Update example plots

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, bounds, n_samples, add_noise=True, random_state=0):
+def make_func_Xy(func, n_samples, bounds, add_noise=True, random_state=0):
     """Make a dataset from a specified function."""
     random_state = check_random_state(random_state)
 
@@ -146,7 +146,7 @@ def _get_tree_weight_matrix(X, Y, X_eval=None, n_trees=100, rng=None, **kwargs):
             tree.fit(X[split1, :], Y[split1].flatten())
 
             # Extract tree weight matrix.
-            y_train_leaves = tree._get_y_train_leaves(X[split2, :], Y)
+            y_train_leaves = tree._get_y_train_leaves(X[split2, :], Y.reshape(-1, 1))
             nrows = X[split2, :].shape[0]
             matrix = np.zeros((nrows, nrows))
             for leaf in y_train_leaves[0]:
@@ -368,7 +368,7 @@ def get_coverage_xtr(bounds_list, train_indices, test_indices, y_train, level, *
 
 
 # Create a dataset that requires extrapolation.
-X, y = make_func_Xy(func, bounds, n_samples, add_noise=True, random_state=0)
+X, y = make_func_Xy(func, n_samples, bounds, add_noise=True, random_state=0)
 
 # Fit and extrapolate based on train-test split (depending on X).
 extrap_min_idx = int(n_samples * (extrap_frac / 2))

examples/plot_quantile_multioutput.py

@@ -39,8 +39,8 @@
 legend = {k: v for f in funcs for k, v in f["legend"].items()}
 
 
-def make_func_Xy(funcs, bounds, n_samples):
-    """Make a dataset from specified function(s)."""
+def make_funcs_Xy(funcs, bounds, n_samples):
+    """Make a dataset from specified function(s) with signal and noise."""
     x = np.linspace(*bounds, n_samples)
     y = np.empty((len(x), len(funcs)))
     for i, func in enumerate(funcs):
@@ -49,7 +49,7 @@ def make_func_Xy(funcs, bounds, n_samples):
 
 
 # Create a dataset with multiple target variables.
-X, y = make_func_Xy(funcs, bounds, n_samples)
+X, y = make_funcs_Xy(funcs, bounds, n_samples)
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)
 

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(a=7, loc=-1, scale=1, random_state=0):
+def make_skewed_dataset(n_samples, a=7, loc=-1, scale=1, random_state=0):
     """Make a skewed dataset."""
     random_state = check_random_state(random_state)
     skewnorm_rv = sp.stats.skewnorm(a, loc, scale)
@@ -37,7 +37,7 @@ def make_skewed_dataset(a=7, loc=-1, scale=1, random_state=0):
 
 
 # Create a right-skewed toy dataset.
-X, y = make_skewed_dataset(a=7, loc=-1, scale=1, random_state=0)
+X, y = make_skewed_dataset(n_samples, a=7, loc=-1, scale=1, random_state=0)
 
 regr_rf = RandomForestRegressor(random_state=random_state)
 regr_qrf = RandomForestQuantileRegressor(random_state=random_state)