MAINT Clean up deprecations for 1.5: in NearestCentroid (#28813)

Co-authored-by: Guillaume Lemaitre <guillaume@probabl.ai>

sklearn/neighbors/_nearest_centroid.py

@@ -7,14 +7,11 @@
 #
 # License: BSD 3 clause
 
-import warnings
 from numbers import Real
 
 import numpy as np
 from scipy import sparse as sp
 
-from sklearn.metrics.pairwise import _VALID_METRICS
-
 from ..base import BaseEstimator, ClassifierMixin, _fit_context
 from ..metrics.pairwise import pairwise_distances_argmin
 from ..preprocessing import LabelEncoder
@@ -34,25 +31,17 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):
 
     Parameters
     ----------
-    metric : str or callable, default="euclidean"
-        Metric to use for distance computation. See the documentation of
-        `scipy.spatial.distance
-        <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
-        the metrics listed in
-        :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
-        values. Note that "wminkowski", "seuclidean" and "mahalanobis" are not
-        supported.
-
-        The centroids for the samples corresponding to each class is
-        the point from which the sum of the distances (according to the metric)
-        of all samples that belong to that particular class are minimized.
-        If the `"manhattan"` metric is provided, this centroid is the median
-        and for all other metrics, the centroid is now set to be the mean.
-
-        .. deprecated:: 1.3
-            Support for metrics other than `euclidean` and `manhattan` and for
-            callables was deprecated in version 1.3 and will be removed in
-            version 1.5.
+    metric : {"euclidean", "manhattan"}, default="euclidean"
+        Metric to use for distance computation.
+
+        If `metric="euclidean"`, the centroid for the samples corresponding to each
+        class is the arithmetic mean, which minimizes the sum of squared L1 distances.
+        If `metric="manhattan"`, the centroid is the feature-wise median, which
+        minimizes the sum of L1 distances.
+
+        .. versionchanged:: 1.5
+            All metrics but `"euclidean"` and `"manhattan"` were deprecated and
+            now raise an error.
 
         .. versionchanged:: 0.19
             `metric='precomputed'` was deprecated and now raises an error
@@ -108,15 +97,8 @@ class NearestCentroid(ClassifierMixin, BaseEstimator):
     [1]
     """
 
-    _valid_metrics = set(_VALID_METRICS) - {"mahalanobis", "seuclidean", "wminkowski"}
-
     _parameter_constraints: dict = {
-        "metric": [
-            StrOptions(
-                _valid_metrics, deprecated=_valid_metrics - {"manhattan", "euclidean"}
-            ),
-            callable,
-        ],
+        "metric": [StrOptions({"manhattan", "euclidean"})],
         "shrink_threshold": [Interval(Real, 0, None, closed="neither"), None],
     }
 
@@ -143,19 +125,6 @@ def fit(self, X, y):
         self : object
             Fitted estimator.
         """
-        if isinstance(self.metric, str) and self.metric not in (
-            "manhattan",
-            "euclidean",
-        ):
-            warnings.warn(
-                (
-                    "Support for distance metrics other than euclidean and "
-                    "manhattan and for callables was deprecated in version "
-                    "1.3 and will be removed in version 1.5."
-                ),
-                FutureWarning,
-            )
-
         # If X is sparse and the metric is "manhattan", store it in a csc
         # format is easier to calculate the median.
         if self.metric == "manhattan":
@@ -195,14 +164,7 @@ def fit(self, X, y):
                     self.centroids_[cur_class] = np.median(X[center_mask], axis=0)
                 else:
                     self.centroids_[cur_class] = csc_median_axis_0(X[center_mask])
-            else:
-                # TODO(1.5) remove warning when metric is only manhattan or euclidean
-                if self.metric != "euclidean":
-                    warnings.warn(
-                        "Averaging for metrics other than "
-                        "euclidean and manhattan not supported. "
-                        "The average is set to be the mean."
-                    )
+            else:  # metric == "euclidean"
                 self.centroids_[cur_class] = X[center_mask].mean(axis=0)
 
         if self.shrink_threshold:
@@ -231,7 +193,6 @@ def fit(self, X, y):
             self.centroids_ = dataset_centroid_[np.newaxis, :] + msd
         return self
 
-    # TODO(1.5) remove note about precomputed metric
     def predict(self, X):
         """Perform classification on an array of test vectors `X`.
 
@@ -246,12 +207,6 @@ def predict(self, X):
         -------
         C : ndarray of shape (n_samples,)
             The predicted classes.
-
-        Notes
-        -----
-        If the metric constructor parameter is `"precomputed"`, `X` is assumed
-        to be the distance matrix between the data to be predicted and
-        `self.centroids_`.
         """
         check_is_fitted(self)
 

sklearn/neighbors/tests/test_nearest_centroid.py

@@ -56,21 +56,17 @@ def test_classification_toy(csr_container):
     assert_array_equal(clf.predict(T_csr.tolil()), true_result)
 
 
-# TODO(1.5): Remove filterwarnings when support for some metrics is removed
-@pytest.mark.filterwarnings("ignore:Support for distance metrics:FutureWarning:sklearn")
 def test_iris():
     # Check consistency on dataset iris.
-    for metric in ("euclidean", "cosine"):
+    for metric in ("euclidean", "manhattan"):
         clf = NearestCentroid(metric=metric).fit(iris.data, iris.target)
         score = np.mean(clf.predict(iris.data) == iris.target)
         assert score > 0.9, "Failed with score = " + str(score)
 
 
-# TODO(1.5): Remove filterwarnings when support for some metrics is removed
-@pytest.mark.filterwarnings("ignore:Support for distance metrics:FutureWarning:sklearn")
 def test_iris_shrinkage():
     # Check consistency on dataset iris, when using shrinkage.
-    for metric in ("euclidean", "cosine"):
+    for metric in ("euclidean", "manhattan"):
         for shrink_threshold in [None, 0.1, 0.5]:
             clf = NearestCentroid(metric=metric, shrink_threshold=shrink_threshold)
             clf = clf.fit(iris.data, iris.target)
@@ -151,20 +147,6 @@ def test_manhattan_metric(csr_container):
     assert_array_equal(dense_centroid, [[-1, -1], [1, 1]])
 
 
-# TODO(1.5): remove this test
-@pytest.mark.parametrize(
-    "metric", sorted(list(NearestCentroid._valid_metrics - {"manhattan", "euclidean"}))
-)
-def test_deprecated_distance_metric_supports(metric):
-    # Check that a warning is raised for all deprecated distance metric supports
-    clf = NearestCentroid(metric=metric)
-    with pytest.warns(
-        FutureWarning,
-        match="Support for distance metrics other than euclidean and manhattan",
-    ):
-        clf.fit(X, y)
-
-
 def test_features_zero_var():
     # Test that features with 0 variance throw error