09-classification-carbon-based-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c772a64f-823c-4973-bf41-3c9a239c601a",
   "metadata": {},
   "source": [
    "# Group-based Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc25fc3-e84a-4d87-ace3-e947218d58aa",
   "metadata": {},
   "source": [
    "### Importing libraries and define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5e31b3ce-df35-4b10-a99a-2f753f523fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import math\n",
    "import os\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8375cb30",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Managing Warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    os.environ[\"PYTHONWARNINGS\"] = \"ignore\" # Also affect subprocesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2d2c745b-9e8f-415c-a43c-f08fb5937b46",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import preprocessing\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b9378dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick.classifier import ROCAUC\n",
    "from yellowbrick.classifier import ClassPredictionError\n",
    "from yellowbrick.classifier import PrecisionRecallCurve\n",
    "from yellowbrick.classifier import ConfusionMatrix\n",
    "from yellowbrick.classifier import DiscriminationThreshold\n",
    "from yellowbrick.model_selection import FeatureImportances\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03a6e29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycaret.classification import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d5a3c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import functions as f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d563849c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a1f4ce5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ccba6e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = ['NP_type', 'Coating','shape','Cell_name','Cell_species','cell_Organ','Cell_morphology',\n",
    "                       'Cell_age','cell_type','sex','Assay','Test_indicator']\n",
    "numerical_columns = ['core_size_nm','hydro_size_nm','Surf_charge_mV','Surface_area_m2_g','Dose_microg_mL','Duration_h',\n",
    "                    'Viability']\n",
    "\n",
    "numerical_columns_no_target = ['core_size_nm','hydro_size_nm','Surf_charge_mV','Dose_microg_mL','Duration_h']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5e030a",
   "metadata": {},
   "source": [
    "# Carbon-based NMs classification model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a92e9968",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_group_name = \"carbon_based\"\n",
    "group_name = \"carbon\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6d7f7e",
   "metadata": {},
   "source": [
    "### Load the data and filter instances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "da49b7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_df = pd.read_csv(\"output/datasets/universal/data_classification_train_df.csv\", encoding=\"utf-8\")\n",
    "data_test_df = pd.read_csv(\"output/datasets/universal/data_classification_test_df.csv\", encoding=\"utf-8\")\n",
    "data_df = pd.concat([data_train_df,data_test_df])\n",
    "\n",
    "del data_train_df\n",
    "del data_test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f38a5c13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4656, 20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4e127fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_df = data_df[data_df['nanomaterial_group'] == group_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "037994ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(116, 20)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_group_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ea457428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NP_type</th>\n",
       "      <th>Coating</th>\n",
       "      <th>core_size_nm</th>\n",
       "      <th>hydro_size_nm</th>\n",
       "      <th>Surf_charge_mV</th>\n",
       "      <th>Surface_area_m2_g</th>\n",
       "      <th>shape</th>\n",
       "      <th>Dose_microg_mL</th>\n",
       "      <th>Duration_h</th>\n",
       "      <th>Cell_name</th>\n",
       "      <th>Cell_species</th>\n",
       "      <th>cell_Organ</th>\n",
       "      <th>Cell_morphology</th>\n",
       "      <th>Cell_age</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>sex</th>\n",
       "      <th>Assay</th>\n",
       "      <th>Test_indicator</th>\n",
       "      <th>nanomaterial_group</th>\n",
       "      <th>viability_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carbon</td>\n",
       "      <td>None</td>\n",
       "      <td>53.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-39.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>50.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>THP_1</td>\n",
       "      <td>Human</td>\n",
       "      <td>Blood</td>\n",
       "      <td>monocyte</td>\n",
       "      <td>neonate</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>carbon</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C60</td>\n",
       "      <td>None</td>\n",
       "      <td>34.64</td>\n",
       "      <td>142.0</td>\n",
       "      <td>-37.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>0.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>mDF</td>\n",
       "      <td>Mouse</td>\n",
       "      <td>Skin</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>carbon</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Diamond</td>\n",
       "      <td>None</td>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-35.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>C3A</td>\n",
       "      <td>Human</td>\n",
       "      <td>Liver</td>\n",
       "      <td>Epithelial</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>carbon</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>C60</td>\n",
       "      <td>None</td>\n",
       "      <td>34.64</td>\n",
       "      <td>142.0</td>\n",
       "      <td>-37.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>1.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>hPDLF</td>\n",
       "      <td>Human</td>\n",
       "      <td>Teeth</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>Adult</td>\n",
       "      <td>cancer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>carbon</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>C70</td>\n",
       "      <td>None</td>\n",
       "      <td>8.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sphere</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>V79</td>\n",
       "      <td>Hamster</td>\n",
       "      <td>Lung</td>\n",
       "      <td>Fibroblast</td>\n",
       "      <td>Adult</td>\n",
       "      <td>Normal</td>\n",
       "      <td>male</td>\n",
       "      <td>MTT</td>\n",
       "      <td>TetrazoliumSalt</td>\n",
       "      <td>carbon</td>\n",
       "      <td>NonToxic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NP_type Coating  core_size_nm  hydro_size_nm  Surf_charge_mV  \\\n",
       "0     Carbon    None         53.70            NaN           -39.9   \n",
       "5        C60    None         34.64          142.0           -37.4   \n",
       "91   Diamond    None          4.00            NaN           -35.6   \n",
       "104      C60    None         34.64          142.0           -37.4   \n",
       "154      C70    None          8.00            NaN           -20.0   \n",
       "\n",
       "     Surface_area_m2_g   shape  Dose_microg_mL  Duration_h Cell_name  \\\n",
       "0                  NaN  Sphere            50.0        24.0     THP_1   \n",
       "5                  NaN  Sphere             0.1         6.0       mDF   \n",
       "91                 NaN  Sphere           100.0         2.0       C3A   \n",
       "104                NaN  Sphere             1.0        48.0     hPDLF   \n",
       "154                NaN  Sphere             5.0        24.0       V79   \n",
       "\n",
       "    Cell_species cell_Organ Cell_morphology Cell_age cell_type   sex Assay  \\\n",
       "0          Human      Blood        monocyte  neonate    cancer  male   MTT   \n",
       "5          Mouse       Skin      Fibroblast    Adult    Normal   NaN   MTT   \n",
       "91         Human      Liver      Epithelial    Adult    cancer  male   MTT   \n",
       "104        Human      Teeth      Fibroblast    Adult    cancer   NaN   MTT   \n",
       "154      Hamster       Lung      Fibroblast    Adult    Normal  male   MTT   \n",
       "\n",
       "      Test_indicator nanomaterial_group viability_class  \n",
       "0    TetrazoliumSalt             carbon        NonToxic  \n",
       "5    TetrazoliumSalt             carbon        NonToxic  \n",
       "91   TetrazoliumSalt             carbon        NonToxic  \n",
       "104  TetrazoliumSalt             carbon        NonToxic  \n",
       "154  TetrazoliumSalt             carbon        NonToxic  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_group_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25ca8bfa",
   "metadata": {},
   "source": [
    "### Create training set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bcf76419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ec60d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ec60d_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_ec60d_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_ec60d_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_ec60d_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_ec60d_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_ec60d_row1_col1\" class=\"data row1 col1\" >viability_class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_ec60d_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "      <td id=\"T_ec60d_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_ec60d_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "      <td id=\"T_ec60d_row3_col1\" class=\"data row3 col1\" >NonToxic: 0, Toxic: 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_ec60d_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "      <td id=\"T_ec60d_row4_col1\" class=\"data row4 col1\" >(116, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_ec60d_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "      <td id=\"T_ec60d_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_ec60d_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_ec60d_row6_col1\" class=\"data row6 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_ec60d_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_ec60d_row7_col1\" class=\"data row7 col1\" >14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_ec60d_row8_col0\" class=\"data row8 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_ec60d_row8_col1\" class=\"data row8 col1\" >(92, 18)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_ec60d_row9_col0\" class=\"data row9 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_ec60d_row9_col1\" class=\"data row9 col1\" >(24, 18)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_ec60d_row10_col0\" class=\"data row10 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_ec60d_row10_col1\" class=\"data row10 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_ec60d_row11_col0\" class=\"data row11 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_ec60d_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_ec60d_row12_col0\" class=\"data row12 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_ec60d_row12_col1\" class=\"data row12 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_ec60d_row13_col0\" class=\"data row13 col0\" >Fold Number</td>\n",
       "      <td id=\"T_ec60d_row13_col1\" class=\"data row13 col1\" >10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_ec60d_row14_col0\" class=\"data row14 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_ec60d_row14_col1\" class=\"data row14 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_ec60d_row15_col0\" class=\"data row15 col0\" >Use GPU</td>\n",
       "      <td id=\"T_ec60d_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_ec60d_row16_col0\" class=\"data row16 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_ec60d_row16_col1\" class=\"data row16 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_ec60d_row17_col0\" class=\"data row17 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_ec60d_row17_col1\" class=\"data row17 col1\" >splitter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_ec60d_row18_col0\" class=\"data row18 col0\" >USI</td>\n",
       "      <td id=\"T_ec60d_row18_col1\" class=\"data row18 col1\" >474e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_ec60d_row19_col0\" class=\"data row19 col0\" >Fix Imbalance</td>\n",
       "      <td id=\"T_ec60d_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ec60d_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_ec60d_row20_col0\" class=\"data row20 col0\" >Fix Imbalance Method</td>\n",
       "      <td id=\"T_ec60d_row20_col1\" class=\"data row20 col1\" >SMOTE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f34a99b11f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "splitter = setup(data = data_group_df, target = 'viability_class',\n",
    "                 preprocess = False,\n",
    "                 train_size=0.8,\n",
    "                 data_split_stratify=True,\n",
    "                 # don't change the following\n",
    "                 session_id = 123,\n",
    "                 use_gpu=False,\n",
    "                 log_experiment = False,\n",
    "                 silent=True,\n",
    "                 experiment_name = 'splitter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3898aa43",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_train_df = get_config(variable=\"X_train\").copy()\n",
    "data_group_train_df['viability_class'] = get_config(variable=\"y_train\").copy()\n",
    "\n",
    "data_group_test_df = get_config(variable=\"X_test\").copy()\n",
    "data_group_test_df['viability_class'] = get_config(variable=\"y_test\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0804450",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 19)\n",
      "(24, 19)\n"
     ]
    }
   ],
   "source": [
    "print(data_group_train_df.shape)\n",
    "print(data_group_test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e53cd4f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x3000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f.plot_distribution_train_test(data_group_train_df, data_group_test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9b00e8",
   "metadata": {},
   "source": [
    "### Save cleaned datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d2f791ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_train_df.to_csv('output/datasets/'+model_group_name+'/data_classification_train_df.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28b9aa49",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_group_test_df.to_csv('output/datasets/'+model_group_name+'/data_classification_test_df.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cffe023c-bf03-4ba8-a278-8b1261da6572",
   "metadata": {},
   "source": [
    "### Use the best scenario identified in step 01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2cf8d7ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_ignore_features = None\n",
    "sc_bin_numeric_features = None\n",
    "sc_high_cardinality_features = None\n",
    "\n",
    "sc_imputation_type = 'simple'\n",
    "sc_numeric_imputation = 'mean'\n",
    "\n",
    "sc_normalize = True\n",
    "sc_remove_outliers = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb0fb96c",
   "metadata": {},
   "source": [
    "### Define seeds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7db1802e",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_inner_seed = 123456\n",
    "cv_outer_seed = 123"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b866744f",
   "metadata": {},
   "source": [
    "### Data Preprcoessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a9f4499b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from imblearn.over_sampling import SMOTE\n",
    "\n",
    "#smote = SMOTE(k_neighbors=3, n_jobs=None, random_state=cv_inner_seed, sampling_strategy='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cb2a02f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_42a77_row5_col1, #T_42a77_row29_col1, #T_42a77_row36_col1, #T_42a77_row44_col1, #T_42a77_row58_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_42a77\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_42a77_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_42a77_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_42a77_row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "      <td id=\"T_42a77_row0_col1\" class=\"data row0 col1\" >123456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_42a77_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_42a77_row1_col1\" class=\"data row1 col1\" >viability_class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_42a77_row2_col0\" class=\"data row2 col0\" >Target Type</td>\n",
       "      <td id=\"T_42a77_row2_col1\" class=\"data row2 col1\" >Binary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_42a77_row3_col0\" class=\"data row3 col0\" >Label Encoded</td>\n",
       "      <td id=\"T_42a77_row3_col1\" class=\"data row3 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_42a77_row4_col0\" class=\"data row4 col0\" >Original Data</td>\n",
       "      <td id=\"T_42a77_row4_col1\" class=\"data row4 col1\" >(92, 19)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_42a77_row5_col0\" class=\"data row5 col0\" >Missing Values</td>\n",
       "      <td id=\"T_42a77_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_42a77_row6_col0\" class=\"data row6 col0\" >Numeric Features</td>\n",
       "      <td id=\"T_42a77_row6_col1\" class=\"data row6 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_42a77_row7_col0\" class=\"data row7 col0\" >Categorical Features</td>\n",
       "      <td id=\"T_42a77_row7_col1\" class=\"data row7 col1\" >13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_42a77_row8_col0\" class=\"data row8 col0\" >Ordinal Features</td>\n",
       "      <td id=\"T_42a77_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_42a77_row9_col0\" class=\"data row9 col0\" >High Cardinality Features</td>\n",
       "      <td id=\"T_42a77_row9_col1\" class=\"data row9 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_42a77_row10_col0\" class=\"data row10 col0\" >High Cardinality Method</td>\n",
       "      <td id=\"T_42a77_row10_col1\" class=\"data row10 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_42a77_row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "      <td id=\"T_42a77_row11_col1\" class=\"data row11 col1\" >(92, 31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_42a77_row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "      <td id=\"T_42a77_row12_col1\" class=\"data row12 col1\" >(24, 31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_42a77_row13_col0\" class=\"data row13 col0\" >Shuffle Train-Test</td>\n",
       "      <td id=\"T_42a77_row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_42a77_row14_col0\" class=\"data row14 col0\" >Stratify Train-Test</td>\n",
       "      <td id=\"T_42a77_row14_col1\" class=\"data row14 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_42a77_row15_col0\" class=\"data row15 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_42a77_row15_col1\" class=\"data row15 col1\" >StratifiedKFold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_42a77_row16_col0\" class=\"data row16 col0\" >Fold Number</td>\n",
       "      <td id=\"T_42a77_row16_col1\" class=\"data row16 col1\" >5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_42a77_row17_col0\" class=\"data row17 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_42a77_row17_col1\" class=\"data row17 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_42a77_row18_col0\" class=\"data row18 col0\" >Use GPU</td>\n",
       "      <td id=\"T_42a77_row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_42a77_row19_col0\" class=\"data row19 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_42a77_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_42a77_row20_col0\" class=\"data row20 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_42a77_row20_col1\" class=\"data row20 col1\" >group_based</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_42a77_row21_col0\" class=\"data row21 col0\" >USI</td>\n",
       "      <td id=\"T_42a77_row21_col1\" class=\"data row21 col1\" >5898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_42a77_row22_col0\" class=\"data row22 col0\" >Imputation Type</td>\n",
       "      <td id=\"T_42a77_row22_col1\" class=\"data row22 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_42a77_row23_col0\" class=\"data row23 col0\" >Iterative Imputation Iteration</td>\n",
       "      <td id=\"T_42a77_row23_col1\" class=\"data row23 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_42a77_row24_col0\" class=\"data row24 col0\" >Numeric Imputer</td>\n",
       "      <td id=\"T_42a77_row24_col1\" class=\"data row24 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_42a77_row25_col0\" class=\"data row25 col0\" >Iterative Imputation Numeric Model</td>\n",
       "      <td id=\"T_42a77_row25_col1\" class=\"data row25 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_42a77_row26_col0\" class=\"data row26 col0\" >Categorical Imputer</td>\n",
       "      <td id=\"T_42a77_row26_col1\" class=\"data row26 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_42a77_row27_col0\" class=\"data row27 col0\" >Iterative Imputation Categorical Model</td>\n",
       "      <td id=\"T_42a77_row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_42a77_row28_col0\" class=\"data row28 col0\" >Unknown Categoricals Handling</td>\n",
       "      <td id=\"T_42a77_row28_col1\" class=\"data row28 col1\" >least_frequent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_42a77_row29_col0\" class=\"data row29 col0\" >Normalize</td>\n",
       "      <td id=\"T_42a77_row29_col1\" class=\"data row29 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_42a77_row30_col0\" class=\"data row30 col0\" >Normalize Method</td>\n",
       "      <td id=\"T_42a77_row30_col1\" class=\"data row30 col1\" >zscore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_42a77_row31_col0\" class=\"data row31 col0\" >Transformation</td>\n",
       "      <td id=\"T_42a77_row31_col1\" class=\"data row31 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_42a77_row32_col0\" class=\"data row32 col0\" >Transformation Method</td>\n",
       "      <td id=\"T_42a77_row32_col1\" class=\"data row32 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_42a77_row33_col0\" class=\"data row33 col0\" >PCA</td>\n",
       "      <td id=\"T_42a77_row33_col1\" class=\"data row33 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_42a77_row34_col0\" class=\"data row34 col0\" >PCA Method</td>\n",
       "      <td id=\"T_42a77_row34_col1\" class=\"data row34 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_42a77_row35_col0\" class=\"data row35 col0\" >PCA Components</td>\n",
       "      <td id=\"T_42a77_row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_42a77_row36_col0\" class=\"data row36 col0\" >Ignore Low Variance</td>\n",
       "      <td id=\"T_42a77_row36_col1\" class=\"data row36 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_42a77_row37_col0\" class=\"data row37 col0\" >Combine Rare Levels</td>\n",
       "      <td id=\"T_42a77_row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_42a77_row38_col0\" class=\"data row38 col0\" >Rare Level Threshold</td>\n",
       "      <td id=\"T_42a77_row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_42a77_row39_col0\" class=\"data row39 col0\" >Numeric Binning</td>\n",
       "      <td id=\"T_42a77_row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_42a77_row40_col0\" class=\"data row40 col0\" >Remove Outliers</td>\n",
       "      <td id=\"T_42a77_row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_42a77_row41_col0\" class=\"data row41 col0\" >Outliers Threshold</td>\n",
       "      <td id=\"T_42a77_row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_42a77_row42_col0\" class=\"data row42 col0\" >Remove Multicollinearity</td>\n",
       "      <td id=\"T_42a77_row42_col1\" class=\"data row42 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_42a77_row43_col0\" class=\"data row43 col0\" >Multicollinearity Threshold</td>\n",
       "      <td id=\"T_42a77_row43_col1\" class=\"data row43 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_42a77_row44_col0\" class=\"data row44 col0\" >Remove Perfect Collinearity</td>\n",
       "      <td id=\"T_42a77_row44_col1\" class=\"data row44 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_42a77_row45_col0\" class=\"data row45 col0\" >Clustering</td>\n",
       "      <td id=\"T_42a77_row45_col1\" class=\"data row45 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_42a77_row46_col0\" class=\"data row46 col0\" >Clustering Iteration</td>\n",
       "      <td id=\"T_42a77_row46_col1\" class=\"data row46 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_42a77_row47_col0\" class=\"data row47 col0\" >Polynomial Features</td>\n",
       "      <td id=\"T_42a77_row47_col1\" class=\"data row47 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_42a77_row48_col0\" class=\"data row48 col0\" >Polynomial Degree</td>\n",
       "      <td id=\"T_42a77_row48_col1\" class=\"data row48 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_42a77_row49_col0\" class=\"data row49 col0\" >Trignometry Features</td>\n",
       "      <td id=\"T_42a77_row49_col1\" class=\"data row49 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_42a77_row50_col0\" class=\"data row50 col0\" >Polynomial Threshold</td>\n",
       "      <td id=\"T_42a77_row50_col1\" class=\"data row50 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_42a77_row51_col0\" class=\"data row51 col0\" >Group Features</td>\n",
       "      <td id=\"T_42a77_row51_col1\" class=\"data row51 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_42a77_row52_col0\" class=\"data row52 col0\" >Feature Selection</td>\n",
       "      <td id=\"T_42a77_row52_col1\" class=\"data row52 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_42a77_row53_col0\" class=\"data row53 col0\" >Feature Selection Method</td>\n",
       "      <td id=\"T_42a77_row53_col1\" class=\"data row53 col1\" >classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_42a77_row54_col0\" class=\"data row54 col0\" >Features Selection Threshold</td>\n",
       "      <td id=\"T_42a77_row54_col1\" class=\"data row54 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_42a77_row55_col0\" class=\"data row55 col0\" >Feature Interaction</td>\n",
       "      <td id=\"T_42a77_row55_col1\" class=\"data row55 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_42a77_row56_col0\" class=\"data row56 col0\" >Feature Ratio</td>\n",
       "      <td id=\"T_42a77_row56_col1\" class=\"data row56 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_42a77_row57_col0\" class=\"data row57 col0\" >Interaction Threshold</td>\n",
       "      <td id=\"T_42a77_row57_col1\" class=\"data row57 col1\" >None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "      <td id=\"T_42a77_row58_col0\" class=\"data row58 col0\" >Fix Imbalance</td>\n",
       "      <td id=\"T_42a77_row58_col1\" class=\"data row58 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_42a77_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
       "      <td id=\"T_42a77_row59_col0\" class=\"data row59 col0\" >Fix Imbalance Method</td>\n",
       "      <td id=\"T_42a77_row59_col1\" class=\"data row59 col1\" >SMOTE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f348980bd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stup = setup(data = data_group_train_df, target = 'viability_class', \n",
    "              \n",
    "        test_data = data_group_test_df,\n",
    "             \n",
    "        categorical_features=categorical_columns,\n",
    "        numeric_features=numerical_columns_no_target,\n",
    "\n",
    "        imputation_type = sc_imputation_type, \n",
    "        numeric_imputation = sc_numeric_imputation, #default: mean\n",
    "\n",
    "        ignore_features = sc_ignore_features,\n",
    "        bin_numeric_features = sc_bin_numeric_features,\n",
    "        high_cardinality_features = sc_high_cardinality_features,\n",
    "\n",
    "        normalize = sc_normalize, #default: False\n",
    "        remove_outliers= sc_remove_outliers, #default: False\n",
    "\n",
    "        transformation= False, #default\n",
    "        transformation_method = 'yeo-johnson', #default\n",
    "        combine_rare_levels = False,\n",
    "        rare_level_threshold = 0.1, #default: 0.1\n",
    "\n",
    "        remove_multicollinearity = False, #default \n",
    "        multicollinearity_threshold = 0.9, #default\n",
    "\n",
    "        ignore_low_variance = True, \n",
    "\n",
    "        feature_interaction = False, \n",
    "        feature_ratio = False,\n",
    "        polynomial_features = False, \n",
    "        trigonometry_features = False,\n",
    "\n",
    "        fix_imbalance = True,\n",
    "        #fix_imbalance_method = smote,\n",
    "        fold = 5,\n",
    "\n",
    "        # don't change the following\n",
    "        session_id = cv_inner_seed,\n",
    "        use_gpu=False,\n",
    "        log_experiment = False,\n",
    "        silent=True,\n",
    "        experiment_name = 'group_based')\n",
    "\n",
    "set_config('seed', cv_inner_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e46f2714",
   "metadata": {},
   "outputs": [],
   "source": [
    "#stup[39].steps[0][1].k_neighbors = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e6f5d4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#stup[39].steps[0][1].k_neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ebe27ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 31)\n",
      "(92,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X_train\").shape)\n",
    "print(get_config(variable=\"y_train\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "34f3d922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 31)\n",
      "(92,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X\").shape)\n",
    "print(get_config(variable=\"y\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f8e4ec4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set_config('X_train', get_config(variable=\"X\").copy()) \n",
    "#set_config('y_train', get_config(variable=\"y\").copy()) \n",
    "#print(get_config(variable=\"X_train\").shape)\n",
    "#print(get_config(variable=\"y_train\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1b417175",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24, 31)\n",
      "(24,)\n"
     ]
    }
   ],
   "source": [
    "print(get_config(variable=\"X_test\").shape)\n",
    "print(get_config(variable=\"y_test\").shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e4387c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load_config('output/models/universal_classification_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6fff2182",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_transformed_df = get_config(variable=\"X\").copy()\n",
    "data_train_transformed_df['Viability'] = get_config(variable=\"y\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cf253b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test_transformed_df = get_config(variable=\"X_test\").copy()\n",
    "data_test_transformed_df['Viability'] = get_config(variable=\"y_test\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "43f293b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train_transformed_df.to_csv('output/datasets/'+model_group_name+'/transformed/data_classification_train_transformed_df.csv', encoding='utf-8', index=False)\n",
    "data_test_transformed_df.to_csv('output/datasets/'+model_group_name+'/transformed/data_classification_test_transformed_df.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe54e2d4",
   "metadata": {},
   "source": [
    "## Double cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db53e62",
   "metadata": {},
   "source": [
    "### Inner CV: 5-fold cross-validation for model selection / hyperparameter tuning for 7 models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8922c9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model_names = ['lr', 'knn', 'svm', 'rf', 'et', 'gbc', 'lightgbm']\n",
    "model_names = ['knn', 'svm', 'rf', 'et', 'gbc', 'lightgbm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "69c30960",
   "metadata": {},
   "outputs": [],
   "source": [
    "inner_results = dict()\n",
    "inner_results_tuned = dict()\n",
    "outer_results = dict()\n",
    "predicted_results = dict()\n",
    "tuned_models = dict()\n",
    "final_models = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c2a73417",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "IntProgress(value=0, description='Processing: ', max=4)"
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     "output_type": "display_data"
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     "data": {
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       "      <th>Initiated</th>\n",
       "      <td>. . . . . . . . . . . . . . . . . .</td>\n",
       "      <td>21:20:53</td>\n",
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       "      <th>Estimator</th>\n",
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       "      <td>K Neighbors Classifier</td>\n",
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       "                                                                      \n",
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       "Initiated  . . . . . . . . . . . . . . . . . .                21:20:53\n",
       "Status     . . . . . . . . . . . . . . . . . .        Finalizing Model\n",
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    {
     "ename": "ValueError",
     "evalue": "Expected n_neighbors <= n_samples,  but n_samples = 5, n_neighbors = 6",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [37]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m model_name \u001b[38;5;129;01min\u001b[39;00m model_names:\n\u001b[0;32m----> 3\u001b[0m     default_model \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mround\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     results_df \u001b[38;5;241m=\u001b[39m pull()\n\u001b[1;32m      6\u001b[0m     results_df \u001b[38;5;241m=\u001b[39m results_df\u001b[38;5;241m.\u001b[39mreset_index(level\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/pycaret/classification.py:959\u001b[0m, in \u001b[0;36mcreate_model\u001b[0;34m(estimator, fold, round, cross_validation, fit_kwargs, groups, probability_threshold, verbose, return_train_score, **kwargs)\u001b[0m\n\u001b[1;32m    843\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_model\u001b[39m(\n\u001b[1;32m    844\u001b[0m     estimator: Union[\u001b[38;5;28mstr\u001b[39m, Any],\n\u001b[1;32m    845\u001b[0m     fold: Optional[Union[\u001b[38;5;28mint\u001b[39m, Any]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    853\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m    854\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    856\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    857\u001b[0m \u001b[38;5;124;03m    This function trains and evaluates the performance of a given estimator\u001b[39;00m\n\u001b[1;32m    858\u001b[0m \u001b[38;5;124;03m    using cross validation. The output of this function is a score grid with\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    956\u001b[0m \n\u001b[1;32m    957\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 959\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpycaret\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minternal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtabular\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_model_supervised\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    960\u001b[0m \u001b[43m        \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    961\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    962\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mround\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mround\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    963\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcross_validation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcross_validation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    964\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfit_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    965\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroups\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    966\u001b[0m \u001b[43m        \u001b[49m\u001b[43mprobability_threshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprobability_threshold\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    967\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    968\u001b[0m \u001b[43m        \u001b[49m\u001b[43mreturn_train_score\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_train_score\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    969\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    970\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/pycaret/internal/tabular.py:3282\u001b[0m, in \u001b[0;36mcreate_model_supervised\u001b[0;34m(estimator, fold, round, cross_validation, predict, fit_kwargs, groups, refit, verbose, system, X_train_data, y_train_data, metrics, experiment_custom_tags, add_to_model_list, probability_threshold, display, return_train_score, **kwargs)\u001b[0m\n\u001b[1;32m   3280\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFinalizing model\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   3281\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m io\u001b[38;5;241m.\u001b[39mcapture_output():\n\u001b[0;32m-> 3282\u001b[0m     \u001b[43mpipeline_with_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_X\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_y\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3284\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m return_train_score:\n\u001b[1;32m   3285\u001b[0m         predict_model(\n\u001b[1;32m   3286\u001b[0m             pipeline_with_model, data\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mconcat([data_X, data_y], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m   3287\u001b[0m         )\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/pycaret/internal/pipeline.py:118\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[0;34m(self, X, y, **fit_kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_kwargs):\n\u001b[0;32m--> 118\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_carry_over_final_estimator_fit_vars()\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/imblearn/pipeline.py:277\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    250\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params):\n\u001b[1;32m    251\u001b[0m     \u001b[38;5;124;03m\"\"\"Fit the model.\u001b[39;00m\n\u001b[1;32m    252\u001b[0m \n\u001b[1;32m    253\u001b[0m \u001b[38;5;124;03m    Fit all the transforms/samplers one after the other and\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[38;5;124;03m        This estimator.\u001b[39;00m\n\u001b[1;32m    276\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 277\u001b[0m     Xt, yt, fit_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    278\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    279\u001b[0m                              \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)):\n\u001b[1;32m    280\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/imblearn/pipeline.py:236\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m    229\u001b[0m     X, fitted_transformer \u001b[38;5;241m=\u001b[39m fit_transform_one_cached(\n\u001b[1;32m    230\u001b[0m         cloned_transformer, X, y, \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    231\u001b[0m         message_clsname\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    232\u001b[0m         message\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(step_idx),\n\u001b[1;32m    233\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params_steps[name]\n\u001b[1;32m    234\u001b[0m     )\n\u001b[1;32m    235\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(cloned_transformer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_resample\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 236\u001b[0m     X, y, fitted_transformer \u001b[38;5;241m=\u001b[39m \u001b[43mfit_resample_one_cached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    237\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcloned_transformer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    238\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmessage_clsname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPipeline\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    239\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep_idx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    240\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params_steps\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    241\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    242\u001b[0m \u001b[38;5;66;03m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[1;32m    243\u001b[0m \u001b[38;5;66;03m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[1;32m    244\u001b[0m \u001b[38;5;66;03m# from the cache.\u001b[39;00m\n\u001b[1;32m    245\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[step_idx] \u001b[38;5;241m=\u001b[39m (name, fitted_transformer)\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/joblib/memory.py:349\u001b[0m, in \u001b[0;36mNotMemorizedFunc.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 349\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/imblearn/pipeline.py:403\u001b[0m, in \u001b[0;36m_fit_resample_one\u001b[0;34m(sampler, X, y, message_clsname, message, **fit_params)\u001b[0m\n\u001b[1;32m    396\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_fit_resample_one\u001b[39m(sampler,\n\u001b[1;32m    397\u001b[0m                       X,\n\u001b[1;32m    398\u001b[0m                       y,\n\u001b[1;32m    399\u001b[0m                       message_clsname\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m    400\u001b[0m                       message\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    401\u001b[0m                       \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfit_params):\n\u001b[1;32m    402\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[0;32m--> 403\u001b[0m         X_res, y_res \u001b[38;5;241m=\u001b[39m \u001b[43msampler\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mfit_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    405\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m X_res, y_res, sampler\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/imblearn/base.py:83\u001b[0m, in \u001b[0;36mSamplerMixin.fit_resample\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     77\u001b[0m X, y, binarize_y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_X_y(X, y)\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampling_strategy_ \u001b[38;5;241m=\u001b[39m check_sampling_strategy(\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msampling_strategy, y, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampling_type\n\u001b[1;32m     81\u001b[0m )\n\u001b[0;32m---> 83\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     85\u001b[0m y_ \u001b[38;5;241m=\u001b[39m (label_binarize(output[\u001b[38;5;241m1\u001b[39m], np\u001b[38;5;241m.\u001b[39munique(y))\n\u001b[1;32m     86\u001b[0m       \u001b[38;5;28;01mif\u001b[39;00m binarize_y \u001b[38;5;28;01melse\u001b[39;00m output[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m     88\u001b[0m X_, y_ \u001b[38;5;241m=\u001b[39m arrays_transformer\u001b[38;5;241m.\u001b[39mtransform(output[\u001b[38;5;241m0\u001b[39m], y_)\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/imblearn/over_sampling/_smote.py:732\u001b[0m, in \u001b[0;36mSMOTE._fit_resample\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    729\u001b[0m X_class \u001b[38;5;241m=\u001b[39m _safe_indexing(X, target_class_indices)\n\u001b[1;32m    731\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnn_k_\u001b[38;5;241m.\u001b[39mfit(X_class)\n\u001b[0;32m--> 732\u001b[0m nns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnn_k_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkneighbors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_class\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreturn_distance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m[:, \u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m    733\u001b[0m X_new, y_new \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_samples(\n\u001b[1;32m    734\u001b[0m     X_class, y\u001b[38;5;241m.\u001b[39mdtype, class_sample, X_class, nns, n_samples, \u001b[38;5;241m1.0\u001b[39m\n\u001b[1;32m    735\u001b[0m )\n\u001b[1;32m    736\u001b[0m X_resampled\u001b[38;5;241m.\u001b[39mappend(X_new)\n",
      "File \u001b[0;32m/opt/conda/envs/pycaret_full/lib/python3.8/site-packages/sklearn/neighbors/_base.py:616\u001b[0m, in \u001b[0;36mKNeighborsMixin.kneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    614\u001b[0m n_samples_fit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_samples_fit_\n\u001b[1;32m    615\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_neighbors \u001b[38;5;241m>\u001b[39m n_samples_fit:\n\u001b[0;32m--> 616\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    617\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected n_neighbors <= n_samples, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    618\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m but n_samples = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m, n_neighbors = \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m\n\u001b[1;32m    619\u001b[0m         (n_samples_fit, n_neighbors)\n\u001b[1;32m    620\u001b[0m     )\n\u001b[1;32m    622\u001b[0m n_jobs \u001b[38;5;241m=\u001b[39m effective_n_jobs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n\u001b[1;32m    623\u001b[0m chunked_results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Expected n_neighbors <= n_samples,  but n_samples = 5, n_neighbors = 6"
     ]
    }
   ],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    default_model = create_model(model_name, round=2)\n",
    "\n",
    "    results_df = pull()\n",
    "    results_df = results_df.reset_index(level=0)\n",
    "    \n",
    "    inner_results[model_name] = results_df.copy()\n",
    "    results_df.to_csv('output/models_results/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_inner_cross_validation_results.tsv\", \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")\n",
    "\n",
    "\n",
    "    tuned_model = tune_model(default_model, choose_better = True, search_library = 'optuna', n_iter = 50, round=2)\n",
    "\n",
    "    results_df = pull()\n",
    "    results_df = results_df.reset_index(level=0)\n",
    "    \n",
    "    inner_results_tuned[model_name] = results_df.copy()\n",
    "    results_df.to_csv('output/models_results/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_tuned_inner_cross_validation_results.tsv\", \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")\n",
    "    \n",
    "    tuned_models[model_name] = deepcopy(tuned_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2543bed8",
   "metadata": {},
   "source": [
    "### Outer CV: 10-fold cross-validation for the tuned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d9c9302",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = get_config(variable=\"X_train\").copy()\n",
    "y = get_config(variable=\"y_train\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c21760e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a9c93d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f052eebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_outer = StratifiedKFold(n_splits=10, shuffle=True, random_state=cv_outer_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "564a8934",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "    scores = cross_validate(tuned_models[model_name], X, y, cv=cv_outer, scoring=('f1','accuracy','roc_auc'))\n",
    "    scores_f1 = np.round(scores['test_f1'], 2);\n",
    "    scores_accuracy = np.round(scores['test_accuracy'], 2);\n",
    "    scores_roc_auc = np.round(scores['test_roc_auc'], 2);\n",
    "    outer_results[model_name] = scores.copy()\n",
    "    \n",
    "    with open('output/models_results/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_tuned_outer_cross_validation_results.tsv\", 'w', newline='') as file:\n",
    "        file.write('F1\\tAccuracy\\tAUC' + \"\\n\")\n",
    "        for idx, x in enumerate(scores_f1):\n",
    "            file.write(str(scores_f1[idx]) + '\\t' + str(scores_accuracy[idx]) + '\\t' + str(scores_roc_auc[idx]) + \"\\n\")\n",
    "\n",
    "    print(model_name + \" scores: \" + ', '.join(scores_f1.astype('str')))\n",
    "    print(model_name + \" mean/std: \" + str(np.round(scores_f1.mean(),2)) + \" / \" + str(np.round(scores_f1.std(), 2)))\n",
    "    \n",
    "    #with open(the_filename, 'r') as f:\n",
    "    #    my_list = [line.rstrip('\\n') for line in f]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567af0bf",
   "metadata": {},
   "source": [
    "### Finalize the models and evaluate on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfae263b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    final_model = finalize_model(tuned_models[model_name])\n",
    "    \n",
    "    final_models[model_name] = deepcopy(final_model)\n",
    "    \n",
    "    hyperparams = pd.DataFrame.from_dict(final_model.get_params(), orient='index', columns=['parameter_value'])\n",
    "    hyperparams = hyperparams.reset_index(level=0)\n",
    "    hyperparams.to_csv('output/models_parameters/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_hyperparams.tsv\", \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")\n",
    "    \n",
    "    model_predict = predict_model(final_model, round=2)\n",
    "    \n",
    "    results_df = pull()\n",
    "    results_df = results_df.reset_index(level=0)\n",
    "    \n",
    "    predicted_results[model_name] = results_df.copy()\n",
    "    results_df.to_csv('output/models_results/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_test_set_validation_results.tsv\", \n",
    "                    index=False, encoding=\"utf-8\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cef3c2f",
   "metadata": {},
   "source": [
    "### Save models and configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7dbf7e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_config('output/models/'+model_group_name+'/classification_config.cfg')\n",
    "\n",
    "for model_name in model_names:\n",
    "    save_model(tuned_models[model_name], 'output/models/'+model_group_name+'/classification_model_'+model_name+'_tuned.mdl')\n",
    "    save_model(final_models[model_name], 'output/models/'+model_group_name+'/classification_model_'+model_name+'_final.mdl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f8dee9d",
   "metadata": {},
   "source": [
    "### Create Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "521ec3e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = get_config(variable=\"X_test\").copy()\n",
    "y_test = get_config(variable=\"y_test\").copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44efd5ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in ['rf', 'et', 'gbc', 'lr']:\n",
    "    \n",
    "    #import matplotlib.pyplot as plt\n",
    "    #plt.rcParams[\"figure.figsize\"] = [16,9]\n",
    "\n",
    "    visualizer1 = FeatureImportances(final_models[model_name], size=(800,550), topn=10)\n",
    "    visualizer1.fit(X, y)\n",
    "    visualizer1.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_feature_importance_plot.png\",dpi = 300)\n",
    "\n",
    "    plt.clf() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4489866",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer2 = PrecisionRecallCurve(final_models[model_name], hist=False, qqplot=True, size=(800,550))\n",
    "    visualizer2.fit(X, y)\n",
    "    visualizer2.score(X_test, y_test)\n",
    "    visualizer2.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_PrecisionRecallCurve_plot.png\",dpi = 300)\n",
    "    \n",
    "    plt.clf() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a279e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer3 = ClassPredictionError(final_models[model_name], size=(800,550))\n",
    "    visualizer3.fit(X, y)\n",
    "    visualizer3.score(X_test, y_test)\n",
    "    visualizer3.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_error_plot.png\",dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23777c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer4 = ConfusionMatrix(final_models[model_name], size=(800,550), percent=True)\n",
    "    visualizer4.fit(X, y)\n",
    "    visualizer4.score(X_test, y_test)\n",
    "    for label in visualizer4.ax.texts:\n",
    "        label.set_size(18)\n",
    "    visualizer4.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_ConfusionMatrix_plot.png\",dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef7e1f19",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "\n",
    "    visualizer5 = DiscriminationThreshold(final_models[model_name], size=(800,550))\n",
    "    visualizer5.fit(X, y)\n",
    "    visualizer5.score(X_test, y_test)\n",
    "    visualizer5.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_DiscriminationThreshold_plot.png\",dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf552eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in ['lr', 'knn', 'rf', 'et', 'gbc', 'lightgbm']:\n",
    "    \n",
    "    visualizer6 = ROCAUC(final_models[model_name], size=(800,550))\n",
    "    visualizer6.fit(X, y)\n",
    "    visualizer6.score(X_test, y_test)\n",
    "    visualizer6.show(outpath='output/plots/'+model_group_name+'/' + \"classification_model_\"+model_name+\"_ROCAUC_plot.png\",dpi = 300)    \n",
    "    \n",
    "    plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a2eafd5",
   "metadata": {},
   "source": [
    "### Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2406f629",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_names = ['lr', 'knn', 'svm', 'rf', 'et', 'gbc', 'lightgbm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f5acd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_config = load_config('output/models/'+model_group_name+'/classification_config.cfg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7503a083",
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_models_load = dict()\n",
    "final_models_load = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af29b475",
   "metadata": {},
   "outputs": [],
   "source": [
    "for model_name in model_names:\n",
    "    tuned_models_load[model_name] = load_model('output/models/'+model_group_name+'/classification_model_'+model_name+'_tuned.mdl')\n",
    "    final_models_load[model_name] = load_model('output/models/'+model_group_name+'/classification_model_'+model_name+'_final.mdl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acba0082",
   "metadata": {},
   "outputs": [],
   "source": [
    "vars(final_models_load['lightgbm']._final_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a1bbb79",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(tuned_models_load['lightgbm']._final_estimator, 'feature')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd66f15e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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