Update docs

docs/user_guide.rst

@@ -37,7 +37,7 @@ This approach was first proposed by :cite:t:`2006:meinshausen`.
 Fitting and Predicting
 ----------------------
 
-Quantile forests can be fit and used to predict like standard scikit-learn estimators. In this package, the quantile forests extend standard scikit-learn forest regressors and inherent their model parameters, in addition to offering additional parameters related to quantile regression. We'll discuss the many of the important model parameters below.
+Quantile forests can be fit and used to predict like standard scikit-learn estimators. In this package, the quantile forests extend standard scikit-learn forest regressors and inherent their model parameters, in addition to offering additional parameters related to quantile regression. We'll discuss many of the important model parameters below.
 
 Fitting a Model
 ~~~~~~~~~~~~~~~

examples/plot_quantile_multioutput.py

@@ -5,7 +5,7 @@
 
 An example on a toy dataset that demonstrates fitting a single quantile
 regressor for multiple target variables. For each target, multiple quantiles
-can be estimated.
+can be estimated simulatenously.
 
 """
 
@@ -29,7 +29,7 @@
 ]
 
 
-def make_func_Xy(funcs, bounds, n_samples):
+def make_Xy(funcs, bounds, n_samples):
     x = np.linspace(bounds[0], bounds[1], n_samples)
     y = np.empty((len(x), 3))
     y[:, 0] = funcs[0](x) + np.random.normal(scale=0.01 * np.abs(x))
@@ -38,7 +38,7 @@ def make_func_Xy(funcs, bounds, n_samples):
     return x, y
 
 
-X, y = make_func_Xy(funcs, bounds, n_samples)
+X, y = make_Xy(funcs, bounds, n_samples)
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
 
 qrf = RandomForestQuantileRegressor(random_state=0)
@@ -49,7 +49,9 @@ def make_func_Xy(funcs, bounds, n_samples):
 
 def plot_multioutputs(colors, funcs, X, y):
     for i in range(y.shape[-1]):
-        plt.fill_between(X, y_pred[:, 0, i], y_pred[:, 2, i], color=colors[i], label=f"Target {i}")
+        y1 = y_pred[:, 0, i]
+        y2 = y_pred[:, 2, i]
+        plt.fill_between(X, y1, y2, color=colors[i], label=f"Target {i}")
         plt.plot(X, funcs[i](X), c="black")
     plt.xlim(bounds)
     plt.ylim([-8, 8])

quantile_forest/_quantile_forest.py

@@ -535,13 +535,6 @@ def predict(
         if not isinstance(interpolation, (bytes, bytearray)):
             interpolation = interpolation.encode()
 
-        if weighted_leaves and not weighted_quantile:
-            warn(
-                "`weighted_leaves` is True, but is only used if `weighted_quantile=True`. "
-                "`weighted_leaves` will be set to False."
-            )
-            weighted_leaves = False
-
         if oob_score:
             if not self.bootstrap:
                 raise ValueError("Out-of-bag estimation only available if bootstrap=True.")