deploy: 830f492

_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)

_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,

_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")

_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"),

_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]

_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

_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())))

_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.

_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,