FIX use same API for CalibrationDisplay than other Display

sklearn/calibration.py

@@ -980,8 +980,8 @@ class CalibrationDisplay:
     y_prob : ndarray of shape (n_samples,)
         Probability estimates for the positive class, for each sample.
 
-    name : str, default=None
-        Name for labeling curve.
+    estimator_name : str, default=None
+        Name of estimator. If None, the estimator name is not shown.
 
     Attributes
     ----------
@@ -1022,11 +1022,11 @@ class CalibrationDisplay:
     <...>
     """
 
-    def __init__(self, prob_true, prob_pred, y_prob, *, name=None):
+    def __init__(self, prob_true, prob_pred, y_prob, *, estimator_name=None):
         self.prob_true = prob_true
         self.prob_pred = prob_pred
         self.y_prob = y_prob
-        self.name = name
+        self.estimator_name = estimator_name
 
     def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
         """Plot visualization.
@@ -1041,7 +1041,8 @@ def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
             created.
 
         name : str, default=None
-            Name for labeling curve.
+            Name for labeling curve. If `None`, use `estimator_name` if
+            not `None`, otherwise no labeling is shown.
 
         ref_line : bool, default=True
             If `True`, plots a reference line representing a perfectly
@@ -1061,8 +1062,7 @@ def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
         if ax is None:
             fig, ax = plt.subplots()
 
-        name = self.name if name is None else name
-        self.name = name
+        name = self.estimator_name if name is None else name
 
         line_kwargs = {}
         if name is not None:
@@ -1298,6 +1298,9 @@ def from_predictions(
         prob_true, prob_pred = calibration_curve(
             y_true, y_prob, n_bins=n_bins, strategy=strategy
         )
+        name = name if name is not None else "Classifier"
 
-        disp = cls(prob_true=prob_true, prob_pred=prob_pred, y_prob=y_prob, name=name)
+        disp = cls(
+            prob_true=prob_true, prob_pred=prob_pred, y_prob=y_prob, estimator_name=name
+        )
         return disp.plot(ax=ax, ref_line=ref_line, **kwargs)

sklearn/metrics/_plot/det_curve.py

@@ -294,8 +294,8 @@ def plot(self, ax=None, *, name=None, **kwargs):
             created.
 
         name : str, default=None
-            Name of DET curve for labeling. If `None`, use the name of the
-            estimator.
+            Name of DET curve for labeling. If `None`, use `estimator_name` if
+            it is not `None`, otherwise no labeling is shown.
 
         **kwargs : dict
             Additional keywords arguments passed to matplotlib `plot` function.

sklearn/metrics/_plot/precision_recall_curve.py

@@ -109,8 +109,8 @@ def plot(self, ax=None, *, name=None, **kwargs):
             created.
 
         name : str, default=None
-            Name of precision recall curve for labeling. If `None`, use the
-            name of the estimator.
+            Name of precision recall curve for labeling. If `None`, use
+            `estimator_name` if not `None`, otherwise no labeling is shown.
 
         **kwargs : dict
             Keyword arguments to be passed to matplotlib's `plot`.

sklearn/metrics/_plot/roc_curve.py

@@ -94,8 +94,8 @@ def plot(self, ax=None, *, name=None, **kwargs):
             created.
 
         name : str, default=None
-            Name of ROC Curve for labeling. If `None`, use the name of the
-            estimator.
+            Name of ROC Curve for labeling. If `None`, use `estimator_name` if
+            not `None`, otherwise no labeling is shown.
 
         Returns
         -------

sklearn/tests/test_calibration.py

@@ -693,7 +693,7 @@ def test_calibration_display_compute(pyplot, iris_data_binary, n_bins, strategy)
     assert_allclose(viz.prob_pred, prob_pred)
     assert_allclose(viz.y_prob, y_prob)
 
-    assert viz.name == "LogisticRegression"
+    assert viz.estimator_name == "LogisticRegression"
 
     # cannot fail thanks to pyplot fixture
     import matplotlib as mpl  # noqa
@@ -715,7 +715,7 @@ def test_plot_calibration_curve_pipeline(pyplot, iris_data_binary):
     clf.fit(X, y)
     viz = CalibrationDisplay.from_estimator(clf, X, y)
     assert clf.__class__.__name__ in viz.line_.get_label()
-    assert viz.name == clf.__class__.__name__
+    assert viz.estimator_name == clf.__class__.__name__
 
 
 @pytest.mark.parametrize(
@@ -726,24 +726,23 @@ def test_calibration_display_default_labels(pyplot, name, expected_label):
     prob_pred = np.array([0.2, 0.8, 0.8, 0.4])
     y_prob = np.array([])
 
-    viz = CalibrationDisplay(prob_true, prob_pred, y_prob, name=name)
+    viz = CalibrationDisplay(prob_true, prob_pred, y_prob, estimator_name=name)
     viz.plot()
     assert viz.line_.get_label() == expected_label
 
 
 def test_calibration_display_label_class_plot(pyplot):
     # Checks that when instantiating `CalibrationDisplay` class then calling
-    # `plot`, `self.name` is the one given in `plot`
+    # `plot`, `self.estimator_name` is the one given in `plot`
     prob_true = np.array([0, 1, 1, 0])
     prob_pred = np.array([0.2, 0.8, 0.8, 0.4])
     y_prob = np.array([])
 
     name = "name one"
-    viz = CalibrationDisplay(prob_true, prob_pred, y_prob, name=name)
-    assert viz.name == name
+    viz = CalibrationDisplay(prob_true, prob_pred, y_prob, estimator_name=name)
+    assert viz.estimator_name == name
     name = "name two"
     viz.plot(name=name)
-    assert viz.name == name
     assert viz.line_.get_label() == name
 
 
@@ -764,7 +763,7 @@ def test_calibration_display_name_multiple_calls(
     params = (clf, X, y) if constructor_name == "from_estimator" else (y, y_prob)
 
     viz = constructor(*params, name=clf_name)
-    assert viz.name == clf_name
+    assert viz.estimator_name == clf_name
     pyplot.close("all")
     viz.plot()
     assert clf_name == viz.line_.get_label()