congo\" : \"Democratic Republic of the Congo\" , \"Palestine, State of\" : \"Palestine\" , } df_pd [ \"Surname_Country\" ] = df_pd [ \"Surname_Country\" ] . replace ( country_info_problematic_vals ) country_info_problematic_vals = { \"Iran, Islamic Republic of\": \"Iran\", \"Moldova, Republic of\": \"Moldova\", \"Taiwan, Province of China\": \"Taiwan\", \"Czechia\": \"Czech republic\", \"C\u00f4te d'Ivoire\": \"Ivory Coast\", \"Brunei Darussalam\": \"Brunei\", \"Viet Nam\": \"Vietnam\", \"Macao\": \"Macau\", \"T\u00fcrkiye\": \"Turkey\", \"Congo\": \"Democratic Republic of the Congo\", \"Palestine, State of\": \"Palestine\", } df_pd[\"Surname_Country\"] = df_pd[\"Surname_Country\"].replace( country_info_problematic_vals ) Additional country info \u00b6 Country region and subregion \u00b6 In [21]: Copied! df_pd [[ \"Surname_Country_region\" , \"Surname_Country_subregion\" ]] = ( auxiliary_data . get_country_region_subregion ( df = df_pd , country_name_col = \"Surname_Country\" ) ) df_pd[[\"Surname_Country_region\", \"Surname_Country_subregion\"]] = ( auxiliary_data.get_country_region_subregion( df=df_pd, country_name_col=\"Surname_Country\" ) ) In [22]: Copied! df_pd [ ( df_pd [ \"Surname_Country_region\" ] == \"NA\" ) | ( df_pd [ \"Surname_Country_subregion\" ] == \"NA\" ) ][[ \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" ]] df_pd[ (df_pd[\"Surname_Country_region\"] == \"NA\") | (df_pd[\"Surname_Country_subregion\"] == \"NA\") ][[\"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\"]] Out[22]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Surname_Country Surname_Country_region Surname_Country_subregion 2024 Myanmar NA NA 3322 Myanmar NA NA 4237 Myanmar NA NA 4626 Myanmar NA NA 4892 Myanmar NA NA 5348 Palestine NA NA 6190 Myanmar NA NA 7064 Myanmar NA NA 7098 Myanmar NA NA 7693 Myanmar NA NA 7895 Myanmar NA NA 8299 Myanmar NA NA 8789 Myanmar NA NA 9600 Myanmar NA NA In [23]: Copied! # https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Palestine\" , [ \"Surname_Country_region\" , \"Surname_Country_subregion\" ], ] = ( \"Asia\" , \"Western Asia\" ) # https://en.wikipedia.org/wiki/Myanmar - 'South-Eastern Asia' to follow countryinfo naming df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Myanmar\" , [ \"Surname_Country_region\" , \"Surname_Country_subregion\" ], ] = ( \"Asia\" , \"South-Eastern Asia\" ) df_pd [ ( df_pd [ \"Surname_Country_region\" ] == \"NA\" ) | ( df_pd [ \"Surname_Country_subregion\" ] == \"NA\" ) ] # https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming df_pd.loc[ df_pd[\"Surname_Country\"] == \"Palestine\", [\"Surname_Country_region\", \"Surname_Country_subregion\"], ] = (\"Asia\", \"Western Asia\") # https://en.wikipedia.org/wiki/Myanmar - 'South-Eastern Asia' to follow countryinfo naming df_pd.loc[ df_pd[\"Surname_Country\"] == \"Myanmar\", [\"Surname_Country_region\", \"Surname_Country_subregion\"], ] = (\"Asia\", \"South-Eastern Asia\") df_pd[ (df_pd[\"Surname_Country_region\"] == \"NA\") | (df_pd[\"Surname_Country_subregion\"] == \"NA\") ] Out[23]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score Surname_Country Surname_Country_region Surname_Country_subregion 0 rows \u00d7 24 columns In [24]: Copied! df_pd [[ \"Country_region\" , \"Country_subregion\" ]] = ( auxiliary_data . get_country_region_subregion ( df = df_pd , country_name_col = \"Country\" ) ) df_pd[[\"Country_region\", \"Country_subregion\"]] = ( auxiliary_data.get_country_region_subregion(df=df_pd, country_name_col=\"Country\") ) In [25]: Copied! df_pd [( df_pd [ \"Country_region\" ] == \"NA\" ) | ( df_pd [ \"Country_subregion\" ] == \"NA\" )][ [ \"Country\" , \"Country_region\" , \"Country_subregion\" ] ] df_pd[(df_pd[\"Country_region\"] == \"NA\") | (df_pd[\"Country_subregion\"] == \"NA\")][ [\"Country\", \"Country_region\", \"Country_subregion\"] ] Out[25]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Country Country_region Country_subregion Native or not \u00b6 if the client is possibly an immigrant(from immigrant family?) or not In [26]: Copied! df_pd [ \"is_native\" ] = ( df_pd [ \"Surname_Country\" ] == df_pd [ \"Country\" ]) . astype ( int ) df_pd[\"is_native\"] = (df_pd[\"Surname_Country\"] == df_pd[\"Country\"]).astype(int) Country hemisphere \u00b6 could be usefull with some timestamp combination to know the season(oposite season in hemispeheres) etc.; anything that might affect mood/behaviour In [27]: Copied! # no need to know the hemisphere of the surname origin country... df_pd = auxiliary_data . hemisphere ( df = df_pd , cc_col = \"Country\" ) . rename ( columns = { \"hemisphere\" : \"Country_hemisphere\" } ) # no need to know the hemisphere of the surname origin country... df_pd = auxiliary_data.hemisphere(df=df_pd, cc_col=\"Country\").rename( columns={\"hemisphere\": \"Country_hemisphere\"} ) Country gdpp \u00b6 check 'wealthiness' of the current and origin country In [28]: Copied! df_pd = auxiliary_data . gdppc ( df = df_pd , country_name_col = \"Country\" ) . rename ( columns = { \"gdp_per_capita\" : \"Country_gdp_per_capita\" , \"IncomeGroup\" : \"Country_IncomeGroup\" , } ) df_pd = auxiliary_data . gdppc ( df = df_pd , country_name_col = \"Surname_Country\" ) . rename ( columns = { \"gdp_per_capita\" : \"Surname_Country_gdp_per_capita\" , \"IncomeGroup\" : \"Surname_Country_IncomeGroup\" , } ) df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col=\"Country\").rename( columns={ \"gdp_per_capita\": \"Country_gdp_per_capita\", \"IncomeGroup\": \"Country_IncomeGroup\", } ) df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col=\"Surname_Country\").rename( columns={ \"gdp_per_capita\": \"Surname_Country_gdp_per_capita\", \"IncomeGroup\": \"Surname_Country_IncomeGroup\", } ) /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:283: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy gdp_per_capita.rename(columns={\"Country Code\": \"iso_a3\"}, inplace=True) /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:284: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy gdp_per_capita[\"gdp_per_capita\"] = gdp_per_capita_raw.drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"]).ffill(axis=1).iloc[:, -1] /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:283: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy gdp_per_capita.rename(columns={\"Country Code\": \"iso_a3\"}, inplace=True) /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:284: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy gdp_per_capita[\"gdp_per_capita\"] = gdp_per_capita_raw.drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"]).ffill(axis=1).iloc[:, -1] SubdivisionHierarchy(code='MX-MEX', country_code='MX', name='M\u00e9xico', parent_code=None, type='State') SubdivisionHierarchy(code='PA-8', country_code='PA', name='Panam\u00e1', parent_code=None, type='Province') SubdivisionHierarchy(code='NG-NI', country_code='NG', name='Niger', parent_code=None, type='State') SubdivisionHierarchy(code='GN-ML', country_code='GN', name='Mali', parent='L', parent_code='GN-L', type='Prefecture') Additional age info \u00b6 In [29]: Copied! df_pd = auxiliary_data . age_categories ( df = df_pd , age_col = \"Age\" ) df_pd = auxiliary_data.age_categories(df=df_pd, age_col=\"Age\") In [30]: Copied! df_pd [ df_pd [ \"Surname_Country_gdp_per_capita\" ] . isna ()][ \"Surname_Country\" ] . unique () df_pd[df_pd[\"Surname_Country_gdp_per_capita\"].isna()][\"Surname_Country\"].unique() Out[30]: array(['Taiwan', 'Macau', 'Turkey', 'Ivory Coast', 'Democratic Republic of the Congo'], dtype=object) In [31]: Copied! # Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Taiwan\" , [ \"Surname_Country_gdp_per_capita\" ]] = ( 32756.0 ) # remaining countries have some naming issues - ToBeResolved; e.g. Macau -> Macao, Turkey -> Turkiye, etc df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Macau\" , [ \"Surname_Country_gdp_per_capita\" ]] = ( 61230.96247 ) df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Turkey\" , [ \"Surname_Country_gdp_per_capita\" ]] = ( 38355.15397 ) df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Ivory Coast\" , [ \"Surname_Country_gdp_per_capita\" ] ] = 6540.462355 df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Democratic Republic of the Congo\" , [ \"Surname_Country_gdp_per_capita\" ], ] = 1337.834149 # Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita df_pd.loc[df_pd[\"Surname_Country\"] == \"Taiwan\", [\"Surname_Country_gdp_per_capita\"]] = ( 32756.0 ) # remaining countries have some naming issues - ToBeResolved; e.g. Macau -> Macao, Turkey -> Turkiye, etc df_pd.loc[df_pd[\"Surname_Country\"] == \"Macau\", [\"Surname_Country_gdp_per_capita\"]] = ( 61230.96247 ) df_pd.loc[df_pd[\"Surname_Country\"] == \"Turkey\", [\"Surname_Country_gdp_per_capita\"]] = ( 38355.15397 ) df_pd.loc[ df_pd[\"Surname_Country\"] == \"Ivory Coast\", [\"Surname_Country_gdp_per_capita\"] ] = 6540.462355 df_pd.loc[ df_pd[\"Surname_Country\"] == \"Democratic Republic of the Congo\", [\"Surname_Country_gdp_per_capita\"], ] = 1337.834149 In [32]: Copied! df_pd . to_parquet ( \"data/dataset_auxiliary_features_raw.parquet\" ) df_pd.to_parquet(\"data/dataset_auxiliary_features_raw.parquet\") In [33]: Copied! df_pd . columns df_pd.columns Out[33]: Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Country', 'Gender', 'Age', 'Tenure', 'CustomerFeedback', 'Balance (EUR)', 'NumberOfProducts', 'HasCreditCard', 'IsActiveMember', 'EstimatedSalary', 'Exited', 'CustomerFeedback_language', 'CustomerFeedback_language_score', 'CustomerFeedback_sentiment3', 'CustomerFeedback_sentiment3_score', 'CustomerFeedback_sentiment5', 'CustomerFeedback_sentiment5_score', 'Surname_Country', 'Surname_Country_region', 'Surname_Country_subregion', 'Country_region', 'Country_subregion', 'is_native', 'Country_hemisphere', 'Country_gdp_per_capita', 'Country_IncomeGroup', 'Surname_Country_gdp_per_capita', 'Surname_Country_IncomeGroup', 'working_class', 'stage_of_life', 'generation'], dtype='object') In [34]: Copied! df_pd . drop ( columns = [ \"RowNumber\" , \"Surname\" , \"CustomerFeedback\" , \"CustomerFeedback_language\" , \"CustomerFeedback_language_score\" , \"CustomerFeedback_sentiment3_score\" , \"CustomerFeedback_sentiment5_score\" , ] ) . to_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd.drop( columns=[ \"RowNumber\", \"Surname\", \"CustomerFeedback\", \"CustomerFeedback_language\", \"CustomerFeedback_language_score\", \"CustomerFeedback_sentiment3_score\", \"CustomerFeedback_sentiment5_score\", ] ).to_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") Appendix [TBD] \u00b6 analyze score and probabilities of the sentiment and country detection methods make an object or procedure to do this automatically","title":"00_auxiliary_features"},{"location":"notebooks/00_auxiliary_features.html#add-auxiliary-features-to-the-dataset","text":"sudo apt install nvidia-utils-535 to get GPU running In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from churn_pred.preprocessing import auxiliary_data , preprocess_text import spacy_fastlang import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from churn_pred.preprocessing import auxiliary_data, preprocess_text import spacy_fastlang In [2]: Copied! df_pd = pd . read_csv ( \"data/dataset.csv\" ) df_pd . head () df_pd = pd.read_csv(\"data/dataset.csv\") df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited 0 747 15787619 Hsieh 844 France Male 18 2 I can not link my card to any third-party plat... 160980.03 1 0 0 145936.28 0 1 1620 15770309 McDonald 656 France Male 18 10 Last week, I reached out to my bank's support ... 151762.74 1 0 1 127014.32 0 2 1679 15569178 Kharlamov 570 France Female 18 4 NaN 82767.42 1 1 0 71811.90 0 3 2022 15795519 Vasiliev 716 Germany Female 18 3 NaN 128743.80 1 0 0 197322.13 0 4 2137 15621893 Bellucci 727 France Male 18 4 Yo, I gotta give some love to this bank's cust... 133550.67 1 1 1 46941.41 0","title":"Add auxiliary features to the dataset"},{"location":"notebooks/00_auxiliary_features.html#sentiment-analysis","text":"","title":"Sentiment analysis"},{"location":"notebooks/00_auxiliary_features.html#cleaning-and-langauge-detection","text":"In [3]: Copied! df_pd = preprocess_text . text_cleaning ( df = df_pd , text_col = \"CustomerFeedback\" ) df_pd = preprocess_text . language_detection ( df = df_pd , text_col = \"CustomerFeedback\" , model_type = \"fasttext\" ) df_pd = preprocess_text.text_cleaning(df=df_pd, text_col=\"CustomerFeedback\") df_pd = preprocess_text.language_detection( df=df_pd, text_col=\"CustomerFeedback\", model_type=\"fasttext\" ) Cleaning Progress: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 10000/10000 [00:34<00:00, 287.95it/s] Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar. In [4]: Copied! df_pd . head () df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 1 0 0 145936.28 0 en 0.786177 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 1 0 1 127014.32 0 en 0.911405 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 1 1 0 71811.90 0 ht 0.974604 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 1 0 0 197322.13 0 ht 0.974604 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 1 1 1 46941.41 0 en 0.758962 In [5]: Copied! df_pd [ df_pd [ \"CustomerFeedback\" ] . astype ( str ) != \"nan\" ][ \"CustomerFeedback_language\" ] . unique () df_pd[df_pd[\"CustomerFeedback\"].astype(str) != \"nan\"][ \"CustomerFeedback_language\" ].unique() Out[5]: array(['en'], dtype=object) In [6]: Copied! df_pd [ ( df_pd [ \"CustomerFeedback\" ] . astype ( str ) != \"nan\" ) & ( df_pd [ \"CustomerFeedback_language_score\" ] < 0.5 ) ] df_pd[ (df_pd[\"CustomerFeedback\"].astype(str) != \"nan\") & (df_pd[\"CustomerFeedback_language_score\"] < 0.5) ] Out[6]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score 105 2866 15590228 Greenwalt 715 France Male 21 6 hsi 76467.16 1 1 1 173511.72 0 en 0.124504 6270 7107 15723989 Carroll 646 France Male 40 5 fund accessible anticipate date _IS_PUNCT_ _IS... 93680.43 2 1 1 179473.26 0 en 0.468742 In [7]: Copied! df_pd . loc [ 105 , [ \"CustomerFeedback\" ]] = \"nan\" df_pd.loc[105, [\"CustomerFeedback\"]] = \"nan\" In [8]: Copied! df_pd [ df_pd [ \"CustomerFeedback\" ] . astype ( str ) != \"nan\" ][ \"CustomerFeedback_language_score\" ] . plot . hist ( bins = 50 ) df_pd[df_pd[\"CustomerFeedback\"].astype(str) != \"nan\"][ \"CustomerFeedback_language_score\" ].plot.hist(bins=50) Out[8]: <Axes: ylabel='Frequency'>","title":"Cleaning and langauge detection"},{"location":"notebooks/00_auxiliary_features.html#sentiment-analysis-prediction","text":"In [9]: Copied! df_pd [[ \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment3_score\" ]] = ( preprocess_text . sentiment_analysis ( df = df_pd , text_col = \"CustomerFeedback\" , sentiment_depth = 3 )[[ \"CustomerFeedback_sentiment\" , \"CustomerFeedback_sentiment_score\" ]] ) df_pd . head () df_pd[[\"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment3_score\"]] = ( preprocess_text.sentiment_analysis( df=df_pd, text_col=\"CustomerFeedback\", sentiment_depth=3 )[[\"CustomerFeedback_sentiment\", \"CustomerFeedback_sentiment_score\"]] ) df_pd.head() Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight'] - This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Out[9]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 1 0 0 145936.28 0 en 0.786177 neutral 0.827256 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 1 0 1 127014.32 0 en 0.911405 neutral 0.671348 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 1 1 0 71811.90 0 ht 0.974604 neutral 0.680865 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 1 0 0 197322.13 0 ht 0.974604 neutral 0.680865 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 1 1 1 46941.41 0 en 0.758962 positive 0.878306 In [10]: Copied! df_pd [[ \"CustomerFeedback_sentiment5\" , \"CustomerFeedback_sentiment5_score\" ]] = ( preprocess_text . sentiment_analysis ( df = df_pd , text_col = \"CustomerFeedback\" , sentiment_depth = 5 )[[ \"CustomerFeedback_sentiment\" , \"CustomerFeedback_sentiment_score\" ]] ) df_pd . head () df_pd[[\"CustomerFeedback_sentiment5\", \"CustomerFeedback_sentiment5_score\"]] = ( preprocess_text.sentiment_analysis( df=df_pd, text_col=\"CustomerFeedback\", sentiment_depth=5 )[[\"CustomerFeedback_sentiment\", \"CustomerFeedback_sentiment_score\"]] ) df_pd.head() Out[10]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 ... 0 0 145936.28 0 en 0.786177 neutral 0.827256 4 stars 0.259499 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 ... 0 1 127014.32 0 en 0.911405 neutral 0.671348 1 star 0.359483 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 ... 1 0 71811.90 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 ... 0 0 197322.13 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 ... 1 1 46941.41 0 en 0.758962 positive 0.878306 1 star 0.291449 5 rows \u00d7 21 columns In [11]: Copied! # save intermediate results df_pd . to_parquet ( \"data/dataset_sentiment.parquet\" ) # save intermediate results df_pd.to_parquet(\"data/dataset_sentiment.parquet\")","title":"Sentiment analysis prediction"},{"location":"notebooks/00_auxiliary_features.html#surname-origin-country","text":"In [3]: Copied! # df_pd = pd.read_parquet(\"data/dataset_sentiment.parquet\") # df_pd = pd.read_parquet(\"data/dataset_sentiment.parquet\") In [12]: Copied! df_pd [ \"Surname\" + \"_Country\" ] = auxiliary_data . surname_origin ( df = df_pd , surname_col = \"Surname\" ) df_pd . head () df_pd[\"Surname\" + \"_Country\"] = auxiliary_data.surname_origin( df=df_pd, surname_col=\"Surname\" ) df_pd.head() Out[12]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... IsActiveMember EstimatedSalary Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score Surname_Country 0 747 15787619 Hsieh 844 France Male 18 2 link card _IS_PUNCT_ party platform _IS_PUNCT_ 160980.03 ... 0 145936.28 0 en 0.786177 neutral 0.827256 4 stars 0.259499 Taiwan, Province of China 1 1620 15770309 McDonald 656 France Male 18 10 week _IS_PUNCT_ reach bank support team confus... 151762.74 ... 1 127014.32 0 en 0.911405 neutral 0.671348 1 star 0.359483 United States 2 1679 15569178 Kharlamov 570 France Female 18 4 nan 82767.42 ... 0 71811.90 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 Russian Federation 3 2022 15795519 Vasiliev 716 Germany Female 18 3 nan 128743.80 ... 0 197322.13 0 ht 0.974604 neutral 0.680865 2 stars 0.260568 Russian Federation 4 2137 15621893 Bellucci 727 France Male 18 4 Yo _IS_PUNCT_ get to love bank customer servic... 133550.67 ... 1 46941.41 0 en 0.758962 positive 0.878306 1 star 0.291449 Italy 5 rows \u00d7 22 columns In [13]: Copied! df_pd [( df_pd [ \"Surname_Country\" ] == \"NA\" )][ \"Surname\" ] . nunique () df_pd[(df_pd[\"Surname_Country\"] == \"NA\")][\"Surname\"].nunique() Out[13]: 496 less than 500 so I can use Namsor free trial to get data, I will use their online csv tool and not API function auxiliary_data.surname_origin_namsor to avoid wasting another email address for free trail In [6]: Copied! namsor_csv = pd . DataFrame ( { \"Surname\" : df_pd [( df_pd [ \"Surname_Country\" ] == \"NA\" )][ \"Surname\" ] . unique ()} ) namsor_csv [ \"first_name\" ] = \"unset\" namsor_csv . to_csv ( \"data/surnames/surnames_for_namsor.csv\" , index = False ) namsor_csv = pd.DataFrame( {\"Surname\": df_pd[(df_pd[\"Surname_Country\"] == \"NA\")][\"Surname\"].unique()} ) namsor_csv[\"first_name\"] = \"unset\" namsor_csv.to_csv(\"data/surnames/surnames_for_namsor.csv\", index=False) after namsor processing In [14]: Copied! # keep_default_na=False as NA-Namibia is intrepreted as NaN namsor_surnames = pd . read_csv ( \"data/surnames/namsor_name-origin_surnames_for_namsor.csv\" , keep_default_na = False , na_values = [ \"_\" ], ) namsor_surnames = namsor_surnames [[ \"lastName\" , \"countryOrigin\" ]] . rename ( columns = { \"lastName\" : \"Surname\" , \"countryOrigin\" : \"Surname_Country_namsor\" } ) # keep_default_na=False as NA-Namibia is intrepreted as NaN namsor_surnames = pd.read_csv( \"data/surnames/namsor_name-origin_surnames_for_namsor.csv\", keep_default_na=False, na_values=[\"_\"], ) namsor_surnames = namsor_surnames[[\"lastName\", \"countryOrigin\"]].rename( columns={\"lastName\": \"Surname\", \"countryOrigin\": \"Surname_Country_namsor\"} ) In [15]: Copied! namsor_surnames [ \"Surname_Country_namsor\" ] = auxiliary_data . get_country_name ( df = namsor_surnames , country_name_col = \"Surname_Country_namsor\" ) namsor_surnames . head () namsor_surnames[\"Surname_Country_namsor\"] = auxiliary_data.get_country_name( df=namsor_surnames, country_name_col=\"Surname_Country_namsor\" ) namsor_surnames.head() Out[15]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Surname Surname_Country_namsor 0 Vaguine France 1 Onyekaozulu Nigeria 2 Chiazagomekpele Nigeria 3 Fleetwood-Smith United Kingdom 4 Anayolisa Nigeria In [16]: Copied! df_pd = df_pd . merge ( namsor_surnames , on = \"Surname\" , how = \"left\" ) df_pd = df_pd.merge(namsor_surnames, on=\"Surname\", how=\"left\") In [17]: Copied! df_pd . loc [( df_pd [ \"Surname_Country\" ] == \"NA\" ), \"Surname_Country\" ] = df_pd . loc [ ( df_pd [ \"Surname_Country\" ] == \"NA\" ), \"Surname_Country_namsor\" ] df_pd.loc[(df_pd[\"Surname_Country\"] == \"NA\"), \"Surname_Country\"] = df_pd.loc[ (df_pd[\"Surname_Country\"] == \"NA\"), \"Surname_Country_namsor\" ] In [18]: Copied! df_pd = df_pd . drop ( columns = [ \"Surname_Country_namsor\" ]) df_pd = df_pd.drop(columns=[\"Surname_Country_namsor\"]) In [19]: Copied! df_pd [ \"Surname_Country\" ] . unique () df_pd[\"Surname_Country\"].unique() Out[19]: array(['Taiwan, Province of China', 'United States', 'Russian Federation', 'Italy', 'Macao', 'Ireland', 'United Kingdom', 'Nigeria', 'France', 'Algeria', 'Germany', 'Malaysia', 'Mexico', 'Hong Kong', 'China', 'Israel', 'Kazakhstan', 'Colombia', 'Lesotho', 'Saudi Arabia', 'Uruguay', 'Togo', 'Ethiopia', 'Spain', 'Egypt', 'Ukraine', 'Lebanon', 'Zimbabwe', 'India', 'Thailand', 'Brazil', 'Panama', 'T\u00fcrkiye', \"C\u00f4te d'Ivoire\", 'South Africa', 'Azerbaijan', 'Congo', 'Czechia', 'Burkina Faso', 'Uzbekistan', 'Morocco', 'Ghana', 'Portugal', 'Austria', 'Sri Lanka', 'Bulgaria', 'Norway', 'Greece', 'Estonia', 'Bangladesh', 'Viet Nam', 'Netherlands', 'Cameroon', 'Sweden', 'Costa Rica', 'Japan', 'Liberia', 'Myanmar', 'Brunei Darussalam', 'Canada', 'Moldova, Republic of', 'Denmark', 'Benin', 'Niger', 'Latvia', 'Finland', 'Chile', 'Poland', 'Switzerland', 'Mozambique', 'Indonesia', 'Rwanda', 'Argentina', 'Namibia', 'Belgium', 'Botswana', 'Romania', 'Mali', 'Belarus', 'Cyprus', 'Palestine, State of', 'Hungary', 'Mauritius', 'Pakistan', 'Senegal', 'Slovakia', 'Iran, Islamic Republic of', 'Malawi', 'Jamaica'], dtype=object) In [20]: Copied! country_info_problematic_vals = { \"Iran, Islamic Republic of\" : \"Iran\" , \"Moldova, Republic of\" : \"Moldova\" , \"Taiwan, Province of China\" : \"Taiwan\" , \"Czechia\" : \"Czech republic\" , \"C\u00f4te d'Ivoire\" : \"Ivory Coast\" , \"Brunei Darussalam\" : \"Brunei\" , \"Viet Nam\" : \"Vietnam\" , \"Macao\" : \"Macau\" , \"T\u00fcrkiye\" : \"Turkey\" , \"Congo\" : \"Democratic Republic of the Congo\" , \"Palestine, State of\" : \"Palestine\" , } df_pd [ \"Surname_Country\" ] = df_pd [ \"Surname_Country\" ] . replace ( country_info_problematic_vals ) country_info_problematic_vals = { \"Iran, Islamic Republic of\": \"Iran\", \"Moldova, Republic of\": \"Moldova\", \"Taiwan, Province of China\": \"Taiwan\", \"Czechia\": \"Czech republic\", \"C\u00f4te d'Ivoire\": \"Ivory Coast\", \"Brunei Darussalam\": \"Brunei\", \"Viet Nam\": \"Vietnam\", \"Macao\": \"Macau\", \"T\u00fcrkiye\": \"Turkey\", \"Congo\": \"Democratic Republic of the Congo\", \"Palestine, State of\": \"Palestine\", } df_pd[\"Surname_Country\"] = df_pd[\"Surname_Country\"].replace( country_info_problematic_vals )","title":"Surname origin country"},{"location":"notebooks/00_auxiliary_features.html#additional-country-info","text":"","title":"Additional country info"},{"location":"notebooks/00_auxiliary_features.html#country-region-and-subregion","text":"In [21]: Copied! df_pd [[ \"Surname_Country_region\" , \"Surname_Country_subregion\" ]] = ( auxiliary_data . get_country_region_subregion ( df = df_pd , country_name_col = \"Surname_Country\" ) ) df_pd[[\"Surname_Country_region\", \"Surname_Country_subregion\"]] = ( auxiliary_data.get_country_region_subregion( df=df_pd, country_name_col=\"Surname_Country\" ) ) In [22]: Copied! df_pd [ ( df_pd [ \"Surname_Country_region\" ] == \"NA\" ) | ( df_pd [ \"Surname_Country_subregion\" ] == \"NA\" ) ][[ \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" ]] df_pd[ (df_pd[\"Surname_Country_region\"] == \"NA\") | (df_pd[\"Surname_Country_subregion\"] == \"NA\") ][[\"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\"]] Out[22]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Surname_Country Surname_Country_region Surname_Country_subregion 2024 Myanmar NA NA 3322 Myanmar NA NA 4237 Myanmar NA NA 4626 Myanmar NA NA 4892 Myanmar NA NA 5348 Palestine NA NA 6190 Myanmar NA NA 7064 Myanmar NA NA 7098 Myanmar NA NA 7693 Myanmar NA NA 7895 Myanmar NA NA 8299 Myanmar NA NA 8789 Myanmar NA NA 9600 Myanmar NA NA In [23]: Copied! # https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Palestine\" , [ \"Surname_Country_region\" , \"Surname_Country_subregion\" ], ] = ( \"Asia\" , \"Western Asia\" ) # https://en.wikipedia.org/wiki/Myanmar - 'South-Eastern Asia' to follow countryinfo naming df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Myanmar\" , [ \"Surname_Country_region\" , \"Surname_Country_subregion\" ], ] = ( \"Asia\" , \"South-Eastern Asia\" ) df_pd [ ( df_pd [ \"Surname_Country_region\" ] == \"NA\" ) | ( df_pd [ \"Surname_Country_subregion\" ] == \"NA\" ) ] # https://en.wikipedia.org/wiki/Palestine_(region) - 'Western Asia' to follow countryinfo naming df_pd.loc[ df_pd[\"Surname_Country\"] == \"Palestine\", [\"Surname_Country_region\", \"Surname_Country_subregion\"], ] = (\"Asia\", \"Western Asia\") # https://en.wikipedia.org/wiki/Myanmar - 'South-Eastern Asia' to follow countryinfo naming df_pd.loc[ df_pd[\"Surname_Country\"] == \"Myanmar\", [\"Surname_Country_region\", \"Surname_Country_subregion\"], ] = (\"Asia\", \"South-Eastern Asia\") df_pd[ (df_pd[\"Surname_Country_region\"] == \"NA\") | (df_pd[\"Surname_Country_subregion\"] == \"NA\") ] Out[23]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } RowNumber CustomerId Surname CreditScore Country Gender Age Tenure CustomerFeedback Balance (EUR) ... Exited CustomerFeedback_language CustomerFeedback_language_score CustomerFeedback_sentiment3 CustomerFeedback_sentiment3_score CustomerFeedback_sentiment5 CustomerFeedback_sentiment5_score Surname_Country Surname_Country_region Surname_Country_subregion 0 rows \u00d7 24 columns In [24]: Copied! df_pd [[ \"Country_region\" , \"Country_subregion\" ]] = ( auxiliary_data . get_country_region_subregion ( df = df_pd , country_name_col = \"Country\" ) ) df_pd[[\"Country_region\", \"Country_subregion\"]] = ( auxiliary_data.get_country_region_subregion(df=df_pd, country_name_col=\"Country\") ) In [25]: Copied! df_pd [( df_pd [ \"Country_region\" ] == \"NA\" ) | ( df_pd [ \"Country_subregion\" ] == \"NA\" )][ [ \"Country\" , \"Country_region\" , \"Country_subregion\" ] ] df_pd[(df_pd[\"Country_region\"] == \"NA\") | (df_pd[\"Country_subregion\"] == \"NA\")][ [\"Country\", \"Country_region\", \"Country_subregion\"] ] Out[25]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Country Country_region Country_subregion","title":"Country region and subregion"},{"location":"notebooks/00_auxiliary_features.html#native-or-not","text":"if the client is possibly an immigrant(from immigrant family?) or not In [26]: Copied! df_pd [ \"is_native\" ] = ( df_pd [ \"Surname_Country\" ] == df_pd [ \"Country\" ]) . astype ( int ) df_pd[\"is_native\"] = (df_pd[\"Surname_Country\"] == df_pd[\"Country\"]).astype(int)","title":"Native or not"},{"location":"notebooks/00_auxiliary_features.html#country-hemisphere","text":"could be usefull with some timestamp combination to know the season(oposite season in hemispeheres) etc.; anything that might affect mood/behaviour In [27]: Copied! # no need to know the hemisphere of the surname origin country... df_pd = auxiliary_data . hemisphere ( df = df_pd , cc_col = \"Country\" ) . rename ( columns = { \"hemisphere\" : \"Country_hemisphere\" } ) # no need to know the hemisphere of the surname origin country... df_pd = auxiliary_data.hemisphere(df=df_pd, cc_col=\"Country\").rename( columns={\"hemisphere\": \"Country_hemisphere\"} )","title":"Country hemisphere"},{"location":"notebooks/00_auxiliary_features.html#country-gdpp","text":"check 'wealthiness' of the current and origin country In [28]: Copied! df_pd = auxiliary_data . gdppc ( df = df_pd , country_name_col = \"Country\" ) . rename ( columns = { \"gdp_per_capita\" : \"Country_gdp_per_capita\" , \"IncomeGroup\" : \"Country_IncomeGroup\" , } ) df_pd = auxiliary_data . gdppc ( df = df_pd , country_name_col = \"Surname_Country\" ) . rename ( columns = { \"gdp_per_capita\" : \"Surname_Country_gdp_per_capita\" , \"IncomeGroup\" : \"Surname_Country_IncomeGroup\" , } ) df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col=\"Country\").rename( columns={ \"gdp_per_capita\": \"Country_gdp_per_capita\", \"IncomeGroup\": \"Country_IncomeGroup\", } ) df_pd = auxiliary_data.gdppc(df=df_pd, country_name_col=\"Surname_Country\").rename( columns={ \"gdp_per_capita\": \"Surname_Country_gdp_per_capita\", \"IncomeGroup\": \"Surname_Country_IncomeGroup\", } ) /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:283: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy gdp_per_capita.rename(columns={\"Country Code\": \"iso_a3\"}, inplace=True) /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:284: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy gdp_per_capita[\"gdp_per_capita\"] = gdp_per_capita_raw.drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"]).ffill(axis=1).iloc[:, -1] /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:283: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy gdp_per_capita.rename(columns={\"Country Code\": \"iso_a3\"}, inplace=True) /home/jovyan/assignment/ecovadis_assignment/churn_pred/preprocessing/auxiliary_data.py:284: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy gdp_per_capita[\"gdp_per_capita\"] = gdp_per_capita_raw.drop(columns=[\"Country Name\", \"Country Code\", \"Indicator Name\", \"Indicator Code\"]).ffill(axis=1).iloc[:, -1] SubdivisionHierarchy(code='MX-MEX', country_code='MX', name='M\u00e9xico', parent_code=None, type='State') SubdivisionHierarchy(code='PA-8', country_code='PA', name='Panam\u00e1', parent_code=None, type='Province') SubdivisionHierarchy(code='NG-NI', country_code='NG', name='Niger', parent_code=None, type='State') SubdivisionHierarchy(code='GN-ML', country_code='GN', name='Mali', parent='L', parent_code='GN-L', type='Prefecture')","title":"Country gdpp"},{"location":"notebooks/00_auxiliary_features.html#additional-age-info","text":"In [29]: Copied! df_pd = auxiliary_data . age_categories ( df = df_pd , age_col = \"Age\" ) df_pd = auxiliary_data.age_categories(df=df_pd, age_col=\"Age\") In [30]: Copied! df_pd [ df_pd [ \"Surname_Country_gdp_per_capita\" ] . isna ()][ \"Surname_Country\" ] . unique () df_pd[df_pd[\"Surname_Country_gdp_per_capita\"].isna()][\"Surname_Country\"].unique() Out[30]: array(['Taiwan', 'Macau', 'Turkey', 'Ivory Coast', 'Democratic Republic of the Congo'], dtype=object) In [31]: Copied! # Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Taiwan\" , [ \"Surname_Country_gdp_per_capita\" ]] = ( 32756.0 ) # remaining countries have some naming issues - ToBeResolved; e.g. Macau -> Macao, Turkey -> Turkiye, etc df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Macau\" , [ \"Surname_Country_gdp_per_capita\" ]] = ( 61230.96247 ) df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Turkey\" , [ \"Surname_Country_gdp_per_capita\" ]] = ( 38355.15397 ) df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Ivory Coast\" , [ \"Surname_Country_gdp_per_capita\" ] ] = 6540.462355 df_pd . loc [ df_pd [ \"Surname_Country\" ] == \"Democratic Republic of the Congo\" , [ \"Surname_Country_gdp_per_capita\" ], ] = 1337.834149 # Taiwan is missing info in worldbank but it is present e.g. here https://www.ceicdata.com/en/indicator/taiwan/gdp-per-capita df_pd.loc[df_pd[\"Surname_Country\"] == \"Taiwan\", [\"Surname_Country_gdp_per_capita\"]] = ( 32756.0 ) # remaining countries have some naming issues - ToBeResolved; e.g. Macau -> Macao, Turkey -> Turkiye, etc df_pd.loc[df_pd[\"Surname_Country\"] == \"Macau\", [\"Surname_Country_gdp_per_capita\"]] = ( 61230.96247 ) df_pd.loc[df_pd[\"Surname_Country\"] == \"Turkey\", [\"Surname_Country_gdp_per_capita\"]] = ( 38355.15397 ) df_pd.loc[ df_pd[\"Surname_Country\"] == \"Ivory Coast\", [\"Surname_Country_gdp_per_capita\"] ] = 6540.462355 df_pd.loc[ df_pd[\"Surname_Country\"] == \"Democratic Republic of the Congo\", [\"Surname_Country_gdp_per_capita\"], ] = 1337.834149 In [32]: Copied! df_pd . to_parquet ( \"data/dataset_auxiliary_features_raw.parquet\" ) df_pd.to_parquet(\"data/dataset_auxiliary_features_raw.parquet\") In [33]: Copied! df_pd . columns df_pd.columns Out[33]: Index(['RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Country', 'Gender', 'Age', 'Tenure', 'CustomerFeedback', 'Balance (EUR)', 'NumberOfProducts', 'HasCreditCard', 'IsActiveMember', 'EstimatedSalary', 'Exited', 'CustomerFeedback_language', 'CustomerFeedback_language_score', 'CustomerFeedback_sentiment3', 'CustomerFeedback_sentiment3_score', 'CustomerFeedback_sentiment5', 'CustomerFeedback_sentiment5_score', 'Surname_Country', 'Surname_Country_region', 'Surname_Country_subregion', 'Country_region', 'Country_subregion', 'is_native', 'Country_hemisphere', 'Country_gdp_per_capita', 'Country_IncomeGroup', 'Surname_Country_gdp_per_capita', 'Surname_Country_IncomeGroup', 'working_class', 'stage_of_life', 'generation'], dtype='object') In [34]: Copied! df_pd . drop ( columns = [ \"RowNumber\" , \"Surname\" , \"CustomerFeedback\" , \"CustomerFeedback_language\" , \"CustomerFeedback_language_score\" , \"CustomerFeedback_sentiment3_score\" , \"CustomerFeedback_sentiment5_score\" , ] ) . to_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd.drop( columns=[ \"RowNumber\", \"Surname\", \"CustomerFeedback\", \"CustomerFeedback_language\", \"CustomerFeedback_language_score\", \"CustomerFeedback_sentiment3_score\", \"CustomerFeedback_sentiment5_score\", ] ).to_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\")","title":"Additional age info"},{"location":"notebooks/00_auxiliary_features.html#appendix-tbd","text":"analyze score and probabilities of the sentiment and country detection methods make an object or procedure to do this automatically","title":"Appendix [TBD]"},{"location":"notebooks/00_auxiliary_features_surname_origin_country_classification.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); THIS IS A WORK IN PROGRESS \u00b6 Surname origin country classification \u00b6 approach shamelessly copied from https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert NOTE from author: Based on the discussion [here](https://www.kaggle.com/competitions/playground-series-s4e1/discussion/465517), I created a Bert based Surname classifier. possible improvement of the base dataset by scraping wiki: https://github.com/greenelab/wiki-nationality-estimate In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../../...\" ) import pandas as pd import torch from churn_pred.preprocessing import surname_classification from churn_pred import utils from torch.utils.data import DataLoader import pickle from transformers import ( BertForSequenceClassification , get_linear_schedule_with_warmup , BertTokenizer , ) from transformers import BertForSequenceClassification import os import sys sys.path.append(os.getcwd()) os.chdir(\"../../...\") import pandas as pd import torch from churn_pred.preprocessing import surname_classification from churn_pred import utils from torch.utils.data import DataLoader import pickle from transformers import ( BertForSequenceClassification, get_linear_schedule_with_warmup, BertTokenizer, ) from transformers import BertForSequenceClassification In [2]: Copied! surname_data = pd . read_csv ( \"data/surnames/surname-nationality.csv\" ) splitted_data = pd . read_csv ( \"data/surnames/surnames_with_splits.csv\" ) surname_data = pd.read_csv(\"data/surnames/surname-nationality.csv\") splitted_data = pd.read_csv(\"data/surnames/surnames_with_splits.csv\") In [5]: Copied! splitted_data splitted_data Out[5]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } nationality nationality_index split surname 0 Arabic 15 train Totah 1 Arabic 15 train Abboud 2 Arabic 15 train Fakhoury 3 Arabic 15 train Srour 4 Arabic 15 train Sayegh ... ... ... ... ... 10975 Vietnamese 11 test Dinh 10976 Vietnamese 11 test Phung 10977 Vietnamese 11 test Quang 10978 Vietnamese 11 test Vu 10979 Vietnamese 11 test Ha 10980 rows \u00d7 4 columns In [6]: Copied! surname_data . surname . nunique () surname_data.surname.nunique() Out[6]: 30923 In [4]: Copied! splitted_data [ \"nationality_index\" ] splitted_data[\"nationality_index\"] Out[4]: 0 15 1 15 2 15 3 15 4 15 .. 10975 11 10976 11 10977 11 10978 11 10979 11 Name: nationality_index, Length: 10980, dtype: int64 In [3]: Copied! labeld_decoder = dict ( zip ( splitted_data [ \"nationality\" ], splitted_data [ \"nationality_index\" ]) ) inverse_label_dict = { v : k for k , v in labeld_decoder . items ()} labeld_decoder = dict( zip(splitted_data[\"nationality\"], splitted_data[\"nationality_index\"]) ) inverse_label_dict = {v: k for k, v in labeld_decoder.items()} In [4]: Copied! # Split data into train, validation, and test sets train_df = splitted_data [ splitted_data [ \"split\" ] == \"train\" ] val_df = splitted_data [ splitted_data [ \"split\" ] == \"val\" ] # Assuming \"val\" is used for validation set test_df = splitted_data [ splitted_data [ \"split\" ] == \"test\" ] # Split data into train, validation, and test sets train_df = splitted_data[splitted_data[\"split\"] == \"train\"] val_df = splitted_data[ splitted_data[\"split\"] == \"val\" ] # Assuming \"val\" is used for validation set test_df = splitted_data[splitted_data[\"split\"] == \"test\"] In [5]: Copied! # Tokenize each dataset train_encodings = surname_classification . tokenize_data ( train_df ) val_encodings = surname_classification . tokenize_data ( val_df ) test_encodings = surname_classification . tokenize_data ( test_df ) # Tokenize each dataset train_encodings = surname_classification.tokenize_data(train_df) val_encodings = surname_classification.tokenize_data(val_df) test_encodings = surname_classification.tokenize_data(test_df) In [8]: Copied! batch_size = 32 train_dataset = surname_classification . create_dataset ( train_encodings , train_df ) val_dataset = surname_classification . create_dataset ( val_encodings , val_df ) test_dataset = surname_classification . create_dataset ( test_encodings , test_df ) train_dataloader = DataLoader ( train_dataset , batch_size = batch_size , shuffle = True ) val_dataloader = DataLoader ( val_dataset , batch_size = batch_size ) test_dataloader = DataLoader ( test_dataset , batch_size = batch_size ) batch_size = 32 train_dataset = surname_classification.create_dataset(train_encodings, train_df) val_dataset = surname_classification.create_dataset(val_encodings, val_df) test_dataset = surname_classification.create_dataset(test_encodings, test_df) train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) val_dataloader = DataLoader(val_dataset, batch_size=batch_size) test_dataloader = DataLoader(test_dataset, batch_size=batch_size) In [9]: Copied! num_labels = splitted_data [ \"nationality_index\" ] . nunique () num_epochs = 10 model = BertForSequenceClassification . from_pretrained ( \"bert-base-cased\" , num_labels = num_labels , hidden_dropout_prob = 0.4 ) # freeze layers for param in model . bert . parameters (): param . requires_grad = False optimizer = surname_classification . create_optimizer ( model , learning_rate = 5e-5 , eps = 1e-8 ) total_steps = len ( train_dataloader ) * num_epochs scheduler = get_linear_schedule_with_warmup ( optimizer , num_warmup_steps = 0.2 , # Usually a fraction of total_steps num_training_steps = total_steps , ) device = torch . device ( \"cuda\" if torch . cuda . is_available () else \"cpu\" ) model . to ( device ) num_labels = splitted_data[\"nationality_index\"].nunique() num_epochs = 10 model = BertForSequenceClassification.from_pretrained( \"bert-base-cased\", num_labels=num_labels, hidden_dropout_prob=0.4 ) # freeze layers for param in model.bert.parameters(): param.requires_grad = False optimizer = surname_classification.create_optimizer(model, learning_rate=5e-5, eps=1e-8) total_steps = len(train_dataloader) * num_epochs scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=0.2, # Usually a fraction of total_steps num_training_steps=total_steps, ) device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") model.to(device) Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. /mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( Out[9]: BertForSequenceClassification( (bert): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(28996, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.4, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.4, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.4, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) (dropout): Dropout(p=0.4, inplace=False) (classifier): Linear(in_features=768, out_features=18, bias=True) ) In [10]: Copied! trained_model = surname_classification . train_model ( model , train_dataloader , val_dataloader , optimizer , scheduler , device , num_epochs = num_epochs , ) trained_model = surname_classification.train_model( model, train_dataloader, val_dataloader, optimizer, scheduler, device, num_epochs=num_epochs, ) log from running a script on a server (ecovadis) pmulinka@supercom-wssonata:~/assignment/ecovadis_assignment$ python notebooks/eda/surname_classification_with_bert.py /home/pmulinka/assignment/ecovadis_assignment Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. /home/pmulinka/assignment/ecovadis/lib/python3.10/site-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( Epoch 1/10 Training Loss: 2.394 Validation Loss: 2.256, Validation Accuracy: 0.267 Epoch 2/10 Training Loss: 2.267 Validation Loss: 2.251, Validation Accuracy: 0.267 Epoch 3/10 Training Loss: 2.265 Validation Loss: 2.243, Validation Accuracy: 0.306 Epoch 4/10 Training Loss: 2.250 Validation Loss: 2.237, Validation Accuracy: 0.385 Epoch 5/10 Training Loss: 2.251 Validation Loss: 2.227, Validation Accuracy: 0.317 Epoch 6/10 Training Loss: 1.620 Validation Loss: 0.982, Validation Accuracy: 0.735 Epoch 7/10 Training Loss: 1.062 Validation Loss: 0.826, Validation Accuracy: 0.772 Epoch 8/10 Training Loss: 0.911 Validation Loss: 0.799, Validation Accuracy: 0.785 Epoch 9/10 Training Loss: 0.832 Validation Loss: 0.775, Validation Accuracy: 0.791 Epoch 10/10 Training Loss: 0.780 Validation Loss: 0.768, Validation Accuracy: 0.790 Training complete Evaluation on test dataset yield Loss: 0.7684005849206677 Accuracy: 0.7960164835164836 In [ ]: Copied! loss , accuracy = surname_classification . evaluate_model ( trained_model , test_dataloader , device ) print ( f \"Evaluation on test dataset yield \\n Loss: { loss } \\n Accuracy: { accuracy } \" ) loss, accuracy = surname_classification.evaluate_model( trained_model, test_dataloader, device ) print(f\"Evaluation on test dataset yield\\nLoss: {loss}\\nAccuracy: {accuracy}\") In [8]: Copied! inverse_label_dict inverse_label_dict Out[8]: {15: 'Arabic', 3: 'Chinese', 5: 'Czech', 2: 'Dutch', 12: 'English', 8: 'French', 9: 'German', 4: 'Greek', 1: 'Irish', 17: 'Italian', 7: 'Japanese', 16: 'Korean', 14: 'Polish', 0: 'Portuguese', 13: 'Russian', 10: 'Scottish', 6: 'Spanish', 11: 'Vietnamese'} In [7]: Copied! utils . dill_dump ( file_loc = \"data/surnames/inverse_label_dict.dill\" , content = inverse_label_dict ) inverse_label_dict = utils . dill_load ( file_loc = \"data/surnames/inverse_label_dict.dill\" ) utils.dill_dump( file_loc=\"data/surnames/inverse_label_dict.dill\", content=inverse_label_dict ) inverse_label_dict = utils.dill_load(file_loc=\"data/surnames/inverse_label_dict.dill\") In [ ]: Copied! # Save the model torch . save ( trained_model . state_dict (), \"surname_model_state_dict.pth\" ) torch . save ( trained_model , \"data/surnames/surname_model.pth\" ) utils . json_dump ( file_loc = \"data/surnames/inverse_label_dict.json\" , content = inverse_label_dict ) # Save the model torch.save(trained_model.state_dict(), \"surname_model_state_dict.pth\") torch.save(trained_model, \"data/surnames/surname_model.pth\") utils.json_dump( file_loc=\"data/surnames/inverse_label_dict.json\", content=inverse_label_dict ) In [10]: Copied! # Load the model device = torch . device ( \"cuda\" if torch . cuda . is_available () else \"cpu\" ) trained_model = torch . load ( \"data/surnames/surname_model.pth\" , map_location = device ) inverse_label_dict = utils . dill_load ( file_loc = \"data/surnames/inverse_label_dict.dill\" ) # model.eval() # trained_model.load_state_dict(torch.load(\"surname_model.pth\")) # Load the model device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") trained_model = torch.load(\"data/surnames/surname_model.pth\", map_location=device) inverse_label_dict = utils.dill_load(file_loc=\"data/surnames/inverse_label_dict.dill\") # model.eval() # trained_model.load_state_dict(torch.load(\"surname_model.pth\")) In [15]: Copied! predicted_surnames predicted_surnames Out[15]: [('Hsieh', 'English'), ('McDonald', 'Spanish'), ('Kharlamov', 'Arabic'), ('Vasiliev', 'English'), ('Bellucci', 'English'), ('Wallace', 'English'), ('Chao', 'Chinese'), ('Boylan', 'English'), ('Burgess', 'Chinese'), ('Cattaneo', 'English')] In [14]: Copied! df_pd = pd . read_csv ( \"data/dataset.csv\" ) uq_surnames = df_pd . Surname . unique () tokenizer = BertTokenizer . from_pretrained ( \"bert-base-uncased\" ) predicted_surnames = surname_classification . predict_nationality ( trained_model , uq_surnames [: 10 ], tokenizer , inverse_label_dict , device ) df_pd = pd.read_csv(\"data/dataset.csv\") uq_surnames = df_pd.Surname.unique() tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\") predicted_surnames = surname_classification.predict_nationality( trained_model, uq_surnames[:10], tokenizer, inverse_label_dict, device ) Predicting Nationalities: 0%| | 0/10 [00:00<?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`. /mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2674: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert). warnings.warn( In [ ]: Copied! predicted_df = pd . DataFrame ( predicted_surnames , columns = [ \"Surname\" , \"Nationality\" ]) predicted_df . to_csv ( \"predicted_surnames_dataset.csv\" ) predicted_df . head () predicted_df = pd.DataFrame(predicted_surnames, columns=[\"Surname\", \"Nationality\"]) predicted_df.to_csv(\"predicted_surnames_dataset.csv\") predicted_df.head() In [ ]: Copied!","title":"TBD_00_auxiliary_features_surname_origin_country_classification.ipynb"},{"location":"notebooks/00_auxiliary_features_surname_origin_country_classification.html#this-is-a-work-in-progress","text":"","title":"THIS IS A WORK IN PROGRESS"},{"location":"notebooks/00_auxiliary_features_surname_origin_country_classification.html#surname-origin-country-classification","text":"approach shamelessly copied from https://www.kaggle.com/code/yonatankpl/surname-classification-with-bert NOTE from author: Based on the discussion [here](https://www.kaggle.com/competitions/playground-series-s4e1/discussion/465517), I created a Bert based Surname classifier. possible improvement of the base dataset by scraping wiki: https://github.com/greenelab/wiki-nationality-estimate In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../../...\" ) import pandas as pd import torch from churn_pred.preprocessing import surname_classification from churn_pred import utils from torch.utils.data import DataLoader import pickle from transformers import ( BertForSequenceClassification , get_linear_schedule_with_warmup , BertTokenizer , ) from transformers import BertForSequenceClassification import os import sys sys.path.append(os.getcwd()) os.chdir(\"../../...\") import pandas as pd import torch from churn_pred.preprocessing import surname_classification from churn_pred import utils from torch.utils.data import DataLoader import pickle from transformers import ( BertForSequenceClassification, get_linear_schedule_with_warmup, BertTokenizer, ) from transformers import BertForSequenceClassification In [2]: Copied! surname_data = pd . read_csv ( \"data/surnames/surname-nationality.csv\" ) splitted_data = pd . read_csv ( \"data/surnames/surnames_with_splits.csv\" ) surname_data = pd.read_csv(\"data/surnames/surname-nationality.csv\") splitted_data = pd.read_csv(\"data/surnames/surnames_with_splits.csv\") In [5]: Copied! splitted_data splitted_data Out[5]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } nationality nationality_index split surname 0 Arabic 15 train Totah 1 Arabic 15 train Abboud 2 Arabic 15 train Fakhoury 3 Arabic 15 train Srour 4 Arabic 15 train Sayegh ... ... ... ... ... 10975 Vietnamese 11 test Dinh 10976 Vietnamese 11 test Phung 10977 Vietnamese 11 test Quang 10978 Vietnamese 11 test Vu 10979 Vietnamese 11 test Ha 10980 rows \u00d7 4 columns In [6]: Copied! surname_data . surname . nunique () surname_data.surname.nunique() Out[6]: 30923 In [4]: Copied! splitted_data [ \"nationality_index\" ] splitted_data[\"nationality_index\"] Out[4]: 0 15 1 15 2 15 3 15 4 15 .. 10975 11 10976 11 10977 11 10978 11 10979 11 Name: nationality_index, Length: 10980, dtype: int64 In [3]: Copied! labeld_decoder = dict ( zip ( splitted_data [ \"nationality\" ], splitted_data [ \"nationality_index\" ]) ) inverse_label_dict = { v : k for k , v in labeld_decoder . items ()} labeld_decoder = dict( zip(splitted_data[\"nationality\"], splitted_data[\"nationality_index\"]) ) inverse_label_dict = {v: k for k, v in labeld_decoder.items()} In [4]: Copied! # Split data into train, validation, and test sets train_df = splitted_data [ splitted_data [ \"split\" ] == \"train\" ] val_df = splitted_data [ splitted_data [ \"split\" ] == \"val\" ] # Assuming \"val\" is used for validation set test_df = splitted_data [ splitted_data [ \"split\" ] == \"test\" ] # Split data into train, validation, and test sets train_df = splitted_data[splitted_data[\"split\"] == \"train\"] val_df = splitted_data[ splitted_data[\"split\"] == \"val\" ] # Assuming \"val\" is used for validation set test_df = splitted_data[splitted_data[\"split\"] == \"test\"] In [5]: Copied! # Tokenize each dataset train_encodings = surname_classification . tokenize_data ( train_df ) val_encodings = surname_classification . tokenize_data ( val_df ) test_encodings = surname_classification . tokenize_data ( test_df ) # Tokenize each dataset train_encodings = surname_classification.tokenize_data(train_df) val_encodings = surname_classification.tokenize_data(val_df) test_encodings = surname_classification.tokenize_data(test_df) In [8]: Copied! batch_size = 32 train_dataset = surname_classification . create_dataset ( train_encodings , train_df ) val_dataset = surname_classification . create_dataset ( val_encodings , val_df ) test_dataset = surname_classification . create_dataset ( test_encodings , test_df ) train_dataloader = DataLoader ( train_dataset , batch_size = batch_size , shuffle = True ) val_dataloader = DataLoader ( val_dataset , batch_size = batch_size ) test_dataloader = DataLoader ( test_dataset , batch_size = batch_size ) batch_size = 32 train_dataset = surname_classification.create_dataset(train_encodings, train_df) val_dataset = surname_classification.create_dataset(val_encodings, val_df) test_dataset = surname_classification.create_dataset(test_encodings, test_df) train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) val_dataloader = DataLoader(val_dataset, batch_size=batch_size) test_dataloader = DataLoader(test_dataset, batch_size=batch_size) In [9]: Copied! num_labels = splitted_data [ \"nationality_index\" ] . nunique () num_epochs = 10 model = BertForSequenceClassification . from_pretrained ( \"bert-base-cased\" , num_labels = num_labels , hidden_dropout_prob = 0.4 ) # freeze layers for param in model . bert . parameters (): param . requires_grad = False optimizer = surname_classification . create_optimizer ( model , learning_rate = 5e-5 , eps = 1e-8 ) total_steps = len ( train_dataloader ) * num_epochs scheduler = get_linear_schedule_with_warmup ( optimizer , num_warmup_steps = 0.2 , # Usually a fraction of total_steps num_training_steps = total_steps , ) device = torch . device ( \"cuda\" if torch . cuda . is_available () else \"cpu\" ) model . to ( device ) num_labels = splitted_data[\"nationality_index\"].nunique() num_epochs = 10 model = BertForSequenceClassification.from_pretrained( \"bert-base-cased\", num_labels=num_labels, hidden_dropout_prob=0.4 ) # freeze layers for param in model.bert.parameters(): param.requires_grad = False optimizer = surname_classification.create_optimizer(model, learning_rate=5e-5, eps=1e-8) total_steps = len(train_dataloader) * num_epochs scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=0.2, # Usually a fraction of total_steps num_training_steps=total_steps, ) device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") model.to(device) Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. /mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( Out[9]: BertForSequenceClassification( (bert): BertModel( (embeddings): BertEmbeddings( (word_embeddings): Embedding(28996, 768, padding_idx=0) (position_embeddings): Embedding(512, 768) (token_type_embeddings): Embedding(2, 768) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.4, inplace=False) ) (encoder): BertEncoder( (layer): ModuleList( (0-11): 12 x BertLayer( (attention): BertAttention( (self): BertSelfAttention( (query): Linear(in_features=768, out_features=768, bias=True) (key): Linear(in_features=768, out_features=768, bias=True) (value): Linear(in_features=768, out_features=768, bias=True) (dropout): Dropout(p=0.1, inplace=False) ) (output): BertSelfOutput( (dense): Linear(in_features=768, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.4, inplace=False) ) ) (intermediate): BertIntermediate( (dense): Linear(in_features=768, out_features=3072, bias=True) (intermediate_act_fn): GELUActivation() ) (output): BertOutput( (dense): Linear(in_features=3072, out_features=768, bias=True) (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True) (dropout): Dropout(p=0.4, inplace=False) ) ) ) ) (pooler): BertPooler( (dense): Linear(in_features=768, out_features=768, bias=True) (activation): Tanh() ) ) (dropout): Dropout(p=0.4, inplace=False) (classifier): Linear(in_features=768, out_features=18, bias=True) ) In [10]: Copied! trained_model = surname_classification . train_model ( model , train_dataloader , val_dataloader , optimizer , scheduler , device , num_epochs = num_epochs , ) trained_model = surname_classification.train_model( model, train_dataloader, val_dataloader, optimizer, scheduler, device, num_epochs=num_epochs, ) log from running a script on a server (ecovadis) pmulinka@supercom-wssonata:~/assignment/ecovadis_assignment$ python notebooks/eda/surname_classification_with_bert.py /home/pmulinka/assignment/ecovadis_assignment Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['classifier.bias', 'classifier.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. /home/pmulinka/assignment/ecovadis/lib/python3.10/site-packages/transformers/optimization.py:521: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning warnings.warn( Epoch 1/10 Training Loss: 2.394 Validation Loss: 2.256, Validation Accuracy: 0.267 Epoch 2/10 Training Loss: 2.267 Validation Loss: 2.251, Validation Accuracy: 0.267 Epoch 3/10 Training Loss: 2.265 Validation Loss: 2.243, Validation Accuracy: 0.306 Epoch 4/10 Training Loss: 2.250 Validation Loss: 2.237, Validation Accuracy: 0.385 Epoch 5/10 Training Loss: 2.251 Validation Loss: 2.227, Validation Accuracy: 0.317 Epoch 6/10 Training Loss: 1.620 Validation Loss: 0.982, Validation Accuracy: 0.735 Epoch 7/10 Training Loss: 1.062 Validation Loss: 0.826, Validation Accuracy: 0.772 Epoch 8/10 Training Loss: 0.911 Validation Loss: 0.799, Validation Accuracy: 0.785 Epoch 9/10 Training Loss: 0.832 Validation Loss: 0.775, Validation Accuracy: 0.791 Epoch 10/10 Training Loss: 0.780 Validation Loss: 0.768, Validation Accuracy: 0.790 Training complete Evaluation on test dataset yield Loss: 0.7684005849206677 Accuracy: 0.7960164835164836 In [ ]: Copied! loss , accuracy = surname_classification . evaluate_model ( trained_model , test_dataloader , device ) print ( f \"Evaluation on test dataset yield \\n Loss: { loss } \\n Accuracy: { accuracy } \" ) loss, accuracy = surname_classification.evaluate_model( trained_model, test_dataloader, device ) print(f\"Evaluation on test dataset yield\\nLoss: {loss}\\nAccuracy: {accuracy}\") In [8]: Copied! inverse_label_dict inverse_label_dict Out[8]: {15: 'Arabic', 3: 'Chinese', 5: 'Czech', 2: 'Dutch', 12: 'English', 8: 'French', 9: 'German', 4: 'Greek', 1: 'Irish', 17: 'Italian', 7: 'Japanese', 16: 'Korean', 14: 'Polish', 0: 'Portuguese', 13: 'Russian', 10: 'Scottish', 6: 'Spanish', 11: 'Vietnamese'} In [7]: Copied! utils . dill_dump ( file_loc = \"data/surnames/inverse_label_dict.dill\" , content = inverse_label_dict ) inverse_label_dict = utils . dill_load ( file_loc = \"data/surnames/inverse_label_dict.dill\" ) utils.dill_dump( file_loc=\"data/surnames/inverse_label_dict.dill\", content=inverse_label_dict ) inverse_label_dict = utils.dill_load(file_loc=\"data/surnames/inverse_label_dict.dill\") In [ ]: Copied! # Save the model torch . save ( trained_model . state_dict (), \"surname_model_state_dict.pth\" ) torch . save ( trained_model , \"data/surnames/surname_model.pth\" ) utils . json_dump ( file_loc = \"data/surnames/inverse_label_dict.json\" , content = inverse_label_dict ) # Save the model torch.save(trained_model.state_dict(), \"surname_model_state_dict.pth\") torch.save(trained_model, \"data/surnames/surname_model.pth\") utils.json_dump( file_loc=\"data/surnames/inverse_label_dict.json\", content=inverse_label_dict ) In [10]: Copied! # Load the model device = torch . device ( \"cuda\" if torch . cuda . is_available () else \"cpu\" ) trained_model = torch . load ( \"data/surnames/surname_model.pth\" , map_location = device ) inverse_label_dict = utils . dill_load ( file_loc = \"data/surnames/inverse_label_dict.dill\" ) # model.eval() # trained_model.load_state_dict(torch.load(\"surname_model.pth\")) # Load the model device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") trained_model = torch.load(\"data/surnames/surname_model.pth\", map_location=device) inverse_label_dict = utils.dill_load(file_loc=\"data/surnames/inverse_label_dict.dill\") # model.eval() # trained_model.load_state_dict(torch.load(\"surname_model.pth\")) In [15]: Copied! predicted_surnames predicted_surnames Out[15]: [('Hsieh', 'English'), ('McDonald', 'Spanish'), ('Kharlamov', 'Arabic'), ('Vasiliev', 'English'), ('Bellucci', 'English'), ('Wallace', 'English'), ('Chao', 'Chinese'), ('Boylan', 'English'), ('Burgess', 'Chinese'), ('Cattaneo', 'English')] In [14]: Copied! df_pd = pd . read_csv ( \"data/dataset.csv\" ) uq_surnames = df_pd . Surname . unique () tokenizer = BertTokenizer . from_pretrained ( \"bert-base-uncased\" ) predicted_surnames = surname_classification . predict_nationality ( trained_model , uq_surnames [: 10 ], tokenizer , inverse_label_dict , device ) df_pd = pd.read_csv(\"data/dataset.csv\") uq_surnames = df_pd.Surname.unique() tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\") predicted_surnames = surname_classification.predict_nationality( trained_model, uq_surnames[:10], tokenizer, inverse_label_dict, device ) Predicting Nationalities: 0%| | 0/10 [00:00<?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`. /mnt/c/#work/ecovadis/ecovadis/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:2674: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert). warnings.warn( In [ ]: Copied! predicted_df = pd . DataFrame ( predicted_surnames , columns = [ \"Surname\" , \"Nationality\" ]) predicted_df . to_csv ( \"predicted_surnames_dataset.csv\" ) predicted_df . head () predicted_df = pd.DataFrame(predicted_surnames, columns=[\"Surname\", \"Nationality\"]) predicted_df.to_csv(\"predicted_surnames_dataset.csv\") predicted_df.head() In [ ]: Copied!","title":"Surname origin country classification"},{"location":"notebooks/01_feature_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Feature analysis \u00b6 Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from churn_pred.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from churn_pred.eda.features.plotting import cross_correlation , distributions import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from churn_pred.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from churn_pred.eda.features.plotting import cross_correlation, distributions Dataset \u00b6 In [2]: Copied! df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd . head () df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In [3]: Copied! target_col = \"Exited\" id_cols = [ \"CustomerId\" ] cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" , ] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [ \"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\", \"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\", ] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In [4]: Copied! df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) df_pd[cat_cols] = df_pd[cat_cols].astype(str) Features \u00b6 Analysis \u00b6 In [5]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 0 [CHECK] Numerical columns count mean std min \\ Age 10000.0 38.92 10.49 18.00 EstimatedSalary 10000.0 100090.24 57510.49 11.58 Balance (EUR) 10000.0 76485.89 62397.41 0.00 CreditScore 10000.0 650.53 96.65 350.00 Tenure 10000.0 5.01 2.89 0.00 Country_gdp_per_capita 10000.0 57651.01 6330.96 48685.50 NumberOfProducts 10000.0 1.53 0.58 1.00 Surname_Country_gdp_per_capita 10000.0 51228.52 25976.50 1337.83 25% 50% 75% max Age 32.00 37.00 44.00 92.00 EstimatedSalary 51002.11 100193.91 149388.25 199992.48 Balance (EUR) 0.00 97198.54 127644.24 250898.09 CreditScore 584.00 652.00 718.00 850.00 Tenure 3.00 5.00 7.00 10.00 Country_gdp_per_capita 57594.03 57594.03 66616.02 66616.02 NumberOfProducts 1.00 1.00 2.00 4.00 Surname_Country_gdp_per_capita 33525.30 57460.51 76329.58 133822.76 [CHECK] Categorical columns count unique top freq Gender 10000 2 Male 5457 Surname_Country_subregion 10000 18 Northern America 2410 Country_subregion 10000 2 Western Europe 7523 Country_hemisphere 10000 1 northern 10000 Country_region 10000 1 Europe 10000 is_native 10000 2 0 9914 Surname_Country 10000 89 United States 2401 CustomerFeedback_sentiment3 10000 3 neutral 8352 Surname_Country_region 10000 4 Europe 4392 working_class 10000 2 working_age 9736 stage_of_life 10000 4 adult 6330 IsActiveMember 10000 2 1 5151 CustomerFeedback_sentiment5 10000 5 2 stars 7121 HasCreditCard 10000 2 1 7055 generation 10000 6 millennials 6367 Country_IncomeGroup 10000 1 High income 10000 Surname_Country_IncomeGroup 10000 5 High income 6686 Country 10000 3 France 5014 In [6]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[6]: CreditScore 0.0 Surname_Country_region 0.0 stage_of_life 0.0 working_class 0.0 Surname_Country_IncomeGroup 0.0 dtype: float64 In [7]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[7]: Exited 0.7963 Balance (EUR) 0.3617 Tenure 0.0413 CreditScore 0.0000 Surname_Country_subregion 0.0000 dtype: float64 In [8]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[8]: Country_IncomeGroup 1 Country_hemisphere 1 Country_region 1 IsActiveMember 2 is_native 2 dtype: int64 In [9]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[9]: Balance (EUR) 62397.405202 EstimatedSalary 57510.492818 Surname_Country_gdp_per_capita 25976.502611 Country_gdp_per_capita 6330.960841 CreditScore 96.653299 dtype: float64 In [10]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[10]: EstimatedSalary 9.210202 Balance (EUR) 6.246510 CreditScore 5.873574 Age 3.677614 Surname_Country 2.481289 dtype: float64 Plotting \u00b6 In [11]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations Balance (EUR) Country_gdp_per_capita 0.329762 NumberOfProducts 0.304180 Age NumberOfProducts 0.030680 Country_gdp_per_capita 0.029999 Balance (EUR) 0.028308 Tenure Surname_Country_gdp_per_capita 0.017138 NumberOfProducts EstimatedSalary 0.014204 Tenure NumberOfProducts 0.013444 Balance (EUR) EstimatedSalary 0.012797 Tenure Balance (EUR) 0.012254 dtype: float64 In [12]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( df=df_pd[cont_cols], low_per_cut=0, high_per_cut=1, type=\"box\", )","title":"01_feature_analysis"},{"location":"notebooks/01_feature_analysis.html#feature-analysis","text":"Purpose : analyse dataset features without inclusion of target value if it looks healthy duplicate values NA values zero/0 values if the values are \"too constant\", ie. most [robably bad features for classifcation/regression small std values entropy is too high/too low(not sure right now?) - \"Entropy is a measure of disorder or uncertainty\" number of unique values in categorical features is too high - most probably they are unable to identify any group of targets drop highly correlated values to improve performance of the model training to decrease dataset size for further exploration","title":"Feature analysis"},{"location":"notebooks/01_feature_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd # import plotly.graph_objects as go from churn_pred.eda.features.analysis import ( missing , zero , nunique , init_check , std , entropy , nunique , ) pd . set_option ( \"display.max_columns\" , 100 ) pd . set_option ( \"display.max_rows\" , 300 ) from churn_pred.eda.features.plotting import cross_correlation , distributions import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd # import plotly.graph_objects as go from churn_pred.eda.features.analysis import ( missing, zero, nunique, init_check, std, entropy, nunique, ) pd.set_option(\"display.max_columns\", 100) pd.set_option(\"display.max_rows\", 300) from churn_pred.eda.features.plotting import cross_correlation, distributions","title":"Imports"},{"location":"notebooks/01_feature_analysis.html#dataset","text":"In [2]: Copied! df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd . head () df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In [3]: Copied! target_col = \"Exited\" id_cols = [ \"CustomerId\" ] cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" , ] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [ \"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\", \"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\", ] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In [4]: Copied! df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) df_pd[cat_cols] = df_pd[cat_cols].astype(str)","title":"Dataset"},{"location":"notebooks/01_feature_analysis.html#features","text":"","title":"Features"},{"location":"notebooks/01_feature_analysis.html#analysis","text":"In [5]: Copied! duplicated_ids , cont_cols_desc , cat_cols_desc = init_check ( df = df_pd , identifier = id_cols , cat_cols = cat_cols , cont_cols = cont_cols , verbose = True , ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() duplicated_ids, cont_cols_desc, cat_cols_desc = init_check( df=df_pd, identifier=id_cols, cat_cols=cat_cols, cont_cols=cont_cols, verbose=True, ) # duplicated_ids, cont_cols_desc.head(), cat_cols_desc.head() [CHECK] Number of duplicated ids: 0 [CHECK] Numerical columns count mean std min \\ Age 10000.0 38.92 10.49 18.00 EstimatedSalary 10000.0 100090.24 57510.49 11.58 Balance (EUR) 10000.0 76485.89 62397.41 0.00 CreditScore 10000.0 650.53 96.65 350.00 Tenure 10000.0 5.01 2.89 0.00 Country_gdp_per_capita 10000.0 57651.01 6330.96 48685.50 NumberOfProducts 10000.0 1.53 0.58 1.00 Surname_Country_gdp_per_capita 10000.0 51228.52 25976.50 1337.83 25% 50% 75% max Age 32.00 37.00 44.00 92.00 EstimatedSalary 51002.11 100193.91 149388.25 199992.48 Balance (EUR) 0.00 97198.54 127644.24 250898.09 CreditScore 584.00 652.00 718.00 850.00 Tenure 3.00 5.00 7.00 10.00 Country_gdp_per_capita 57594.03 57594.03 66616.02 66616.02 NumberOfProducts 1.00 1.00 2.00 4.00 Surname_Country_gdp_per_capita 33525.30 57460.51 76329.58 133822.76 [CHECK] Categorical columns count unique top freq Gender 10000 2 Male 5457 Surname_Country_subregion 10000 18 Northern America 2410 Country_subregion 10000 2 Western Europe 7523 Country_hemisphere 10000 1 northern 10000 Country_region 10000 1 Europe 10000 is_native 10000 2 0 9914 Surname_Country 10000 89 United States 2401 CustomerFeedback_sentiment3 10000 3 neutral 8352 Surname_Country_region 10000 4 Europe 4392 working_class 10000 2 working_age 9736 stage_of_life 10000 4 adult 6330 IsActiveMember 10000 2 1 5151 CustomerFeedback_sentiment5 10000 5 2 stars 7121 HasCreditCard 10000 2 1 7055 generation 10000 6 millennials 6367 Country_IncomeGroup 10000 1 High income 10000 Surname_Country_IncomeGroup 10000 5 High income 6686 Country 10000 3 France 5014 In [6]: Copied! missing_val_frac , fig = missing ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) missing_val_frac . head () missing_val_frac, fig = missing( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) missing_val_frac.head() Out[6]: CreditScore 0.0 Surname_Country_region 0.0 stage_of_life 0.0 working_class 0.0 Surname_Country_IncomeGroup 0.0 dtype: float64 In [7]: Copied! zero_val_frac , fig = zero ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) zero_val_frac . head () zero_val_frac, fig = zero( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) zero_val_frac.head() Out[7]: Exited 0.7963 Balance (EUR) 0.3617 Tenure 0.0413 CreditScore 0.0000 Surname_Country_subregion 0.0000 dtype: float64 In [8]: Copied! data_nunique , fig = nunique ( df = df_pd . drop ( columns = id_cols ), scale = \"log\" , plot = True , ) data_nunique . head () data_nunique, fig = nunique( df=df_pd.drop(columns=id_cols), scale=\"log\", plot=True, ) data_nunique.head() Out[8]: Country_IncomeGroup 1 Country_hemisphere 1 Country_region 1 IsActiveMember 2 is_native 2 dtype: int64 In [9]: Copied! data_std , fig = std ( df = df_pd [ cont_cols ], scale = \"log\" , plot = True , ) data_std . head () data_std, fig = std( df=df_pd[cont_cols], scale=\"log\", plot=True, ) data_std.head() Out[9]: Balance (EUR) 62397.405202 EstimatedSalary 57510.492818 Surname_Country_gdp_per_capita 25976.502611 Country_gdp_per_capita 6330.960841 CreditScore 96.653299 dtype: float64 In [10]: Copied! col_entropies , fig = entropy ( df = df_pd . drop ( columns = id_cols ), scale = \"linear\" , plot = True , ) col_entropies . head () col_entropies, fig = entropy( df=df_pd.drop(columns=id_cols), scale=\"linear\", plot=True, ) col_entropies.head() Out[10]: EstimatedSalary 9.210202 Balance (EUR) 6.246510 CreditScore 5.873574 Age 3.677614 Surname_Country 2.481289 dtype: float64","title":"Analysis"},{"location":"notebooks/01_feature_analysis.html#plotting","text":"In [11]: Copied! fig = cross_correlation ( df = df_pd [ cont_cols ], n = 10 , verbose = True ) fig = cross_correlation(df=df_pd[cont_cols], n=10, verbose=True) Top 10 absolute correlations Balance (EUR) Country_gdp_per_capita 0.329762 NumberOfProducts 0.304180 Age NumberOfProducts 0.030680 Country_gdp_per_capita 0.029999 Balance (EUR) 0.028308 Tenure Surname_Country_gdp_per_capita 0.017138 NumberOfProducts EstimatedSalary 0.014204 Tenure NumberOfProducts 0.013444 Balance (EUR) EstimatedSalary 0.012797 Tenure Balance (EUR) 0.012254 dtype: float64 In [12]: Copied! fig = distributions ( df = df_pd [ cont_cols ], low_per_cut = 0 , high_per_cut = 1 , type = \"box\" , ) fig = distributions( 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.highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Target analysis \u00b6 Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is perfroming check the possible connection between feature and target values (eg. if the distributions of features are different for each class) Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import ( distributions_in_binary_cls , ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import ( distributions_in_binary_cls, ) Dataset \u00b6 In [2]: Copied! df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd . head () df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember ... Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 ... Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 ... Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 ... Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 ... Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 ... Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z 5 rows \u00d7 28 columns In [3]: Copied! target_col = \"Exited\" id_cols = [ \"CustomerId\" ] cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" , ] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [ \"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\", \"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\", ] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In [4]: Copied! df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) df_pd[cat_cols] = df_pd[cat_cols].astype(str) Analysis \u00b6 In [5]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[5]: Age 0.285323 Country_gdp_per_capita 0.139180 Balance (EUR) 0.118533 EstimatedSalary 0.012097 Surname_Country_gdp_per_capita 0.002470 Name: Exited, dtype: float64 Plotting \u00b6 Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [6]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, )","title":"02_target_analysis"},{"location":"notebooks/02_target_analysis.html#target-analysis","text":"Purpose : put features into context with predicted or actual target values analyse feature distribution per group in binary classification check correlation of features with target value check 'certainty' of the model predictions by plotting prob_distrib_per_class to see how certain the model is in predicting each class visually check predicted vs actual(ground truth values) by simple scatter plot by 'improved scatter plot' ( joint_dist ) - this can be usefull if the actual values form a cluster (eg. regression applied to clusters of users) and we want check if the model predictions also form a nice cluster with similar distribution as actual values Intended use : check how your model is perfroming check the possible connection between feature and target values (eg. if the distributions of features are different for each class)","title":"Target analysis"},{"location":"notebooks/02_target_analysis.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"../..\" ) import pandas as pd from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import ( distributions_in_binary_cls , ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"../..\") import pandas as pd from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import ( distributions_in_binary_cls, )","title":"Imports"},{"location":"notebooks/02_target_analysis.html#dataset","text":"In [2]: Copied! df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd . head () df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[2]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember ... Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 ... Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 ... Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 ... Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 ... Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 ... Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z 5 rows \u00d7 28 columns In [3]: Copied! target_col = \"Exited\" id_cols = [ \"CustomerId\" ] cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" , ] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [ \"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\", \"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\", ] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In [4]: Copied! df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) df_pd[cat_cols] = df_pd[cat_cols].astype(str)","title":"Dataset"},{"location":"notebooks/02_target_analysis.html#analysis","text":"In [5]: Copied! sorted_corr_cols , fig = correlation ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], scale = \"linear\" , plot = True , ) sorted_corr_cols . head () sorted_corr_cols, fig = correlation( df=df_pd[cont_cols], target=df_pd[target_col], scale=\"linear\", plot=True, ) sorted_corr_cols.head() Out[5]: Age 0.285323 Country_gdp_per_capita 0.139180 Balance (EUR) 0.118533 EstimatedSalary 0.012097 Surname_Country_gdp_per_capita 0.002470 Name: Exited, dtype: float64","title":"Analysis"},{"location":"notebooks/02_target_analysis.html#plotting","text":"Predicted and actual/ground_truth values were gathered using LightGBM with Optuna optimizer on the dataset In [6]: Copied! fig = distributions_in_binary_cls ( df = df_pd [ cont_cols ], target = df_pd [ target_col ], low_per_cut = 0 , high_per_cut = 1 , ) fig = distributions_in_binary_cls( df=df_pd[cont_cols], target=df_pd[target_col], low_per_cut=0, high_per_cut=1, )","title":"Plotting"},{"location":"notebooks/03_models.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Models \u00b6 Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from churn_pred.preprocessing.scaler import scaler_mapper from churn_pred.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from churn_pred.preprocessing.label_encoder import LabelEncoder # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from churn_pred.preprocessing.scaler import scaler_mapper from churn_pred.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from churn_pred.preprocessing.label_encoder import LabelEncoder # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() Dataset \u00b6 In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd . head () df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In [5]: Copied! target_col = \"Exited\" id_cols = [ \"CustomerId\" ] cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" , ] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [ \"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\", \"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\", ] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In [6]: Copied! df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [7]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[7]: Exited 0 7963 1 2037 Name: count, dtype: int64 Preprocessing \u00b6 divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [8]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid) Logistic Regression \u00b6 In [9]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 0.91 0.71 0.79 796 1 0.38 0.71 0.50 204 accuracy 0.71 1000 macro avg 0.64 0.71 0.65 1000 weighted avg 0.80 0.71 0.73 1000 In [10]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.6100000000000003 , with score: 0.7973245061612856 In [11]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [12]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [0.76928761 0.76928761] Weighted AUC: 0.7692876145433049 H2O AutoML \u00b6 NOTE : java is required : sudo apt install openjdk-17-jdk In [13]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmpaylgzon8 JVM stdout: /tmp/tmpaylgzon8/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmpaylgzon8/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.1 H2O_cluster_version_age: 1 month and 18 days H2O_cluster_name: H2O_from_python_unknownUser_3enaho H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 21:14:36.227: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588\u2588\u2588 21:14:47.43: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:14:50.191: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:14:58.843: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:14:59.823: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:15:06.470: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588\u2588 21:15:14.629: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:18.475: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:22.46: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:25.584: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:26.398: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] 21:15:27.398: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:15:33.757: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588\u2588 21:15:41.23: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:44.787: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:15:51.131: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:52.50: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 21:17:30.497: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] 21:17:31.185: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo StackedEnsemble_BestOfFamily_4_AutoML_1_20240501_211436 0.880101 0.321353 0.718049 0.19825 0.313431 0.0982391 587 0.08015 StackedEnsemble GBM_grid_1_AutoML_1_20240501_211436_model_7 0.880077 0.324523 0.715814 0.227877 0.315328 0.0994315 1275 0.048422 GBM GBM_grid_1_AutoML_1_20240501_211436_model_2 0.879564 0.32755 0.700805 0.214585 0.317341 0.100705 1284 0.047805 GBM GBM_5_AutoML_1_20240501_211436 0.879162 0.324106 0.720173 0.206754 0.31347 0.0982632 1324 0.030257 GBM GBM_grid_1_AutoML_1_20240501_211436_model_6 0.876891 0.329434 0.696693 0.223137 0.318794 0.10163 1338 0.048563 GBM StackedEnsemble_AllModels_3_AutoML_1_20240501_211436 0.876439 0.326157 0.715138 0.201247 0.316194 0.0999788 1252 0.067674 StackedEnsemble StackedEnsemble_BestOfFamily_3_AutoML_1_20240501_211436 0.875775 0.325977 0.719837 0.217583 0.315933 0.0998134 814 0.073439 StackedEnsemble GBM_3_AutoML_1_20240501_211436 0.875664 0.333065 0.70305 0.232269 0.318954 0.101732 1345 0.043399 GBM StackedEnsemble_BestOfFamily_2_AutoML_1_20240501_211436 0.875284 0.330522 0.690455 0.223718 0.318198 0.10125 720 0.060753 StackedEnsemble StackedEnsemble_AllModels_2_AutoML_1_20240501_211436 0.875203 0.326942 0.718431 0.225461 0.316482 0.100161 847 0.080788 StackedEnsemble [36 rows x 10 columns] ModelMetricsBinomialGLM: stackedensemble ** Reported on test data. ** MSE: 0.09823909719810361 RMSE: 0.31343116819822436 LogLoss: 0.3213528582843091 AUC: 0.8801014889579766 AUCPR: 0.7180486621898251 Gini: 0.7602029779159531 Null degrees of freedom: 999 Residual degrees of freedom: 995 Null deviance: 1009.0700428262837 Residual deviance: 642.7057165686182 AIC: 652.7057165686182 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.28080823635190166 0 1 Error Rate ----- --- --- ------- -------------- 0 693 104 0.1305 (104.0/797.0) 1 54 149 0.266 (54.0/203.0) Total 747 253 0.158 (158.0/1000.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- -------- ----- max f1 0.280808 0.653509 191 max f2 0.160323 0.717949 247 max f0point5 0.663482 0.710974 85 max accuracy 0.499443 0.872 122 max precision 0.991398 1 0 max recall 0.0175152 1 376 max specificity 0.991398 1 0 max absolute_mcc 0.449611 0.574975 137 max min_per_class_accuracy 0.187182 0.795483 232 max mean_per_class_accuracy 0.280808 0.80175 191 max tns 0.991398 797 0 max fns 0.991398 202 0 max fps 0.0032163 797 399 max tps 0.0175152 203 376 max tnr 0.991398 1 0 max fnr 0.991398 0.995074 0 max fpr 0.0032163 1 399 max tpr 0.0175152 1 376 Gains/Lift Table: Avg response rate: 20.30 %, avg score: 20.95 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- --------- ----------------- --------------- ---------- -------------------------- ------------------ -------------- ------------------------- -------- ----------------- -------------------- 1 0.01 0.940779 4.92611 4.92611 1 0.964295 1 0.964295 0.0492611 0.0492611 392.611 392.611 0.0492611 2 0.02 0.904438 3.94089 4.4335 0.8 0.92715 0.9 0.945722 0.0394089 0.08867 294.089 343.35 0.0861605 3 0.03 0.878852 4.92611 4.5977 1 0.893414 0.933333 0.928286 0.0492611 0.137931 392.611 359.77 0.135422 4 0.04 0.867626 4.92611 4.6798 1 0.871928 0.95 0.914197 0.0492611 0.187192 392.611 367.98 0.184683 5 0.05 0.834646 4.4335 4.63054 0.9 0.850263 0.94 0.90141 0.044335 0.231527 343.35 363.054 0.227763 6 0.1 0.687835 3.64532 4.13793 0.74 0.771377 0.84 0.836393 0.182266 0.413793 264.532 313.793 0.393718 7 0.15 0.508264 2.6601 3.64532 0.54 0.608741 0.74 0.760509 0.133005 0.546798 166.01 264.532 0.497865 8 0.2 0.388087 1.67488 3.15271 0.34 0.448371 0.64 0.682474 0.0837438 0.630542 67.4877 215.271 0.540203 9 0.3 0.207693 1.47783 2.59442 0.3 0.291717 0.526667 0.552222 0.147783 0.778325 47.7833 159.442 0.600157 10 0.4 0.130789 0.640394 2.10591 0.13 0.165365 0.4275 0.455508 0.0640394 0.842365 -35.9606 110.591 0.555037 11 0.5 0.0857897 0.640394 1.81281 0.13 0.104819 0.368 0.38537 0.0640394 0.906404 -35.9606 81.2808 0.509917 12 0.6 0.057346 0.44335 1.58456 0.09 0.0704711 0.321667 0.332887 0.044335 0.950739 -55.665 58.4565 0.440074 13 0.7 0.0338547 0.246305 1.39338 0.05 0.0452273 0.282857 0.291793 0.0246305 0.975369 -75.3695 39.3385 0.345507 14 0.8 0.0216684 0.147783 1.23768 0.03 0.027519 0.25125 0.258759 0.0147783 0.990148 -85.2217 23.7685 0.238579 15 0.9 0.0120087 0.0985222 1.11111 0.02 0.0167146 0.225556 0.231865 0.00985222 1 -90.1478 11.1111 0.125471 16 1 0.0032163 0 1 0 0.00824799 0.203 0.209503 0 1 -100 0 0 stackedensemble prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [14]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [15]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [0.90083383 0.90083383] Weighted AUC: 0.9008338259927087 In [18]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [19]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 0.94 0.90 0.92 796 1 0.66 0.76 0.71 204 accuracy 0.87 1000 macro avg 0.80 0.83 0.81 1000 weighted avg 0.88 0.87 0.87 1000 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" LightGBM \u00b6 In [20]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [21]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Warning] Found whitespace in feature_names, replace with underlines [LightGBM] [Info] Number of positive: 1630, number of negative: 6370 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002671 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1048 [LightGBM] [Info] Number of data points in the train set: 8000, number of used features: 23 [LightGBM] [Warning] Found whitespace in feature_names, replace with underlines [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203750 -> initscore=-1.363019 [LightGBM] [Info] Start training from score -1.363019 In [22]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 0.90 0.95 0.92 796 1 0.76 0.58 0.66 204 accuracy 0.88 1000 macro avg 0.83 0.77 0.79 1000 weighted avg 0.87 0.88 0.87 1000 In [23]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [0.89585797 0.89585797] Weighted AUC: 0.8958579663020987","title":"03_models"},{"location":"notebooks/03_models.html#models","text":"","title":"Models"},{"location":"notebooks/03_models.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report , RocCurveDisplay , roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from churn_pred.preprocessing.scaler import scaler_mapper from churn_pred.training.utils import ( predict_cls_lgbm_from_raw , predict_proba_lgbm_from_raw , ) from churn_pred.preprocessing.label_encoder import LabelEncoder # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start() import dill import numpy as np import matplotlib.pyplot as plt import h2o from h2o.automl import H2OAutoML from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report, RocCurveDisplay, roc_auc_score from sklearn.linear_model import LogisticRegression import lightgbm as lgbm from churn_pred.preprocessing.scaler import scaler_mapper from churn_pred.training.utils import ( predict_cls_lgbm_from_raw, predict_proba_lgbm_from_raw, ) from churn_pred.preprocessing.label_encoder import LabelEncoder # import tracemalloc # import warnings # from typing import Optional, Dict # tracemalloc.start()","title":"Imports"},{"location":"notebooks/03_models.html#dataset","text":"In [3]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True valid_size = 0.2 test_size = 0.5 random_state = 1 test_n_valid_combined = True In [4]: Copied! df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd . head () df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[4]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In [5]: Copied! target_col = \"Exited\" id_cols = [ \"CustomerId\" ] cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" , ] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ] ) . columns . values . tolist () target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [ \"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\", \"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\", ] cont_cols = df_pd.drop( columns=id_cols + cat_cols + [target_col] ).columns.values.tolist() In [6]: Copied! df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) df_pd[cat_cols] = df_pd[cat_cols].astype(str) check possible class imbalance In [7]: Copied! df_pd [ target_col ] . value_counts () df_pd[target_col].value_counts() Out[7]: Exited 0 7963 1 2037 Name: count, dtype: int64","title":"Dataset"},{"location":"notebooks/03_models.html#preprocessing","text":"divide dataset into train, test, valid scale continuous columns by standard scaler(not needed for LightGBM but for other mdoels...) In [8]: Copied! df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state , ) df_train . reset_index ( inplace = True , drop = True ) df_valid . reset_index ( inplace = True , drop = True ) df_test . reset_index ( inplace = True , drop = True ) scaler_mapper_def = { \"cont_cols\" : StandardScaler , \"cat_cols\" : None , \"id_cols\" : None , } scaler = scaler_mapper ( cont_cols = cont_cols , cat_cols = cat_cols , id_cols = [ target_col ] + id_cols , scaler_mapper_def = scaler_mapper_def , ) df_train_scaled = scaler . fit_transform ( df_train ) df_test_scaled = scaler . transform ( df_test ) df_valid_scaled = scaler . transform ( df_valid ) df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state, ) df_train.reset_index(inplace=True, drop=True) df_valid.reset_index(inplace=True, drop=True) df_test.reset_index(inplace=True, drop=True) scaler_mapper_def = { \"cont_cols\": StandardScaler, \"cat_cols\": None, \"id_cols\": None, } scaler = scaler_mapper( cont_cols=cont_cols, cat_cols=cat_cols, id_cols=[target_col] + id_cols, scaler_mapper_def=scaler_mapper_def, ) df_train_scaled = scaler.fit_transform(df_train) df_test_scaled = scaler.transform(df_test) df_valid_scaled = scaler.transform(df_valid)","title":"Preprocessing"},{"location":"notebooks/03_models.html#logistic-regression","text":"In [9]: Copied! LR_clf = LogisticRegression ( class_weight = \"balanced\" ) LR_clf . fit ( df_train_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ), df_train_scaled [ target_col ], ) LR_predicted = LR_clf . predict ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) print ( \"LR classification report : \\n \" + str ( classification_report ( df_test_scaled [ target_col ], LR_predicted )) ) LR_clf = LogisticRegression(class_weight=\"balanced\") LR_clf.fit( df_train_scaled.drop(columns=[target_col] + id_cols + cat_cols), df_train_scaled[target_col], ) LR_predicted = LR_clf.predict( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) print( \"LR classification report :\\n\" + str(classification_report(df_test_scaled[target_col], LR_predicted)) ) LR classification report : precision recall f1-score support 0 0.91 0.71 0.79 796 1 0.38 0.71 0.50 204 accuracy 0.71 1000 macro avg 0.64 0.71 0.65 1000 weighted avg 0.80 0.71 0.73 1000 In [10]: Copied! # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np . arange ( 0.2 , 0.8 , 0.01 ): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) )[:, 1 ] >= t ) . astype ( int ) threshold_score . append ( ( t , criterion ( df_test_scaled [ target_col ], preds_bin , average = \"weighted\" )) ) threshold_score = sorted ( threshold_score , key = lambda x : x [ 1 ], reverse = True ) best_threshold , best_score = threshold_score [ 0 ][ 0 ], threshold_score [ 0 ][ 1 ] print ( f \"The best threshold \\n { best_threshold } \\n , with score: \\n { best_score } \" ) # which metrics am I focusing on? it is more importan to precisely predict non spenders or spenders? from sklearn.metrics import f1_score criterion = f1_score threshold_score = [] for t in np.arange(0.2, 0.8, 0.01): # preds_bin = [int(p > t) for p in y_pred] preds_bin = ( LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) )[:, 1] >= t ).astype(int) threshold_score.append( (t, criterion(df_test_scaled[target_col], preds_bin, average=\"weighted\")) ) threshold_score = sorted(threshold_score, key=lambda x: x[1], reverse=True) best_threshold, best_score = threshold_score[0][0], threshold_score[0][1] print(f\"The best threshold\\n{best_threshold}\\n, with score:\\n{best_score}\") The best threshold 0.6100000000000003 , with score: 0.7973245061612856 In [11]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = LR_clf . predict_proba ( df_test_scaled . drop ( columns = [ target_col ] + id_cols + cat_cols ) ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = LR_clf.predict_proba( df_test_scaled.drop(columns=[target_col] + id_cols + cat_cols) ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) In [12]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [0.76928761 0.76928761] Weighted AUC: 0.7692876145433049","title":"Logistic Regression"},{"location":"notebooks/03_models.html#h2o-automl","text":"NOTE : java is required : sudo apt install openjdk-17-jdk In [13]: Copied! # initialize H2O h2o . init ( log_dir = \"h2o_logs\" , log_level = \"WARN\" ) # read as h2o file h2o_train = h2o . H2OFrame ( df_train_scaled . drop ( columns = id_cols )) h2o_valid = h2o . H2OFrame ( df_valid_scaled . drop ( columns = id_cols )) h2o_test = h2o . H2OFrame ( df_test_scaled . drop ( columns = id_cols )) # For binary classification, response should be a factor h2o_train [ target_col ] = h2o_train [ target_col ] . asfactor () h2o_valid [ target_col ] = h2o_valid [ target_col ] . asfactor () h2o_test [ target_col ] = h2o_test [ target_col ] . asfactor () # Define AML task aml = H2OAutoML ( seed = random_state , max_runtime_secs = 180 ) # over/under sample for classification tasks aml . balance_classes = True # Run it _ = aml . train ( x = list ( cont_cols + cat_cols ), y = target_col , training_frame = h2o_train , leaderboard_frame = h2o_valid , ) m = aml . get_best_model () # Leaderboard, show and save lb = h2o . automl . get_leaderboard ( aml , extra_columns = \"ALL\" ) print ( lb ) print ( m . model_performance ( h2o_valid )) predictions = m . predict ( h2o_test ) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") # initialize H2O h2o.init(log_dir=\"h2o_logs\", log_level=\"WARN\") # read as h2o file h2o_train = h2o.H2OFrame(df_train_scaled.drop(columns=id_cols)) h2o_valid = h2o.H2OFrame(df_valid_scaled.drop(columns=id_cols)) h2o_test = h2o.H2OFrame(df_test_scaled.drop(columns=id_cols)) # For binary classification, response should be a factor h2o_train[target_col] = h2o_train[target_col].asfactor() h2o_valid[target_col] = h2o_valid[target_col].asfactor() h2o_test[target_col] = h2o_test[target_col].asfactor() # Define AML task aml = H2OAutoML(seed=random_state, max_runtime_secs=180) # over/under sample for classification tasks aml.balance_classes = True # Run it _ = aml.train( x=list(cont_cols + cat_cols), y=target_col, training_frame=h2o_train, leaderboard_frame=h2o_valid, ) m = aml.get_best_model() # Leaderboard, show and save lb = h2o.automl.get_leaderboard(aml, extra_columns=\"ALL\") print(lb) print(m.model_performance(h2o_valid)) predictions = m.predict(h2o_test) # save results and model # h2o.export_file(lb, path=\"h2o_logs/leaderboard.csv\", force=True) # MOJO is h2o version agnostic # m.save_mojo(\"h2o_logs/bestmodel.zip\") Checking whether there is an H2O instance running at http://localhost:54321..... not found. Attempting to start a local H2O server... Java Version: openjdk version \"17.0.10\" 2024-01-16; OpenJDK Runtime Environment (build 17.0.10+7-Ubuntu-122.04.1); OpenJDK 64-Bit Server VM (build 17.0.10+7-Ubuntu-122.04.1, mixed mode, sharing) Starting server from /opt/conda/lib/python3.10/site-packages/h2o/backend/bin/h2o.jar Ice root: /tmp/tmpaylgzon8 JVM stdout: /tmp/tmpaylgzon8/h2o_unknownUser_started_from_python.out JVM stderr: /tmp/tmpaylgzon8/h2o_unknownUser_started_from_python.err Server is running at http://127.0.0.1:54321 Connecting to H2O server at http://127.0.0.1:54321 ... successful. #h2o-table-1.h2o-container { overflow-x: auto; } #h2o-table-1 .h2o-table { /* width: 100%; */ margin-top: 1em; margin-bottom: 1em; } #h2o-table-1 .h2o-table caption { white-space: nowrap; caption-side: top; text-align: left; /* margin-left: 1em; */ margin: 0; font-size: larger; } #h2o-table-1 .h2o-table thead { white-space: nowrap; position: sticky; top: 0; box-shadow: 0 -1px inset; } #h2o-table-1 .h2o-table tbody { overflow: auto; } #h2o-table-1 .h2o-table th, #h2o-table-1 .h2o-table td { text-align: right; /* border: 1px solid; */ } #h2o-table-1 .h2o-table tr:nth-child(even) { /* background: #F5F5F5 */ } H2O_cluster_uptime: 01 secs H2O_cluster_timezone: Etc/UTC H2O_data_parsing_timezone: UTC H2O_cluster_version: 3.46.0.1 H2O_cluster_version_age: 1 month and 18 days H2O_cluster_name: H2O_from_python_unknownUser_3enaho H2O_cluster_total_nodes: 1 H2O_cluster_free_memory: 16 Gb H2O_cluster_total_cores: 12 H2O_cluster_allowed_cores: 12 H2O_cluster_status: locked, healthy H2O_connection_url: http://127.0.0.1:54321 H2O_connection_proxy: {\"http\": null, \"https\": null} H2O_internal_security: False Python_version: 3.10.11 final Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% Parse progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% AutoML progress: | 21:14:36.227: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588\u2588\u2588 21:14:47.43: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:14:50.191: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:14:58.843: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:14:59.823: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:15:06.470: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588\u2588 21:15:14.629: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:18.475: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:22.46: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:25.584: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:26.398: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] 21:15:27.398: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:15:33.757: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588\u2588 21:15:41.23: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:44.787: _train param, Dropping bad and constant columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588 21:15:51.131: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588 21:15:52.50: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 21:17:30.497: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] 21:17:31.185: _train param, Dropping unused columns: [Country_region, Country_IncomeGroup, Country_hemisphere] \u2588\u2588| (done) 100% model_id auc logloss aucpr mean_per_class_error rmse mse training_time_ms predict_time_per_row_ms algo StackedEnsemble_BestOfFamily_4_AutoML_1_20240501_211436 0.880101 0.321353 0.718049 0.19825 0.313431 0.0982391 587 0.08015 StackedEnsemble GBM_grid_1_AutoML_1_20240501_211436_model_7 0.880077 0.324523 0.715814 0.227877 0.315328 0.0994315 1275 0.048422 GBM GBM_grid_1_AutoML_1_20240501_211436_model_2 0.879564 0.32755 0.700805 0.214585 0.317341 0.100705 1284 0.047805 GBM GBM_5_AutoML_1_20240501_211436 0.879162 0.324106 0.720173 0.206754 0.31347 0.0982632 1324 0.030257 GBM GBM_grid_1_AutoML_1_20240501_211436_model_6 0.876891 0.329434 0.696693 0.223137 0.318794 0.10163 1338 0.048563 GBM StackedEnsemble_AllModels_3_AutoML_1_20240501_211436 0.876439 0.326157 0.715138 0.201247 0.316194 0.0999788 1252 0.067674 StackedEnsemble StackedEnsemble_BestOfFamily_3_AutoML_1_20240501_211436 0.875775 0.325977 0.719837 0.217583 0.315933 0.0998134 814 0.073439 StackedEnsemble GBM_3_AutoML_1_20240501_211436 0.875664 0.333065 0.70305 0.232269 0.318954 0.101732 1345 0.043399 GBM StackedEnsemble_BestOfFamily_2_AutoML_1_20240501_211436 0.875284 0.330522 0.690455 0.223718 0.318198 0.10125 720 0.060753 StackedEnsemble StackedEnsemble_AllModels_2_AutoML_1_20240501_211436 0.875203 0.326942 0.718431 0.225461 0.316482 0.100161 847 0.080788 StackedEnsemble [36 rows x 10 columns] ModelMetricsBinomialGLM: stackedensemble ** Reported on test data. ** MSE: 0.09823909719810361 RMSE: 0.31343116819822436 LogLoss: 0.3213528582843091 AUC: 0.8801014889579766 AUCPR: 0.7180486621898251 Gini: 0.7602029779159531 Null degrees of freedom: 999 Residual degrees of freedom: 995 Null deviance: 1009.0700428262837 Residual deviance: 642.7057165686182 AIC: 652.7057165686182 Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.28080823635190166 0 1 Error Rate ----- --- --- ------- -------------- 0 693 104 0.1305 (104.0/797.0) 1 54 149 0.266 (54.0/203.0) Total 747 253 0.158 (158.0/1000.0) Maximum Metrics: Maximum metrics at their respective thresholds metric threshold value idx --------------------------- ----------- -------- ----- max f1 0.280808 0.653509 191 max f2 0.160323 0.717949 247 max f0point5 0.663482 0.710974 85 max accuracy 0.499443 0.872 122 max precision 0.991398 1 0 max recall 0.0175152 1 376 max specificity 0.991398 1 0 max absolute_mcc 0.449611 0.574975 137 max min_per_class_accuracy 0.187182 0.795483 232 max mean_per_class_accuracy 0.280808 0.80175 191 max tns 0.991398 797 0 max fns 0.991398 202 0 max fps 0.0032163 797 399 max tps 0.0175152 203 376 max tnr 0.991398 1 0 max fnr 0.991398 0.995074 0 max fpr 0.0032163 1 399 max tpr 0.0175152 1 376 Gains/Lift Table: Avg response rate: 20.30 %, avg score: 20.95 % group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain kolmogorov_smirnov ------- -------------------------- ----------------- --------- ----------------- --------------- ---------- -------------------------- ------------------ -------------- ------------------------- -------- ----------------- -------------------- 1 0.01 0.940779 4.92611 4.92611 1 0.964295 1 0.964295 0.0492611 0.0492611 392.611 392.611 0.0492611 2 0.02 0.904438 3.94089 4.4335 0.8 0.92715 0.9 0.945722 0.0394089 0.08867 294.089 343.35 0.0861605 3 0.03 0.878852 4.92611 4.5977 1 0.893414 0.933333 0.928286 0.0492611 0.137931 392.611 359.77 0.135422 4 0.04 0.867626 4.92611 4.6798 1 0.871928 0.95 0.914197 0.0492611 0.187192 392.611 367.98 0.184683 5 0.05 0.834646 4.4335 4.63054 0.9 0.850263 0.94 0.90141 0.044335 0.231527 343.35 363.054 0.227763 6 0.1 0.687835 3.64532 4.13793 0.74 0.771377 0.84 0.836393 0.182266 0.413793 264.532 313.793 0.393718 7 0.15 0.508264 2.6601 3.64532 0.54 0.608741 0.74 0.760509 0.133005 0.546798 166.01 264.532 0.497865 8 0.2 0.388087 1.67488 3.15271 0.34 0.448371 0.64 0.682474 0.0837438 0.630542 67.4877 215.271 0.540203 9 0.3 0.207693 1.47783 2.59442 0.3 0.291717 0.526667 0.552222 0.147783 0.778325 47.7833 159.442 0.600157 10 0.4 0.130789 0.640394 2.10591 0.13 0.165365 0.4275 0.455508 0.0640394 0.842365 -35.9606 110.591 0.555037 11 0.5 0.0857897 0.640394 1.81281 0.13 0.104819 0.368 0.38537 0.0640394 0.906404 -35.9606 81.2808 0.509917 12 0.6 0.057346 0.44335 1.58456 0.09 0.0704711 0.321667 0.332887 0.044335 0.950739 -55.665 58.4565 0.440074 13 0.7 0.0338547 0.246305 1.39338 0.05 0.0452273 0.282857 0.291793 0.0246305 0.975369 -75.3695 39.3385 0.345507 14 0.8 0.0216684 0.147783 1.23768 0.03 0.027519 0.25125 0.258759 0.0147783 0.990148 -85.2217 23.7685 0.238579 15 0.9 0.0120087 0.0985222 1.11111 0.02 0.0167146 0.225556 0.231865 0.00985222 1 -90.1478 11.1111 0.125471 16 1 0.0032163 0 1 0 0.00824799 0.203 0.209503 0 1 -100 0 0 stackedensemble prediction progress: |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| (done) 100% In [14]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = h2o . as_list ( predictions . drop ([ \"predict\" ])) . values micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = h2o.as_list(predictions.drop([\"predict\"])).values micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\" In [15]: Copied! print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [0.90083383 0.90083383] Weighted AUC: 0.9008338259927087 In [18]: Copied! fig , ax = plt . subplots ( figsize = ( 6 , 6 )) n_classes = 2 for class_id in range ( n_classes ): RocCurveDisplay . from_predictions ( y_onehot_test [:, class_id ], y_score [:, class_id ], name = f \"ROC curve for class { class_id } \" , ax = ax , # plot_chance_level=(class_id == 2), ) fig, ax = plt.subplots(figsize=(6, 6)) n_classes = 2 for class_id in range(n_classes): RocCurveDisplay.from_predictions( y_onehot_test[:, class_id], y_score[:, class_id], name=f\"ROC curve for class {class_id}\", ax=ax, # plot_chance_level=(class_id == 2), ) In [19]: Copied! print ( \"Classification report: \\n {} \" . format ( classification_report ( df_test [ target_col ], h2o . as_list ( predictions [ \"predict\" ])) ) ) print( \"Classification report:\\n{}\".format( classification_report(df_test[target_col], h2o.as_list(predictions[\"predict\"])) ) ) Classification report: precision recall f1-score support 0 0.94 0.90 0.92 796 1 0.66 0.76 0.71 204 accuracy 0.87 1000 macro avg 0.80 0.83 0.81 1000 weighted avg 0.88 0.87 0.87 1000 /opt/conda/lib/python3.10/site-packages/h2o/frame.py:1983: H2ODependencyWarning: Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install datatable (for Python 3.9 or lower), or polars and pyarrow (for Python 3.10 or above) and activate it using: with h2o.utils.threading.local_context(polars_enabled=True, datatable_enabled=True): pandas_df = h2o_df.as_data_frame() warnings.warn(\"Converting H2O frame to pandas dataframe using single-thread. For faster conversion using\"","title":"H2O AutoML"},{"location":"notebooks/03_models.html#lightgbm","text":"In [20]: Copied! config = {} config [ \"objective\" ] = \"binary\" df_train_scaled_enc = df_train_scaled . copy () df_valid_scaled_enc = df_valid_scaled . copy () df_test_scaled_enc = df_test_scaled . copy () lgb_cat_cols = cat_cols label_encoder = LabelEncoder ( lgb_cat_cols ) df_train_scaled_enc = label_encoder . fit_transform ( df_train_scaled_enc ) df_valid_scaled_enc = label_encoder . transform ( df_valid_scaled_enc ) df_test_scaled_enc = label_encoder . transform ( df_test_scaled_enc ) lgbtrain = lgbm . Dataset ( df_train_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_train_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbvalid = lgbm . Dataset ( df_valid_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_valid_scaled_enc [ target_col ], reference = lgbtrain , free_raw_data = False , ) # Final TRAIN/TEST ftrain = pd . concat ([ df_train_scaled_enc , df_valid_scaled_enc ]) . reset_index ( drop = True ) flgbtrain = lgbm . Dataset ( ftrain . drop ( columns = [ target_col ] + id_cols ), ftrain [ target_col ], categorical_feature = lgb_cat_cols , free_raw_data = False , ) lgbtest = lgbm . Dataset ( df_test_scaled_enc . drop ( columns = [ target_col ] + id_cols ), df_test_scaled_enc [ target_col ], categorical_feature = lgb_cat_cols , reference = flgbtrain , free_raw_data = False , ) config = {} config[\"objective\"] = \"binary\" df_train_scaled_enc = df_train_scaled.copy() df_valid_scaled_enc = df_valid_scaled.copy() df_test_scaled_enc = df_test_scaled.copy() lgb_cat_cols = cat_cols label_encoder = LabelEncoder(lgb_cat_cols) df_train_scaled_enc = label_encoder.fit_transform(df_train_scaled_enc) df_valid_scaled_enc = label_encoder.transform(df_valid_scaled_enc) df_test_scaled_enc = label_encoder.transform(df_test_scaled_enc) lgbtrain = lgbm.Dataset( df_train_scaled_enc.drop(columns=[target_col] + id_cols), df_train_scaled_enc[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbvalid = lgbm.Dataset( df_valid_scaled_enc.drop(columns=[target_col] + id_cols), df_valid_scaled_enc[target_col], reference=lgbtrain, free_raw_data=False, ) # Final TRAIN/TEST ftrain = pd.concat([df_train_scaled_enc, df_valid_scaled_enc]).reset_index(drop=True) flgbtrain = lgbm.Dataset( ftrain.drop(columns=[target_col] + id_cols), ftrain[target_col], categorical_feature=lgb_cat_cols, free_raw_data=False, ) lgbtest = lgbm.Dataset( df_test_scaled_enc.drop(columns=[target_col] + id_cols), df_test_scaled_enc[target_col], categorical_feature=lgb_cat_cols, reference=flgbtrain, free_raw_data=False, ) In [21]: Copied! model = lgbm . train ( config , lgbtrain , valid_sets = [ lgbvalid ], valid_names = [ \"\" ], feval = None , ) model = lgbm.train( config, lgbtrain, valid_sets=[lgbvalid], valid_names=[\"\"], feval=None, ) [LightGBM] [Warning] Found whitespace in feature_names, replace with underlines [LightGBM] [Info] Number of positive: 1630, number of negative: 6370 [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002671 seconds. You can set `force_row_wise=true` to remove the overhead. And if memory is not enough, you can set `force_col_wise=true`. [LightGBM] [Info] Total Bins 1048 [LightGBM] [Info] Number of data points in the train set: 8000, number of used features: 23 [LightGBM] [Warning] Found whitespace in feature_names, replace with underlines [LightGBM] [Info] [binary:BoostFromScore]: pavg=0.203750 -> initscore=-1.363019 [LightGBM] [Info] Start training from score -1.363019 In [22]: Copied! res = predict_cls_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data , raw_score = True ), task = \"binary\" , ) result = pd . DataFrame ( { \"predicted\" : res , \"ground_truth\" : df_test [ target_col ] . values , } ) print ( \"Classification report: \\n {} \" . format ( classification_report ( result [ \"ground_truth\" ], result [ \"predicted\" ]) ) ) res = predict_cls_lgbm_from_raw( preds_raw=model.predict(lgbtest.data, raw_score=True), task=\"binary\", ) result = pd.DataFrame( { \"predicted\": res, \"ground_truth\": df_test[target_col].values, } ) print( \"Classification report:\\n{}\".format( classification_report(result[\"ground_truth\"], result[\"predicted\"]) ) ) Classification report: precision recall f1-score support 0 0.90 0.95 0.92 796 1 0.76 0.58 0.66 204 accuracy 0.88 1000 macro avg 0.83 0.77 0.79 1000 weighted avg 0.87 0.88 0.87 1000 In [23]: Copied! y_onehot_test = pd . get_dummies ( df_test_scaled [ target_col ]) . values y_score = predict_proba_lgbm_from_raw ( preds_raw = model . predict ( lgbtest . data ), task = \"binary\" , binary2d = True , ) micro_roc_auc_per_class = roc_auc_score ( y_onehot_test , y_score , average = None , ) micro_roc_auc_weighted = roc_auc_score ( y_onehot_test , y_score , average = \"weighted\" , ) print ( f \"Per class AUC: \\n { micro_roc_auc_per_class } \" ) print ( f \"Weighted AUC: \\n { micro_roc_auc_weighted } \" ) y_onehot_test = pd.get_dummies(df_test_scaled[target_col]).values y_score = predict_proba_lgbm_from_raw( preds_raw=model.predict(lgbtest.data), task=\"binary\", binary2d=True, ) micro_roc_auc_per_class = roc_auc_score( y_onehot_test, y_score, average=None, ) micro_roc_auc_weighted = roc_auc_score( y_onehot_test, y_score, average=\"weighted\", ) print(f\"Per class AUC:\\n{micro_roc_auc_per_class}\") print(f\"Weighted AUC:\\n{micro_roc_auc_weighted}\") Per class AUC: [0.89585797 0.89585797] Weighted AUC: 0.8958579663020987","title":"LightGBM"},{"location":"notebooks/04_model_training_pipeline.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); 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We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Example usage notebook \u00b6 Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation Imports \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import os import json from typing import Literal import pandas as pd from churn_pred.preprocessing.preprocess import PreprocessData from churn_pred.training.trainer import Trainer from churn_pred.training.optuna_optimizer import LGBOptunaOptimizer from churn_pred.training.utils import flatten_dict , get_or_create_experiment import dill import numpy as np from churn_pred.utils import dill_dump , dill_load from sklearn.model_selection import train_test_split from pprint import pprint import os import json from typing import Literal import pandas as pd from churn_pred.preprocessing.preprocess import PreprocessData from churn_pred.training.trainer import Trainer from churn_pred.training.optuna_optimizer import LGBOptunaOptimizer from churn_pred.training.utils import flatten_dict, get_or_create_experiment import dill import numpy as np from churn_pred.utils import dill_dump, dill_load from sklearn.model_selection import train_test_split from pprint import pprint Train a testing model \u00b6 In [3]: Copied! # 1. get the data df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd . head () # 1. get the data df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In [4]: Copied! target_col = \"Exited\" id_cols = [ \"CustomerId\" ] cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" ] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ]) . columns . values . tolist () target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [\"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\",\"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\"] cont_cols = df_pd.drop(columns=id_cols + cat_cols + [target_col]).columns.values.tolist() In [5]: Copied! df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [28]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split(df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state) df_valid, df_test = train_test_split(df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-06-18 21:05:47,850] A new study created in memory with name: no-name-72ade7dc-1867-41c2-b5a4-df3b58765bc7 feature_fraction, val_score: inf: 0%| | 0/7 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.388064: 14%|#4 | 1/7 [00:08<00:48, 8.11s/it][I 2024-06-18 21:05:55,971] Trial 0 finished with value: 0.38806351066689354 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 0.38806351066689354. feature_fraction, val_score: 0.388064: 14%|#4 | 1/7 [00:08<00:48, 8.11s/it] Early stopping, best iteration is: [241] valid_0's binary_logloss: 0.388064 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.384552: 29%|##8 | 2/7 [00:15<00:39, 7.81s/it][I 2024-06-18 21:06:03,568] Trial 1 finished with value: 0.38455247747199556 and parameters: {'feature_fraction': 0.5}. Best is trial 1 with value: 0.38455247747199556. feature_fraction, val_score: 0.384552: 29%|##8 | 2/7 [00:15<00:39, 7.81s/it] Early stopping, best iteration is: [184] valid_0's binary_logloss: 0.384552 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 43%|####2 | 3/7 [00:20<00:25, 6.48s/it][I 2024-06-18 21:06:08,471] Trial 2 finished with value: 0.3813271028603074 and parameters: {'feature_fraction': 0.8}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 43%|####2 | 3/7 [00:20<00:25, 6.48s/it] Early stopping, best iteration is: [154] valid_0's binary_logloss: 0.381327 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 57%|#####7 | 4/7 [00:22<00:13, 4.64s/it][I 2024-06-18 21:06:10,281] Trial 3 finished with value: 0.40303264639386804 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 57%|#####7 | 4/7 [00:22<00:13, 4.64s/it] Early stopping, best iteration is: [9] valid_0's binary_logloss: 0.403033 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 71%|#######1 | 5/7 [00:29<00:10, 5.45s/it][I 2024-06-18 21:06:17,181] Trial 4 finished with value: 0.385808915879594 and parameters: {'feature_fraction': 1.0}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 71%|#######1 | 5/7 [00:29<00:10, 5.45s/it] Early stopping, best iteration is: [196] valid_0's binary_logloss: 0.385809 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 86%|########5 | 6/7 [00:35<00:05, 5.57s/it][I 2024-06-18 21:06:22,970] Trial 5 finished with value: 0.3905588715180784 and parameters: {'feature_fraction': 0.7}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 86%|########5 | 6/7 [00:35<00:05, 5.57s/it] Early stopping, best iteration is: [152] valid_0's binary_logloss: 0.390559 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41<00:00, 5.97s/it][I 2024-06-18 21:06:29,778] Trial 6 finished with value: 0.38245734022417816 and parameters: {'feature_fraction': 0.6}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41<00:00, 5.99s/it] Early stopping, best iteration is: [201] valid_0's binary_logloss: 0.382457 num_leaves, val_score: 0.381327: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.381327: 5%|5 | 1/20 [00:06<02:12, 6.99s/it][I 2024-06-18 21:06:36,772] Trial 7 finished with value: 0.3867130065464061 and parameters: {'num_leaves': 28}. Best is trial 7 with value: 0.3867130065464061. num_leaves, val_score: 0.381327: 5%|5 | 1/20 [00:06<02:12, 6.99s/it] Early stopping, best iteration is: [225] valid_0's binary_logloss: 0.386713 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 10%|# | 2/20 [00:14<02:15, 7.51s/it][I 2024-06-18 21:06:44,655] Trial 8 finished with value: 0.3704743259843756 and parameters: {'num_leaves': 92}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 10%|# | 2/20 [00:14<02:15, 7.51s/it] Early stopping, best iteration is: [64] valid_0's binary_logloss: 0.370474 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 15%|#5 | 3/20 [00:26<02:42, 9.53s/it][I 2024-06-18 21:06:56,583] Trial 9 finished with value: 0.3718260260505307 and parameters: {'num_leaves': 149}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 15%|#5 | 3/20 [00:26<02:42, 9.53s/it] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.371826 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 20%|## | 4/20 [00:44<03:21, 12.59s/it][I 2024-06-18 21:07:13,876] Trial 10 finished with value: 0.37095297012345396 and parameters: {'num_leaves': 248}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 20%|## | 4/20 [00:44<03:21, 12.59s/it] Early stopping, best iteration is: [41] valid_0's binary_logloss: 0.370953 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 25%|##5 | 5/20 [00:59<03:25, 13.71s/it][I 2024-06-18 21:07:29,563] Trial 11 finished with value: 0.3738263141664088 and parameters: {'num_leaves': 252}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 25%|##5 | 5/20 [00:59<03:25, 13.71s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.373826 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 30%|### | 6/20 [01:08<02:48, 12.01s/it][I 2024-06-18 21:07:38,283] Trial 12 finished with value: 0.38055573216311783 and parameters: {'num_leaves': 96}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 30%|### | 6/20 [01:08<02:48, 12.01s/it] Early stopping, best iteration is: [52] valid_0's binary_logloss: 0.380556 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 35%|###5 | 7/20 [01:25<02:58, 13.73s/it][I 2024-06-18 21:07:55,556] Trial 13 finished with value: 0.36708245318754934 and parameters: {'num_leaves': 253}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 35%|###5 | 7/20 [01:25<02:58, 13.73s/it] Early stopping, best iteration is: [39] valid_0's binary_logloss: 0.367082 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 40%|#### | 8/20 [01:36<02:33, 12.81s/it][I 2024-06-18 21:08:06,381] Trial 14 finished with value: 0.3760206407150123 and parameters: {'num_leaves': 132}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 40%|#### | 8/20 [01:36<02:33, 12.81s/it] Early stopping, best iteration is: [51] valid_0's binary_logloss: 0.376021 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 45%|####5 | 9/20 [01:44<02:05, 11.43s/it][I 2024-06-18 21:08:14,782] Trial 15 finished with value: 0.378543416834411 and parameters: {'num_leaves': 64}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 45%|####5 | 9/20 [01:44<02:05, 11.43s/it] Early stopping, best iteration is: [103] valid_0's binary_logloss: 0.378543 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 50%|##### | 10/20 [01:56<01:55, 11.58s/it][I 2024-06-18 21:08:26,682] Trial 16 finished with value: 0.37346546696167743 and parameters: {'num_leaves': 184}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 50%|##### | 10/20 [01:56<01:55, 11.58s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.373465 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 55%|#####5 | 11/20 [02:08<01:45, 11.67s/it][I 2024-06-18 21:08:38,573] Trial 17 finished with value: 0.3716035258824437 and parameters: {'num_leaves': 178}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 55%|#####5 | 11/20 [02:08<01:45, 11.67s/it] Early stopping, best iteration is: [33] valid_0's binary_logloss: 0.371604 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 60%|###### | 12/20 [02:10<01:08, 8.58s/it][I 2024-06-18 21:08:40,079] Trial 18 finished with value: 0.4090573635738217 and parameters: {'num_leaves': 6}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 60%|###### | 12/20 [02:10<01:08, 8.58s/it] Early stopping, best iteration is: [219] valid_0's binary_logloss: 0.409057 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 65%|######5 | 13/20 [02:19<01:00, 8.68s/it][I 2024-06-18 21:08:48,983] Trial 19 finished with value: 0.3704743259843756 and parameters: {'num_leaves': 92}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 65%|######5 | 13/20 [02:19<01:00, 8.68s/it] Early stopping, best iteration is: [64] valid_0's binary_logloss: 0.370474 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 70%|####### | 14/20 [02:34<01:03, 10.58s/it][I 2024-06-18 21:09:03,972] Trial 20 finished with value: 0.37479451809569353 and parameters: {'num_leaves': 211}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 70%|####### | 14/20 [02:34<01:03, 10.58s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.374795 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 75%|#######5 | 15/20 [02:43<00:50, 10.14s/it][I 2024-06-18 21:09:13,084] Trial 21 finished with value: 0.3772431537086629 and parameters: {'num_leaves': 90}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 75%|#######5 | 15/20 [02:43<00:50, 10.14s/it] Early stopping, best iteration is: [69] valid_0's binary_logloss: 0.377243 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 80%|######## | 16/20 [02:53<00:40, 10.13s/it][I 2024-06-18 21:09:23,178] Trial 22 finished with value: 0.3728508215083201 and parameters: {'num_leaves': 94}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 80%|######## | 16/20 [02:53<00:40, 10.13s/it] Early stopping, best iteration is: [76] valid_0's binary_logloss: 0.372851 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 85%|########5 | 17/20 [03:01<00:28, 9.57s/it][I 2024-06-18 21:09:31,462] Trial 23 finished with value: 0.3741261012500593 and parameters: {'num_leaves': 65}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 85%|########5 | 17/20 [03:01<00:28, 9.57s/it] Early stopping, best iteration is: [130] valid_0's binary_logloss: 0.374126 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 90%|######### | 18/20 [03:11<00:19, 9.61s/it][I 2024-06-18 21:09:41,151] Trial 24 finished with value: 0.3809976671401048 and parameters: {'num_leaves': 116}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 90%|######### | 18/20 [03:11<00:19, 9.61s/it] Early stopping, best iteration is: [61] valid_0's binary_logloss: 0.380998 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 95%|#########5| 19/20 [03:18<00:08, 8.92s/it][I 2024-06-18 21:09:48,483] Trial 25 finished with value: 0.3773659816174925 and parameters: {'num_leaves': 45}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 95%|#########5| 19/20 [03:18<00:08, 8.92s/it] Early stopping, best iteration is: [150] valid_0's binary_logloss: 0.377366 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27<00:00, 8.98s/it][I 2024-06-18 21:09:57,579] Trial 26 finished with value: 0.379797423356925 and parameters: {'num_leaves': 148}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27<00:00, 10.39s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.379797 bagging, val_score: 0.367082: 0%| | 0/10 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 10%|# | 1/10 [00:15<02:17, 15.28s/it][I 2024-06-18 21:10:12,864] Trial 27 finished with value: 0.3754694447762392 and parameters: {'bagging_fraction': 0.6017118946353502, 'bagging_freq': 3}. Best is trial 27 with value: 0.3754694447762392. bagging, val_score: 0.367082: 10%|# | 1/10 [00:15<02:17, 15.28s/it] Early stopping, best iteration is: [54] valid_0's binary_logloss: 0.375469 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 20%|## | 2/10 [00:32<02:10, 16.29s/it][I 2024-06-18 21:10:29,858] Trial 28 finished with value: 0.37140708223458174 and parameters: {'bagging_fraction': 0.9697760068067878, 'bagging_freq': 7}. Best is trial 28 with value: 0.37140708223458174. bagging, val_score: 0.367082: 20%|## | 2/10 [00:32<02:10, 16.29s/it] Early stopping, best iteration is: [51] valid_0's binary_logloss: 0.371407 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 30%|### | 3/10 [00:40<01:27, 12.51s/it][I 2024-06-18 21:10:37,880] Trial 29 finished with value: 0.3763001794627212 and parameters: {'bagging_fraction': 0.4336686702739523, 'bagging_freq': 1}. Best is trial 28 with value: 0.37140708223458174. bagging, val_score: 0.367082: 30%|### | 3/10 [00:40<01:27, 12.51s/it] Early stopping, best iteration is: [35] valid_0's binary_logloss: 0.3763 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 40%|#### | 4/10 [00:57<01:25, 14.27s/it][I 2024-06-18 21:10:54,855] Trial 30 finished with value: 0.37399860180838584 and parameters: {'bagging_fraction': 0.9955847594220013, 'bagging_freq': 7}. Best is trial 28 with value: 0.37140708223458174. bagging, val_score: 0.367082: 40%|#### | 4/10 [00:57<01:25, 14.27s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.373999 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 50%|##### | 5/10 [01:13<01:15, 15.04s/it][I 2024-06-18 21:11:11,265] Trial 31 finished with value: 0.36851832518824595 and parameters: {'bagging_fraction': 0.7559826069760041, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 50%|##### | 5/10 [01:13<01:15, 15.04s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.368518 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 60%|###### | 6/10 [01:29<01:00, 15.17s/it][I 2024-06-18 21:11:26,675] Trial 32 finished with value: 0.3695574686675923 and parameters: {'bagging_fraction': 0.7373615298502163, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 60%|###### | 6/10 [01:29<01:00, 15.17s/it] Early stopping, best iteration is: [51] valid_0's binary_logloss: 0.369557 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 70%|####### | 7/10 [01:44<00:45, 15.17s/it][I 2024-06-18 21:11:41,852] Trial 33 finished with value: 0.37298644850012186 and parameters: {'bagging_fraction': 0.7616544959864899, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 70%|####### | 7/10 [01:44<00:45, 15.17s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.372986 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 80%|######## | 8/10 [01:58<00:29, 14.94s/it][I 2024-06-18 21:11:56,282] Trial 34 finished with value: 0.3745164899611851 and parameters: {'bagging_fraction': 0.7510199039916521, 'bagging_freq': 5}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 80%|######## | 8/10 [01:58<00:29, 14.94s/it] Early stopping, best iteration is: [35] valid_0's binary_logloss: 0.374516 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 90%|######### | 9/10 [02:13<00:14, 14.95s/it][I 2024-06-18 21:12:11,256] Trial 35 finished with value: 0.3790678087224149 and parameters: {'bagging_fraction': 0.6376411296778922, 'bagging_freq': 3}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 90%|######### | 9/10 [02:13<00:14, 14.95s/it] Early stopping, best iteration is: [41] valid_0's binary_logloss: 0.379068 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 100%|##########| 10/10 [02:28<00:00, 14.82s/it][I 2024-06-18 21:12:25,777] Trial 36 finished with value: 0.36759282443526115 and parameters: {'bagging_fraction': 0.8438639692641409, 'bagging_freq': 5}. Best is trial 36 with value: 0.36759282443526115. bagging, val_score: 0.367082: 100%|##########| 10/10 [02:28<00:00, 14.82s/it] Early stopping, best iteration is: [30] valid_0's binary_logloss: 0.367593 feature_fraction_stage2, val_score: 0.367082: 0%| | 0/6 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.367082: 17%|#6 | 1/6 [00:14<01:11, 14.28s/it][I 2024-06-18 21:12:40,067] Trial 37 finished with value: 0.3751194682458601 and parameters: {'feature_fraction': 0.8480000000000001}. Best is trial 37 with value: 0.3751194682458601. feature_fraction_stage2, val_score: 0.367082: 17%|#6 | 1/6 [00:14<01:11, 14.28s/it] Early stopping, best iteration is: [31] valid_0's binary_logloss: 0.375119 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.367082: 33%|###3 | 2/6 [00:31<01:03, 15.82s/it][I 2024-06-18 21:12:56,969] Trial 38 finished with value: 0.37511946824586 and parameters: {'feature_fraction': 0.88}. Best is trial 38 with value: 0.37511946824586. feature_fraction_stage2, val_score: 0.367082: 33%|###3 | 2/6 [00:31<01:03, 15.82s/it] Early stopping, best iteration is: [31] valid_0's binary_logloss: 0.375119 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.367082: 50%|##### | 3/6 [00:48<00:50, 16.73s/it][I 2024-06-18 21:13:14,771] Trial 39 finished with value: 0.37230053743309227 and parameters: {'feature_fraction': 0.7520000000000001}. Best is trial 39 with value: 0.37230053743309227. feature_fraction_stage2, val_score: 0.367082: 50%|##### | 3/6 [00:48<00:50, 16.73s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.372301 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.367082: 67%|######6 | 4/6 [01:05<00:33, 16.76s/it][I 2024-06-18 21:13:31,580] Trial 40 finished with value: 0.37230053743309227 and parameters: {'feature_fraction': 0.7200000000000001}. Best is trial 39 with value: 0.37230053743309227. feature_fraction_stage2, val_score: 0.367082: 67%|######6 | 4/6 [01:05<00:33, 16.76s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.372301 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.366281: 83%|########3 | 5/6 [01:21<00:16, 16.48s/it][I 2024-06-18 21:13:47,556] Trial 41 finished with value: 0.3662812971282284 and parameters: {'feature_fraction': 0.8160000000000001}. Best is trial 41 with value: 0.3662812971282284. feature_fraction_stage2, val_score: 0.366281: 83%|########3 | 5/6 [01:21<00:16, 16.48s/it] Early stopping, best iteration is: [30] valid_0's binary_logloss: 0.366281 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38<00:00, 16.66s/it][I 2024-06-18 21:14:04,578] Trial 42 finished with value: 0.3670824531875493 and parameters: {'feature_fraction': 0.784}. Best is trial 41 with value: 0.3662812971282284. feature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38<00:00, 16.47s/it] Early stopping, best iteration is: [39] valid_0's binary_logloss: 0.367082 regularization_factors, val_score: 0.366281: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 5%|5 | 1/20 [00:13<04:12, 13.29s/it][I 2024-06-18 21:14:17,877] Trial 43 finished with value: 0.36913074695699283 and parameters: {'lambda_l1': 0.26740014043884963, 'lambda_l2': 0.001760870184331642}. Best is trial 43 with value: 0.36913074695699283. regularization_factors, val_score: 0.366281: 5%|5 | 1/20 [00:13<04:12, 13.29s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.369131 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 10%|# | 2/20 [00:28<04:21, 14.52s/it][I 2024-06-18 21:14:33,256] Trial 44 finished with value: 0.3744642189562516 and parameters: {'lambda_l1': 1.1442585672872564e-08, 'lambda_l2': 4.849247272391423e-08}. Best is trial 43 with value: 0.36913074695699283. regularization_factors, val_score: 0.366281: 10%|# | 2/20 [00:28<04:21, 14.52s/it] Early stopping, best iteration is: [37] valid_0's binary_logloss: 0.374464 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 15%|#5 | 3/20 [00:45<04:23, 15.52s/it][I 2024-06-18 21:14:49,962] Trial 45 finished with value: 0.3703169935094016 and parameters: {'lambda_l1': 9.96832677827828e-07, 'lambda_l2': 6.3332349736895}. Best is trial 43 with value: 0.36913074695699283. regularization_factors, val_score: 0.366281: 15%|#5 | 3/20 [00:45<04:23, 15.52s/it] Early stopping, best iteration is: [75] valid_0's binary_logloss: 0.370317 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 20%|## | 4/20 [00:58<03:54, 14.69s/it][I 2024-06-18 21:15:03,375] Trial 46 finished with value: 0.3672993919740384 and parameters: {'lambda_l1': 1.5127153863704856, 'lambda_l2': 4.478393628964223e-08}. Best is trial 46 with value: 0.3672993919740384. regularization_factors, val_score: 0.366281: 20%|## | 4/20 [00:58<03:54, 14.69s/it] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.367299 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 25%|##5 | 5/20 [01:12<03:36, 14.40s/it][I 2024-06-18 21:15:17,268] Trial 47 finished with value: 0.3673221145565776 and parameters: {'lambda_l1': 2.3927205612539093, 'lambda_l2': 5.0167732343674086e-08}. Best is trial 46 with value: 0.3672993919740384. regularization_factors, val_score: 0.366281: 25%|##5 | 5/20 [01:12<03:36, 14.40s/it] Early stopping, best iteration is: [73] valid_0's binary_logloss: 0.367322 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 30%|### | 6/20 [01:25<03:15, 13.96s/it][I 2024-06-18 21:15:30,372] Trial 48 finished with value: 0.38611937264133733 and parameters: {'lambda_l1': 6.054665616489558, 'lambda_l2': 1.800720533496738e-08}. Best is trial 46 with value: 0.3672993919740384. regularization_factors, val_score: 0.366281: 30%|### | 6/20 [01:25<03:15, 13.96s/it] Early stopping, best iteration is: [156] valid_0's binary_logloss: 0.386119 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 35%|###5 | 7/20 [01:41<03:08, 14.53s/it][I 2024-06-18 21:15:46,069] Trial 49 finished with value: 0.36481873368079354 and parameters: {'lambda_l1': 0.018981605475779886, 'lambda_l2': 4.496278591408666e-06}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 35%|###5 | 7/20 [01:41<03:08, 14.53s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.364819 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 40%|#### | 8/20 [01:58<03:02, 15.22s/it][I 2024-06-18 21:16:02,773] Trial 50 finished with value: 0.3676098962913463 and parameters: {'lambda_l1': 0.005938438143650016, 'lambda_l2': 1.6486829620090715e-05}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 40%|#### | 8/20 [01:58<03:02, 15.22s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.36761 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 45%|####5 | 9/20 [02:13<02:46, 15.12s/it][I 2024-06-18 21:16:17,683] Trial 51 finished with value: 0.3693663505867317 and parameters: {'lambda_l1': 0.14870260759215176, 'lambda_l2': 8.827878170241735e-07}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 45%|####5 | 9/20 [02:13<02:46, 15.12s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.369366 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 50%|##### | 10/20 [02:21<02:11, 13.17s/it][I 2024-06-18 21:16:26,468] Trial 52 finished with value: 0.3995526935417914 and parameters: {'lambda_l1': 9.914097223337537, 'lambda_l2': 2.507766754016921e-06}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 50%|##### | 10/20 [02:21<02:11, 13.17s/it] Early stopping, best iteration is: [108] valid_0's binary_logloss: 0.399553 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 55%|#####5 | 11/20 [02:36<02:02, 13.60s/it][I 2024-06-18 21:16:41,058] Trial 53 finished with value: 0.37135627608204047 and parameters: {'lambda_l1': 0.004317296224576123, 'lambda_l2': 1.0694628305152963e-08}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 55%|#####5 | 11/20 [02:36<02:02, 13.60s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.371356 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 60%|###### | 12/20 [02:50<01:50, 13.82s/it][I 2024-06-18 21:16:55,362] Trial 54 finished with value: 0.3656757773203038 and parameters: {'lambda_l1': 0.1528681952118711, 'lambda_l2': 0.0003078435151438368}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 60%|###### | 12/20 [02:50<01:50, 13.82s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.365676 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 65%|######5 | 13/20 [03:03<01:34, 13.48s/it][I 2024-06-18 21:17:08,074] Trial 55 finished with value: 0.3683612678266087 and parameters: {'lambda_l1': 0.026844520069898837, 'lambda_l2': 0.0010041201106681954}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 65%|######5 | 13/20 [03:03<01:34, 13.48s/it] Early stopping, best iteration is: [30] valid_0's binary_logloss: 0.368361 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 70%|####### | 14/20 [03:18<01:24, 14.03s/it][I 2024-06-18 21:17:23,369] Trial 56 finished with value: 0.367596997615459 and parameters: {'lambda_l1': 0.0003242973314637712, 'lambda_l2': 5.561136449228501e-05}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 70%|####### | 14/20 [03:18<01:24, 14.03s/it] Early stopping, best iteration is: [30] valid_0's binary_logloss: 0.367597 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 75%|#######5 | 15/20 [03:31<01:07, 13.57s/it][I 2024-06-18 21:17:35,879] Trial 57 finished with value: 0.36589916181880006 and parameters: {'lambda_l1': 0.2057156258629452, 'lambda_l2': 0.024885518547422405}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 75%|#######5 | 15/20 [03:31<01:07, 13.57s/it] Early stopping, best iteration is: [31] valid_0's binary_logloss: 0.365899 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 80%|######## | 16/20 [03:45<00:55, 13.85s/it][I 2024-06-18 21:17:50,372] Trial 58 finished with value: 0.36736185428940016 and parameters: {'lambda_l1': 0.0002150419853087141, 'lambda_l2': 0.034107683119902095}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 80%|######## | 16/20 [03:45<00:55, 13.85s/it] Early stopping, best iteration is: [34] valid_0's binary_logloss: 0.367362 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 85%|########5 | 17/20 [04:00<00:42, 14.20s/it][I 2024-06-18 21:18:05,384] Trial 59 finished with value: 0.3702898033719683 and parameters: {'lambda_l1': 0.0583471784381045, 'lambda_l2': 0.3274496387110711}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 85%|########5 | 17/20 [04:00<00:42, 14.20s/it] Early stopping, best iteration is: [40] valid_0's binary_logloss: 0.37029 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 90%|######### | 18/20 [04:18<00:30, 15.10s/it][I 2024-06-18 21:18:22,586] Trial 60 finished with value: 0.3651493917986752 and parameters: {'lambda_l1': 0.001458069822509784, 'lambda_l2': 0.008248248782706902}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 90%|######### | 18/20 [04:18<00:30, 15.10s/it] Early stopping, best iteration is: [43] valid_0's binary_logloss: 0.365149 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 95%|#########5| 19/20 [04:33<00:15, 15.10s/it][I 2024-06-18 21:18:37,676] Trial 61 finished with value: 0.3696068718370661 and parameters: {'lambda_l1': 0.0006748569060520587, 'lambda_l2': 0.01603839211588336}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 95%|#########5| 19/20 [04:33<00:15, 15.10s/it] Early stopping, best iteration is: [41] valid_0's binary_logloss: 0.369607 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48<00:00, 15.16s/it][I 2024-06-18 21:18:52,985] Trial 62 finished with value: 0.3700319935632785 and parameters: {'lambda_l1': 0.004313680126647664, 'lambda_l2': 0.008355812202217454}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48<00:00, 14.42s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.370032 min_child_samples, val_score: 0.364819: 0%| | 0/5 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 20%|## | 1/5 [00:10<00:40, 10.19s/it][I 2024-06-18 21:19:03,252] Trial 63 finished with value: 0.3897736671401978 and parameters: {'min_child_samples': 50}. Best is trial 63 with value: 0.3897736671401978. min_child_samples, val_score: 0.364819: 20%|## | 1/5 [00:10<00:40, 10.19s/it] Early stopping, best iteration is: [51] valid_0's binary_logloss: 0.389774 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 40%|#### | 2/5 [00:29<00:46, 15.39s/it][I 2024-06-18 21:19:22,277] Trial 64 finished with value: 0.3683263542462511 and parameters: {'min_child_samples': 5}. Best is trial 64 with value: 0.3683263542462511. min_child_samples, val_score: 0.364819: 40%|#### | 2/5 [00:29<00:46, 15.39s/it] Early stopping, best iteration is: [43] valid_0's binary_logloss: 0.368326 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 60%|###### | 3/5 [00:36<00:23, 11.88s/it][I 2024-06-18 21:19:29,977] Trial 65 finished with value: 0.3938280500430312 and parameters: {'min_child_samples': 100}. Best is trial 64 with value: 0.3683263542462511. min_child_samples, val_score: 0.364819: 60%|###### | 3/5 [00:36<00:23, 11.88s/it] Early stopping, best iteration is: [113] valid_0's binary_logloss: 0.393828 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 80%|######## | 4/5 [00:53<00:13, 13.86s/it][I 2024-06-18 21:19:46,881] Trial 66 finished with value: 0.36712449039705003 and parameters: {'min_child_samples': 10}. Best is trial 66 with value: 0.36712449039705003. min_child_samples, val_score: 0.364819: 80%|######## | 4/5 [00:53<00:13, 13.86s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.367124 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07<00:00, 13.80s/it][I 2024-06-18 21:20:00,561] Trial 67 finished with value: 0.3719291335673048 and parameters: {'min_child_samples': 25}. Best is trial 66 with value: 0.36712449039705003. min_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07<00:00, 13.50s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.371929 In [8]: Copied! # save/load trainer # dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load ( \"lgbm_trainer.dill\" ) # save/load trainer # dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load(\"lgbm_trainer.dill\") In [11]: Copied! pprint ( metrics_dict ) pprint(metrics_dict) {'cls_report': {'0': {'f1-score': 0.9244823386114495, 'precision': 0.8971631205673759, 'recall': 0.9535175879396985, 'support': 796}, '1': {'f1-score': 0.6536312849162011, 'precision': 0.7597402597402597, 'recall': 0.5735294117647058, 'support': 204}, 'accuracy': 0.876, 'macro avg': {'f1-score': 0.7890568117638252, 'precision': 0.8284516901538178, 'recall': 0.7635234998522022, 'support': 1000}, 'weighted avg': {'f1-score': 0.8692287236576187, 'precision': 0.8691288569586443, 'recall': 0.876, 'support': 1000}}, 'cm': [[759, 37], [87, 117]], 'prec_rec_curve': [[0.204, 0.7597402597402597, 1.0], [1.0, 0.5735294117647058, 0.0], [0.0, 1.0]]} In [32]: Copied! pprint ( trainer . compute_metrics ( df_test , with_dynamic_binary_threshold = True )) pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True)) {'cls_report': {'0': {'f1-score': 0.9244823386114495, 'precision': 0.8971631205673759, 'recall': 0.9535175879396985, 'support': 796}, '1': {'f1-score': 0.6536312849162011, 'precision': 0.7597402597402597, 'recall': 0.5735294117647058, 'support': 204}, 'accuracy': 0.876, 'macro avg': {'f1-score': 0.7890568117638252, 'precision': 0.8284516901538178, 'recall': 0.7635234998522022, 'support': 1000}, 'weighted avg': {'f1-score': 0.8692287236576187, 'precision': 0.8691288569586443, 'recall': 0.876, 'support': 1000}}, 'cm': [[759, 37], [87, 117]], 'prec_rec_curve': [[0.204, 0.7597402597402597, 1.0], [1.0, 0.5735294117647058, 0.0], [0.0, 1.0]]} Mlflow \u00b6 tracking model registration in Minio In [12]: Copied! mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" mlflow . set_tracking_uri ( \"http://\" + mlflow_host + \":\" + mlflow_port ) experiment_id = get_or_create_experiment ( \"ecovadis\" ) mlflow . set_experiment ( experiment_id = experiment_id ) mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" mlflow.set_tracking_uri(\"http://\" + mlflow_host + \":\" + mlflow_port) experiment_id = get_or_create_experiment(\"ecovadis\") mlflow.set_experiment(experiment_id=experiment_id) Out[12]: <Experiment: artifact_location='s3://mlflow/6', creation_time=1718745770093, experiment_id='6', last_update_time=1718745770093, lifecycle_stage='active', name='ecovadis', tags={}> In [13]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) metrics_dict_flattened = flatten_dict(metrics_dict) In [14]: Copied! # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [15]: Copied! run_name = \"init_run\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"ecovadis\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"ecovadis_model\" registered_model_name = \"ecovadis_lgbm_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , code_paths = [ \"churn_pred\" ]) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"init_run\" with mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"ecovadis\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"ecovadis_model\" registered_model_name = \"ecovadis_lgbm_model\" mlflow.pyfunc.log_model(python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, code_paths=[\"churn_pred\"]) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference 2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference 2024/06/18 21:23:58 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpfsv2c_xg/model, flavor: python_function). Fall back to return ['cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback. Successfully registered model 'ecovadis_lgbm_model'. 2024/06/18 21:27:30 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: ecovadis_lgbm_model, version 1 Run ID: 761906b692e94248940c6dbf6b12c23c Model uri: s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model Created version '1' of model 'ecovadis_lgbm_model'. Screenshots from MLflow GUI (Nodeport service in Kubernetes) \u00b6 Predictions testing \u00b6 IMPORTANT : the predictions are in raw_score format(for testing to see if I get same predictions), i.e. strange numbers and not classes in the output Example data \u00b6 In [16]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[16]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z In [16]: Copied! df_pd . iloc [: 2 ] . transpose () . to_dict ( orient = 'index' ) df_pd.iloc[:2].transpose().to_dict(orient='index') Out[16]: {'CustomerId': {0: 15787619, 1: 15770309}, 'CreditScore': {0: 844, 1: 656}, 'Country': {0: 'France', 1: 'France'}, 'Gender': {0: 'Male', 1: 'Male'}, 'Age': {0: 18, 1: 18}, 'Tenure': {0: 2, 1: 10}, 'Balance (EUR)': {0: 160980.03, 1: 151762.74}, 'NumberOfProducts': {0: 1, 1: 1}, 'HasCreditCard': {0: '0', 1: '0'}, 'IsActiveMember': {0: '0', 1: '1'}, 'EstimatedSalary': {0: 145936.28, 1: 127014.32}, 'Exited': {0: 0, 1: 0}, 'CustomerFeedback_sentiment3': {0: 'neutral', 1: 'neutral'}, 'CustomerFeedback_sentiment5': {0: '4 stars', 1: '1 star'}, 'Surname_Country': {0: 'Taiwan', 1: 'United States'}, 'Surname_Country_region': {0: 'Asia', 1: 'Americas'}, 'Surname_Country_subregion': {0: 'Eastern Asia', 1: 'Northern America'}, 'Country_region': {0: 'Europe', 1: 'Europe'}, 'Country_subregion': {0: 'Western Europe', 1: 'Western Europe'}, 'is_native': {0: '0', 1: '0'}, 'Country_hemisphere': {0: 'northern', 1: 'northern'}, 'Country_gdp_per_capita': {0: 57594.03402, 1: 57594.03402}, 'Country_IncomeGroup': {0: 'High income', 1: 'High income'}, 'Surname_Country_gdp_per_capita': {0: 32756.0, 1: 76329.58227}, 'Surname_Country_IncomeGroup': {0: 'None', 1: 'High income'}, 'working_class': {0: 'working_age', 1: 'working_age'}, 'stage_of_life': {0: 'teen', 1: 'teen'}, 'generation': {0: 'gen_z', 1: 'gen_z'}} after some notepad magic: { \"CustomerId\": [15787619, 15770309], \"CreditScore\": [844, 656], \"Country\": [\"France\", \"France\"], \"Gender\": [\"Male\", \"Male\"], \"Age\": [18, 18], \"Tenure\": [2, 10], \"Balance (EUR)\": [160980.03, 151762.74], \"NumberOfProducts\": [1, 1], \"HasCreditCard\": [\"0\", \"0\"], \"IsActiveMember\": [\"0\", \"1\"], \"EstimatedSalary\": [145936.28, 127014.32], \"Exited\": [0, 0], \"CustomerFeedback_sentiment3\": [\"neutral\", \"neutral\"], \"CustomerFeedback_sentiment5\": [\"4 stars\", \"1 star\"], \"Surname_Country\": [\"Taiwan\", \"United States\"], \"Surname_Country_region\": [\"Asia\", \"Americas\"], \"Surname_Country_subregion\": [\"Eastern Asia\", \"Northern America\"], \"Country_region\": [\"Europe\", \"Europe\"], \"Country_subregion\": [\"Western Europe\", \"Western Europe\"], \"is_native\": [\"0\", \"0\"], \"Country_hemisphere\": [\"northern\", \"northern\"], \"Country_gdp_per_capita\": [57594.03402, 57594.03402], \"Country_IncomeGroup\": [\"High income\", \"High income\"], \"Surname_Country_gdp_per_capita\": [32756.0, 76329.58227], \"Surname_Country_IncomeGroup\": [\"None\", \"High income\"], \"working_class\": [\"working_age\", \"working_age\"], \"stage_of_life\": [\"teen\", \"teen\"], \"generation\": [\"gen_z\", \"gen_z\"], } Trainer \u00b6 In [16]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"Exited\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"Exited\"]), context={}) Out[16]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Exited 0 -9.958064 1 -12.560959 Downloaded Trainer \u00b6 In [17]: Copied! model_uri model_uri Out[17]: 's3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model' In [18]: Copied! # loaded_trainer = mlflow.pyfunc.load_model(model_uri) loaded_trainer = mlflow . pyfunc . load_model ( 's3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model' ) # loaded_trainer = mlflow.pyfunc.load_model(model_uri) loaded_trainer = mlflow.pyfunc.load_model('s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model') Downloading artifacts: 0%| | 0/61 [00:00<?, ?it/s] In [19]: Copied! loaded_trainer . unwrap_python_model () loaded_trainer.unwrap_python_model() Out[19]: <churn_pred.training.trainer.Trainer at 0x7f3e17f31060> In [21]: Copied! # mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle') mlflow . pyfunc . save_model ( \"data/model\" , python_model = loaded_trainer . unwrap_python_model ()) # mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle') mlflow.pyfunc.save_model(\"data/model\", python_model=loaded_trainer.unwrap_python_model()) 2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference 2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference 2024/06/18 21:31:37 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: data/model, flavor: python_function). Fall back to return ['cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback. In [22]: Copied! loaded_trainer . predict ( df_pd . iloc [: 2 ] . drop ( columns = [ \"Exited\" ])) loaded_trainer.predict(df_pd.iloc[:2].drop(columns=[\"Exited\"])) Out[22]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Exited 0 -9.958064 1 -12.560959 Downloaded/Served Trainer \u00b6 i.e. testing model locally In [23]: Copied! model_uri model_uri Out[23]: 's3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model' In [24]: Copied! ! mlflow models serve - m \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\" -- env - manager local - p 5000 ! mlflow models serve -m \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\" --env-manager local -p 5000 Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 61/61 [00:00<00:00, 691.42it/s] 2024/06/18 21:32:14 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 61/61 [00:00<00:00, 304.59it/s] 2024/06/18 21:32:15 INFO mlflow.pyfunc.backend: === Running command 'exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app' [2024-06-18 21:32:15 +0000] [1380] [INFO] Starting gunicorn 22.0.0 [2024-06-18 21:32:15 +0000] [1380] [INFO] Listening at: http://127.0.0.1:5000 (1380) [2024-06-18 21:32:15 +0000] [1380] [INFO] Using worker: sync [2024-06-18 21:32:15 +0000] [1381] [INFO] Booting worker with pid: 1381 ^C [2024-06-18 21:36:45 +0000] [1380] [INFO] Handling signal: int [2024-06-18 21:36:45 +0000] [1381] [INFO] Worker exiting (pid: 1381) output from terminal: root@jupyter-5uperpalo:~/assignment/ecovadis_assignment# curl -X POST -H \"Content-Type:application/json\" --data '{\"inputs\": {\"CustomerId\": [15787619, 15770309], \"CreditScore\": [844, 656], \"Country\": [\"France\", \"France\"], \"Gender\": [\"Male\", \"Male\"], \"Age\": [18, 18], \"Tenure\": [2, 10], \"Balance (EUR)\": [160980.03, 151762.74], \"NumberOfProducts\": [1, 1], \"HasCreditCard\": [\"0\", \"0\"], \"IsActiveMember\": [\"0\", \"1\"], \"EstimatedSalary\": [145936.28, 127014.32], \"CustomerFeedback_sentiment3\": [\"neutral\", \"neutral\"], \"CustomerFeedback_sentiment5\": [\"4 stars\", \"1 star\"], \"Surname_Country\": [\"Taiwan\", \"United States\"], \"Surname_Country_region\": [\"Asia\", \"Americas\"], \"Surname_Country_subregion\": [\"Eastern Asia\", \"Northern America\"], \"Country_region\": [\"Europe\", \"Europe\"], \"Country_subregion\": [\"Western Europe\", \"Western Europe\"], \"is_native\": [\"0\", \"0\"], \"Country_hemisphere\": [\"northern\", \"northern\"], \"Country_gdp_per_capita\": [57594.03402, 57594.03402], \"Country_IncomeGroup\": [\"High income\", \"High income\"], \"Surname_Country_gdp_per_capita\": [32756.0, 76329.58227], \"Surname_Country_IncomeGroup\": [\"None\", \"High income\"], \"working_class\": [\"working_age\", \"working_age\"], \"stage_of_life\": [\"teen\", \"teen\"], \"generation\": [\"gen_z\", \"gen_z\"]}}' http://127.0.0.1:5000/invocations {\"predictions\": [{\"Exited\": -9.958063834023024}, {\"Exited\": -12.560959268044444}]} Model deployment using Kserve + Knative + Istio \u00b6 reasons: Kserve is less mature than Seldon core but is primarly adopted by Kubeflow and MLflow \"Serverless installation using Knative\" is a primary installation option of Ksserve as KServe Serverless installation enables autoscaling based on request volume and supports scale down to and from zero. It also supports revision management and canary rollout based on revisions. Istio is a primary choice of service routing for Knative Kserve uses MLserver from Seldoncore but according to \"quick internet search\" it is adopted more by community than Seldon-core as Seldon-core focuses more on enterprise version of its software issues: Kserve uses MLserver which ignores requirements.txt and needs prepackaged conda environment in tar.gz file Kserve uses old version of MLserver(1.3.2) resulting in incorrect usage of conda-unpack related code has to be included with model and conda environment mlserver with correct version has to be defined in the conda env: - mlserver==1.3.5 - mlserver-mlflow==1.3.5 related github issue discussions/troubleshooting: https://github.com/kserve/kserve/issues/3733#issuecomment-2168261646 https://github.com/SeldonIO/MLServer/issues/1801 Create conda environment, tar.gz it and upload it to Minio \u00b6 In [7]: Copied! from minio import Minio # to download conda.yaml client = Minio ( \"10.152.183.156:9000\" , \"minioadmin\" , \"minioadmin\" , secure = False ) bucket = \"mlflow\" object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml\" file_path = \"notebooks/conda.yaml\" client . fget_object ( bucket_name = bucket , object_name = object_name , file_path = file_path ) from minio import Minio # to download conda.yaml client = Minio(\"10.152.183.156:9000\", \"minioadmin\", \"minioadmin\", secure=False) bucket = \"mlflow\" object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml\" file_path = \"notebooks/conda.yaml\" client.fget_object(bucket_name=bucket, object_name=object_name, file_path=file_path) Out[7]: <minio.datatypes.Object at 0x730858235e70> following commands were run in terminal to create environment: pip install conda_pack conda env create --force -f conda.yaml conda-pack --name mlflow-env --output environment.tar.gz --force --ignore-editable-packages --ignore-missing-files In [28]: Copied! # upload environmnet.tar.gz object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz\" file_path = \"notebooks/environment.tar.gz\" client . fput_object ( bucket_name = bucket , object_name = object_name , file_path = file_path ) # upload environmnet.tar.gz object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz\" file_path = \"notebooks/environment.tar.gz\" client.fput_object(bucket_name=bucket, object_name=object_name, file_path=file_path) Out[28]: <minio.helpers.ObjectWriteResult at 0x7f409c5bbac0> Create Kserver Inference Service in Kubernetes \u00b6 apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"custom-ecovadis-model\" namespace: \"mlflow-kserve-success6g\" spec: predictor: serviceAccountName: success6g model: modelFormat: name: mlflow protocolVersion: v2 storageUri: \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\" Test the Inference service \u00b6 export INGRESS_NAME=istio-ingressgateway export INGRESS_NS=istio-system export INGRESS_PORT=$(kubectl -n \"${INGRESS_NS}\" get service \"${INGRESS_NAME}\" -o jsonpath='{.spec.ports[?(@.name==\"http2\")].nodePort}') export INGRESS_HOST=$(kubectl get po -l istio=ingressgateway -n \"${INGRESS_NS}\" -o jsonpath='{.items[0].status.hostIP}') export SERVICE_HOSTNAME=$(kubectl get inferenceservice custom-ecovadis-model -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3) curl -v \\ -H \"Host: ${SERVICE_HOSTNAME}\" \\ -H \"Content-Type: application/json\" \\ -d @./test.json \\ http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-model/infer SUMMARY \u00b6 This unfortunately crashes due to the size of conda environment that is being downloaded from minio database by Kserve. COnda env has 3.36GB due to language models (libraries). Note: this approach was succesfully tested on a custom model that used small conda env ~360MB WORKAROUND \u00b6 save/serve(deploy) only the LightGBM model preprocess the data locally; this could be done in production using (i) Kserve InferenceGpraph or (ii) pipelines in Kubeflow send preprocessed data to API In [15]: Copied! run_name = \"init_run\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"ecovadis\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"ecovadis_raw_model\" registered_model_name = \"ecovadis_raw_lgbm_model\" mlflow . lightgbm . log_model ( lgb_model = trainer . model , artifact_path = artifact_path , registered_model_name = registered_model_name ) # mlflow.pyfunc.log_model(python_model=trainer, # artifact_path=artifact_path, # registered_model_name=registered_model_name, # code_paths=[\"churn_pred\"]) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"init_run\" with mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"ecovadis\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"ecovadis_raw_model\" registered_model_name = \"ecovadis_raw_lgbm_model\" mlflow.lightgbm.log_model( lgb_model=trainer.model, artifact_path=artifact_path, registered_model_name=registered_model_name) # mlflow.pyfunc.log_model(python_model=trainer, # artifact_path=artifact_path, # registered_model_name=registered_model_name, # code_paths=[\"churn_pred\"]) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") Successfully registered model 'ecovadis_raw_lgbm_model'. 2024/06/19 05:43:13 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: ecovadis_raw_lgbm_model, version 1 Run ID: 9a74f8e9087044b9b9d4935a268c26d0 Model uri: s3://mlflow/6/9a74f8e9087044b9b9d4935a268c26d0/artifacts/ecovadis_raw_model Created version '1' of model 'ecovadis_raw_lgbm_model'. In [39]: Copied! df_infser_test = df_pd . iloc [: 2 ] for prep in trainer . preprocessors : df_infser_test = prep . transform ( df_infser_test ) if hasattr ( trainer , \"optimizer\" ): if hasattr ( trainer . optimizer , \"best_to_drop\" ): # type: ignore df_infser_test . drop ( columns = trainer . optimizer . best_to_drop , # type: ignore inplace = True , ) df_infser_test = df_infser_test . drop ( columns = trainer . id_cols + [ trainer . target_col ]) df_infser_test = df_pd.iloc[:2] for prep in trainer.preprocessors: df_infser_test = prep.transform(df_infser_test) if hasattr(trainer, \"optimizer\"): if hasattr(trainer.optimizer, \"best_to_drop\"): # type: ignore df_infser_test.drop( columns=trainer.optimizer.best_to_drop, # type: ignore inplace=True, ) df_infser_test = df_infser_test.drop(columns=trainer.id_cols + [trainer.target_col]) In [40]: Copied! df_infser_test . transpose () . to_dict ( orient = 'index' ) df_infser_test.transpose().to_dict(orient='index') Out[40]: {'is_native': {0: 1.0, 1: 1.0}, 'generation': {0: 1.0, 1: 1.0}, 'Surname_Country': {0: 1.0, 1: 2.0}, 'Country_subregion': {0: 1.0, 1: 1.0}, 'Surname_Country_subregion': {0: 1.0, 1: 2.0}, 'Surname_Country_IncomeGroup': {0: 1.0, 1: 2.0}, 'stage_of_life': {0: 1.0, 1: 1.0}, 'IsActiveMember': {0: 1.0, 1: 2.0}, 'Country': {0: 1.0, 1: 1.0}, 'CustomerFeedback_sentiment5': {0: 1.0, 1: 2.0}, 'CustomerFeedback_sentiment3': {0: 1.0, 1: 1.0}, 'Gender': {0: 1.0, 1: 1.0}, 'Surname_Country_region': {0: 1.0, 1: 2.0}, 'working_class': {0: 1.0, 1: 1.0}, 'HasCreditCard': {0: 1.0, 1: 1.0}, 'EstimatedSalary': {0: 145936.28125, 1: 127014.3203125}, 'Age': {0: 18.0, 1: 18.0}, 'Tenure': {0: 2.0, 1: 10.0}, 'NumberOfProducts': {0: 1.0, 1: 1.0}, 'Country_gdp_per_capita': {0: 57594.03515625, 1: 57594.03515625}, 'Surname_Country_gdp_per_capita': {0: 32756.0, 1: 76329.5859375}, 'CreditScore': {0: 844.0, 1: 656.0}, 'Balance (EUR)': {0: 160980.03125, 1: 151762.734375}} create test.json: cat test.json { \"parameters\": { \"content_type\": \"pd\" }, \"inputs\": [ { \"name\": \"is_native\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"generation\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Surname_Country\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Country_subregion\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Surname_Country_subregion\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Surname_Country_IncomeGroup\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"stage_of_life\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"IsActiveMember\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Country\", \"shape\": [1], \"datatype\": \"BYTES\", \"data\": [1.0] }, { \"name\": \"CustomerFeedback_sentiment5\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"CustomerFeedback_sentiment3\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Gender\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Surname_Country_region\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"working_class\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"HasCreditCard\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"EstimatedSalary\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [145936.28] }, { \"name\": \"Age\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [18] }, { \"name\": \"Tenure\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [2] }, { \"name\": \"NumberOfProducts\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Country_gdp_per_capita\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [57594.03402] }, { \"name\": \"Surname_Country_gdp_per_capita\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [32756.0] }, { \"name\": \"CreditScore\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [844.0] }, { \"name\": \"Balance (EUR)\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [160980.03125] } ] test service: export INGRESS_NAME=istio-ingressgateway export INGRESS_NS=istio-system export SERVICE_HOSTNAME=custom-ecovadis-raw-model.mlflow-kserve-success6g.example.com export INGRESS_PORT=$(kubectl -n \"${INGRESS_NS}\" get service \"${INGRESS_NAME}\" -o jsonpath='{.spec.ports[?(@.name==\"http2\")].nodePort}') export SERVICE_HOSTNAME=$(kubectl get inferenceservice custom-ecovadis-raw-model -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3) curl -v \\ -H \"Host: ${SERVICE_HOSTNAME}\" \\ -H \"Content-Type: application/json\" \\ -d @./test.json \\ http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer output: {INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer * Trying 10.1.24.50:32702... * Connected to 10.1.24.50 (10.1.24.50) port 32702 (#0) > POST /v2/models/custom-ecovadis-raw-model/infer HTTP/1.1 > Host: custom-ecovadis-raw-model.mlflow-kserve-success6g.example.com > User-Agent: curl/8.1.1 > Accept: */* > Content-Type: application/json > Content-Length: 2823 > < HTTP/1.1 200 OK < ce-endpoint: custom-ecovadis-raw-model < ce-id: 0cd6e630-3a2b-44de-8284-299a10cf5283 < ce-inferenceservicename: mlserver < ce-modelid: custom-ecovadis-raw-model < ce-namespace: mlflow-kserve-success6g < ce-requestid: 0cd6e630-3a2b-44de-8284-299a10cf5283 < ce-source: io.seldon.serving.deployment.mlserver.mlflow-kserve-success6g < ce-specversion: 0.3 < ce-type: io.seldon.serving.inference.response < content-length: 251 < content-type: application/json < date: Wed, 19 Jun 2024 06:49:46 GMT < server: istio-envoy < x-envoy-upstream-service-time: 712 < * Connection #0 to host 10.1.24.50 left intact {\"model_name\":\"custom-ecovadis-raw-model\",\"id\":\"0cd6e630-3a2b-44de-8284-299a10cf5283\",\"parameters\":{\"content_type\":\"np\"},\"outputs\":[{\"name\":\"output-1\",\"shape\":[1,1],\"datatype\":\"FP64\",\"parameters\":{\"content_type\":\"np\"},\"data\":[-9.958064]}]}","title":"04_model_training_pipeline"},{"location":"notebooks/04_model_training_pipeline.html#example-usage-notebook","text":"Objects and functions in this notebook are listed with all paramaters to ilustrate their capabillities. Most of the paramaters have default values in the implementation","title":"Example usage notebook"},{"location":"notebooks/04_model_training_pipeline.html#imports","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"..\" ) import pandas as pd import mlflow pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"..\") import pandas as pd import mlflow pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! import os import json from typing import Literal import pandas as pd from churn_pred.preprocessing.preprocess import PreprocessData from churn_pred.training.trainer import Trainer from churn_pred.training.optuna_optimizer import LGBOptunaOptimizer from churn_pred.training.utils import flatten_dict , get_or_create_experiment import dill import numpy as np from churn_pred.utils import dill_dump , dill_load from sklearn.model_selection import train_test_split from pprint import pprint import os import json from typing import Literal import pandas as pd from churn_pred.preprocessing.preprocess import PreprocessData from churn_pred.training.trainer import Trainer from churn_pred.training.optuna_optimizer import LGBOptunaOptimizer from churn_pred.training.utils import flatten_dict, get_or_create_experiment import dill import numpy as np from churn_pred.utils import dill_dump, dill_load from sklearn.model_selection import train_test_split from pprint import pprint","title":"Imports"},{"location":"notebooks/04_model_training_pipeline.html#train-a-testing-model","text":"In [3]: Copied! # 1. get the data df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) df_pd . head () # 1. get the data df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") df_pd.head() Out[3]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z 2 15569178 570 France Female 18 4 82767.42 1 1 0 71811.90 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 57594.03402 High income 34637.76172 Upper middle income working_age teen gen_z 3 15795519 716 Germany Female 18 3 128743.80 1 0 0 197322.13 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Western Europe 0 northern 66616.02225 High income 34637.76172 Upper middle income working_age teen gen_z 4 15621893 727 France Male 18 4 133550.67 1 1 1 46941.41 0 positive 1 star Italy Europe Southern Europe Europe Western Europe 0 northern 57594.03402 High income 55442.07843 High income working_age teen gen_z In [4]: Copied! target_col = \"Exited\" id_cols = [ \"CustomerId\" ] cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" ] cont_cols = df_pd . drop ( columns = id_cols + cat_cols + [ target_col ]) . columns . values . tolist () target_col = \"Exited\" id_cols = [\"CustomerId\"] cat_cols = [\"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\",\"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\"] cont_cols = df_pd.drop(columns=id_cols + cat_cols + [target_col]).columns.values.tolist() In [5]: Copied! df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) df_pd[cat_cols] = df_pd[cat_cols].astype(str) In [28]: Copied! valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ target_col ], random_state = random_state ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ target_col ], random_state = random_state ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split(df_pd, test_size=valid_size, stratify=df_pd[target_col], random_state=random_state) df_valid, df_test = train_test_split(df_valid, test_size=test_size, stratify=df_valid[target_col], random_state=random_state) In [7]: Copied! prepare_data = PreprocessData ( id_cols = id_cols , target_col = target_col , cat_cols = cat_cols , cont_cols = cont_cols , ) # this should be fitted only on training data _ = prepare_data . fit ( df = df_pd ) prepare_data = PreprocessData( id_cols=id_cols, target_col=target_col, cat_cols=cat_cols, cont_cols=cont_cols, ) # this should be fitted only on training data _ = prepare_data.fit(df=df_pd) In [8]: Copied! optimizer = LGBOptunaOptimizer ( objective = \"binary\" , n_class = 2 , ) trainer = Trainer ( cat_cols = prepare_data . cat_cols , target_col = prepare_data . target_col , id_cols = id_cols , objective = \"binary\" , n_class = 2 , optimizer = optimizer , preprocessors = [ prepare_data ], ) metrics_dict = trainer . fit ( df_train = df_train , df_valid = df_valid , df_test = df_test , ) optimizer = LGBOptunaOptimizer( objective=\"binary\", n_class=2, ) trainer = Trainer( cat_cols=prepare_data.cat_cols, target_col=prepare_data.target_col, id_cols=id_cols, objective=\"binary\", n_class=2, optimizer=optimizer, preprocessors=[prepare_data], ) metrics_dict = trainer.fit( df_train=df_train, df_valid=df_valid, df_test=df_test, ) [I 2024-06-18 21:05:47,850] A new study created in memory with name: no-name-72ade7dc-1867-41c2-b5a4-df3b58765bc7 feature_fraction, val_score: inf: 0%| | 0/7 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.388064: 14%|#4 | 1/7 [00:08<00:48, 8.11s/it][I 2024-06-18 21:05:55,971] Trial 0 finished with value: 0.38806351066689354 and parameters: {'feature_fraction': 0.4}. Best is trial 0 with value: 0.38806351066689354. feature_fraction, val_score: 0.388064: 14%|#4 | 1/7 [00:08<00:48, 8.11s/it] Early stopping, best iteration is: [241] valid_0's binary_logloss: 0.388064 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.384552: 29%|##8 | 2/7 [00:15<00:39, 7.81s/it][I 2024-06-18 21:06:03,568] Trial 1 finished with value: 0.38455247747199556 and parameters: {'feature_fraction': 0.5}. Best is trial 1 with value: 0.38455247747199556. feature_fraction, val_score: 0.384552: 29%|##8 | 2/7 [00:15<00:39, 7.81s/it] Early stopping, best iteration is: [184] valid_0's binary_logloss: 0.384552 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 43%|####2 | 3/7 [00:20<00:25, 6.48s/it][I 2024-06-18 21:06:08,471] Trial 2 finished with value: 0.3813271028603074 and parameters: {'feature_fraction': 0.8}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 43%|####2 | 3/7 [00:20<00:25, 6.48s/it] Early stopping, best iteration is: [154] valid_0's binary_logloss: 0.381327 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 57%|#####7 | 4/7 [00:22<00:13, 4.64s/it][I 2024-06-18 21:06:10,281] Trial 3 finished with value: 0.40303264639386804 and parameters: {'feature_fraction': 0.8999999999999999}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 57%|#####7 | 4/7 [00:22<00:13, 4.64s/it] Early stopping, best iteration is: [9] valid_0's binary_logloss: 0.403033 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 71%|#######1 | 5/7 [00:29<00:10, 5.45s/it][I 2024-06-18 21:06:17,181] Trial 4 finished with value: 0.385808915879594 and parameters: {'feature_fraction': 1.0}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 71%|#######1 | 5/7 [00:29<00:10, 5.45s/it] Early stopping, best iteration is: [196] valid_0's binary_logloss: 0.385809 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 86%|########5 | 6/7 [00:35<00:05, 5.57s/it][I 2024-06-18 21:06:22,970] Trial 5 finished with value: 0.3905588715180784 and parameters: {'feature_fraction': 0.7}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 86%|########5 | 6/7 [00:35<00:05, 5.57s/it] Early stopping, best iteration is: [152] valid_0's binary_logloss: 0.390559 Training until validation scores don't improve for 50 rounds feature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41<00:00, 5.97s/it][I 2024-06-18 21:06:29,778] Trial 6 finished with value: 0.38245734022417816 and parameters: {'feature_fraction': 0.6}. Best is trial 2 with value: 0.3813271028603074. feature_fraction, val_score: 0.381327: 100%|##########| 7/7 [00:41<00:00, 5.99s/it] Early stopping, best iteration is: [201] valid_0's binary_logloss: 0.382457 num_leaves, val_score: 0.381327: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.381327: 5%|5 | 1/20 [00:06<02:12, 6.99s/it][I 2024-06-18 21:06:36,772] Trial 7 finished with value: 0.3867130065464061 and parameters: {'num_leaves': 28}. Best is trial 7 with value: 0.3867130065464061. num_leaves, val_score: 0.381327: 5%|5 | 1/20 [00:06<02:12, 6.99s/it] Early stopping, best iteration is: [225] valid_0's binary_logloss: 0.386713 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 10%|# | 2/20 [00:14<02:15, 7.51s/it][I 2024-06-18 21:06:44,655] Trial 8 finished with value: 0.3704743259843756 and parameters: {'num_leaves': 92}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 10%|# | 2/20 [00:14<02:15, 7.51s/it] Early stopping, best iteration is: [64] valid_0's binary_logloss: 0.370474 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 15%|#5 | 3/20 [00:26<02:42, 9.53s/it][I 2024-06-18 21:06:56,583] Trial 9 finished with value: 0.3718260260505307 and parameters: {'num_leaves': 149}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 15%|#5 | 3/20 [00:26<02:42, 9.53s/it] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.371826 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 20%|## | 4/20 [00:44<03:21, 12.59s/it][I 2024-06-18 21:07:13,876] Trial 10 finished with value: 0.37095297012345396 and parameters: {'num_leaves': 248}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 20%|## | 4/20 [00:44<03:21, 12.59s/it] Early stopping, best iteration is: [41] valid_0's binary_logloss: 0.370953 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 25%|##5 | 5/20 [00:59<03:25, 13.71s/it][I 2024-06-18 21:07:29,563] Trial 11 finished with value: 0.3738263141664088 and parameters: {'num_leaves': 252}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 25%|##5 | 5/20 [00:59<03:25, 13.71s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.373826 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.370474: 30%|### | 6/20 [01:08<02:48, 12.01s/it][I 2024-06-18 21:07:38,283] Trial 12 finished with value: 0.38055573216311783 and parameters: {'num_leaves': 96}. Best is trial 8 with value: 0.3704743259843756. num_leaves, val_score: 0.370474: 30%|### | 6/20 [01:08<02:48, 12.01s/it] Early stopping, best iteration is: [52] valid_0's binary_logloss: 0.380556 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 35%|###5 | 7/20 [01:25<02:58, 13.73s/it][I 2024-06-18 21:07:55,556] Trial 13 finished with value: 0.36708245318754934 and parameters: {'num_leaves': 253}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 35%|###5 | 7/20 [01:25<02:58, 13.73s/it] Early stopping, best iteration is: [39] valid_0's binary_logloss: 0.367082 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 40%|#### | 8/20 [01:36<02:33, 12.81s/it][I 2024-06-18 21:08:06,381] Trial 14 finished with value: 0.3760206407150123 and parameters: {'num_leaves': 132}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 40%|#### | 8/20 [01:36<02:33, 12.81s/it] Early stopping, best iteration is: [51] valid_0's binary_logloss: 0.376021 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 45%|####5 | 9/20 [01:44<02:05, 11.43s/it][I 2024-06-18 21:08:14,782] Trial 15 finished with value: 0.378543416834411 and parameters: {'num_leaves': 64}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 45%|####5 | 9/20 [01:44<02:05, 11.43s/it] Early stopping, best iteration is: [103] valid_0's binary_logloss: 0.378543 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 50%|##### | 10/20 [01:56<01:55, 11.58s/it][I 2024-06-18 21:08:26,682] Trial 16 finished with value: 0.37346546696167743 and parameters: {'num_leaves': 184}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 50%|##### | 10/20 [01:56<01:55, 11.58s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.373465 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 55%|#####5 | 11/20 [02:08<01:45, 11.67s/it][I 2024-06-18 21:08:38,573] Trial 17 finished with value: 0.3716035258824437 and parameters: {'num_leaves': 178}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 55%|#####5 | 11/20 [02:08<01:45, 11.67s/it] Early stopping, best iteration is: [33] valid_0's binary_logloss: 0.371604 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 60%|###### | 12/20 [02:10<01:08, 8.58s/it][I 2024-06-18 21:08:40,079] Trial 18 finished with value: 0.4090573635738217 and parameters: {'num_leaves': 6}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 60%|###### | 12/20 [02:10<01:08, 8.58s/it] Early stopping, best iteration is: [219] valid_0's binary_logloss: 0.409057 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 65%|######5 | 13/20 [02:19<01:00, 8.68s/it][I 2024-06-18 21:08:48,983] Trial 19 finished with value: 0.3704743259843756 and parameters: {'num_leaves': 92}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 65%|######5 | 13/20 [02:19<01:00, 8.68s/it] Early stopping, best iteration is: [64] valid_0's binary_logloss: 0.370474 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 70%|####### | 14/20 [02:34<01:03, 10.58s/it][I 2024-06-18 21:09:03,972] Trial 20 finished with value: 0.37479451809569353 and parameters: {'num_leaves': 211}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 70%|####### | 14/20 [02:34<01:03, 10.58s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.374795 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 75%|#######5 | 15/20 [02:43<00:50, 10.14s/it][I 2024-06-18 21:09:13,084] Trial 21 finished with value: 0.3772431537086629 and parameters: {'num_leaves': 90}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 75%|#######5 | 15/20 [02:43<00:50, 10.14s/it] Early stopping, best iteration is: [69] valid_0's binary_logloss: 0.377243 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 80%|######## | 16/20 [02:53<00:40, 10.13s/it][I 2024-06-18 21:09:23,178] Trial 22 finished with value: 0.3728508215083201 and parameters: {'num_leaves': 94}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 80%|######## | 16/20 [02:53<00:40, 10.13s/it] Early stopping, best iteration is: [76] valid_0's binary_logloss: 0.372851 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 85%|########5 | 17/20 [03:01<00:28, 9.57s/it][I 2024-06-18 21:09:31,462] Trial 23 finished with value: 0.3741261012500593 and parameters: {'num_leaves': 65}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 85%|########5 | 17/20 [03:01<00:28, 9.57s/it] Early stopping, best iteration is: [130] valid_0's binary_logloss: 0.374126 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 90%|######### | 18/20 [03:11<00:19, 9.61s/it][I 2024-06-18 21:09:41,151] Trial 24 finished with value: 0.3809976671401048 and parameters: {'num_leaves': 116}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 90%|######### | 18/20 [03:11<00:19, 9.61s/it] Early stopping, best iteration is: [61] valid_0's binary_logloss: 0.380998 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 95%|#########5| 19/20 [03:18<00:08, 8.92s/it][I 2024-06-18 21:09:48,483] Trial 25 finished with value: 0.3773659816174925 and parameters: {'num_leaves': 45}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 95%|#########5| 19/20 [03:18<00:08, 8.92s/it] Early stopping, best iteration is: [150] valid_0's binary_logloss: 0.377366 Training until validation scores don't improve for 50 rounds num_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27<00:00, 8.98s/it][I 2024-06-18 21:09:57,579] Trial 26 finished with value: 0.379797423356925 and parameters: {'num_leaves': 148}. Best is trial 13 with value: 0.36708245318754934. num_leaves, val_score: 0.367082: 100%|##########| 20/20 [03:27<00:00, 10.39s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.379797 bagging, val_score: 0.367082: 0%| | 0/10 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 10%|# | 1/10 [00:15<02:17, 15.28s/it][I 2024-06-18 21:10:12,864] Trial 27 finished with value: 0.3754694447762392 and parameters: {'bagging_fraction': 0.6017118946353502, 'bagging_freq': 3}. Best is trial 27 with value: 0.3754694447762392. bagging, val_score: 0.367082: 10%|# | 1/10 [00:15<02:17, 15.28s/it] Early stopping, best iteration is: [54] valid_0's binary_logloss: 0.375469 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 20%|## | 2/10 [00:32<02:10, 16.29s/it][I 2024-06-18 21:10:29,858] Trial 28 finished with value: 0.37140708223458174 and parameters: {'bagging_fraction': 0.9697760068067878, 'bagging_freq': 7}. Best is trial 28 with value: 0.37140708223458174. bagging, val_score: 0.367082: 20%|## | 2/10 [00:32<02:10, 16.29s/it] Early stopping, best iteration is: [51] valid_0's binary_logloss: 0.371407 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 30%|### | 3/10 [00:40<01:27, 12.51s/it][I 2024-06-18 21:10:37,880] Trial 29 finished with value: 0.3763001794627212 and parameters: {'bagging_fraction': 0.4336686702739523, 'bagging_freq': 1}. Best is trial 28 with value: 0.37140708223458174. bagging, val_score: 0.367082: 30%|### | 3/10 [00:40<01:27, 12.51s/it] Early stopping, best iteration is: [35] valid_0's binary_logloss: 0.3763 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 40%|#### | 4/10 [00:57<01:25, 14.27s/it][I 2024-06-18 21:10:54,855] Trial 30 finished with value: 0.37399860180838584 and parameters: {'bagging_fraction': 0.9955847594220013, 'bagging_freq': 7}. Best is trial 28 with value: 0.37140708223458174. bagging, val_score: 0.367082: 40%|#### | 4/10 [00:57<01:25, 14.27s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.373999 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 50%|##### | 5/10 [01:13<01:15, 15.04s/it][I 2024-06-18 21:11:11,265] Trial 31 finished with value: 0.36851832518824595 and parameters: {'bagging_fraction': 0.7559826069760041, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 50%|##### | 5/10 [01:13<01:15, 15.04s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.368518 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 60%|###### | 6/10 [01:29<01:00, 15.17s/it][I 2024-06-18 21:11:26,675] Trial 32 finished with value: 0.3695574686675923 and parameters: {'bagging_fraction': 0.7373615298502163, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 60%|###### | 6/10 [01:29<01:00, 15.17s/it] Early stopping, best iteration is: [51] valid_0's binary_logloss: 0.369557 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 70%|####### | 7/10 [01:44<00:45, 15.17s/it][I 2024-06-18 21:11:41,852] Trial 33 finished with value: 0.37298644850012186 and parameters: {'bagging_fraction': 0.7616544959864899, 'bagging_freq': 4}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 70%|####### | 7/10 [01:44<00:45, 15.17s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.372986 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 80%|######## | 8/10 [01:58<00:29, 14.94s/it][I 2024-06-18 21:11:56,282] Trial 34 finished with value: 0.3745164899611851 and parameters: {'bagging_fraction': 0.7510199039916521, 'bagging_freq': 5}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 80%|######## | 8/10 [01:58<00:29, 14.94s/it] Early stopping, best iteration is: [35] valid_0's binary_logloss: 0.374516 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 90%|######### | 9/10 [02:13<00:14, 14.95s/it][I 2024-06-18 21:12:11,256] Trial 35 finished with value: 0.3790678087224149 and parameters: {'bagging_fraction': 0.6376411296778922, 'bagging_freq': 3}. Best is trial 31 with value: 0.36851832518824595. bagging, val_score: 0.367082: 90%|######### | 9/10 [02:13<00:14, 14.95s/it] Early stopping, best iteration is: [41] valid_0's binary_logloss: 0.379068 Training until validation scores don't improve for 50 rounds bagging, val_score: 0.367082: 100%|##########| 10/10 [02:28<00:00, 14.82s/it][I 2024-06-18 21:12:25,777] Trial 36 finished with value: 0.36759282443526115 and parameters: {'bagging_fraction': 0.8438639692641409, 'bagging_freq': 5}. Best is trial 36 with value: 0.36759282443526115. bagging, val_score: 0.367082: 100%|##########| 10/10 [02:28<00:00, 14.82s/it] Early stopping, best iteration is: [30] valid_0's binary_logloss: 0.367593 feature_fraction_stage2, val_score: 0.367082: 0%| | 0/6 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.367082: 17%|#6 | 1/6 [00:14<01:11, 14.28s/it][I 2024-06-18 21:12:40,067] Trial 37 finished with value: 0.3751194682458601 and parameters: {'feature_fraction': 0.8480000000000001}. Best is trial 37 with value: 0.3751194682458601. feature_fraction_stage2, val_score: 0.367082: 17%|#6 | 1/6 [00:14<01:11, 14.28s/it] Early stopping, best iteration is: [31] valid_0's binary_logloss: 0.375119 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.367082: 33%|###3 | 2/6 [00:31<01:03, 15.82s/it][I 2024-06-18 21:12:56,969] Trial 38 finished with value: 0.37511946824586 and parameters: {'feature_fraction': 0.88}. Best is trial 38 with value: 0.37511946824586. feature_fraction_stage2, val_score: 0.367082: 33%|###3 | 2/6 [00:31<01:03, 15.82s/it] Early stopping, best iteration is: [31] valid_0's binary_logloss: 0.375119 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.367082: 50%|##### | 3/6 [00:48<00:50, 16.73s/it][I 2024-06-18 21:13:14,771] Trial 39 finished with value: 0.37230053743309227 and parameters: {'feature_fraction': 0.7520000000000001}. Best is trial 39 with value: 0.37230053743309227. feature_fraction_stage2, val_score: 0.367082: 50%|##### | 3/6 [00:48<00:50, 16.73s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.372301 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.367082: 67%|######6 | 4/6 [01:05<00:33, 16.76s/it][I 2024-06-18 21:13:31,580] Trial 40 finished with value: 0.37230053743309227 and parameters: {'feature_fraction': 0.7200000000000001}. Best is trial 39 with value: 0.37230053743309227. feature_fraction_stage2, val_score: 0.367082: 67%|######6 | 4/6 [01:05<00:33, 16.76s/it] Early stopping, best iteration is: [46] valid_0's binary_logloss: 0.372301 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.366281: 83%|########3 | 5/6 [01:21<00:16, 16.48s/it][I 2024-06-18 21:13:47,556] Trial 41 finished with value: 0.3662812971282284 and parameters: {'feature_fraction': 0.8160000000000001}. Best is trial 41 with value: 0.3662812971282284. feature_fraction_stage2, val_score: 0.366281: 83%|########3 | 5/6 [01:21<00:16, 16.48s/it] Early stopping, best iteration is: [30] valid_0's binary_logloss: 0.366281 Training until validation scores don't improve for 50 rounds feature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38<00:00, 16.66s/it][I 2024-06-18 21:14:04,578] Trial 42 finished with value: 0.3670824531875493 and parameters: {'feature_fraction': 0.784}. Best is trial 41 with value: 0.3662812971282284. feature_fraction_stage2, val_score: 0.366281: 100%|##########| 6/6 [01:38<00:00, 16.47s/it] Early stopping, best iteration is: [39] valid_0's binary_logloss: 0.367082 regularization_factors, val_score: 0.366281: 0%| | 0/20 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 5%|5 | 1/20 [00:13<04:12, 13.29s/it][I 2024-06-18 21:14:17,877] Trial 43 finished with value: 0.36913074695699283 and parameters: {'lambda_l1': 0.26740014043884963, 'lambda_l2': 0.001760870184331642}. Best is trial 43 with value: 0.36913074695699283. regularization_factors, val_score: 0.366281: 5%|5 | 1/20 [00:13<04:12, 13.29s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.369131 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 10%|# | 2/20 [00:28<04:21, 14.52s/it][I 2024-06-18 21:14:33,256] Trial 44 finished with value: 0.3744642189562516 and parameters: {'lambda_l1': 1.1442585672872564e-08, 'lambda_l2': 4.849247272391423e-08}. Best is trial 43 with value: 0.36913074695699283. regularization_factors, val_score: 0.366281: 10%|# | 2/20 [00:28<04:21, 14.52s/it] Early stopping, best iteration is: [37] valid_0's binary_logloss: 0.374464 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 15%|#5 | 3/20 [00:45<04:23, 15.52s/it][I 2024-06-18 21:14:49,962] Trial 45 finished with value: 0.3703169935094016 and parameters: {'lambda_l1': 9.96832677827828e-07, 'lambda_l2': 6.3332349736895}. Best is trial 43 with value: 0.36913074695699283. regularization_factors, val_score: 0.366281: 15%|#5 | 3/20 [00:45<04:23, 15.52s/it] Early stopping, best iteration is: [75] valid_0's binary_logloss: 0.370317 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 20%|## | 4/20 [00:58<03:54, 14.69s/it][I 2024-06-18 21:15:03,375] Trial 46 finished with value: 0.3672993919740384 and parameters: {'lambda_l1': 1.5127153863704856, 'lambda_l2': 4.478393628964223e-08}. Best is trial 46 with value: 0.3672993919740384. regularization_factors, val_score: 0.366281: 20%|## | 4/20 [00:58<03:54, 14.69s/it] Early stopping, best iteration is: [49] valid_0's binary_logloss: 0.367299 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 25%|##5 | 5/20 [01:12<03:36, 14.40s/it][I 2024-06-18 21:15:17,268] Trial 47 finished with value: 0.3673221145565776 and parameters: {'lambda_l1': 2.3927205612539093, 'lambda_l2': 5.0167732343674086e-08}. Best is trial 46 with value: 0.3672993919740384. regularization_factors, val_score: 0.366281: 25%|##5 | 5/20 [01:12<03:36, 14.40s/it] Early stopping, best iteration is: [73] valid_0's binary_logloss: 0.367322 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.366281: 30%|### | 6/20 [01:25<03:15, 13.96s/it][I 2024-06-18 21:15:30,372] Trial 48 finished with value: 0.38611937264133733 and parameters: {'lambda_l1': 6.054665616489558, 'lambda_l2': 1.800720533496738e-08}. Best is trial 46 with value: 0.3672993919740384. regularization_factors, val_score: 0.366281: 30%|### | 6/20 [01:25<03:15, 13.96s/it] Early stopping, best iteration is: [156] valid_0's binary_logloss: 0.386119 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 35%|###5 | 7/20 [01:41<03:08, 14.53s/it][I 2024-06-18 21:15:46,069] Trial 49 finished with value: 0.36481873368079354 and parameters: {'lambda_l1': 0.018981605475779886, 'lambda_l2': 4.496278591408666e-06}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 35%|###5 | 7/20 [01:41<03:08, 14.53s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.364819 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 40%|#### | 8/20 [01:58<03:02, 15.22s/it][I 2024-06-18 21:16:02,773] Trial 50 finished with value: 0.3676098962913463 and parameters: {'lambda_l1': 0.005938438143650016, 'lambda_l2': 1.6486829620090715e-05}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 40%|#### | 8/20 [01:58<03:02, 15.22s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.36761 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 45%|####5 | 9/20 [02:13<02:46, 15.12s/it][I 2024-06-18 21:16:17,683] Trial 51 finished with value: 0.3693663505867317 and parameters: {'lambda_l1': 0.14870260759215176, 'lambda_l2': 8.827878170241735e-07}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 45%|####5 | 9/20 [02:13<02:46, 15.12s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.369366 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 50%|##### | 10/20 [02:21<02:11, 13.17s/it][I 2024-06-18 21:16:26,468] Trial 52 finished with value: 0.3995526935417914 and parameters: {'lambda_l1': 9.914097223337537, 'lambda_l2': 2.507766754016921e-06}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 50%|##### | 10/20 [02:21<02:11, 13.17s/it] Early stopping, best iteration is: [108] valid_0's binary_logloss: 0.399553 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 55%|#####5 | 11/20 [02:36<02:02, 13.60s/it][I 2024-06-18 21:16:41,058] Trial 53 finished with value: 0.37135627608204047 and parameters: {'lambda_l1': 0.004317296224576123, 'lambda_l2': 1.0694628305152963e-08}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 55%|#####5 | 11/20 [02:36<02:02, 13.60s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.371356 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 60%|###### | 12/20 [02:50<01:50, 13.82s/it][I 2024-06-18 21:16:55,362] Trial 54 finished with value: 0.3656757773203038 and parameters: {'lambda_l1': 0.1528681952118711, 'lambda_l2': 0.0003078435151438368}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 60%|###### | 12/20 [02:50<01:50, 13.82s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.365676 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 65%|######5 | 13/20 [03:03<01:34, 13.48s/it][I 2024-06-18 21:17:08,074] Trial 55 finished with value: 0.3683612678266087 and parameters: {'lambda_l1': 0.026844520069898837, 'lambda_l2': 0.0010041201106681954}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 65%|######5 | 13/20 [03:03<01:34, 13.48s/it] Early stopping, best iteration is: [30] valid_0's binary_logloss: 0.368361 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 70%|####### | 14/20 [03:18<01:24, 14.03s/it][I 2024-06-18 21:17:23,369] Trial 56 finished with value: 0.367596997615459 and parameters: {'lambda_l1': 0.0003242973314637712, 'lambda_l2': 5.561136449228501e-05}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 70%|####### | 14/20 [03:18<01:24, 14.03s/it] Early stopping, best iteration is: [30] valid_0's binary_logloss: 0.367597 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 75%|#######5 | 15/20 [03:31<01:07, 13.57s/it][I 2024-06-18 21:17:35,879] Trial 57 finished with value: 0.36589916181880006 and parameters: {'lambda_l1': 0.2057156258629452, 'lambda_l2': 0.024885518547422405}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 75%|#######5 | 15/20 [03:31<01:07, 13.57s/it] Early stopping, best iteration is: [31] valid_0's binary_logloss: 0.365899 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 80%|######## | 16/20 [03:45<00:55, 13.85s/it][I 2024-06-18 21:17:50,372] Trial 58 finished with value: 0.36736185428940016 and parameters: {'lambda_l1': 0.0002150419853087141, 'lambda_l2': 0.034107683119902095}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 80%|######## | 16/20 [03:45<00:55, 13.85s/it] Early stopping, best iteration is: [34] valid_0's binary_logloss: 0.367362 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 85%|########5 | 17/20 [04:00<00:42, 14.20s/it][I 2024-06-18 21:18:05,384] Trial 59 finished with value: 0.3702898033719683 and parameters: {'lambda_l1': 0.0583471784381045, 'lambda_l2': 0.3274496387110711}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 85%|########5 | 17/20 [04:00<00:42, 14.20s/it] Early stopping, best iteration is: [40] valid_0's binary_logloss: 0.37029 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 90%|######### | 18/20 [04:18<00:30, 15.10s/it][I 2024-06-18 21:18:22,586] Trial 60 finished with value: 0.3651493917986752 and parameters: {'lambda_l1': 0.001458069822509784, 'lambda_l2': 0.008248248782706902}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 90%|######### | 18/20 [04:18<00:30, 15.10s/it] Early stopping, best iteration is: [43] valid_0's binary_logloss: 0.365149 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 95%|#########5| 19/20 [04:33<00:15, 15.10s/it][I 2024-06-18 21:18:37,676] Trial 61 finished with value: 0.3696068718370661 and parameters: {'lambda_l1': 0.0006748569060520587, 'lambda_l2': 0.01603839211588336}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 95%|#########5| 19/20 [04:33<00:15, 15.10s/it] Early stopping, best iteration is: [41] valid_0's binary_logloss: 0.369607 Training until validation scores don't improve for 50 rounds regularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48<00:00, 15.16s/it][I 2024-06-18 21:18:52,985] Trial 62 finished with value: 0.3700319935632785 and parameters: {'lambda_l1': 0.004313680126647664, 'lambda_l2': 0.008355812202217454}. Best is trial 49 with value: 0.36481873368079354. regularization_factors, val_score: 0.364819: 100%|##########| 20/20 [04:48<00:00, 14.42s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.370032 min_child_samples, val_score: 0.364819: 0%| | 0/5 [00:00<?, ?it/s] Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 20%|## | 1/5 [00:10<00:40, 10.19s/it][I 2024-06-18 21:19:03,252] Trial 63 finished with value: 0.3897736671401978 and parameters: {'min_child_samples': 50}. Best is trial 63 with value: 0.3897736671401978. min_child_samples, val_score: 0.364819: 20%|## | 1/5 [00:10<00:40, 10.19s/it] Early stopping, best iteration is: [51] valid_0's binary_logloss: 0.389774 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 40%|#### | 2/5 [00:29<00:46, 15.39s/it][I 2024-06-18 21:19:22,277] Trial 64 finished with value: 0.3683263542462511 and parameters: {'min_child_samples': 5}. Best is trial 64 with value: 0.3683263542462511. min_child_samples, val_score: 0.364819: 40%|#### | 2/5 [00:29<00:46, 15.39s/it] Early stopping, best iteration is: [43] valid_0's binary_logloss: 0.368326 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 60%|###### | 3/5 [00:36<00:23, 11.88s/it][I 2024-06-18 21:19:29,977] Trial 65 finished with value: 0.3938280500430312 and parameters: {'min_child_samples': 100}. Best is trial 64 with value: 0.3683263542462511. min_child_samples, val_score: 0.364819: 60%|###### | 3/5 [00:36<00:23, 11.88s/it] Early stopping, best iteration is: [113] valid_0's binary_logloss: 0.393828 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 80%|######## | 4/5 [00:53<00:13, 13.86s/it][I 2024-06-18 21:19:46,881] Trial 66 finished with value: 0.36712449039705003 and parameters: {'min_child_samples': 10}. Best is trial 66 with value: 0.36712449039705003. min_child_samples, val_score: 0.364819: 80%|######## | 4/5 [00:53<00:13, 13.86s/it] Early stopping, best iteration is: [32] valid_0's binary_logloss: 0.367124 Training until validation scores don't improve for 50 rounds min_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07<00:00, 13.80s/it][I 2024-06-18 21:20:00,561] Trial 67 finished with value: 0.3719291335673048 and parameters: {'min_child_samples': 25}. Best is trial 66 with value: 0.36712449039705003. min_child_samples, val_score: 0.364819: 100%|##########| 5/5 [01:07<00:00, 13.50s/it] Early stopping, best iteration is: [38] valid_0's binary_logloss: 0.371929 In [8]: Copied! # save/load trainer # dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load ( \"lgbm_trainer.dill\" ) # save/load trainer # dill_dump(\"lgbm_trainer.dill\", trainer) trainer = dill_load(\"lgbm_trainer.dill\") In [11]: Copied! pprint ( metrics_dict ) pprint(metrics_dict) {'cls_report': {'0': {'f1-score': 0.9244823386114495, 'precision': 0.8971631205673759, 'recall': 0.9535175879396985, 'support': 796}, '1': {'f1-score': 0.6536312849162011, 'precision': 0.7597402597402597, 'recall': 0.5735294117647058, 'support': 204}, 'accuracy': 0.876, 'macro avg': {'f1-score': 0.7890568117638252, 'precision': 0.8284516901538178, 'recall': 0.7635234998522022, 'support': 1000}, 'weighted avg': {'f1-score': 0.8692287236576187, 'precision': 0.8691288569586443, 'recall': 0.876, 'support': 1000}}, 'cm': [[759, 37], [87, 117]], 'prec_rec_curve': [[0.204, 0.7597402597402597, 1.0], [1.0, 0.5735294117647058, 0.0], [0.0, 1.0]]} In [32]: Copied! pprint ( trainer . compute_metrics ( df_test , with_dynamic_binary_threshold = True )) pprint(trainer.compute_metrics(df_test, with_dynamic_binary_threshold=True)) {'cls_report': {'0': {'f1-score': 0.9244823386114495, 'precision': 0.8971631205673759, 'recall': 0.9535175879396985, 'support': 796}, '1': {'f1-score': 0.6536312849162011, 'precision': 0.7597402597402597, 'recall': 0.5735294117647058, 'support': 204}, 'accuracy': 0.876, 'macro avg': {'f1-score': 0.7890568117638252, 'precision': 0.8284516901538178, 'recall': 0.7635234998522022, 'support': 1000}, 'weighted avg': {'f1-score': 0.8692287236576187, 'precision': 0.8691288569586443, 'recall': 0.876, 'support': 1000}}, 'cm': [[759, 37], [87, 117]], 'prec_rec_curve': [[0.204, 0.7597402597402597, 1.0], [1.0, 0.5735294117647058, 0.0], [0.0, 1.0]]}","title":"Train a testing model"},{"location":"notebooks/04_model_training_pipeline.html#mlflow","text":"tracking model registration in Minio In [12]: Copied! mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os . environ [ \"AWS_ACCESS_KEY_ID\" ] = \"minioadmin\" os . environ [ \"AWS_SECRET_ACCESS_KEY\" ] = \"minioadmin\" os . environ [ \"MLFLOW_S3_ENDPOINT_URL\" ] = f \"http://10.152.183.156:9000\" mlflow . set_tracking_uri ( \"http://\" + mlflow_host + \":\" + mlflow_port ) experiment_id = get_or_create_experiment ( \"ecovadis\" ) mlflow . set_experiment ( experiment_id = experiment_id ) mlflow_host = \"10.152.183.54\" mlflow_host_url = \"mlflow.mlflow.svc.cluster.local\" mlflow_port = \"5000\" os.environ[\"AWS_ACCESS_KEY_ID\"] = \"minioadmin\" os.environ[\"AWS_SECRET_ACCESS_KEY\"] = \"minioadmin\" os.environ[\"MLFLOW_S3_ENDPOINT_URL\"] = f\"http://10.152.183.156:9000\" mlflow.set_tracking_uri(\"http://\" + mlflow_host + \":\" + mlflow_port) experiment_id = get_or_create_experiment(\"ecovadis\") mlflow.set_experiment(experiment_id=experiment_id) Out[12]: <Experiment: artifact_location='s3://mlflow/6', creation_time=1718745770093, experiment_id='6', last_update_time=1718745770093, lifecycle_stage='active', name='ecovadis', tags={}> In [13]: Copied! metrics_dict_flattened = flatten_dict ( metrics_dict ) metrics_dict_flattened = flatten_dict(metrics_dict) In [14]: Copied! # mlflow metrics can be only int, float not list del metrics_dict_flattened [ \"cm\" ] del metrics_dict_flattened [ \"prec_rec_curve\" ] # mlflow metrics can be only int, float not list del metrics_dict_flattened[\"cm\"] del metrics_dict_flattened[\"prec_rec_curve\"] In [15]: Copied! run_name = \"init_run\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"ecovadis\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"ecovadis_model\" registered_model_name = \"ecovadis_lgbm_model\" mlflow . pyfunc . log_model ( python_model = trainer , artifact_path = artifact_path , registered_model_name = registered_model_name , code_paths = [ \"churn_pred\" ]) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"init_run\" with mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"ecovadis\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"ecovadis_model\" registered_model_name = \"ecovadis_lgbm_model\" mlflow.pyfunc.log_model(python_model=trainer, artifact_path=artifact_path, registered_model_name=registered_model_name, code_paths=[\"churn_pred\"]) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") 2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference 2024/06/18 21:23:54 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference 2024/06/18 21:23:58 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: /tmp/tmpfsv2c_xg/model, flavor: python_function). Fall back to return ['cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback. Successfully registered model 'ecovadis_lgbm_model'. 2024/06/18 21:27:30 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: ecovadis_lgbm_model, version 1 Run ID: 761906b692e94248940c6dbf6b12c23c Model uri: s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model Created version '1' of model 'ecovadis_lgbm_model'.","title":"Mlflow"},{"location":"notebooks/04_model_training_pipeline.html#screenshots-from-mlflow-gui-nodeport-service-in-kubernetes","text":"","title":"Screenshots from MLflow GUI (Nodeport service in Kubernetes)"},{"location":"notebooks/04_model_training_pipeline.html#predictions-testing","text":"IMPORTANT : the predictions are in raw_score format(for testing to see if I get same predictions), i.e. strange numbers and not classes in the output","title":"Predictions testing"},{"location":"notebooks/04_model_training_pipeline.html#example-data","text":"In [16]: Copied! df_pd . iloc [: 2 ] df_pd.iloc[:2] Out[16]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 0 15787619 844 France Male 18 2 160980.03 1 0 0 145936.28 0 neutral 4 stars Taiwan Asia Eastern Asia Europe Western Europe 0 northern 57594.03402 High income 32756.00000 None working_age teen gen_z 1 15770309 656 France Male 18 10 151762.74 1 0 1 127014.32 0 neutral 1 star United States Americas Northern America Europe Western Europe 0 northern 57594.03402 High income 76329.58227 High income working_age teen gen_z In [16]: Copied! df_pd . iloc [: 2 ] . transpose () . to_dict ( orient = 'index' ) df_pd.iloc[:2].transpose().to_dict(orient='index') Out[16]: {'CustomerId': {0: 15787619, 1: 15770309}, 'CreditScore': {0: 844, 1: 656}, 'Country': {0: 'France', 1: 'France'}, 'Gender': {0: 'Male', 1: 'Male'}, 'Age': {0: 18, 1: 18}, 'Tenure': {0: 2, 1: 10}, 'Balance (EUR)': {0: 160980.03, 1: 151762.74}, 'NumberOfProducts': {0: 1, 1: 1}, 'HasCreditCard': {0: '0', 1: '0'}, 'IsActiveMember': {0: '0', 1: '1'}, 'EstimatedSalary': {0: 145936.28, 1: 127014.32}, 'Exited': {0: 0, 1: 0}, 'CustomerFeedback_sentiment3': {0: 'neutral', 1: 'neutral'}, 'CustomerFeedback_sentiment5': {0: '4 stars', 1: '1 star'}, 'Surname_Country': {0: 'Taiwan', 1: 'United States'}, 'Surname_Country_region': {0: 'Asia', 1: 'Americas'}, 'Surname_Country_subregion': {0: 'Eastern Asia', 1: 'Northern America'}, 'Country_region': {0: 'Europe', 1: 'Europe'}, 'Country_subregion': {0: 'Western Europe', 1: 'Western Europe'}, 'is_native': {0: '0', 1: '0'}, 'Country_hemisphere': {0: 'northern', 1: 'northern'}, 'Country_gdp_per_capita': {0: 57594.03402, 1: 57594.03402}, 'Country_IncomeGroup': {0: 'High income', 1: 'High income'}, 'Surname_Country_gdp_per_capita': {0: 32756.0, 1: 76329.58227}, 'Surname_Country_IncomeGroup': {0: 'None', 1: 'High income'}, 'working_class': {0: 'working_age', 1: 'working_age'}, 'stage_of_life': {0: 'teen', 1: 'teen'}, 'generation': {0: 'gen_z', 1: 'gen_z'}} after some notepad magic: { \"CustomerId\": [15787619, 15770309], \"CreditScore\": [844, 656], \"Country\": [\"France\", \"France\"], \"Gender\": [\"Male\", \"Male\"], \"Age\": [18, 18], \"Tenure\": [2, 10], \"Balance (EUR)\": [160980.03, 151762.74], \"NumberOfProducts\": [1, 1], \"HasCreditCard\": [\"0\", \"0\"], \"IsActiveMember\": [\"0\", \"1\"], \"EstimatedSalary\": [145936.28, 127014.32], \"Exited\": [0, 0], \"CustomerFeedback_sentiment3\": [\"neutral\", \"neutral\"], \"CustomerFeedback_sentiment5\": [\"4 stars\", \"1 star\"], \"Surname_Country\": [\"Taiwan\", \"United States\"], \"Surname_Country_region\": [\"Asia\", \"Americas\"], \"Surname_Country_subregion\": [\"Eastern Asia\", \"Northern America\"], \"Country_region\": [\"Europe\", \"Europe\"], \"Country_subregion\": [\"Western Europe\", \"Western Europe\"], \"is_native\": [\"0\", \"0\"], \"Country_hemisphere\": [\"northern\", \"northern\"], \"Country_gdp_per_capita\": [57594.03402, 57594.03402], \"Country_IncomeGroup\": [\"High income\", \"High income\"], \"Surname_Country_gdp_per_capita\": [32756.0, 76329.58227], \"Surname_Country_IncomeGroup\": [\"None\", \"High income\"], \"working_class\": [\"working_age\", \"working_age\"], \"stage_of_life\": [\"teen\", \"teen\"], \"generation\": [\"gen_z\", \"gen_z\"], }","title":"Example data"},{"location":"notebooks/04_model_training_pipeline.html#trainer","text":"In [16]: Copied! trainer . predict ( df = df_pd . iloc [: 2 ] . drop ( columns = [ \"Exited\" ]), context = {}) trainer.predict(df=df_pd.iloc[:2].drop(columns=[\"Exited\"]), context={}) Out[16]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Exited 0 -9.958064 1 -12.560959","title":"Trainer"},{"location":"notebooks/04_model_training_pipeline.html#downloaded-trainer","text":"In [17]: Copied! model_uri model_uri Out[17]: 's3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model' In [18]: Copied! # loaded_trainer = mlflow.pyfunc.load_model(model_uri) loaded_trainer = mlflow . pyfunc . load_model ( 's3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model' ) # loaded_trainer = mlflow.pyfunc.load_model(model_uri) loaded_trainer = mlflow.pyfunc.load_model('s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model') Downloading artifacts: 0%| | 0/61 [00:00<?, ?it/s] In [19]: Copied! loaded_trainer . unwrap_python_model () loaded_trainer.unwrap_python_model() Out[19]: <churn_pred.training.trainer.Trainer at 0x7f3e17f31060> In [21]: Copied! # mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle') mlflow . pyfunc . save_model ( \"data/model\" , python_model = loaded_trainer . unwrap_python_model ()) # mlflow.statsmodels.save_model(loaded_trainer, 'data/model')#, serialization_format='pickle') mlflow.pyfunc.save_model(\"data/model\", python_model=loaded_trainer.unwrap_python_model()) 2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference 2024/06/18 21:31:34 INFO mlflow.types.utils: Unsupported type hint: <class 'pandas.core.frame.DataFrame'>, skipping schema inference 2024/06/18 21:31:37 WARNING mlflow.utils.environment: Encountered an unexpected error while inferring pip requirements (model URI: data/model, flavor: python_function). Fall back to return ['cloudpickle==2.2.1']. Set logging level to DEBUG to see the full traceback. In [22]: Copied! loaded_trainer . predict ( df_pd . iloc [: 2 ] . drop ( columns = [ \"Exited\" ])) loaded_trainer.predict(df_pd.iloc[:2].drop(columns=[\"Exited\"])) Out[22]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Exited 0 -9.958064 1 -12.560959","title":"Downloaded Trainer"},{"location":"notebooks/04_model_training_pipeline.html#downloadedserved-trainer","text":"i.e. testing model locally In [23]: Copied! model_uri model_uri Out[23]: 's3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model' In [24]: Copied! ! mlflow models serve - m \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\" -- env - manager local - p 5000 ! mlflow models serve -m \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\" --env-manager local -p 5000 Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 61/61 [00:00<00:00, 691.42it/s] 2024/06/18 21:32:14 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function' Downloading artifacts: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 61/61 [00:00<00:00, 304.59it/s] 2024/06/18 21:32:15 INFO mlflow.pyfunc.backend: === Running command 'exec gunicorn --timeout=60 -b 127.0.0.1:5000 -w 1 ${GUNICORN_CMD_ARGS} -- mlflow.pyfunc.scoring_server.wsgi:app' [2024-06-18 21:32:15 +0000] [1380] [INFO] Starting gunicorn 22.0.0 [2024-06-18 21:32:15 +0000] [1380] [INFO] Listening at: http://127.0.0.1:5000 (1380) [2024-06-18 21:32:15 +0000] [1380] [INFO] Using worker: sync [2024-06-18 21:32:15 +0000] [1381] [INFO] Booting worker with pid: 1381 ^C [2024-06-18 21:36:45 +0000] [1380] [INFO] Handling signal: int [2024-06-18 21:36:45 +0000] [1381] [INFO] Worker exiting (pid: 1381) output from terminal: root@jupyter-5uperpalo:~/assignment/ecovadis_assignment# curl -X POST -H \"Content-Type:application/json\" --data '{\"inputs\": {\"CustomerId\": [15787619, 15770309], \"CreditScore\": [844, 656], \"Country\": [\"France\", \"France\"], \"Gender\": [\"Male\", \"Male\"], \"Age\": [18, 18], \"Tenure\": [2, 10], \"Balance (EUR)\": [160980.03, 151762.74], \"NumberOfProducts\": [1, 1], \"HasCreditCard\": [\"0\", \"0\"], \"IsActiveMember\": [\"0\", \"1\"], \"EstimatedSalary\": [145936.28, 127014.32], \"CustomerFeedback_sentiment3\": [\"neutral\", \"neutral\"], \"CustomerFeedback_sentiment5\": [\"4 stars\", \"1 star\"], \"Surname_Country\": [\"Taiwan\", \"United States\"], \"Surname_Country_region\": [\"Asia\", \"Americas\"], \"Surname_Country_subregion\": [\"Eastern Asia\", \"Northern America\"], \"Country_region\": [\"Europe\", \"Europe\"], \"Country_subregion\": [\"Western Europe\", \"Western Europe\"], \"is_native\": [\"0\", \"0\"], \"Country_hemisphere\": [\"northern\", \"northern\"], \"Country_gdp_per_capita\": [57594.03402, 57594.03402], \"Country_IncomeGroup\": [\"High income\", \"High income\"], \"Surname_Country_gdp_per_capita\": [32756.0, 76329.58227], \"Surname_Country_IncomeGroup\": [\"None\", \"High income\"], \"working_class\": [\"working_age\", \"working_age\"], \"stage_of_life\": [\"teen\", \"teen\"], \"generation\": [\"gen_z\", \"gen_z\"]}}' http://127.0.0.1:5000/invocations {\"predictions\": [{\"Exited\": -9.958063834023024}, {\"Exited\": -12.560959268044444}]}","title":"Downloaded/Served Trainer"},{"location":"notebooks/04_model_training_pipeline.html#model-deployment-using-kserve-knative-istio","text":"reasons: Kserve is less mature than Seldon core but is primarly adopted by Kubeflow and MLflow \"Serverless installation using Knative\" is a primary installation option of Ksserve as KServe Serverless installation enables autoscaling based on request volume and supports scale down to and from zero. It also supports revision management and canary rollout based on revisions. Istio is a primary choice of service routing for Knative Kserve uses MLserver from Seldoncore but according to \"quick internet search\" it is adopted more by community than Seldon-core as Seldon-core focuses more on enterprise version of its software issues: Kserve uses MLserver which ignores requirements.txt and needs prepackaged conda environment in tar.gz file Kserve uses old version of MLserver(1.3.2) resulting in incorrect usage of conda-unpack related code has to be included with model and conda environment mlserver with correct version has to be defined in the conda env: - mlserver==1.3.5 - mlserver-mlflow==1.3.5 related github issue discussions/troubleshooting: https://github.com/kserve/kserve/issues/3733#issuecomment-2168261646 https://github.com/SeldonIO/MLServer/issues/1801","title":"Model deployment using Kserve + Knative + Istio"},{"location":"notebooks/04_model_training_pipeline.html#create-conda-environment-targz-it-and-upload-it-to-minio","text":"In [7]: Copied! from minio import Minio # to download conda.yaml client = Minio ( \"10.152.183.156:9000\" , \"minioadmin\" , \"minioadmin\" , secure = False ) bucket = \"mlflow\" object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml\" file_path = \"notebooks/conda.yaml\" client . fget_object ( bucket_name = bucket , object_name = object_name , file_path = file_path ) from minio import Minio # to download conda.yaml client = Minio(\"10.152.183.156:9000\", \"minioadmin\", \"minioadmin\", secure=False) bucket = \"mlflow\" object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/conda.yaml\" file_path = \"notebooks/conda.yaml\" client.fget_object(bucket_name=bucket, object_name=object_name, file_path=file_path) Out[7]: <minio.datatypes.Object at 0x730858235e70> following commands were run in terminal to create environment: pip install conda_pack conda env create --force -f conda.yaml conda-pack --name mlflow-env --output environment.tar.gz --force --ignore-editable-packages --ignore-missing-files In [28]: Copied! # upload environmnet.tar.gz object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz\" file_path = \"notebooks/environment.tar.gz\" client . fput_object ( bucket_name = bucket , object_name = object_name , file_path = file_path ) # upload environmnet.tar.gz object_name = \"6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model/environment.tar.gz\" file_path = \"notebooks/environment.tar.gz\" client.fput_object(bucket_name=bucket, object_name=object_name, file_path=file_path) Out[28]: <minio.helpers.ObjectWriteResult at 0x7f409c5bbac0>","title":"Create conda environment, tar.gz it and upload it to Minio"},{"location":"notebooks/04_model_training_pipeline.html#create-kserver-inference-service-in-kubernetes","text":"apiVersion: \"serving.kserve.io/v1beta1\" kind: \"InferenceService\" metadata: name: \"custom-ecovadis-model\" namespace: \"mlflow-kserve-success6g\" spec: predictor: serviceAccountName: success6g model: modelFormat: name: mlflow protocolVersion: v2 storageUri: \"s3://mlflow/6/761906b692e94248940c6dbf6b12c23c/artifacts/ecovadis_model\"","title":"Create Kserver Inference Service in Kubernetes"},{"location":"notebooks/04_model_training_pipeline.html#test-the-inference-service","text":"export INGRESS_NAME=istio-ingressgateway export INGRESS_NS=istio-system export INGRESS_PORT=$(kubectl -n \"${INGRESS_NS}\" get service \"${INGRESS_NAME}\" -o jsonpath='{.spec.ports[?(@.name==\"http2\")].nodePort}') export INGRESS_HOST=$(kubectl get po -l istio=ingressgateway -n \"${INGRESS_NS}\" -o jsonpath='{.items[0].status.hostIP}') export SERVICE_HOSTNAME=$(kubectl get inferenceservice custom-ecovadis-model -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3) curl -v \\ -H \"Host: ${SERVICE_HOSTNAME}\" \\ -H \"Content-Type: application/json\" \\ -d @./test.json \\ http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-model/infer","title":"Test the Inference service"},{"location":"notebooks/04_model_training_pipeline.html#summary","text":"This unfortunately crashes due to the size of conda environment that is being downloaded from minio database by Kserve. COnda env has 3.36GB due to language models (libraries). Note: this approach was succesfully tested on a custom model that used small conda env ~360MB","title":"SUMMARY"},{"location":"notebooks/04_model_training_pipeline.html#workaround","text":"save/serve(deploy) only the LightGBM model preprocess the data locally; this could be done in production using (i) Kserve InferenceGpraph or (ii) pipelines in Kubeflow send preprocessed data to API In [15]: Copied! run_name = \"init_run\" with mlflow . start_run ( experiment_id = experiment_id , run_name = run_name , nested = True ) as run : mlflow . log_params ( trainer . optimizer . best ) mlflow . log_metrics ( metrics_dict_flattened ) # Log tags mlflow . set_tags ( tags = { \"project\" : \"ecovadis\" , \"optimizer_engine\" : \"optuna\" , \"model_family\" : \"ligtgbm\" , \"feature_set_version\" : 1 , } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"ecovadis_raw_model\" registered_model_name = \"ecovadis_raw_lgbm_model\" mlflow . lightgbm . log_model ( lgb_model = trainer . model , artifact_path = artifact_path , registered_model_name = registered_model_name ) # mlflow.pyfunc.log_model(python_model=trainer, # artifact_path=artifact_path, # registered_model_name=registered_model_name, # code_paths=[\"churn_pred\"]) model_uri = mlflow . get_artifact_uri ( artifact_path ) print ( f \"Run ID: \\n { run . info . run_id } \\n Model uri: \\n { model_uri } \" ) run_name = \"init_run\" with mlflow.start_run(experiment_id=experiment_id, run_name=run_name, nested=True) as run: mlflow.log_params(trainer.optimizer.best) mlflow.log_metrics(metrics_dict_flattened) # Log tags mlflow.set_tags( tags={ \"project\": \"ecovadis\", \"optimizer_engine\": \"optuna\", \"model_family\": \"ligtgbm\", \"feature_set_version\": 1, } ) # Log figure - for future fun # mlflow.log_figure(figure=correlation_plot, artifact_file=\"correlation_plot.png\") artifact_path = \"ecovadis_raw_model\" registered_model_name = \"ecovadis_raw_lgbm_model\" mlflow.lightgbm.log_model( lgb_model=trainer.model, artifact_path=artifact_path, registered_model_name=registered_model_name) # mlflow.pyfunc.log_model(python_model=trainer, # artifact_path=artifact_path, # registered_model_name=registered_model_name, # code_paths=[\"churn_pred\"]) model_uri = mlflow.get_artifact_uri(artifact_path) print(f\"Run ID:\\n{run.info.run_id}\\nModel uri:\\n{model_uri}\") Successfully registered model 'ecovadis_raw_lgbm_model'. 2024/06/19 05:43:13 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: ecovadis_raw_lgbm_model, version 1 Run ID: 9a74f8e9087044b9b9d4935a268c26d0 Model uri: s3://mlflow/6/9a74f8e9087044b9b9d4935a268c26d0/artifacts/ecovadis_raw_model Created version '1' of model 'ecovadis_raw_lgbm_model'. In [39]: Copied! df_infser_test = df_pd . iloc [: 2 ] for prep in trainer . preprocessors : df_infser_test = prep . transform ( df_infser_test ) if hasattr ( trainer , \"optimizer\" ): if hasattr ( trainer . optimizer , \"best_to_drop\" ): # type: ignore df_infser_test . drop ( columns = trainer . optimizer . best_to_drop , # type: ignore inplace = True , ) df_infser_test = df_infser_test . drop ( columns = trainer . id_cols + [ trainer . target_col ]) df_infser_test = df_pd.iloc[:2] for prep in trainer.preprocessors: df_infser_test = prep.transform(df_infser_test) if hasattr(trainer, \"optimizer\"): if hasattr(trainer.optimizer, \"best_to_drop\"): # type: ignore df_infser_test.drop( columns=trainer.optimizer.best_to_drop, # type: ignore inplace=True, ) df_infser_test = df_infser_test.drop(columns=trainer.id_cols + [trainer.target_col]) In [40]: Copied! df_infser_test . transpose () . to_dict ( orient = 'index' ) df_infser_test.transpose().to_dict(orient='index') Out[40]: {'is_native': {0: 1.0, 1: 1.0}, 'generation': {0: 1.0, 1: 1.0}, 'Surname_Country': {0: 1.0, 1: 2.0}, 'Country_subregion': {0: 1.0, 1: 1.0}, 'Surname_Country_subregion': {0: 1.0, 1: 2.0}, 'Surname_Country_IncomeGroup': {0: 1.0, 1: 2.0}, 'stage_of_life': {0: 1.0, 1: 1.0}, 'IsActiveMember': {0: 1.0, 1: 2.0}, 'Country': {0: 1.0, 1: 1.0}, 'CustomerFeedback_sentiment5': {0: 1.0, 1: 2.0}, 'CustomerFeedback_sentiment3': {0: 1.0, 1: 1.0}, 'Gender': {0: 1.0, 1: 1.0}, 'Surname_Country_region': {0: 1.0, 1: 2.0}, 'working_class': {0: 1.0, 1: 1.0}, 'HasCreditCard': {0: 1.0, 1: 1.0}, 'EstimatedSalary': {0: 145936.28125, 1: 127014.3203125}, 'Age': {0: 18.0, 1: 18.0}, 'Tenure': {0: 2.0, 1: 10.0}, 'NumberOfProducts': {0: 1.0, 1: 1.0}, 'Country_gdp_per_capita': {0: 57594.03515625, 1: 57594.03515625}, 'Surname_Country_gdp_per_capita': {0: 32756.0, 1: 76329.5859375}, 'CreditScore': {0: 844.0, 1: 656.0}, 'Balance (EUR)': {0: 160980.03125, 1: 151762.734375}} create test.json: cat test.json { \"parameters\": { \"content_type\": \"pd\" }, \"inputs\": [ { \"name\": \"is_native\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"generation\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Surname_Country\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Country_subregion\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Surname_Country_subregion\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Surname_Country_IncomeGroup\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"stage_of_life\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"IsActiveMember\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Country\", \"shape\": [1], \"datatype\": \"BYTES\", \"data\": [1.0] }, { \"name\": \"CustomerFeedback_sentiment5\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"CustomerFeedback_sentiment3\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Gender\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Surname_Country_region\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"working_class\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"HasCreditCard\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"EstimatedSalary\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [145936.28] }, { \"name\": \"Age\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [18] }, { \"name\": \"Tenure\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [2] }, { \"name\": \"NumberOfProducts\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [1.0] }, { \"name\": \"Country_gdp_per_capita\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [57594.03402] }, { \"name\": \"Surname_Country_gdp_per_capita\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [32756.0] }, { \"name\": \"CreditScore\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [844.0] }, { \"name\": \"Balance (EUR)\", \"shape\": [1], \"datatype\": \"FP64\", \"data\": [160980.03125] } ] test service: export INGRESS_NAME=istio-ingressgateway export INGRESS_NS=istio-system export SERVICE_HOSTNAME=custom-ecovadis-raw-model.mlflow-kserve-success6g.example.com export INGRESS_PORT=$(kubectl -n \"${INGRESS_NS}\" get service \"${INGRESS_NAME}\" -o jsonpath='{.spec.ports[?(@.name==\"http2\")].nodePort}') export SERVICE_HOSTNAME=$(kubectl get inferenceservice custom-ecovadis-raw-model -n mlflow-kserve-success6g -o jsonpath='{.status.url}' | cut -d \"/\" -f 3) curl -v \\ -H \"Host: ${SERVICE_HOSTNAME}\" \\ -H \"Content-Type: application/json\" \\ -d @./test.json \\ http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer output: {INGRESS_HOST}:${INGRESS_PORT}/v2/models/custom-ecovadis-raw-model/infer * Trying 10.1.24.50:32702... * Connected to 10.1.24.50 (10.1.24.50) port 32702 (#0) > POST /v2/models/custom-ecovadis-raw-model/infer HTTP/1.1 > Host: custom-ecovadis-raw-model.mlflow-kserve-success6g.example.com > User-Agent: curl/8.1.1 > Accept: */* > Content-Type: application/json > Content-Length: 2823 > < HTTP/1.1 200 OK < ce-endpoint: custom-ecovadis-raw-model < ce-id: 0cd6e630-3a2b-44de-8284-299a10cf5283 < ce-inferenceservicename: mlserver < ce-modelid: custom-ecovadis-raw-model < ce-namespace: mlflow-kserve-success6g < ce-requestid: 0cd6e630-3a2b-44de-8284-299a10cf5283 < ce-source: io.seldon.serving.deployment.mlserver.mlflow-kserve-success6g < ce-specversion: 0.3 < ce-type: io.seldon.serving.inference.response < content-length: 251 < content-type: application/json < date: Wed, 19 Jun 2024 06:49:46 GMT < server: istio-envoy < x-envoy-upstream-service-time: 712 < * Connection #0 to host 10.1.24.50 left intact {\"model_name\":\"custom-ecovadis-raw-model\",\"id\":\"0cd6e630-3a2b-44de-8284-299a10cf5283\",\"parameters\":{\"content_type\":\"np\"},\"outputs\":[{\"name\":\"output-1\",\"shape\":[1,1],\"datatype\":\"FP64\",\"parameters\":{\"content_type\":\"np\"},\"data\":[-9.958064]}]}","title":"WORKAROUND"},{"location":"notebooks/05_model_analysis.html","text":"(function (global, factory) { typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : typeof define === 'function' && define.amd ? define(factory) : (global = global || self, global.ClipboardCopyElement = factory()); }(this, function () { 'use strict'; function createNode(text) { const node = document.createElement('pre'); node.style.width = '1px'; node.style.height = '1px'; node.style.position = 'fixed'; node.style.top = '5px'; node.textContent = text; return node; } function copyNode(node) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(node.textContent); } const selection = getSelection(); if (selection == null) { return Promise.reject(new Error()); } selection.removeAllRanges(); const range = document.createRange(); range.selectNodeContents(node); selection.addRange(range); document.execCommand('copy'); selection.removeAllRanges(); return Promise.resolve(); } function copyText(text) { if ('clipboard' in navigator) { // eslint-disable-next-line flowtype/no-flow-fix-me-comments // $FlowFixMe Clipboard is not defined in Flow yet. return navigator.clipboard.writeText(text); } const body = document.body; if (!body) { return Promise.reject(new Error()); } const node = createNode(text); body.appendChild(node); copyNode(node); body.removeChild(node); return Promise.resolve(); } function copy(button) { const id = button.getAttribute('for'); const text = button.getAttribute('value'); function trigger() { button.dispatchEvent(new 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false setTimeout(function() { notice.hidden = true }, 1000) }) pre { line-height: 125%; } td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } .highlight-ipynb .hll { background-color: var(--jp-cell-editor-active-background) } .highlight-ipynb { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) } .highlight-ipynb .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */ .highlight-ipynb .err { color: var(--jp-mirror-editor-error-color) } /* Error */ .highlight-ipynb .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */ .highlight-ipynb .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */ .highlight-ipynb .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */ .highlight-ipynb .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */ .highlight-ipynb .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */ .highlight-ipynb .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */ .highlight-ipynb .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */ .highlight-ipynb .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */ .highlight-ipynb .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */ .highlight-ipynb .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */ .highlight-ipynb .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */ .highlight-ipynb .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */ .highlight-ipynb .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */ .highlight-ipynb .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */ .highlight-ipynb .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */ .highlight-ipynb .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */ .highlight-ipynb .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */ .highlight-ipynb .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */ .highlight-ipynb .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */ .highlight-ipynb .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */ .highlight-ipynb .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */ .highlight-ipynb .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */ .highlight-ipynb .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */ .highlight-ipynb .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */ .highlight-ipynb .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */ .highlight-ipynb .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */ .highlight-ipynb .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */ .highlight-ipynb .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */ .highlight-ipynb .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */ .highlight-ipynb .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */ .highlight-ipynb .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */ .highlight-ipynb .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */ .highlight-ipynb .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */ .highlight-ipynb .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */ .highlight-ipynb .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */ .highlight-ipynb .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */ .highlight-ipynb .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */ .highlight-ipynb .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */ /* This file is taken from the built JupyterLab theme.css Found on share/nbconvert/templates/lab/static Some changes have been made and marked with CHANGE */ .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ --jp-shadow-base-lightness: 0; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-400); --jp-border-color1: var(--md-grey-400); --jp-border-color2: var(--md-grey-300); --jp-border-color3: var(--md-grey-200); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(0, 0, 0, 1); --jp-ui-font-color1: rgba(0, 0, 0, 0.87); --jp-ui-font-color2: rgba(0, 0, 0, 0.54); --jp-ui-font-color3: rgba(0, 0, 0, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(0, 0, 0, 1); --jp-content-font-color1: rgba(0, 0, 0, 0.87); --jp-content-font-color2: rgba(0, 0, 0, 0.54); --jp-content-font-color3: rgba(0, 0, 0, 0.38); --jp-content-link-color: var(--md-blue-700); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: white; --jp-layout-color1: white; --jp-layout-color2: var(--md-grey-200); --jp-layout-color3: var(--md-grey-400); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: #111111; --jp-inverse-layout-color1: var(--md-grey-900); --jp-inverse-layout-color2: var(--md-grey-800); --jp-inverse-layout-color3: var(--md-grey-700); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-900); --jp-brand-color1: var(--md-blue-700); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-900); --jp-accent-color1: var(--md-green-700); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-900); --jp-warn-color1: var(--md-orange-700); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-900); --jp-error-color1: var(--md-red-700); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-900); --jp-success-color1: var(--md-green-700); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-900); --jp-info-color1: var(--md-cyan-700); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--md-grey-100); --jp-cell-editor-border-color: var(--md-grey-300); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 0.5; --jp-cell-prompt-not-active-font-color: var(--md-grey-700); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: var(--md-blue-50); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: #fdd; --jp-rendermime-table-row-background: var(--md-grey-100); --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.25); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color1); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--md-grey-300); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color1); --jp-input-hover-background: var(--jp-layout-color1); --jp-input-background: var(--md-grey-100); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: #d9d9d9; --jp-editor-selected-focused-background: #d7d4f0; --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: #008000; --jp-mirror-editor-atom-color: #88f; --jp-mirror-editor-number-color: #080; --jp-mirror-editor-def-color: #00f; --jp-mirror-editor-variable-color: var(--md-grey-900); --jp-mirror-editor-variable-2-color: #05a; --jp-mirror-editor-variable-3-color: #085; --jp-mirror-editor-punctuation-color: #05a; --jp-mirror-editor-property-color: #05a; --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ba2121; --jp-mirror-editor-string-2-color: #708; --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: #008000; --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: #170; --jp-mirror-editor-attribute-color: #00c; --jp-mirror-editor-header-color: blue; --jp-mirror-editor-quote-color: #090; --jp-mirror-editor-link-color: #00c; --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: white; /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.5; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(245, 200, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } [data-md-color-scheme=\"slate\"] .jupyter-wrapper { /* Elevation * * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: * * https://github.com/material-components/material-components-web * https://material-components-web.appspot.com/elevation.html */ /* The dark theme shadows need a bit of work, but this will probably also require work on the core layout * colors used in the theme as well. */ --jp-shadow-base-lightness: 32; --jp-shadow-umbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.2 ); --jp-shadow-penumbra-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.14 ); --jp-shadow-ambient-color: rgba( var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), var(--jp-shadow-base-lightness), 0.12 ); --jp-elevation-z0: none; --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), 0px 1px 1px 0px var(--jp-shadow-penumbra-color), 0px 1px 3px 0px var(--jp-shadow-ambient-color); --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), 0px 2px 2px 0px var(--jp-shadow-penumbra-color), 0px 1px 5px 0px var(--jp-shadow-ambient-color); --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), 0px 4px 5px 0px var(--jp-shadow-penumbra-color), 0px 1px 10px 0px var(--jp-shadow-ambient-color); --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), 0px 6px 10px 0px var(--jp-shadow-penumbra-color), 0px 1px 18px 0px var(--jp-shadow-ambient-color); --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), 0px 8px 10px 1px var(--jp-shadow-penumbra-color), 0px 3px 14px 2px var(--jp-shadow-ambient-color); --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), 0px 12px 17px 2px var(--jp-shadow-penumbra-color), 0px 5px 22px 4px var(--jp-shadow-ambient-color); --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), 0px 16px 24px 2px var(--jp-shadow-penumbra-color), 0px 6px 30px 5px var(--jp-shadow-ambient-color); --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), 0px 20px 31px 3px var(--jp-shadow-penumbra-color), 0px 8px 38px 7px var(--jp-shadow-ambient-color); --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), 0px 24px 38px 3px var(--jp-shadow-penumbra-color), 0px 9px 46px 8px var(--jp-shadow-ambient-color); /* Borders * * The following variables, specify the visual styling of borders in JupyterLab. */ --jp-border-width: 1px; --jp-border-color0: var(--md-grey-700); --jp-border-color1: var(--md-grey-700); --jp-border-color2: var(--md-grey-800); --jp-border-color3: var(--md-grey-900); --jp-border-radius: 2px; /* UI Fonts * * The UI font CSS variables are used for the typography all of the JupyterLab * user interface elements that are not directly user generated content. * * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-ui-font-scale-factor: 1.2; --jp-ui-font-size0: 0.83333em; --jp-ui-font-size1: 13px; /* Base font size */ --jp-ui-font-size2: 1.2em; --jp-ui-font-size3: 1.44em; --jp-ui-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Use these font colors against the corresponding main layout colors. * In a light theme, these go from dark to light. */ /* Defaults use Material Design specification */ --jp-ui-font-color0: rgba(255, 255, 255, 1); --jp-ui-font-color1: rgba(255, 255, 255, 0.87); --jp-ui-font-color2: rgba(255, 255, 255, 0.54); --jp-ui-font-color3: rgba(255, 255, 255, 0.38); /* * Use these against the brand/accent/warn/error colors. * These will typically go from light to darker, in both a dark and light theme. */ --jp-ui-inverse-font-color0: rgba(0, 0, 0, 1); --jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8); --jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5); --jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3); /* Content Fonts * * Content font variables are used for typography of user generated content. * * The font sizing here is done assuming that the body font size of --jp-content-font-size1 * is applied to a parent element. When children elements, such as headings, are sized * in em all things will be computed relative to that body size. */ --jp-content-line-height: 1.6; --jp-content-font-scale-factor: 1.2; --jp-content-font-size0: 0.83333em; --jp-content-font-size1: 14px; /* Base font size */ --jp-content-font-size2: 1.2em; --jp-content-font-size3: 1.44em; --jp-content-font-size4: 1.728em; --jp-content-font-size5: 2.0736em; /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-content-presentation-font-size1: 17px; --jp-content-heading-line-height: 1; --jp-content-heading-margin-top: 1.2em; --jp-content-heading-margin-bottom: 0.8em; --jp-content-heading-font-weight: 500; /* Defaults use Material Design specification */ --jp-content-font-color0: rgba(255, 255, 255, 1); --jp-content-font-color1: rgba(255, 255, 255, 1); --jp-content-font-color2: rgba(255, 255, 255, 0.7); --jp-content-font-color3: rgba(255, 255, 255, 0.5); --jp-content-link-color: var(--md-blue-300); --jp-content-font-family: -apple-system, BlinkMacSystemFont, \"Segoe UI\", Helvetica, Arial, sans-serif, \"Apple Color Emoji\", \"Segoe UI Emoji\", \"Segoe UI Symbol\"; /* * Code Fonts * * Code font variables are used for typography of code and other monospaces content. */ --jp-code-font-size: 13px; --jp-code-line-height: 1.3077; /* 17px for 13px base */ --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ --jp-code-font-family-default: Menlo, Consolas, \"DejaVu Sans Mono\", monospace; --jp-code-font-family: var(--jp-code-font-family-default); /* This gives a magnification of about 125% in presentation mode over normal. */ --jp-code-presentation-font-size: 16px; /* may need to tweak cursor width if you change font size */ --jp-code-cursor-width0: 1.4px; --jp-code-cursor-width1: 2px; --jp-code-cursor-width2: 4px; /* Layout * * The following are the main layout colors use in JupyterLab. In a light * theme these would go from light to dark. */ --jp-layout-color0: #111111; --jp-layout-color1: var(--md-grey-900); --jp-layout-color2: var(--md-grey-800); --jp-layout-color3: var(--md-grey-700); --jp-layout-color4: var(--md-grey-600); /* Inverse Layout * * The following are the inverse layout colors use in JupyterLab. In a light * theme these would go from dark to light. */ --jp-inverse-layout-color0: white; --jp-inverse-layout-color1: white; --jp-inverse-layout-color2: var(--md-grey-200); --jp-inverse-layout-color3: var(--md-grey-400); --jp-inverse-layout-color4: var(--md-grey-600); /* Brand/accent */ --jp-brand-color0: var(--md-blue-700); --jp-brand-color1: var(--md-blue-500); --jp-brand-color2: var(--md-blue-300); --jp-brand-color3: var(--md-blue-100); --jp-brand-color4: var(--md-blue-50); --jp-accent-color0: var(--md-green-700); --jp-accent-color1: var(--md-green-500); --jp-accent-color2: var(--md-green-300); --jp-accent-color3: var(--md-green-100); /* State colors (warn, error, success, info) */ --jp-warn-color0: var(--md-orange-700); --jp-warn-color1: var(--md-orange-500); --jp-warn-color2: var(--md-orange-300); --jp-warn-color3: var(--md-orange-100); --jp-error-color0: var(--md-red-700); --jp-error-color1: var(--md-red-500); --jp-error-color2: var(--md-red-300); --jp-error-color3: var(--md-red-100); --jp-success-color0: var(--md-green-700); --jp-success-color1: var(--md-green-500); --jp-success-color2: var(--md-green-300); --jp-success-color3: var(--md-green-100); --jp-info-color0: var(--md-cyan-700); --jp-info-color1: var(--md-cyan-500); --jp-info-color2: var(--md-cyan-300); --jp-info-color3: var(--md-cyan-100); /* Cell specific styles */ --jp-cell-padding: 5px; --jp-cell-collapser-width: 8px; --jp-cell-collapser-min-height: 20px; --jp-cell-collapser-not-active-hover-opacity: 0.6; --jp-cell-editor-background: var(--jp-layout-color1); --jp-cell-editor-border-color: var(--md-grey-700); --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-cell-editor-active-background: var(--jp-layout-color0); --jp-cell-editor-active-border-color: var(--jp-brand-color1); --jp-cell-prompt-width: 64px; --jp-cell-prompt-font-family: var(--jp-code-font-family-default); --jp-cell-prompt-letter-spacing: 0px; --jp-cell-prompt-opacity: 1; --jp-cell-prompt-not-active-opacity: 1; --jp-cell-prompt-not-active-font-color: var(--md-grey-300); /* A custom blend of MD grey and blue 600 * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ --jp-cell-inprompt-font-color: #307fc1; /* A custom blend of MD grey and orange 600 * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ --jp-cell-outprompt-font-color: #bf5b3d; /* Notebook specific styles */ --jp-notebook-padding: 10px; --jp-notebook-select-background: var(--jp-layout-color1); --jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24); /* The scroll padding is calculated to fill enough space at the bottom of the notebook to show one single-line cell (with appropriate padding) at the top when the notebook is scrolled all the way to the bottom. We also subtract one pixel so that no scrollbar appears if we have just one single-line cell in the notebook. This padding is to enable a 'scroll past end' feature in a notebook. */ --jp-notebook-scroll-padding: calc( 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - var(--jp-code-padding) - var(--jp-cell-padding) - 1px ); /* Rendermime styles */ --jp-rendermime-error-background: rgba(244, 67, 54, 0.28); --jp-rendermime-table-row-background: var(--md-grey-900); --jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2); /* Dialog specific styles */ --jp-dialog-background: rgba(0, 0, 0, 0.6); /* Console specific styles */ --jp-console-padding: 10px; /* Toolbar specific styles */ --jp-toolbar-border-color: var(--jp-border-color2); --jp-toolbar-micro-height: 8px; --jp-toolbar-background: var(--jp-layout-color1); --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8); --jp-toolbar-header-margin: 4px 4px 0px 4px; --jp-toolbar-active-background: var(--jp-layout-color0); /* Statusbar specific styles */ --jp-statusbar-height: 24px; /* Input field styles */ --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); --jp-input-active-background: var(--jp-layout-color0); --jp-input-hover-background: var(--jp-layout-color2); --jp-input-background: var(--md-grey-800); --jp-input-border-color: var(--jp-border-color1); --jp-input-active-border-color: var(--jp-brand-color1); --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); /* General editor styles */ --jp-editor-selected-background: var(--jp-layout-color2); --jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24); --jp-editor-cursor-color: var(--jp-ui-font-color0); /* Code mirror specific styles */ --jp-mirror-editor-keyword-color: var(--md-green-500); --jp-mirror-editor-atom-color: var(--md-blue-300); --jp-mirror-editor-number-color: var(--md-green-400); --jp-mirror-editor-def-color: var(--md-blue-600); --jp-mirror-editor-variable-color: var(--md-grey-300); --jp-mirror-editor-variable-2-color: var(--md-blue-400); --jp-mirror-editor-variable-3-color: var(--md-green-600); --jp-mirror-editor-punctuation-color: var(--md-blue-400); --jp-mirror-editor-property-color: var(--md-blue-400); --jp-mirror-editor-operator-color: #aa22ff; --jp-mirror-editor-comment-color: #408080; --jp-mirror-editor-string-color: #ff7070; --jp-mirror-editor-string-2-color: var(--md-purple-300); --jp-mirror-editor-meta-color: #aa22ff; --jp-mirror-editor-qualifier-color: #555; --jp-mirror-editor-builtin-color: var(--md-green-600); --jp-mirror-editor-bracket-color: #997; --jp-mirror-editor-tag-color: var(--md-green-700); --jp-mirror-editor-attribute-color: var(--md-blue-700); --jp-mirror-editor-header-color: var(--md-blue-500); --jp-mirror-editor-quote-color: var(--md-green-300); --jp-mirror-editor-link-color: var(--md-blue-700); --jp-mirror-editor-error-color: #f00; --jp-mirror-editor-hr-color: #999; /* Vega extension styles */ --jp-vega-background: var(--md-grey-400); /* Sidebar-related styles */ --jp-sidebar-min-width: 250px; /* Search-related styles */ --jp-search-toggle-off-opacity: 0.6; --jp-search-toggle-hover-opacity: 0.8; --jp-search-toggle-on-opacity: 1; --jp-search-selected-match-background-color: rgb(255, 225, 0); --jp-search-selected-match-color: black; --jp-search-unselected-match-background-color: var( --jp-inverse-layout-color0 ); --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); /* scrollbar related styles. Supports every browser except Edge. */ /* colors based on JetBrain's Darcula theme */ --jp-scrollbar-background-color: #3f4244; --jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */ --jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */ /* hacks for setting the thumb shape. These do nothing in Firefox */ --jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */ --jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */ /* Icon colors that work well with light or dark backgrounds */ --jp-icon-contrast-color0: var(--md-purple-600); --jp-icon-contrast-color1: var(--md-green-600); --jp-icon-contrast-color2: var(--md-pink-600); --jp-icon-contrast-color3: var(--md-blue-600); } :root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: 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#7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: 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sourceMappingURL=mkdocs-jupyter.css.map*/ init_mathjax = function() { if (window.MathJax) { // MathJax loaded MathJax.Hub.Config({ TeX: { equationNumbers: { autoNumber: \"AMS\", useLabelIds: true } }, tex2jax: { inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ], displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ], processEscapes: true, processEnvironments: true }, displayAlign: 'center', CommonHTML: { linebreaks: { automatic: true } } }); MathJax.Hub.Queue([\"Typeset\", MathJax.Hub]); } } init_mathjax(); Model analysis \u00b6 In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! from churn_pred.utils import dill_load from churn_pred.training.utils import get_feature_importance from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from churn_pred.training.utils import to_lgbdataset import shap import warnings import numpy as np from churn_pred.utils import dill_load from churn_pred.training.utils import get_feature_importance from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from churn_pred.training.utils import to_lgbdataset import shap import warnings import numpy as np In [3]: Copied! trainer = dill_load ( \"data/model/lgbm_trainer.dill\" ) trainer = dill_load(\"data/model/lgbm_trainer.dill\") In [4]: Copied! df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" , ] df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") cat_cols = [ \"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\", \"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\", ] df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [5]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col)) Model feature importance and \"certainty\" of the model in predicting classes - probability density per class \u00b6 In [6]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[6]: <Axes: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'> In [7]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", ) SHAP analysis \u00b6 In [8]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [9]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [10]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [11]: Copied! df_predicted_cls [ 28 : 30 ] df_predicted_cls[28:30] Out[11]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Exited 28 0 29 1 In [12]: Copied! df_test . iloc [ 28 : 30 ] df_test.iloc[28:30] Out[12]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 9941 15714240 712 Spain Male 74 5 0.0 2 0 0 151425.82 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Southern Europe 0 northern 48685.49631 High income 34637.76172 Upper middle income elderly senior_adult boomers_1 7444 15658057 812 Spain Female 44 8 0.0 3 1 0 66926.83 1 neutral 1 star Italy Europe Southern Europe Europe Southern Europe 0 northern 48685.49631 High income 55442.07843 High income working_age middle_age_adult millennials In [13]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 28 : 30 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[28:30], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"05_model_analysis.ipynb"},{"location":"notebooks/05_model_analysis.html#model-analysis","text":"In [1]: Copied! import os import sys sys . path . append ( os . getcwd ()) os . chdir ( \"..\" ) import pandas as pd pd . set_option ( \"display.max_columns\" , 200 ) pd . set_option ( \"display.max_rows\" , 300 ) import os import sys sys.path.append(os.getcwd()) os.chdir(\"..\") import pandas as pd pd.set_option(\"display.max_columns\", 200) pd.set_option(\"display.max_rows\", 300) In [2]: Copied! from churn_pred.utils import dill_load from churn_pred.training.utils import get_feature_importance from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from churn_pred.training.utils import to_lgbdataset import shap import warnings import numpy as np from churn_pred.utils import dill_load from churn_pred.training.utils import get_feature_importance from churn_pred.eda.target.analysis import correlation from churn_pred.eda.target.plotting import prob_distrib_per_class from sklearn.model_selection import train_test_split from pprint import pprint import lightgbm as lgb from churn_pred.training.utils import to_lgbdataset import shap import warnings import numpy as np In [3]: Copied! trainer = dill_load ( \"data/model/lgbm_trainer.dill\" ) trainer = dill_load(\"data/model/lgbm_trainer.dill\") In [4]: Copied! df_pd = pd . read_parquet ( \"data/dataset_auxiliary_features_cleaned.parquet\" ) cat_cols = [ \"Country\" , \"Gender\" , \"HasCreditCard\" , \"IsActiveMember\" , \"CustomerFeedback_sentiment3\" , \"CustomerFeedback_sentiment5\" , \"Surname_Country\" , \"Surname_Country_region\" , \"Surname_Country_subregion\" , \"Country_region\" , \"Country_subregion\" , \"is_native\" , \"Country_hemisphere\" , \"Country_IncomeGroup\" , \"Surname_Country_IncomeGroup\" , \"working_class\" , \"stage_of_life\" , \"generation\" , ] df_pd [ cat_cols ] = df_pd [ cat_cols ] . astype ( str ) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train , df_valid = train_test_split ( df_pd , test_size = valid_size , stratify = df_pd [ trainer . target_col ], random_state = random_state , ) df_valid , df_test = train_test_split ( df_valid , test_size = test_size , stratify = df_valid [ trainer . target_col ], random_state = random_state , ) df_pd = pd.read_parquet(\"data/dataset_auxiliary_features_cleaned.parquet\") cat_cols = [ \"Country\", \"Gender\", \"HasCreditCard\", \"IsActiveMember\", \"CustomerFeedback_sentiment3\", \"CustomerFeedback_sentiment5\", \"Surname_Country\", \"Surname_Country_region\", \"Surname_Country_subregion\", \"Country_region\", \"Country_subregion\", \"is_native\", \"Country_hemisphere\", \"Country_IncomeGroup\", \"Surname_Country_IncomeGroup\", \"working_class\", \"stage_of_life\", \"generation\", ] df_pd[cat_cols] = df_pd[cat_cols].astype(str) valid_size = 0.2 test_size = 0.5 random_state = 1 df_train, df_valid = train_test_split( df_pd, test_size=valid_size, stratify=df_pd[trainer.target_col], random_state=random_state, ) df_valid, df_test = train_test_split( df_valid, test_size=test_size, stratify=df_valid[trainer.target_col], random_state=random_state, ) In [5]: Copied! df_predicted_proba = trainer . predict_proba ( df = df_test . drop ( columns = trainer . target_col ), binary2d = False ) df_predicted_cls = trainer . predict_cls ( df = df_test . drop ( columns = trainer . target_col )) df_predicted_proba = trainer.predict_proba( df=df_test.drop(columns=trainer.target_col), binary2d=False ) df_predicted_cls = trainer.predict_cls(df=df_test.drop(columns=trainer.target_col))","title":"Model analysis"},{"location":"notebooks/05_model_analysis.html#model-feature-importance-and-certainty-of-the-model-in-predicting-classes-probability-density-per-class","text":"In [6]: Copied! lgb . plot_importance ( trainer . model , importance_type = \"split\" ) # lgb.plot_importance(trainer.model, importance_type=\"gain\") lgb.plot_importance(trainer.model, importance_type=\"split\") # lgb.plot_importance(trainer.model, importance_type=\"gain\") Out[6]: <Axes: title={'center': 'Feature importance'}, xlabel='Feature importance', ylabel='Features'> In [7]: Copied! fig = prob_distrib_per_class ( predicted_probs = df_predicted_proba . values . flatten (), actual = df_test [ trainer . target_col ] . values . flatten (), task = \"binary\" , ) fig = prob_distrib_per_class( predicted_probs=df_predicted_proba.values.flatten(), actual=df_test[trainer.target_col].values.flatten(), task=\"binary\", )","title":"Model feature importance and \"certainty\" of the model in predicting classes - probability density per class"},{"location":"notebooks/05_model_analysis.html#shap-analysis","text":"In [8]: Copied! df_test_prep = trainer . preprocessors [ 0 ] . transform ( df_test . drop ( columns = [ trainer . target_col ]) ) df_test_prep [ trainer . target_col ] = df_test [ trainer . target_col ] . astype ( int ) lgb_test , _ = to_lgbdataset ( train = df_test_prep , cat_cols = trainer . cat_cols , target_col = trainer . target_col , id_cols = trainer . id_cols , ) df_test_prep = trainer.preprocessors[0].transform( df_test.drop(columns=[trainer.target_col]) ) df_test_prep[trainer.target_col] = df_test[trainer.target_col].astype(int) lgb_test, _ = to_lgbdataset( train=df_test_prep, cat_cols=trainer.cat_cols, target_col=trainer.target_col, id_cols=trainer.id_cols, ) In [9]: Copied! base_value = trainer . model . predict ( data = lgb_test . data , pred_contrib = True ) shap_values = base_value [:, : - 1 ] base_value = np . mean ( base_value [:, - 1 ]) base_value = trainer.model.predict(data=lgb_test.data, pred_contrib=True) shap_values = base_value[:, :-1] base_value = np.mean(base_value[:, -1]) In [10]: Copied! shap . summary_plot ( shap_values , features = lgb_test . data , plot_type = \"dot\" , sort = False , show = True ) shap.summary_plot( shap_values, features=lgb_test.data, plot_type=\"dot\", sort=False, show=True ) In [11]: Copied! df_predicted_cls [ 28 : 30 ] df_predicted_cls[28:30] Out[11]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Exited 28 0 29 1 In [12]: Copied! df_test . iloc [ 28 : 30 ] df_test.iloc[28:30] Out[12]: .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } CustomerId CreditScore Country Gender Age Tenure Balance (EUR) NumberOfProducts HasCreditCard IsActiveMember EstimatedSalary Exited CustomerFeedback_sentiment3 CustomerFeedback_sentiment5 Surname_Country Surname_Country_region Surname_Country_subregion Country_region Country_subregion is_native Country_hemisphere Country_gdp_per_capita Country_IncomeGroup Surname_Country_gdp_per_capita Surname_Country_IncomeGroup working_class stage_of_life generation 9941 15714240 712 Spain Male 74 5 0.0 2 0 0 151425.82 0 neutral 2 stars Russian Federation Europe Eastern Europe Europe Southern Europe 0 northern 48685.49631 High income 34637.76172 Upper middle income elderly senior_adult boomers_1 7444 15658057 812 Spain Female 44 8 0.0 3 1 0 66926.83 1 neutral 1 star Italy Europe Southern Europe Europe Southern Europe 0 northern 48685.49631 High income 55442.07843 High income working_age middle_age_adult millennials In [13]: Copied! shap_decision_plot = shap . decision_plot ( base_value = base_value , shap_values = shap_values [ 28 : 30 ], features = trainer . model . feature_name (), feature_names = trainer . model . feature_name (), # legend_labels=legend_labels, link = \"logit\" , # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), ) shap_decision_plot = shap.decision_plot( base_value=base_value, shap_values=shap_values[28:30], features=trainer.model.feature_name(), feature_names=trainer.model.feature_name(), # legend_labels=legend_labels, link=\"logit\", # row_index=0, # feature_order=list(range(len(trainer.model.feature_name()))), )","title":"SHAP analysis"}]}
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diff --git a/docs/site/sitemap.xml b/docs/site/sitemap.xml
index 0f8724e..120606c 100644
--- a/docs/site/sitemap.xml
+++ b/docs/site/sitemap.xml
@@ -1,3 +1,83 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
+    </url>
+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
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+    <url>
+         <loc>None</loc>
+         <lastmod>2024-08-13</lastmod>
+         <changefreq>daily</changefreq>
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diff --git a/docs/site/sitemap.xml.gz b/docs/site/sitemap.xml.gz
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diff --git a/docs/sources/assets/images/logo.pdf b/docs/sources/assets/images/logo.pdf
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diff --git a/docs/sources/index.md b/docs/sources/index.md
index db5ae09..e3f72a1 100644
--- a/docs/sources/index.md
+++ b/docs/sources/index.md
@@ -1,6 +1,6 @@
-# Churn prediction assignment for EcoVadis
+# Churn predictor
 
-Main purpose of this library is to provide solution for an asisgnemnt in the interview process of EcoVadis.
+Main purpose of this library is to provide solution for an asisgnemnt below:
 
 ```
 In this assignment you're tasked with developing a machine learning solution for churn prediction to identify which customers are likely to leave a service (column "Exited" in the attached dataset). This assignment is meant to assess
@@ -11,8 +11,6 @@ In this assignment you're tasked with developing a machine learning solution for
 
 Please note that multiple solutions may exist and we do not expect a production ready solution, though any reflections on how you may wish to productionalise your solution are welcome. You are free to choose the medium (e.g., notebooks, python scripts). 
 
-Please return your solution by May 2 (9am CET). Once returned, our colleagues with the HR department will schedule the final interview where you'll be asked to walk us through your solution and reflect on any decisions made.
-
 Additional explanation of independent variables:
 
 NumberOfProducts - the number of accounts and bank-affiliated products 
diff --git a/setup.py b/setup.py
index 7097fb8..9ad68a4 100644
--- a/setup.py
+++ b/setup.py
@@ -34,17 +34,17 @@ def requirements(fname):
 setup_kwargs = {
     "name": "churn_pred",
     "version": version,
-    "description": "EcoVadis assignment",
+    "description": "Churn predictor",
     "long_description": open("README.MD", "r", encoding="utf-8").read(),
     "long_description_content_type": "text/markdown",
     # "long_description": long_description,
     "author": "Pavol Mulinka",
     "author_email": "mulinka.pavol@gmail.com",
-    "url": "https://github.com/5uperpalo/ecovadis_assignment",
+    "url": "https://github.com/5uperpalo/churnpred",
     "license": "MIT",
     "install_requires": reqs,
     "extras_require": extras,
-    "python_requires": ">=3.9.0",
+    "python_requires": ">=3.8.1",
     "classifiers": [
         "Environment :: Other Environment",
         "Framework :: Jupyter",
@@ -56,7 +56,7 @@ def requirements(fname):
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
     ],
     "zip_safe": True,
-    "package_data": {"ecovadis_assignment": ["data/*"]},
+    "package_data": {"churnpred": ["data/*"]},
     "packages": setuptools.find_packages(exclude=["test_*.py"]),
 }