Update example plots

examples/plot_predict_custom.py

@@ -66,13 +66,14 @@ def predict(qrf, X, quantiles=0.5, what=None):
 
 
 # Load the Diabetes dataset.
-X, y = datasets.load_diabetes(return_X_y=True)
+X, y = datasets.load_diabetes(return_X_y=True, as_frame=True)
 
 X_train, X_test, y_train, y_test = train_test_split(
     X, y, test_size=n_test_samples, random_state=random_state
 )
 
-qrf = RandomForestQuantileRegressor(random_state=random_state).fit(X_train, y_train)
+qrf = RandomForestQuantileRegressor(random_state=random_state)
+qrf.fit(X_train, y_train)
 
 # Define a user-specified function.
 # Here we randomly sample 1,000 values with replacement from the empirical distribution.

examples/plot_quantile_conformalized.py

@@ -35,7 +35,7 @@
 }
 
 # Load the California Housing dataset.
-X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True)
+X, y = datasets.fetch_california_housing(return_X_y=True, as_frame=True)
 perm = random_state.permutation(min(len(X), n_samples))
 X = X.iloc[perm]
 y = y.iloc[perm]

examples/plot_quantile_example.py

@@ -22,7 +22,7 @@
 quantiles = [0.025, 0.5, 0.975]
 
 
-def make_toy_dataset(n_samples, bounds, add_noise=True, random_state=0):
+def make_toy_dataset(n_samples, bounds, add_noise=True, random_state=None):
     """Make a toy dataset."""
     random_state = check_random_state(random_state)
 

examples/plot_quantile_extrapolation.py

@@ -33,7 +33,7 @@
 qrf_params = {"min_samples_leaf": 4, "max_samples_leaf": None, "random_state": random_state}
 
 
-def make_func_Xy(func, n_samples, bounds, add_noise=True, random_state=0):
+def make_func_Xy(func, n_samples, bounds, add_noise=True, random_state=None):
     """Make a dataset from a specified function."""
     random_state = check_random_state(random_state)
 

examples/plot_quantile_intervals.py

@@ -20,7 +20,7 @@
 n_samples = 1000
 
 # Load the California Housing dataset.
-X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True)
+X, y = datasets.fetch_california_housing(return_X_y=True, as_frame=True)
 perm = random_state.permutation(min(len(X), n_samples))
 X = X.iloc[perm]
 y = y.iloc[perm]

examples/plot_quantile_ranks.py

@@ -24,7 +24,7 @@
 bounds = [0, 10]
 
 
-def make_toy_dataset(n_samples, bounds, random_state=0):
+def make_toy_dataset(n_samples, bounds, random_state=None):
     """Make a toy dataset."""
     random_state = check_random_state(random_state)
     X_1d = np.linspace(*bounds, num=n_samples)
@@ -40,7 +40,8 @@ def make_toy_dataset(n_samples, bounds, random_state=0):
     min_samples_leaf=50,
     max_samples_leaf=None,
     random_state=random_state,
-).fit(X, y)
+)
+qrf.fit(X, y)
 
 y_pred = qrf.predict(X, quantiles=0.5)
 

examples/plot_quantile_vs_standard.py

@@ -26,7 +26,7 @@
 quantiles = np.linspace(0, 1, num=101, endpoint=True).round(2).tolist()
 
 
-def make_skewed_dataset(n_samples, a=7, loc=-1, scale=1, random_state=0):
+def make_skewed_dataset(n_samples, a=7, loc=-1, scale=1, random_state=None):
     """Make a skewed dataset."""
     random_state = check_random_state(random_state)
     skewnorm_rv = sp.stats.skewnorm(a, loc, scale)

examples/plot_treeshap_example.py

@@ -89,7 +89,7 @@ def get_shap_value_by_index(shap_values, index):
 
 
 # Load the California Housing dataset.
-X, y = datasets.fetch_california_housing(as_frame=True, return_X_y=True)
+X, y = datasets.fetch_california_housing(return_X_y=True, as_frame=True)
 perm = random_state.permutation(min(len(X), n_samples))
 X = X.iloc[perm]
 y = y.iloc[perm]