diff --git a/_sources/gallery/plot_huggingface_model.rst.txt b/_sources/gallery/plot_huggingface_model.rst.txt index c95bc45..75c5405 100644 --- a/_sources/gallery/plot_huggingface_model.rst.txt +++ b/_sources/gallery/plot_huggingface_model.rst.txt @@ -34,7 +34,6 @@ of each sample. The model used is available on Hugging Face Hub import numpy as np import pandas as pd from sklearn import datasets - from sklearn.utils.validation import check_random_state from skops import hub_utils import quantile_forest @@ -46,7 +45,7 @@ of each sample. The model used is available on Hugging Face Hub repo_id = "quantile-forest/california-housing-example" load_existing = True - random_state = check_random_state(0) + random_state = np.random.RandomState(0) quantiles = np.linspace(0, 1, num=5, endpoint=True).round(2).tolist() sample_frac = 1 @@ -190,13 +189,13 @@ of each sample. The model used is available on Hugging Face Hub def plot_quantiles_by_latlon(df, quantiles, color_scheme="cividis"): # Slider for varying the displayed quantile estimates. slider = alt.binding_range( + name="Predicted Quantile: ", min=0, max=1, step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1), - name="Predicted Quantile: ", ) - quantile_val = alt.param(value=0.5, bind=slider, name="quantile") + quantile_val = alt.param(name="quantile", value=0.5, bind=slider) chart = ( alt.Chart(df) @@ -250,7 +249,6 @@ of each sample. The model used is available on Hugging Face Hub import numpy as np import pandas as pd from sklearn import datasets - from sklearn.utils.validation import check_random_state from skops import hub_utils import quantile_forest @@ -262,7 +260,7 @@ of each sample. The model used is available on Hugging Face Hub repo_id = "quantile-forest/california-housing-example" load_existing = True - random_state = check_random_state(0) + random_state = np.random.RandomState(0) quantiles = np.linspace(0, 1, num=5, endpoint=True).round(2).tolist() sample_frac = 1 @@ -406,13 +404,13 @@ of each sample. The model used is available on Hugging Face Hub def plot_quantiles_by_latlon(df, quantiles, color_scheme="cividis"): # Slider for varying the displayed quantile estimates. slider = alt.binding_range( + name="Predicted Quantile: ", min=0, max=1, step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1), - name="Predicted Quantile: ", ) - quantile_val = alt.param(value=0.5, bind=slider, name="quantile") + quantile_val = alt.param(name="quantile", value=0.5, bind=slider) chart = ( alt.Chart(df) diff --git a/_sources/gallery/plot_predict_custom.rst.txt b/_sources/gallery/plot_predict_custom.rst.txt index 1531927..9cc11a6 100644 --- a/_sources/gallery/plot_predict_custom.rst.txt +++ b/_sources/gallery/plot_predict_custom.rst.txt @@ -31,11 +31,10 @@ this scenario, we compute the empirical cumulative distribution function import scipy as sp from sklearn import datasets from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_test_samples = 100 @@ -118,7 +117,7 @@ this scenario, we compute the empirical cumulative distribution function max_idx = df["index"].max() # 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: ") + slider = alt.binding_range(name="Test Sample Index: ", min=min_idx, max=max_idx, step=1) index_selection = alt.selection_point( value=0, bind=slider, @@ -192,11 +191,10 @@ this scenario, we compute the empirical cumulative distribution function import scipy as sp from sklearn import datasets from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_test_samples = 100 @@ -279,7 +277,7 @@ this scenario, we compute the empirical cumulative distribution function max_idx = df["index"].max() # 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: ") + slider = alt.binding_range(name="Test Sample Index: ", min=min_idx, max=max_idx, step=1) index_selection = alt.selection_point( value=0, bind=slider, diff --git a/_sources/gallery/plot_proximity_counts.rst.txt b/_sources/gallery/plot_proximity_counts.rst.txt index 761de98..831406d 100644 --- a/_sources/gallery/plot_proximity_counts.rst.txt +++ b/_sources/gallery/plot_proximity_counts.rst.txt @@ -37,7 +37,7 @@ conditions. from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_test_samples = 25 noise_std = 0.1 @@ -144,7 +144,7 @@ conditions. subplot_dim = (width - subplot_spacing * (n_subplot_rows - 1)) / n_subplot_rows # Slider for determining the test index for which the data is being visualized. - slider = alt.binding_range(min=0, max=n_samples - 1, step=1, name="Test Sample Index: ") + slider = alt.binding_range(name="Test Sample Index: ", min=0, max=n_samples - 1, step=1) index_selection = alt.selection_point(value=0, bind=slider, fields=["index"]) scale = alt.Scale(domain=[x_min, x_max], scheme="greys") @@ -257,7 +257,7 @@ conditions. from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_test_samples = 25 noise_std = 0.1 @@ -364,7 +364,7 @@ conditions. subplot_dim = (width - subplot_spacing * (n_subplot_rows - 1)) / n_subplot_rows # Slider for determining the test index for which the data is being visualized. - slider = alt.binding_range(min=0, max=n_samples - 1, step=1, name="Test Sample Index: ") + slider = alt.binding_range(name="Test Sample Index: ", min=0, max=n_samples - 1, step=1) index_selection = alt.selection_point(value=0, bind=slider, fields=["index"]) scale = alt.Scale(domain=[x_min, x_max], scheme="greys") diff --git a/_sources/gallery/plot_quantile_conformalized.rst.txt b/_sources/gallery/plot_quantile_conformalized.rst.txt index 186ab2c..f7101c7 100644 --- a/_sources/gallery/plot_quantile_conformalized.rst.txt +++ b/_sources/gallery/plot_quantile_conformalized.rst.txt @@ -33,12 +33,11 @@ by Carl McBride Ellis. import pandas as pd from sklearn import datasets from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor random_seed = 0 - random_state = check_random_state(random_seed) + random_state = np.random.RandomState(random_seed) n_samples = 900 coverages = np.linspace(0, 1, num=11, endpoint=True).round(1).tolist() # the "coverage level" @@ -173,10 +172,10 @@ by Carl McBride Ellis. def plot_prediction_intervals_by_strategy(df): def plot_prediction_intervals(df, domain): # Slider for varying the target coverage level. - slider = alt.binding_range(min=0, max=1, step=0.1, name="Coverage Target: ") - coverage_val = alt.param(value=0.9, bind=slider, name="coverage") + slider = alt.binding_range(name="Coverage Target: ", min=0, max=1, step=0.1) + coverage_val = alt.param(name="coverage", value=0.9, bind=slider) - click = alt.selection_point(fields=["y_label"], bind="legend") + click = alt.selection_point(bind="legend", fields=["y_label"]) tooltip = [ alt.Tooltip("y_test:Q", format="$,d", title="True Price"), @@ -312,12 +311,11 @@ by Carl McBride Ellis. import pandas as pd from sklearn import datasets from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor random_seed = 0 - random_state = check_random_state(random_seed) + random_state = np.random.RandomState(random_seed) n_samples = 900 coverages = np.linspace(0, 1, num=11, endpoint=True).round(1).tolist() # the "coverage level" @@ -452,10 +450,10 @@ by Carl McBride Ellis. def plot_prediction_intervals_by_strategy(df): def plot_prediction_intervals(df, domain): # Slider for varying the target coverage level. - slider = alt.binding_range(min=0, max=1, step=0.1, name="Coverage Target: ") - coverage_val = alt.param(value=0.9, bind=slider, name="coverage") + slider = alt.binding_range(name="Coverage Target: ", min=0, max=1, step=0.1) + coverage_val = alt.param(name="coverage", value=0.9, bind=slider) - click = alt.selection_point(fields=["y_label"], bind="legend") + click = alt.selection_point(bind="legend", fields=["y_label"]) tooltip = [ alt.Tooltip("y_test:Q", format="$,d", title="True Price"), diff --git a/_sources/gallery/plot_quantile_example.rst.txt b/_sources/gallery/plot_quantile_example.rst.txt index c692e7e..39d2b5b 100644 --- a/_sources/gallery/plot_quantile_example.rst.txt +++ b/_sources/gallery/plot_quantile_example.rst.txt @@ -29,7 +29,7 @@ samples. from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 1000 bounds = [0, 10] quantiles = [0.025, 0.5, 0.975] @@ -170,7 +170,7 @@ samples. from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 1000 bounds = [0, 10] quantiles = [0.025, 0.5, 0.975] diff --git a/_sources/gallery/plot_quantile_extrapolation.rst.txt b/_sources/gallery/plot_quantile_extrapolation.rst.txt index 7538922..4a49af2 100644 --- a/_sources/gallery/plot_quantile_extrapolation.rst.txt +++ b/_sources/gallery/plot_quantile_extrapolation.rst.txt @@ -36,7 +36,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 500 bounds = [0, 15] extrap_frac = 0.25 @@ -584,7 +584,7 @@ adapted from `"Extrapolation-Aware Nonparametric Statistical Inference" from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 500 bounds = [0, 15] extrap_frac = 0.25 diff --git a/_sources/gallery/plot_quantile_interpolation.rst.txt b/_sources/gallery/plot_quantile_interpolation.rst.txt index 99fabf1..86c63ea 100644 --- a/_sources/gallery/plot_quantile_interpolation.rst.txt +++ b/_sources/gallery/plot_quantile_interpolation.rst.txt @@ -28,11 +28,10 @@ handled when a quantile does not exactly match a data point. import altair as alt import numpy as np import pandas as pd - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) intervals = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist() # Create toy dataset. @@ -95,10 +94,10 @@ handled when a quantile does not exactly match a data point. def plot_interpolations(df, legend): # Slider for varying the prediction interval that determines the quantiles being interpolated. - slider = alt.binding_range(min=0, max=1, step=0.01, name="Prediction Interval: ") - interval_val = alt.param(value=0.9, bind=slider, name="interval") + slider = alt.binding_range(name="Prediction Interval: ", min=0, max=1, step=0.01) + interval_val = alt.param(name="interval", value=0.9, bind=slider) - click = alt.selection_point(fields=["method"], bind="legend") + click = alt.selection_point(bind="legend", fields=["method"], on="click") color = alt.condition( click, @@ -164,7 +163,7 @@ handled when a quantile does not exactly match a data point. header=alt.Header(labelOrient="bottom", titleOrient="bottom"), title="Samples (Feature Values)", ), - title="QRF Prediction Intervals by Quantile Interpolation on Toy Dataset", + title="QRF Predictions by Quantile Interpolation on Toy Dataset", ) .configure_facet(spacing=15) .configure_range(category=alt.RangeScheme(list(legend.values()))) @@ -186,11 +185,10 @@ handled when a quantile does not exactly match a data point. import altair as alt import numpy as np import pandas as pd - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) intervals = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist() # Create toy dataset. @@ -253,10 +251,10 @@ handled when a quantile does not exactly match a data point. def plot_interpolations(df, legend): # Slider for varying the prediction interval that determines the quantiles being interpolated. - slider = alt.binding_range(min=0, max=1, step=0.01, name="Prediction Interval: ") - interval_val = alt.param(value=0.9, bind=slider, name="interval") + slider = alt.binding_range(name="Prediction Interval: ", min=0, max=1, step=0.01) + interval_val = alt.param(name="interval", value=0.9, bind=slider) - click = alt.selection_point(fields=["method"], bind="legend") + click = alt.selection_point(bind="legend", fields=["method"], on="click") color = alt.condition( click, @@ -322,7 +320,7 @@ handled when a quantile does not exactly match a data point. header=alt.Header(labelOrient="bottom", titleOrient="bottom"), title="Samples (Feature Values)", ), - title="QRF Prediction Intervals by Quantile Interpolation on Toy Dataset", + title="QRF Predictions by Quantile Interpolation on Toy Dataset", ) .configure_facet(spacing=15) .configure_range(category=alt.RangeScheme(list(legend.values()))) diff --git a/_sources/gallery/plot_quantile_intervals.rst.txt b/_sources/gallery/plot_quantile_intervals.rst.txt index 3f8dcd1..59a72ee 100644 --- a/_sources/gallery/plot_quantile_intervals.rst.txt +++ b/_sources/gallery/plot_quantile_intervals.rst.txt @@ -26,11 +26,10 @@ visualization is inspired by Figure 3 of `"Quantile Regression Forests" import pandas as pd from sklearn import datasets from sklearn.model_selection import KFold - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 1000 # Load the California Housing Prices dataset. @@ -208,11 +207,10 @@ visualization is inspired by Figure 3 of `"Quantile Regression Forests" import pandas as pd from sklearn import datasets from sklearn.model_selection import KFold - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 1000 # Load the California Housing Prices dataset. diff --git a/_sources/gallery/plot_quantile_multioutput.rst.txt b/_sources/gallery/plot_quantile_multioutput.rst.txt index 8784ced..933bf08 100644 --- a/_sources/gallery/plot_quantile_multioutput.rst.txt +++ b/_sources/gallery/plot_quantile_multioutput.rst.txt @@ -27,11 +27,10 @@ for each target: the median line and the area defined by the interval points. import numpy as np import pandas as pd from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 2500 bounds = [0, 100] quantiles = np.linspace(0, 1, num=41, endpoint=True).round(3).tolist() @@ -91,10 +90,10 @@ for each target: the median line and the area defined by the interval points. def plot_multitargets(df, legend): # Slider for varying the displayed prediction intervals. - slider = alt.binding_range(min=0, max=1, step=0.05, name="Prediction Interval: ") - interval_val = alt.param(value=0.95, bind=slider, name="interval") + slider = alt.binding_range(name="Prediction Interval: ", min=0, max=1, step=0.05) + interval_val = alt.param(name="interval", value=0.95, bind=slider) - click = alt.selection_point(fields=["target"], bind="legend") + click = alt.selection_point(bind="legend", fields=["target"], on="click") color = alt.condition( click, @@ -181,11 +180,10 @@ for each target: the median line and the area defined by the interval points. import numpy as np import pandas as pd from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 2500 bounds = [0, 100] quantiles = np.linspace(0, 1, num=41, endpoint=True).round(3).tolist() @@ -245,10 +243,10 @@ for each target: the median line and the area defined by the interval points. def plot_multitargets(df, legend): # Slider for varying the displayed prediction intervals. - slider = alt.binding_range(min=0, max=1, step=0.05, name="Prediction Interval: ") - interval_val = alt.param(value=0.95, bind=slider, name="interval") + slider = alt.binding_range(name="Prediction Interval: ", min=0, max=1, step=0.05) + interval_val = alt.param(name="interval", value=0.95, bind=slider) - click = alt.selection_point(fields=["target"], bind="legend") + click = alt.selection_point(bind="legend", fields=["target"], on="click") color = alt.condition( click, diff --git a/_sources/gallery/plot_quantile_ranks.rst.txt b/_sources/gallery/plot_quantile_ranks.rst.txt index cf1dfd7..7fe62cf 100644 --- a/_sources/gallery/plot_quantile_ranks.rst.txt +++ b/_sources/gallery/plot_quantile_ranks.rst.txt @@ -32,7 +32,7 @@ around the median. from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 5000 bounds = [0, 10] @@ -63,10 +63,10 @@ around the median. def plot_pred_and_ranks(df): # Slider for varying the interval that defines the upper and lower quantile rank thresholds. - slider = alt.binding_range(min=0, max=1, step=0.01, name="Rank Interval Threshold: ") - interval_val = alt.param(value=0.05, bind=slider, name="interval") + slider = alt.binding_range(name="Rank Interval Threshold: ", min=0, max=1, step=0.01) + interval_val = alt.param(name="interval", value=0.05, bind=slider) - click = alt.selection_point(fields=["outlier"], bind="legend") + click = alt.selection_point(bind="legend", fields=["outlier"], on="click") base = alt.Chart(df) @@ -139,7 +139,7 @@ around the median. from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 5000 bounds = [0, 10] @@ -170,10 +170,10 @@ around the median. def plot_pred_and_ranks(df): # Slider for varying the interval that defines the upper and lower quantile rank thresholds. - slider = alt.binding_range(min=0, max=1, step=0.01, name="Rank Interval Threshold: ") - interval_val = alt.param(value=0.05, bind=slider, name="interval") + slider = alt.binding_range(name="Rank Interval Threshold: ", min=0, max=1, step=0.01) + interval_val = alt.param(name="interval", value=0.05, bind=slider) - click = alt.selection_point(fields=["outlier"], bind="legend") + click = alt.selection_point(bind="legend", fields=["outlier"], on="click") base = alt.Chart(df) diff --git a/_sources/gallery/plot_quantile_vs_standard.rst.txt b/_sources/gallery/plot_quantile_vs_standard.rst.txt index 68277c2..ec62087 100644 --- a/_sources/gallery/plot_quantile_vs_standard.rst.txt +++ b/_sources/gallery/plot_quantile_vs_standard.rst.txt @@ -30,11 +30,10 @@ distributions. import scipy as sp from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) quantiles = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist() # Create right-skewed dataset. @@ -79,14 +78,14 @@ distributions. def plot_prediction_histograms(df, legend): # Slider for varying the quantile value used for generating the QRF histogram. slider = alt.binding_range( + name="Predicted Quantile: ", min=0, max=1, step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1), - name="Predicted Quantile: ", ) quantile_val = alt.param(value=0.5, bind=slider, name="quantile") - click = alt.selection_point(fields=["label"], bind="legend") + click = alt.selection_point(bind="legend", fields=["label"], on="click") chart = ( alt.Chart(df) @@ -113,6 +112,7 @@ distributions. alt.Color("label:N", sort=list(legend.keys()), title=None), alt.value("lightgray"), ), + opacity=alt.condition(click, alt.value(1), alt.value(0.5)), xOffset=alt.XOffset("label:N"), tooltip=[ alt.Tooltip("label:N", title="Label"), @@ -143,11 +143,10 @@ distributions. import scipy as sp from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) quantiles = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist() # Create right-skewed dataset. @@ -192,14 +191,14 @@ distributions. def plot_prediction_histograms(df, legend): # Slider for varying the quantile value used for generating the QRF histogram. slider = alt.binding_range( + name="Predicted Quantile: ", min=0, max=1, step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1), - name="Predicted Quantile: ", ) quantile_val = alt.param(value=0.5, bind=slider, name="quantile") - click = alt.selection_point(fields=["label"], bind="legend") + click = alt.selection_point(bind="legend", fields=["label"], on="click") chart = ( alt.Chart(df) @@ -226,6 +225,7 @@ distributions. alt.Color("label:N", sort=list(legend.keys()), title=None), alt.value("lightgray"), ), + opacity=alt.condition(click, alt.value(1), alt.value(0.5)), xOffset=alt.XOffset("label:N"), tooltip=[ alt.Tooltip("label:N", title="Label"), diff --git a/_sources/gallery/plot_treeshap_example.rst.txt b/_sources/gallery/plot_treeshap_example.rst.txt index c29b9dc..3369d5d 100644 --- a/_sources/gallery/plot_treeshap_example.rst.txt +++ b/_sources/gallery/plot_treeshap_example.rst.txt @@ -33,11 +33,10 @@ how the SHAP explanations vary with different quantile choices. import shap from sklearn import datasets from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 1000 test_idx = 0 quantiles = np.linspace(0, 1, num=11, endpoint=True).round(1).tolist() @@ -143,12 +142,12 @@ how the SHAP explanations vary with different quantile choices. # Slider for varying the applied quantile estimates. slider = alt.binding_range( + name="Predicted Quantile: ", min=0, max=1, step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1), - name="Predicted Quantile: ", ) - quantile_val = alt.param(value=0.5, bind=slider, name="quantile") + quantile_val = alt.param(name="quantile", value=0.5, bind=slider) df_grouped = ( df.groupby("quantile")[df.columns.tolist()] @@ -336,11 +335,10 @@ how the SHAP explanations vary with different quantile choices. import shap from sklearn import datasets from sklearn.model_selection import train_test_split - from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor - random_state = check_random_state(0) + random_state = np.random.RandomState(0) n_samples = 1000 test_idx = 0 quantiles = np.linspace(0, 1, num=11, endpoint=True).round(1).tolist() @@ -446,12 +444,12 @@ how the SHAP explanations vary with different quantile choices. # Slider for varying the applied quantile estimates. slider = alt.binding_range( + name="Predicted Quantile: ", min=0, max=1, step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1), - name="Predicted Quantile: ", ) - quantile_val = alt.param(value=0.5, bind=slider, name="quantile") + quantile_val = alt.param(name="quantile", value=0.5, bind=slider) df_grouped = ( df.groupby("quantile")[df.columns.tolist()] diff --git a/_sources/getting_started/developers.rst.txt b/_sources/getting_started/developers.rst.txt index 4351b3d..0ff309a 100644 --- a/_sources/getting_started/developers.rst.txt +++ b/_sources/getting_started/developers.rst.txt @@ -36,10 +36,18 @@ On macOS, run:: Test and Coverage ~~~~~~~~~~~~~~~~~ +Ensure that `pytest` and `pytest-cov` are installed:: + + $ pip install pytest pytest-cov + To test the code:: $ python -m pytest quantile_forest -v +To test the code and produce a coverage report:: + + $ python -m pytest quantile_forest --cov-report html --cov=quantile_forest + To test the documentation:: $ python -m pytest docs/user_guide/*rst diff --git a/_static/_image_hashes.json b/_static/_image_hashes.json index 7379c64..5a57561 100644 --- a/_static/_image_hashes.json +++ b/_static/_image_hashes.json @@ -1 +1 @@ -{"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": 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"plot_huggingface_model.png": "54f6ba0d8875e499e02114fd00fb3197"} \ No newline at end of file diff --git a/_static/plot_quantile_interpolation-thumb.png b/_static/plot_quantile_interpolation-thumb.png index 617f86b..960c8f9 100644 Binary files a/_static/plot_quantile_interpolation-thumb.png and b/_static/plot_quantile_interpolation-thumb.png differ diff --git a/_static/plot_quantile_interpolation.png b/_static/plot_quantile_interpolation.png index 6cdb08a..5938d92 100644 Binary files a/_static/plot_quantile_interpolation.png and b/_static/plot_quantile_interpolation.png differ diff --git a/gallery/plot_huggingface_model.html b/gallery/plot_huggingface_model.html index 40142d0..143bfcc 100644 --- a/gallery/plot_huggingface_model.html +++ b/gallery/plot_huggingface_model.html @@ -439,7 +439,6 @@ import numpy as np import pandas as pd from sklearn import datasets -from sklearn.utils.validation import check_random_state from skops import hub_utils import quantile_forest @@ -451,7 +450,7 @@ repo_id = "quantile-forest/california-housing-example" load_existing = True -random_state = check_random_state(0) +random_state = np.random.RandomState(0) quantiles = np.linspace(0, 1, num=5, endpoint=True).round(2).tolist() sample_frac = 1 @@ -595,13 +594,13 @@ def plot_quantiles_by_latlon(df, quantiles, color_scheme="cividis"): # Slider for varying the displayed quantile estimates. slider = alt.binding_range( + name="Predicted Quantile: ", min=0, max=1, step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1), - name="Predicted Quantile: ", ) - quantile_val = alt.param(value=0.5, bind=slider, name="quantile") + quantile_val = alt.param(name="quantile", value=0.5, bind=slider) chart = ( alt.Chart(df) diff --git a/gallery/plot_predict_custom.html b/gallery/plot_predict_custom.html index ae7715e..1d87343 100644 --- a/gallery/plot_predict_custom.html +++ b/gallery/plot_predict_custom.html @@ -436,11 +436,10 @@ import scipy as sp from sklearn import datasets from sklearn.model_selection import train_test_split -from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor -random_state = check_random_state(0) +random_state = np.random.RandomState(0) n_test_samples = 100 @@ -523,7 +522,7 @@ max_idx = df["index"].max() # 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: ") + slider = alt.binding_range(name="Test Sample Index: ", min=min_idx, max=max_idx, step=1) index_selection = alt.selection_point( value=0, bind=slider, diff --git a/gallery/plot_proximity_counts.html b/gallery/plot_proximity_counts.html index 74fb6d7..778811c 100644 --- a/gallery/plot_proximity_counts.html +++ b/gallery/plot_proximity_counts.html @@ -442,7 +442,7 @@ from quantile_forest import RandomForestQuantileRegressor -random_state = check_random_state(0) +random_state = np.random.RandomState(0) n_test_samples = 25 noise_std = 0.1 @@ -549,7 +549,7 @@ subplot_dim = (width - subplot_spacing * (n_subplot_rows - 1)) / n_subplot_rows # Slider for determining the test index for which the data is being visualized. - slider = alt.binding_range(min=0, max=n_samples - 1, step=1, name="Test Sample Index: ") + slider = alt.binding_range(name="Test Sample Index: ", min=0, max=n_samples - 1, step=1) index_selection = alt.selection_point(value=0, bind=slider, fields=["index"]) scale = alt.Scale(domain=[x_min, x_max], scheme="greys") diff --git a/gallery/plot_quantile_conformalized.html b/gallery/plot_quantile_conformalized.html index d3394c6..0405013 100644 --- a/gallery/plot_quantile_conformalized.html +++ b/gallery/plot_quantile_conformalized.html @@ -437,12 +437,11 @@ import pandas as pd from sklearn import datasets from sklearn.model_selection import train_test_split -from sklearn.utils.validation import check_random_state from quantile_forest import RandomForestQuantileRegressor random_seed = 0 -random_state = check_random_state(random_seed) +random_state = np.random.RandomState(random_seed) n_samples = 900 coverages = np.linspace(0, 1, num=11, endpoint=True).round(1).tolist() # the "coverage level" @@ -577,10 +576,10 @@ def plot_prediction_intervals_by_strategy(df): def plot_prediction_intervals(df, domain): # Slider for varying the target coverage level. - slider = alt.binding_range(min=0, max=1, step=0.1, name="Coverage Target: ") - coverage_val = alt.param(value=0.9, bind=slider, name="coverage") + slider = alt.binding_range(name="Coverage Target: ", min=0, max=1, step=0.1) + coverage_val = alt.param(name="coverage", value=0.9, bind=slider) - click = alt.selection_point(fields=["y_label"], bind="legend") + click = alt.selection_point(bind="legend", fields=["y_label"]) tooltip = [ alt.Tooltip("y_test:Q", format="$,d", title="True Price"), diff --git a/gallery/plot_quantile_example.html b/gallery/plot_quantile_example.html index 0ef5adb..d50db2e 100644 --- a/gallery/plot_quantile_example.html +++ b/gallery/plot_quantile_example.html @@ -434,7 +434,7 @@ from quantile_forest import RandomForestQuantileRegressor -random_state = check_random_state(0) +random_state = np.random.RandomState(0) n_samples = 1000 bounds = [0, 10] quantiles = [0.025, 0.5, 0.975] diff --git a/gallery/plot_quantile_extrapolation.html b/gallery/plot_quantile_extrapolation.html index 3fa66a4..bb1509c 100644 --- a/gallery/plot_quantile_extrapolation.html +++ b/gallery/plot_quantile_extrapolation.html @@ -440,7 +440,7 @@ from quantile_forest import RandomForestQuantileRegressor -random_state = check_random_state(0) +random_state = np.random.RandomState(0) n_samples = 500 bounds = [0, 15] extrap_frac = 0.25 diff --git a/gallery/plot_quantile_interpolation.html b/gallery/plot_quantile_interpolation.html index 7e6c596..e98d969 100644 --- a/gallery/plot_quantile_interpolation.html +++ b/gallery/plot_quantile_interpolation.html @@ -420,7 +420,7 @@ // embed when document is 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"quantile_upp": 1.0}, {"method": "Higher", "X": "Sample 5 ([1, 1])", "y_pred": 2.0, "y_pred_low": 1.0, "y_pred_upp": 2.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Midpoint", "X": "Sample 1 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -2.0, "y_pred_upp": 0.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Midpoint", "X": "Sample 2 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -2.0, "y_pred_upp": 0.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Midpoint", "X": "Sample 3 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -2.0, "y_pred_upp": 0.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Midpoint", "X": "Sample 4 ([1, 1])", "y_pred": 1.5, "y_pred_low": 1.0, "y_pred_upp": 2.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Midpoint", "X": "Sample 5 ([1, 1])", "y_pred": 1.5, "y_pred_low": 1.0, "y_pred_upp": 2.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Nearest", "X": "Sample 1 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -2.0, "y_pred_upp": 0.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Nearest", "X": "Sample 2 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -2.0, "y_pred_upp": 0.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Nearest", "X": "Sample 3 ([-1, -1])", "y_pred": -1.0, "y_pred_low": -2.0, "y_pred_upp": 0.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Nearest", "X": "Sample 4 ([1, 1])", "y_pred": 1.0, "y_pred_low": 1.0, "y_pred_upp": 2.0, "quantile_low": 0.0, "quantile_upp": 1.0}, {"method": "Nearest", "X": "Sample 5 ([1, 1])", "y_pred": 1.0, "y_pred_low": 1.0, "y_pred_upp": 2.0, "quantile_low": 0.0, "quantile_upp": 1.0}]}}; var opt = { "mode": "vega-lite", "renderer": "canvas", @@ -432,11 +432,10 @@ 
import altair as alt
 import numpy as np
 import pandas as pd
-from sklearn.utils.validation import check_random_state
 
 from quantile_forest import RandomForestQuantileRegressor
 
-random_state = check_random_state(0)
+random_state = np.random.RandomState(0)
 intervals = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist()
 
 # Create toy dataset.
@@ -499,10 +498,10 @@
 
 def plot_interpolations(df, legend):
     # Slider for varying the prediction interval that determines the quantiles being interpolated.
-    slider = alt.binding_range(min=0, max=1, step=0.01, name="Prediction Interval: ")
-    interval_val = alt.param(value=0.9, bind=slider, name="interval")
+    slider = alt.binding_range(name="Prediction Interval: ", min=0, max=1, step=0.01)
+    interval_val = alt.param(name="interval", value=0.9, bind=slider)
 
-    click = alt.selection_point(fields=["method"], bind="legend")
+    click = alt.selection_point(bind="legend", fields=["method"], on="click")
 
     color = alt.condition(
         click,
@@ -568,7 +567,7 @@
                 header=alt.Header(labelOrient="bottom", titleOrient="bottom"),
                 title="Samples (Feature Values)",
             ),
-            title="QRF Prediction Intervals by Quantile Interpolation on Toy Dataset",
+            title="QRF Predictions by Quantile Interpolation on Toy Dataset",
         )
         .configure_facet(spacing=15)
         .configure_range(category=alt.RangeScheme(list(legend.values())))
diff --git a/gallery/plot_quantile_intervals.html b/gallery/plot_quantile_intervals.html
index 64f15f9..e0c3629 100644
--- a/gallery/plot_quantile_intervals.html
+++ b/gallery/plot_quantile_intervals.html
@@ -430,11 +430,10 @@
 import pandas as pd
 from sklearn import datasets
 from sklearn.model_selection import KFold
-from sklearn.utils.validation import check_random_state
 
 from quantile_forest import RandomForestQuantileRegressor
 
-random_state = check_random_state(0)
+random_state = np.random.RandomState(0)
 n_samples = 1000
 
 # Load the California Housing Prices dataset.
diff --git a/gallery/plot_quantile_multioutput.html b/gallery/plot_quantile_multioutput.html
index e6b758d..088ab12 100644
--- a/gallery/plot_quantile_multioutput.html
+++ b/gallery/plot_quantile_multioutput.html
@@ -419,7 +419,7 @@
   // embed when document is loaded, to ensure vega library is available
   // this works on all modern browsers, except IE8 and older
   document.addEventListener("DOMContentLoaded", function(event) {
-      var spec = {"config": {"view": {"continuousWidth": 300, "continuousHeight": 300}, "range": {"category": ["#f2a619", "#006aff"]}}, "layer": [{"mark": {"type": "circle", "color": "black", "opacity": 0.25, "size": 25}, "encoding": {"color": {"condition": {"param": "param_14", "field": "target", "type": "nominal"}, "value": "lightgray"}, "tooltip": [{"field": "target", "title": "Target", "type": "nominal"}, {"field": "x", "format": ",.3f", "title": "X", "type": "quantitative"}, {"field": "y", "format": ",.3f", "title": "Y", "type": "quantitative"}, {"field": "y_true", "format": ",.3f", "title": "Y", "type": "quantitative"}, {"field": "y_pred", "format": ",.3f", "title": "Predicted Y", "type": "quantitative"}, {"field": "y_pred_low", "format": ",.3f", "title": "Predicted Lower Y", "type": "quantitative"}, {"field": "y_pred_upp", "format": ",.3f", "title": "Predicted Upper Y", "type": "quantitative"}, {"field": "quantile_low", "format": ".3f", "title": "Lower Quantile", "type": "quantitative"}, {"field": "quantile_upp", "format": ".3f", "title": "Upper Quantile", "type": "quantitative"}], "x": {"field": "x", "scale": {"nice": false}, "type": "quantitative"}, "y": {"field": "y", "type": "quantitative"}}, "name": "view_17", "transform": [{"calculate": "round((0.5 - interval / 2) * 1000) / 1000", "as": "quantile_low"}, {"calculate": "round((0.5 + interval / 2) * 1000) / 1000", "as": "quantile_upp"}, {"calculate": "'q_' + datum.quantile_low", "as": "quantile_low_col"}, {"calculate": "'q_' + datum.quantile_upp", "as": "quantile_upp_col"}, {"calculate": "datum[datum.quantile_low_col]", "as": "y_pred_low"}, {"calculate": "datum[datum.quantile_upp_col]", "as": "y_pred_upp"}]}, {"mark": {"type": "area", "opacity": 0.25}, "encoding": {"color": {"condition": {"param": "param_14", "field": "target", "legend": {"symbolOpacity": 1}, "scale": {"range": ["#f2a619", "#006aff"]}, "sort": ["0", "1"], "title": "Target", "type": "nominal"}, "value": "lightgray"}, "tooltip": 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interval / 2) * 1000) / 1000", "as": "quantile_low"}, {"calculate": "round((0.5 + interval / 2) * 1000) / 1000", "as": "quantile_upp"}, {"calculate": "'q_' + datum.quantile_low", "as": "quantile_low_col"}, {"calculate": "'q_' + datum.quantile_upp", "as": "quantile_upp_col"}, {"calculate": "datum[datum.quantile_low_col]", "as": "y_pred_low"}, {"calculate": "datum[datum.quantile_upp_col]", "as": "y_pred_upp"}]}, {"mark": {"type": "line", "color": "black", "size": 3}, "encoding": {"color": {"condition": {"param": "param_14", "field": "target", "legend": {"symbolOpacity": 1}, "scale": {"range": ["#f2a619", "#006aff"]}, "sort": ["0", "1"], "title": "Target", "type": "nominal"}, "value": "lightgray"}, "tooltip": [{"field": "target", "title": "Target", "type": "nominal"}, {"field": "x", "format": ",.3f", "title": "X", "type": "quantitative"}, {"field": "y", "format": ",.3f", "title": "Y", "type": "quantitative"}, {"field": "y_true", "format": ",.3f", "title": "Y", "type": "quantitative"}, 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       var opt = {
         "mode": "vega-lite",
         "renderer": "canvas",
@@ -432,11 +432,10 @@
 import numpy as np
 import pandas as pd
 from sklearn.model_selection import train_test_split
-from sklearn.utils.validation import check_random_state
 
 from quantile_forest import RandomForestQuantileRegressor
 
-random_state = check_random_state(0)
+random_state = np.random.RandomState(0)
 n_samples = 2500
 bounds = [0, 100]
 quantiles = np.linspace(0, 1, num=41, endpoint=True).round(3).tolist()
@@ -496,10 +495,10 @@
 
 def plot_multitargets(df, legend):
     # Slider for varying the displayed prediction intervals.
-    slider = alt.binding_range(min=0, max=1, step=0.05, name="Prediction Interval: ")
-    interval_val = alt.param(value=0.95, bind=slider, name="interval")
+    slider = alt.binding_range(name="Prediction Interval: ", min=0, max=1, step=0.05)
+    interval_val = alt.param(name="interval", value=0.95, bind=slider)
 
-    click = alt.selection_point(fields=["target"], bind="legend")
+    click = alt.selection_point(bind="legend", fields=["target"], on="click")
 
     color = alt.condition(
         click,
diff --git a/gallery/plot_quantile_ranks.html b/gallery/plot_quantile_ranks.html
index 212ae06..bfc75f7 100644
--- a/gallery/plot_quantile_ranks.html
+++ b/gallery/plot_quantile_ranks.html
@@ -419,7 +419,7 @@
   // embed when document is loaded, to ensure vega library is available
   // this works on all modern browsers, except IE8 and older
   document.addEventListener("DOMContentLoaded", function(event) {
-      var spec = {"config": {"view": {"continuousWidth": 300, "continuousHeight": 300}}, "layer": [{"mark": {"type": "line", "opacity": 1}, "encoding": {"opacity": {"field": "model", "scale": {"range": [1, 1]}, "title": "Prediction", "type": "nominal"}}, "transform": [{"calculate": "'Median'", "as": "model"}]}, {"mark": {"type": "circle", "opacity": 0.5, "size": 25}, "encoding": {"color": {"condition": {"param": "param_15", "field": "outlier", "scale": {"domain": ["Yes", "No"], "range": ["red", "#f2a619"]}, "title": "Outlier", "type": "nominal"}, "value": "lightgray"}, "tooltip": [{"field": "x", "format": ",.3f", "title": "X", "type": "quantitative"}, {"field": "y", "format": ",.3f", "title": "Y", "type": "quantitative"}, {"field": "y_rank", "format": ".3f", "title": "Quantile Rank", "type": "quantitative"}, {"field": "outlier", "title": "Outlier", "type": "nominal"}], "x": {"field": "x", "type": "quantitative"}, "y": {"field": "y", "type": "quantitative"}}, "name": "view_18", "transform": [{"calculate": "abs(datum.y_rank - 0.5) > (0.5 - interval / 2) ? 'Yes' : 'No'", "as": "outlier"}]}, {"mark": {"type": "line", "color": "#006aff", "size": 4}, "encoding": {"tooltip": [{"field": "x", "format": ",.3f", "title": "X", "type": "quantitative"}, {"field": "y", "format": ",.3f", "title": "Y", "type": "quantitative"}, {"field": "y_pred", "format": ",.3f", "title": "Predicted Y", "type": "quantitative"}], "x": {"axis": {"title": "X"}, "field": "x", "type": "quantitative"}, "y": {"axis": {"title": "Y"}, "field": "y_pred", "type": "quantitative"}}}], "data": {"name": "data-659ece1cc7cff19a0feb05a2cae2a0d9"}, "height": 400, "params": [{"name": "interval", "bind": {"input": "range", "max": 1, "min": 0, "name": "Rank Interval Threshold: ", "step": 0.01}, "value": 0.05}, {"name": "param_15", "select": {"type": "point", "fields": ["outlier"]}, "bind": "legend", "views": ["view_18"]}], "title": "QRF Predictions with Quantile Rank Thresholding on Toy Dataset", "width": 650, "$schema": "https://vega.github.io/schema/vega-lite/v5.20.1.json", "datasets": 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       var opt = {
         "mode": "vega-lite",
         "renderer": "canvas",
@@ -437,7 +437,7 @@
 
 from quantile_forest import RandomForestQuantileRegressor
 
-random_state = check_random_state(0)
+random_state = np.random.RandomState(0)
 n_samples = 5000
 bounds = [0, 10]
 
@@ -468,10 +468,10 @@
 
 def plot_pred_and_ranks(df):
     # Slider for varying the interval that defines the upper and lower quantile rank thresholds.
-    slider = alt.binding_range(min=0, max=1, step=0.01, name="Rank Interval Threshold: ")
-    interval_val = alt.param(value=0.05, bind=slider, name="interval")
+    slider = alt.binding_range(name="Rank Interval Threshold: ", min=0, max=1, step=0.01)
+    interval_val = alt.param(name="interval", value=0.05, bind=slider)
 
-    click = alt.selection_point(fields=["outlier"], bind="legend")
+    click = alt.selection_point(bind="legend", fields=["outlier"], on="click")
 
     base = alt.Chart(df)
 
diff --git a/gallery/plot_quantile_vs_standard.html b/gallery/plot_quantile_vs_standard.html
index d2d67c4..1448ef0 100644
--- a/gallery/plot_quantile_vs_standard.html
+++ b/gallery/plot_quantile_vs_standard.html
@@ -420,7 +420,7 @@
   // embed when document is loaded, to ensure vega library is available
   // this works on all modern browsers, except IE8 and older
   document.addEventListener("DOMContentLoaded", function(event) {
-      var spec = {"config": {"view": {"continuousWidth": 300, "continuousHeight": 300}, "range": {"category": ["#c0c0c0", "#f2a619", "#006aff"]}}, "data": {"name": "data-c73ea58c9debcb98d70fcf0296e48b0c"}, "mark": {"type": "bar"}, "encoding": {"color": {"condition": {"param": "param_16", "field": "label", "sort": ["Actual", "RF (Mean)", "QRF (Median)"], "title": null, "type": "nominal"}, "value": "lightgray"}, "tooltip": [{"field": "label", "title": "Label", "type": "nominal"}, {"field": "value", "title": "Value (binned)", "type": "quantitative"}, {"aggregate": "count", "format": ",d", "title": "Counts", "type": "quantitative"}], "x": {"axis": {"labelAngle": 0, "labelExpr": "datum.value % 0.5 == 0 ? datum.value : null"}, "field": "value", "title": "Actual and Predicted Target Values", "type": "nominal"}, "xOffset": {"field": "label", "type": "nominal"}, "y": {"aggregate": "count", "axis": {"format": ",d", "title": "Counts"}, "type": "quantitative"}}, "height": 400, "params": [{"name": "quantile", "bind": {"input": "range", "max": 1, "min": 0, "name": "Predicted Quantile: ", "step": 0.01}, "value": 0.5}, {"name": "param_16", "select": {"type": "point", "fields": ["label"]}, "bind": "legend"}], "title": "Distribution of QRF vs. RF Predictions on Right-Skewed Distribution", "transform": [{"calculate": "'qrf_' + quantile", "as": "qrf_col"}, {"calculate": "datum[datum.qrf_col]", "as": "qrf"}, {"calculate": "round(datum.actual * 10) / 10", "as": "Actual"}, {"calculate": "round(datum.rf * 10) / 10", "as": "RF (Mean)"}, {"calculate": "round(datum.qrf * 10) / 10", "as": "QRF (Quantile)"}, {"fold": ["Actual", "RF (Mean)", "QRF (Quantile)"], "as": ["label", "value"]}], "width": 650, "$schema": "https://vega.github.io/schema/vega-lite/v5.20.1.json", "datasets": {"data-c73ea58c9debcb98d70fcf0296e48b0c": [{"actual": -0.5434896097999349, "rf": -0.016532400319418442, "qrf_0": -1.072270262138082, "qrf_0.01": -1.072270262138082, "qrf_0.02": -1.072270262138082, "qrf_0.03": 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-0.11697757302744594, "qrf_0.49": -0.11697757302744594, "qrf_0.5": -0.11697757302744594, "qrf_0.51": -0.11578317463247931, "qrf_0.52": -0.1139436903813025, "qrf_0.53": -0.11329766090487947, "qrf_0.54": -0.11329766090487947, "qrf_0.55": -0.11329766090487947, "qrf_0.56": -0.11329766090487947, "qrf_0.57": -0.11329766090487947, "qrf_0.58": -0.11329766090487947, "qrf_0.59": -0.11329766090487947, "qrf_0.6": -0.11329766090487947, "qrf_0.61": -0.11329766090487947, "qrf_0.62": -0.11329766090487947, "qrf_0.63": -0.11329766090487947, "qrf_0.64": -0.11329766090487947, "qrf_0.65": -0.11329766090487947, "qrf_0.66": -0.11329766090487947, "qrf_0.67": -0.11329766090487947, "qrf_0.68": -0.11329766090487947, "qrf_0.69": -0.11329766090487947, "qrf_0.7": -0.11329766090487947, "qrf_0.71": -0.1084096730713409, "qrf_0.72": -0.09644253044440121, "qrf_0.73": -0.09578977566945551, "qrf_0.74": -0.08372511146337926, "qrf_0.75": -0.051654608931486795, "qrf_0.76": -0.022356400977975973, "qrf_0.77": 0.044242464970106754, "qrf_0.78": 0.044242464970106754, "qrf_0.79": 0.044242464970106754, "qrf_0.8": 0.044242464970106754, "qrf_0.81": 0.044242464970106754, "qrf_0.82": 0.044242464970106754, "qrf_0.83": 0.05091074455651561, "qrf_0.84": 0.08346763900780552, "qrf_0.85": 0.08359742387992967, "qrf_0.86": 0.08433287148863333, "qrf_0.87": 0.08433287148863333, "qrf_0.88": 0.08433287148863333, "qrf_0.89": 0.08433287148863333, "qrf_0.9": 0.08433287148863333, "qrf_0.91": 0.08433287148863333, "qrf_0.92": 0.08433287148863333, "qrf_0.93": 0.08433287148863333, "qrf_0.94": 0.08433287148863333, "qrf_0.95": 0.08433287148863333, "qrf_0.96": 0.08433287148863333, "qrf_0.97": 0.08433287148863333, "qrf_0.98": 0.08484499540437304, "qrf_0.99": 0.11097123059196805, "qrf_1": 0.21315539891000057}, {"actual": -0.5779631729727772, "rf": -0.38771745091762794, "qrf_0": -0.9078178796538621, "qrf_0.01": -0.9078178796538621, "qrf_0.02": -0.9078178796538621, "qrf_0.03": -0.9078178796538621, "qrf_0.04": -0.9078178796538621, "qrf_0.05": -0.838562481756446, "qrf_0.06": -0.8349174608144767, "qrf_0.07": -0.8349174608144767, "qrf_0.08": -0.8349174608144767, "qrf_0.09": -0.8349174608144767, "qrf_0.1": -0.8296202824380443, "qrf_0.11": -0.742502189014699, "qrf_0.12": -0.7137881288437276, "qrf_0.13": -0.7113309378477831, "qrf_0.14": -0.7113309378477831, "qrf_0.15": -0.7113309378477831, "qrf_0.16": -0.7113309378477831, "qrf_0.17": -0.7113309378477831, "qrf_0.18": -0.7113309378477831, "qrf_0.19": -0.7113309378477831, "qrf_0.2": -0.7113309378477831, "qrf_0.21": -0.7113309378477831, "qrf_0.22": -0.7113309378477831, "qrf_0.23": -0.7113309378477831, "qrf_0.24": -0.7113309378477831, "qrf_0.25": -0.7113309378477831, "qrf_0.26": -0.7113309378477831, "qrf_0.27": -0.7113309378477831, "qrf_0.28": -0.7113309378477831, "qrf_0.29": -0.7113309378477831, "qrf_0.3": -0.7113309378477831, "qrf_0.31": -0.7113309378477831, "qrf_0.32": -0.7113309378477831, "qrf_0.33": -0.7113309378477831, "qrf_0.34": -0.7113309378477831, "qrf_0.35": -0.7113309378477831, "qrf_0.36": -0.7113309378477831, "qrf_0.37": -0.7113309378477831, "qrf_0.38": -0.7113309378477831, "qrf_0.39": -0.7113309378477831, "qrf_0.4": -0.7113309378477831, "qrf_0.41": -0.7113309378477831, "qrf_0.42": -0.7113309378477831, "qrf_0.43": -0.6818806059426539, "qrf_0.44": -0.6596636888914161, "qrf_0.45": -0.6596636888914161, "qrf_0.46": -0.6596636888914161, "qrf_0.47": -0.6596636888914161, "qrf_0.48": -0.6596636888914161, "qrf_0.49": -0.6420884027105501, "qrf_0.5": -0.6168695651119888, "qrf_0.51": -0.60853678678524, "qrf_0.52": -0.60853678678524, "qrf_0.53": -0.60853678678524, "qrf_0.54": -0.60853678678524, "qrf_0.55": -0.6023504992814712, "qrf_0.56": -0.5947894812213094, "qrf_0.57": -0.5822307398787562, "qrf_0.58": -0.5530946194242682, "qrf_0.59": -0.48562977069712593, "qrf_0.6": -0.3933794208822398, "qrf_0.61": -0.36340310701612777, "qrf_0.62": -0.35698126678856, "qrf_0.63": -0.3465035274698969, "qrf_0.64": -0.3465035274698969, "qrf_0.65": -0.345658547977317, "qrf_0.66": -0.33877964844114583, "qrf_0.67": -0.32847267709208083, "qrf_0.68": -0.32847267709208083, "qrf_0.69": -0.3113596009895254, "qrf_0.7": -0.27326920579351277, "qrf_0.71": -0.27326920579351277, "qrf_0.72": -0.27326920579351277, "qrf_0.73": -0.27326920579351277, "qrf_0.74": -0.2602594616413945, "qrf_0.75": -0.22323172828536664, "qrf_0.76": -0.22323172828536664, "qrf_0.77": -0.22323172828536664, "qrf_0.78": -0.10181943146359429, "qrf_0.79": 0.3286423481772378, "qrf_0.8": 0.3286423481772378, "qrf_0.81": 0.33220746991022365, "qrf_0.82": 0.34740614677189896, "qrf_0.83": 0.34740614677189896, "qrf_0.84": 0.37592459431284164, "qrf_0.85": 0.5256464439027946, "qrf_0.86": 0.5256464439027946, "qrf_0.87": 0.5256464439027946, "qrf_0.88": 0.5256464439027946, "qrf_0.89": 0.5256464439027946, "qrf_0.9": 0.5256464439027946, "qrf_0.91": 0.5256464439027946, "qrf_0.92": 0.5256464439027946, "qrf_0.93": 0.5256464439027946, "qrf_0.94": 0.5294967344042384, "qrf_0.95": 0.5898179522602034, "qrf_0.96": 0.5898179522602034, "qrf_0.97": 0.5898179522602034, "qrf_0.98": 0.5898179522602034, "qrf_0.99": 0.5952281432334104, "qrf_1": 1.130837049580629}, {"actual": -1.0453981422568246, "rf": -0.0633119861136478, "qrf_0": -1.2191691145584445, "qrf_0.01": -1.2191691145584445, "qrf_0.02": -0.8861030889263601, "qrf_0.03": -0.8793058230971339, "qrf_0.04": -0.8793058230971339, "qrf_0.05": -0.8793058230971339, "qrf_0.06": -0.8793058230971339, "qrf_0.07": -0.8793058230971339, "qrf_0.08": -0.8616591887163613, "qrf_0.09": -0.8451269426779457, "qrf_0.1": -0.8436436481193704, "qrf_0.11": -0.8436436481193704, "qrf_0.12": -0.8436436481193704, "qrf_0.13": -0.8436436481193704, "qrf_0.14": -0.8436436481193704, "qrf_0.15": -0.8436436481193704, "qrf_0.16": -0.8436436481193704, "qrf_0.17": -0.8436436481193704, "qrf_0.18": -0.8436436481193704, "qrf_0.19": -0.8436436481193704, "qrf_0.2": -0.8436436481193704, "qrf_0.21": -0.8436436481193704, "qrf_0.22": -0.8403987048185525, "qrf_0.23": -0.839483464400373, "qrf_0.24": -0.788608089804834, "qrf_0.25": -0.7725421820378215, "qrf_0.26": -0.7725421820378215, "qrf_0.27": -0.7462100069939238, "qrf_0.28": -0.736470709374948, "qrf_0.29": -0.736470709374948, "qrf_0.3": -0.7343257222424259, "qrf_0.31": -0.7334064420427735, "qrf_0.32": -0.7334064420427735, "qrf_0.33": -0.7334064420427735, "qrf_0.34": -0.7334064420427735, "qrf_0.35": -0.7334064420427735, "qrf_0.36": -0.6753034093518983, "qrf_0.37": -0.642620453463281, "qrf_0.38": -0.642620453463281, "qrf_0.39": -0.642620453463281, "qrf_0.4": -0.642620453463281, "qrf_0.41": -0.642620453463281, "qrf_0.42": -0.642620453463281, "qrf_0.43": -0.642620453463281, "qrf_0.44": -0.642620453463281, "qrf_0.45": -0.642620453463281, "qrf_0.46": -0.642620453463281, "qrf_0.47": -0.642620453463281, "qrf_0.48": -0.642620453463281, "qrf_0.49": -0.642620453463281, "qrf_0.5": -0.642620453463281, "qrf_0.51": -0.642620453463281, "qrf_0.52": -0.642620453463281, "qrf_0.53": -0.642620453463281, "qrf_0.54": -0.642620453463281, "qrf_0.55": -0.642620453463281, "qrf_0.56": -0.642620453463281, "qrf_0.57": -0.642620453463281, "qrf_0.58": -0.6324436639896978, "qrf_0.59": -0.6183900023357014, "qrf_0.6": -0.16482725191445985, "qrf_0.61": 0.5155168737174065, "qrf_0.62": 0.5155168737174065, "qrf_0.63": 0.5155168737174065, "qrf_0.64": 0.5155168737174065, "qrf_0.65": 0.5155168737174065, "qrf_0.66": 0.5155168737174065, "qrf_0.67": 0.5155168737174065, "qrf_0.68": 0.5155168737174065, "qrf_0.69": 0.5155168737174065, "qrf_0.7": 0.5155168737174065, "qrf_0.71": 0.5155168737174065, "qrf_0.72": 0.5155168737174065, "qrf_0.73": 0.5155168737174065, "qrf_0.74": 0.5155168737174065, "qrf_0.75": 0.5173588271668572, "qrf_0.76": 0.5780415230022093, "qrf_0.77": 0.87038989249533, "qrf_0.78": 1.264378996758646, "qrf_0.79": 1.264378996758646, "qrf_0.8": 1.264378996758646, "qrf_0.81": 1.264378996758646, "qrf_0.82": 1.264378996758646, "qrf_0.83": 1.264378996758646, "qrf_0.84": 1.264378996758646, "qrf_0.85": 1.264378996758646, "qrf_0.86": 1.264378996758646, "qrf_0.87": 1.264378996758646, "qrf_0.88": 1.264378996758646, "qrf_0.89": 1.264378996758646, "qrf_0.9": 1.264378996758646, "qrf_0.91": 1.264378996758646, "qrf_0.92": 1.264378996758646, "qrf_0.93": 1.264378996758646, "qrf_0.94": 1.264378996758646, "qrf_0.95": 1.264378996758646, "qrf_0.96": 1.264378996758646, "qrf_0.97": 1.264378996758646, "qrf_0.98": 1.264378996758646, "qrf_0.99": 1.267546921745263, "qrf_1": 1.5811714954201816}]}};
       var opt = {
         "mode": "vega-lite",
         "renderer": "canvas",
@@ -435,11 +435,10 @@
 import scipy as sp
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.model_selection import train_test_split
-from sklearn.utils.validation import check_random_state
 
 from quantile_forest import RandomForestQuantileRegressor
 
-random_state = check_random_state(0)
+random_state = np.random.RandomState(0)
 quantiles = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist()
 
 # Create right-skewed dataset.
@@ -484,14 +483,14 @@
 def plot_prediction_histograms(df, legend):
     # Slider for varying the quantile value used for generating the QRF histogram.
     slider = alt.binding_range(
+        name="Predicted Quantile: ",
         min=0,
         max=1,
         step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1),
-        name="Predicted Quantile: ",
     )
     quantile_val = alt.param(value=0.5, bind=slider, name="quantile")
 
-    click = alt.selection_point(fields=["label"], bind="legend")
+    click = alt.selection_point(bind="legend", fields=["label"], on="click")
 
     chart = (
         alt.Chart(df)
@@ -518,6 +517,7 @@
                 alt.Color("label:N", sort=list(legend.keys()), title=None),
                 alt.value("lightgray"),
             ),
+            opacity=alt.condition(click, alt.value(1), alt.value(0.5)),
             xOffset=alt.XOffset("label:N"),
             tooltip=[
                 alt.Tooltip("label:N", title="Label"),
diff --git a/gallery/plot_treeshap_example.html b/gallery/plot_treeshap_example.html
index 2664c35..84c82ec 100644
--- a/gallery/plot_treeshap_example.html
+++ b/gallery/plot_treeshap_example.html
@@ -437,11 +437,10 @@
 import shap
 from sklearn import datasets
 from sklearn.model_selection import train_test_split
-from sklearn.utils.validation import check_random_state
 
 from quantile_forest import RandomForestQuantileRegressor
 
-random_state = check_random_state(0)
+random_state = np.random.RandomState(0)
 n_samples = 1000
 test_idx = 0
 quantiles = np.linspace(0, 1, num=11, endpoint=True).round(1).tolist()
@@ -547,12 +546,12 @@
 
     # Slider for varying the applied quantile estimates.
     slider = alt.binding_range(
+        name="Predicted Quantile: ",
         min=0,
         max=1,
         step=0.5 if len(quantiles) == 1 else 1 / (len(quantiles) - 1),
-        name="Predicted Quantile: ",
     )
-    quantile_val = alt.param(value=0.5, bind=slider, name="quantile")
+    quantile_val = alt.param(name="quantile", value=0.5, bind=slider)
 
     df_grouped = (
         df.groupby("quantile")[df.columns.tolist()]
diff --git a/generated/quantile_forest.ExtraTreesQuantileRegressor.html b/generated/quantile_forest.ExtraTreesQuantileRegressor.html
index ea75877..4dc7fbb 100644
--- a/generated/quantile_forest.ExtraTreesQuantileRegressor.html
+++ b/generated/quantile_forest.ExtraTreesQuantileRegressor.html
@@ -590,7 +590,7 @@ 
ExtraTreesQuantileRegressor
 
  • multioutput regressions (i.e. when n_outputs_ > 1),
    • 
 
  • regressions trained on data with missing values,
    • 
-
  • trees with large leaf samples (i.e. when max_samples_leaf > 1).
    • 
+
  • trees with multi-sample leaves (i.e. when max_samples_leaf > 1).
    • 
 
 
 
diff --git a/generated/quantile_forest.RandomForestQuantileRegressor.html b/generated/quantile_forest.RandomForestQuantileRegressor.html
index bbadd5c..1352a21 100644
--- a/generated/quantile_forest.RandomForestQuantileRegressor.html
+++ b/generated/quantile_forest.RandomForestQuantileRegressor.html
@@ -588,7 +588,7 @@ 
    RandomForestQuantileRegressor
 
  • multioutput regressions (i.e. when n_outputs_ > 1),
    • 
 
  • regressions trained on data with missing values,
    • 
-
  • trees with large leaf samples (i.e. when max_samples_leaf > 1).
    • 
+
  • trees with multi-sample leaves (i.e. when max_samples_leaf > 1).
    • 
 
 
 
diff --git a/getting_started/developers.html b/getting_started/developers.html
index 53935dd..2cb51e0 100644
--- a/getting_started/developers.html
+++ b/getting_started/developers.html
@@ -459,10 +459,18 @@ 
    Troubleshooting
 
    Test and Coverage#
    
+
    Ensure that pytest and pytest-cov are installed:
    
+
    $ pip install pytest pytest-cov
+
    
+
    
 
    To test the code:
    
 
    $ python -m pytest quantile_forest -v
 
    
 
    
+
    To test the code and produce a coverage report:
    
+
    $ python -m pytest quantile_forest --cov-report html --cov=quantile_forest
+
    
+
    
 
    To test the documentation:
    
 
    $ python -m pytest docs/user_guide/*rst
 
    
diff --git a/searchindex.js b/searchindex.js
index c500d4c..70e1f6d 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"API Reference": [[0, null]], "Comparing Quantile Interpolation Methods": [[8, null]], "Computing User-Specified Functions with QRFs": [[3, null]], "Developer\u2019s Guide": [[16, null]], "Development Dependencies": [[16, "development-dependencies"]], "Development Installation": [[16, "development-installation"]], "Documentation": [[16, "documentation"]], "ExtraTreesQuantileRegressor": [[14, null]], "Extrapolation with Quantile Regression Forests": [[7, null]], "Fitting a Model": [[21, "fitting-a-model"]], "Fitting and Predicting": [[21, null]], "General Examples": [[1, null]], "Getting Started": [[17, null]], "Installation": [[17, "installation"]], "Introduction": [[22, null]], "Making Predictions": [[21, "making-predictions"]], "Multi-target Quantile Regression with QRFs": [[10, null]], "Out-of-Bag Estimation": [[21, "out-of-bag-estimation"]], "Predicting with Quantile Regression Forests": [[6, null]], "Prerequisites": [[17, "prerequisites"]], 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