scikit-learn · glemaitre · Sep 9, 2021 · Sep 9, 2021 · Sep 9, 2021 · Sep 9, 2021
diff --git a/sklearn/calibration.py b/sklearn/calibration.py
@@ -25,14 +25,14 @@
     RegressorMixin,
     clone,
     MetaEstimatorMixin,
-    is_classifier,
 )
+from .metrics._base import _check_pos_label_consistency
+from .metrics._plot.base import BinaryClassifierCurveDisplayMixin
 from .preprocessing import label_binarize, LabelEncoder
 from .utils import (
     column_or_1d,
     deprecated,
     indexable,
-    check_matplotlib_support,
 )
 
 from .utils.multiclass import check_classification_targets
@@ -47,8 +47,6 @@
 from .isotonic import IsotonicRegression
 from .svm import LinearSVC
 from .model_selection import check_cv, cross_val_predict
-from .metrics._base import _check_pos_label_consistency
-from .metrics._plot.base import _get_response
 
 
 class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
@@ -987,7 +985,7 @@ def calibration_curve(
     return prob_true, prob_pred
 
 
-class CalibrationDisplay:
+class CalibrationDisplay(BinaryClassifierCurveDisplayMixin):
     """Calibration curve (also known as reliability diagram) visualization.
 
     It is recommended to use
@@ -1098,7 +1096,7 @@ def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
         display : :class:`~sklearn.calibration.CalibrationDisplay`
             Object that stores computed values.
         """
-        check_matplotlib_support("CalibrationDisplay.plot")
+        super().plot()
         import matplotlib.pyplot as plt
 
         if ax is None:
@@ -1234,17 +1232,15 @@ def from_estimator(
         >>> disp = CalibrationDisplay.from_estimator(clf, X_test, y_test)
         >>> plt.show()
         """
-        method_name = f"{cls.__name__}.from_estimator"
-        check_matplotlib_support(method_name)
-
-        if not is_classifier(estimator):
-            raise ValueError("'estimator' should be a fitted classifier.")
-
-        y_prob, pos_label = _get_response(
-            X, estimator, response_method="predict_proba", pos_label=pos_label
+        y_prob, pos_label, name = super().from_estimator(
+            estimator,
+            X,
+            y,
+            response_method="predict_proba",
+            pos_label=pos_label,
+            name=name,
         )
 
-        name = name if name is not None else estimator.__class__.__name__
         return cls.from_predictions(
             y,
             y_prob,
@@ -1354,8 +1350,12 @@ def from_predictions(
         >>> disp = CalibrationDisplay.from_predictions(y_test, y_prob)
         >>> plt.show()
         """
-        method_name = f"{cls.__name__}.from_estimator"
-        check_matplotlib_support(method_name)
+        pos_label, name = super().from_predictions(
+            y_true,
+            y_prob,
+            pos_label=pos_label,
+            name=name,
+        )
 
         prob_true, prob_pred = calibration_curve(
             y_true, y_prob, n_bins=n_bins, strategy=strategy, pos_label=pos_label

diff --git a/sklearn/ensemble/_stacking.py b/sklearn/ensemble/_stacking.py
@@ -30,8 +30,11 @@
 from ..utils import Bunch
 from ..utils.metaestimators import if_delegate_has_method
 from ..utils.multiclass import check_classification_targets
-from ..utils.validation import check_is_fitted
-from ..utils.validation import column_or_1d
+from ..utils.validation import (
+    _check_response_method,
+    check_is_fitted,
+    column_or_1d,
+)
 from ..utils.fixes import delayed
 
 
@@ -104,21 +107,14 @@ def _concatenate_predictions(self, X, predictions):
     def _method_name(name, estimator, method):
         if estimator == "drop":
             return None
-        if method == "auto":
-            if getattr(estimator, "predict_proba", None):
-                return "predict_proba"
-            elif getattr(estimator, "decision_function", None):
-                return "decision_function"
-            else:
-                return "predict"
-        else:
-            if not hasattr(estimator, method):
-                raise ValueError(
-                    "Underlying estimator {} does not implement the method {}.".format(
-                        name, method
-                    )
-                )
-            return method
+        method = None if method == "auto" else method
+        try:
+            method_name = _check_response_method(estimator, method).__name__
+        except AttributeError as e:
+            raise ValueError(
+                f"Underlying estimator {name} does not implement the method {method}."
+            ) from e
+        return method_name
 
     def fit(self, X, y, sample_weight=None):
         """Fit the estimators.