From deafa738d1b47ba3b6f46176def9d50359e922ea Mon Sep 17 00:00:00 2001 From: reidjohnson Date: Fri, 16 Aug 2024 09:45:54 +0000 Subject: [PATCH] deploy: ecceee52017062b4a3c4ee7d5f764de92d4118fb --- _sources/gallery/plot_predict_custom.rst.txt | 46 +++++++++++++++--- .../gallery/plot_quantile_multioutput.rst.txt | 4 +- .../gallery/plot_treeshap_example.rst.txt | 2 + _static/_image_hashes.json | 2 +- _static/plot_predict_custom-thumb.png | Bin 12150 -> 34914 bytes _static/plot_predict_custom.png | Bin 115457 -> 285010 bytes gallery/plot_predict_custom.html | 25 ++++++++-- gallery/plot_quantile_multioutput.html | 2 +- gallery/plot_treeshap_example.html | 1 + searchindex.js | 2 +- 10 files changed, 69 insertions(+), 15 deletions(-) diff --git a/_sources/gallery/plot_predict_custom.rst.txt b/_sources/gallery/plot_predict_custom.rst.txt index 61ecd87..1531927 100644 --- 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 index f5476d1..8784ced 100644 --- 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 index cd8b1ce..c29b9dc 100644 --- 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 index cce864f..7379c64 100644 --- 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": 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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"), ] @@ -537,6 +547,8 @@ .encode( x=alt.X("y_val:Q", title="Response Value"), y=alt.Y("proba:Q", title="Probability"), + color=color, + opacity=opacity, tooltip=tooltip, ) ) @@ -548,14 +560,19 @@ 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/gallery/plot_quantile_multioutput.html b/gallery/plot_quantile_multioutput.html index c6d6aae..e6b758d 100644 --- a/gallery/plot_quantile_multioutput.html +++ b/gallery/plot_quantile_multioutput.html @@ -566,9 +566,9 @@ .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/gallery/plot_treeshap_example.html b/gallery/plot_treeshap_example.html index 38ff6dc..2664c35 100644 --- a/gallery/plot_treeshap_example.html +++ b/gallery/plot_treeshap_example.html @@ -512,6 +512,7 @@ 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, 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