deploy: ecceee5

zillow · Aug 16, 2024 · deafa73 · deafa73
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Showing 10 changed files with 69 additions and 15 deletions.
diff --git a/_sources/gallery/plot_predict_custom.rst.txt b/_sources/gallery/plot_predict_custom.rst.txt
@@ -119,9 +119,19 @@ this scenario, we compute the empirical cumulative distribution function
 
         # Slider for determining the sample index for which the custom function is being visualized.
         slider = alt.binding_range(min=min_idx, max=max_idx, step=1, name="Test Sample Index: ")
-        index_selection = alt.selection_point(value=0, bind=slider, fields=["index"])
+        index_selection = alt.selection_point(
+            value=0,
+            bind=slider,
+            empty=False,
+            fields=["index"],
+            on="click",
+            nearest=True,
+        )
 
+        color = alt.condition(index_selection, alt.value("#006aff"), alt.value("lightgray"))
+        opacity = alt.condition(index_selection, alt.value(1), alt.value(0.2))
         tooltip = [
+            alt.Tooltip("index:Q", title="Sample Index"),
             alt.Tooltip("y_val:Q", title="Response Value"),
             alt.Tooltip("proba:Q", title="Probability"),
         ]
@@ -132,6 +142,8 @@ this scenario, we compute the empirical cumulative distribution function
             .encode(
                 x=alt.X("y_val:Q", title="Response Value"),
                 y=alt.Y("proba:Q", title="Probability"),
+                color=color,
+                opacity=opacity,
                 tooltip=tooltip,
             )
         )
@@ -143,14 +155,19 @@ this scenario, we compute the empirical cumulative distribution function
                 x=alt.X("y_val:Q", title="Response Value"),
                 x2=alt.X2("y_val2:Q"),
                 y=alt.Y("proba:Q", title="Probability"),
+                color=color,
+                opacity=opacity,
                 tooltip=tooltip,
             )
         )
 
+        # Ensure the selected sample index values overlay the unselected values.
+        chart_base = circles + lines
+        chart_selected = (circles + lines).transform_filter(index_selection)
+
         chart = (
-            (circles + lines)
+            (chart_base + chart_selected)
             .add_params(index_selection)
-            .transform_filter(index_selection)
             .properties(
                 title="Empirical Cumulative Distribution Function (ECDF) Plot",
                 height=400,
@@ -263,9 +280,19 @@ this scenario, we compute the empirical cumulative distribution function
 
                 # Slider for determining the sample index for which the custom function is being visualized.
                 slider = alt.binding_range(min=min_idx, max=max_idx, step=1, name="Test Sample Index: ")
-                index_selection = alt.selection_point(value=0, bind=slider, fields=["index"])
+                index_selection = alt.selection_point(
+                    value=0,
+                    bind=slider,
+                    empty=False,
+                    fields=["index"],
+                    on="click",
+                    nearest=True,
+                )
 
+                color = alt.condition(index_selection, alt.value("#006aff"), alt.value("lightgray"))
+                opacity = alt.condition(index_selection, alt.value(1), alt.value(0.2))
                 tooltip = [
+                    alt.Tooltip("index:Q", title="Sample Index"),
                     alt.Tooltip("y_val:Q", title="Response Value"),
                     alt.Tooltip("proba:Q", title="Probability"),
                 ]
@@ -276,6 +303,8 @@ this scenario, we compute the empirical cumulative distribution function
                     .encode(
                         x=alt.X("y_val:Q", title="Response Value"),
                         y=alt.Y("proba:Q", title="Probability"),
+                        color=color,
+                        opacity=opacity,
                         tooltip=tooltip,
                     )
                 )
@@ -287,14 +316,19 @@ this scenario, we compute the empirical cumulative distribution function
                         x=alt.X("y_val:Q", title="Response Value"),
                         x2=alt.X2("y_val2:Q"),
                         y=alt.Y("proba:Q", title="Probability"),
+                        color=color,
+                        opacity=opacity,
                         tooltip=tooltip,
                     )
                 )
 
+                # Ensure the selected sample index values overlay the unselected values.
+                chart_base = circles + lines
+                chart_selected = (circles + lines).transform_filter(index_selection)
+
                 chart = (
-                    (circles + lines)
+                    (chart_base + chart_selected)
                     .add_params(index_selection)
-                    .transform_filter(index_selection)
                     .properties(
                         title="Empirical Cumulative Distribution Function (ECDF) Plot",
                         height=400,

diff --git a/_sources/gallery/plot_quantile_multioutput.rst.txt b/_sources/gallery/plot_quantile_multioutput.rst.txt
@@ -161,9 +161,9 @@ for each target: the median line and the area defined by the interval points.
             .add_params(interval_val, click)
             .configure_range(category=alt.RangeScheme(list(legend.values())))
             .properties(
+                title="Multi-target Predictions and Prediction Intervals on Toy Dataset",
                 height=400,
                 width=650,
-                title="Multi-target Predictions and Prediction Intervals on Toy Dataset",
             )
         )
 
@@ -315,9 +315,9 @@ for each target: the median line and the area defined by the interval points.
                     .add_params(interval_val, click)
                     .configure_range(category=alt.RangeScheme(list(legend.values())))
                     .properties(
+                        title="Multi-target Predictions and Prediction Intervals on Toy Dataset",
                         height=400,
                         width=650,
-                        title="Multi-target Predictions and Prediction Intervals on Toy Dataset",
                     )
                 )
 

diff --git a/_sources/gallery/plot_treeshap_example.rst.txt b/_sources/gallery/plot_treeshap_example.rst.txt
@@ -108,6 +108,7 @@ how the SHAP explanations vary with different quantile choices.
     X = X.iloc[perm]
     y = y.iloc[perm]
     y *= 100_000  # convert to dollars
+
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=random_state)
 
     qrf = RandomForestQuantileRegressor(random_state=random_state)
@@ -410,6 +411,7 @@ how the SHAP explanations vary with different quantile choices.
             X = X.iloc[perm]
             y = y.iloc[perm]
             y *= 100_000  # convert to dollars
+
             X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=random_state)
 
             qrf = RandomForestQuantileRegressor(random_state=random_state)

diff --git a/_static/_image_hashes.json b/_static/_image_hashes.json
@@ -1 +1 @@
-{"plot_quantile_interpolation.png": "e490d90e837fe8ab6cd368dc5af69c39", "plot_predict_custom.png": "337ddedec1648e46bc1a1831d7d4e07c", "plot_quantile_extrapolation.png": "79982e108d80c6b9a29dffb7fb761fa2", "plot_quantile_multioutput.png": "814b6b00bfe0ee6f39c91fe1c5da3dc8", "plot_quantile_example.png": "37b7aa1346e4418c4e23f424a891dd1f", "plot_quantile_conformalized.png": "b0a13f4c4f5c4357e037f0bb87809124", "plot_quantile_intervals.png": "0c2ccc83500678ca048240bc1eaed3c9", "plot_quantile_vs_standard.png": "7d705af48c41c27e7e8b712d3404c80b", "plot_treeshap_example.png": "3c6268285ebf715aeba9c21e4d4161c1", "plot_proximity_counts.png": "506fe1fe5d9cab22dde4d26df44d4dca", "plot_quantile_ranks.png": "9148bdf85c4e3e27c9f8dd295c0bf49d", "plot_huggingface_model.png": "5e60b72cd961ba7a3909203bb3a28083"}
+{"plot_quantile_interpolation.png": "e490d90e837fe8ab6cd368dc5af69c39", "plot_predict_custom.png": "60ec2b76ada2addd7e96a742f02c5464", "plot_quantile_extrapolation.png": "79982e108d80c6b9a29dffb7fb761fa2", "plot_quantile_multioutput.png": "9281997371c80f5ae412c0cc8d71842b", "plot_quantile_example.png": "37b7aa1346e4418c4e23f424a891dd1f", "plot_quantile_conformalized.png": "b0a13f4c4f5c4357e037f0bb87809124", "plot_quantile_intervals.png": "0c2ccc83500678ca048240bc1eaed3c9", "plot_quantile_vs_standard.png": "7d705af48c41c27e7e8b712d3404c80b", "plot_treeshap_example.png": "370a91ac8caaa9d64cef36f0d226e577", "plot_proximity_counts.png": "506fe1fe5d9cab22dde4d26df44d4dca", "plot_quantile_ranks.png": "9148bdf85c4e3e27c9f8dd295c0bf49d", "plot_huggingface_model.png": "5e60b72cd961ba7a3909203bb3a28083"}
diff --git a/_static/plot_predict_custom-thumb.png b/_static/plot_predict_custom-thumb.png
diff --git a/_static/plot_predict_custom.png b/_static/plot_predict_custom.png
diff --git a/gallery/plot_predict_custom.html b/gallery/plot_predict_custom.html
diff --git a/gallery/plot_quantile_multioutput.html b/gallery/plot_quantile_multioutput.html
@@ -566,9 +566,9 @@
         <span class="o">.</span><span class="n">add_params</span><span class="p">(</span><span class="n">interval_val</span><span class="p">,</span> <span class="n">click</span><span class="p">)</span>
         <span class="o">.</span><span class="n">configure_range</span><span class="p">(</span><span class="n">category</span><span class="o">=</span><span class="n">alt</span><span class="o">.</span><span class="n">RangeScheme</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">legend</span><span class="o">.</span><span class="n">values</span><span class="p">())))</span>
         <span class="o">.</span><span class="n">properties</span><span class="p">(</span>
+            <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Multi-target Predictions and Prediction Intervals on Toy Dataset&quot;</span><span class="p">,</span>
             <span class="n">height</span><span class="o">=</span><span class="mi">400</span><span class="p">,</span>
             <span class="n">width</span><span class="o">=</span><span class="mi">650</span><span class="p">,</span>
-            <span class="n">title</span><span class="o">=</span><span class="s2">&quot;Multi-target Predictions and Prediction Intervals on Toy Dataset&quot;</span><span class="p">,</span>
         <span class="p">)</span>
     <span class="p">)</span>
 

diff --git a/gallery/plot_treeshap_example.html b/gallery/plot_treeshap_example.html
@@ -512,6 +512,7 @@
 <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">perm</span><span class="p">]</span>
 <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">perm</span><span class="p">]</span>
 <span class="n">y</span> <span class="o">*=</span> <span class="mi">100_000</span>  <span class="c1"># convert to dollars</span>
+
 <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.1</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">qrf</span> <span class="o">=</span> <span class="n">RandomForestQuantileRegressor</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>

diff --git a/searchindex.js b/searchindex.js
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		{"plot_quantile_interpolation.png": "e490d90e837fe8ab6cd368dc5af69c39", "plot_predict_custom.png": "337ddedec1648e46bc1a1831d7d4e07c", "plot_quantile_extrapolation.png": "79982e108d80c6b9a29dffb7fb761fa2", "plot_quantile_multioutput.png": "814b6b00bfe0ee6f39c91fe1c5da3dc8", "plot_quantile_example.png": "37b7aa1346e4418c4e23f424a891dd1f", "plot_quantile_conformalized.png": "b0a13f4c4f5c4357e037f0bb87809124", "plot_quantile_intervals.png": "0c2ccc83500678ca048240bc1eaed3c9", "plot_quantile_vs_standard.png": "7d705af48c41c27e7e8b712d3404c80b", "plot_treeshap_example.png": "3c6268285ebf715aeba9c21e4d4161c1", "plot_proximity_counts.png": "506fe1fe5d9cab22dde4d26df44d4dca", "plot_quantile_ranks.png": "9148bdf85c4e3e27c9f8dd295c0bf49d", "plot_huggingface_model.png": "5e60b72cd961ba7a3909203bb3a28083"}
		{"plot_quantile_interpolation.png": "e490d90e837fe8ab6cd368dc5af69c39", "plot_predict_custom.png": "60ec2b76ada2addd7e96a742f02c5464", "plot_quantile_extrapolation.png": "79982e108d80c6b9a29dffb7fb761fa2", "plot_quantile_multioutput.png": "9281997371c80f5ae412c0cc8d71842b", "plot_quantile_example.png": "37b7aa1346e4418c4e23f424a891dd1f", "plot_quantile_conformalized.png": "b0a13f4c4f5c4357e037f0bb87809124", "plot_quantile_intervals.png": "0c2ccc83500678ca048240bc1eaed3c9", "plot_quantile_vs_standard.png": "7d705af48c41c27e7e8b712d3404c80b", "plot_treeshap_example.png": "370a91ac8caaa9d64cef36f0d226e577", "plot_proximity_counts.png": "506fe1fe5d9cab22dde4d26df44d4dca", "plot_quantile_ranks.png": "9148bdf85c4e3e27c9f8dd295c0bf49d", "plot_huggingface_model.png": "5e60b72cd961ba7a3909203bb3a28083"}