scikit-learn · jeremiedbb · Jul 6, 2023 · Jul 3, 2023 · Jul 3, 2023 · Jul 3, 2023
diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
@@ -139,9 +139,9 @@ including specification of query strategies, distance metrics, etc. For a list
 of valid metrics use :meth:`KDTree.valid_metrics` and :meth:`BallTree.valid_metrics`:
 
     >>> from sklearn.neighbors import KDTree, BallTree
-    >>> KDTree.valid_metrics()
+    >>> KDTree.valid_metrics
     ['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev', 'infinity']
-    >>> BallTree.valid_metrics()
+    >>> BallTree.valid_metrics
     ['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev', 'infinity', 'seuclidean', 'mahalanobis', 'hamming', 'canberra', 'braycurtis', 'jaccard', 'dice', 'rogerstanimoto', 'russellrao', 'sokalmichener', 'sokalsneath', 'haversine', 'pyfunc']
 
 .. _classification:
@@ -480,7 +480,7 @@ A list of valid metrics for any of the above algorithms can be obtained by using
 ``valid_metric`` attribute. For example, valid metrics for ``KDTree`` can be generated by:
 
     >>> from sklearn.neighbors import KDTree
-    >>> print(sorted(KDTree.valid_metrics()))
+    >>> print(sorted(KDTree.valid_metrics))
     ['chebyshev', 'cityblock', 'euclidean', 'infinity', 'l1', 'l2', 'manhattan', 'minkowski', 'p']
 
 

diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst
@@ -2,6 +2,23 @@
 
 .. currentmodule:: sklearn
 
+.. _changes_1_3_1:
+
+Version 1.3.1
+=============
+
+**TODO: set date**
+
+Changelog
+---------
+
+:mod:`sklearn.neighbors`
+........................
+
+- |Fix| Reintroduce :attr:`sklearn.neighbors.BallTree.valid_metrics` and
+  :attr:`sklearn.neighbors.KDTree.valid_metrics` as public class attributes.
+  :pr:`26754` by :user:`Julien Jerphanion <jjerphan>`.
+
 .. _changes_1_3:
 
 Version 1.3.0

diff --git a/sklearn/cluster/_hdbscan/hdbscan.py b/sklearn/cluster/_hdbscan/hdbscan.py
@@ -56,7 +56,7 @@
 from ._reachability import mutual_reachability_graph
 from ._tree import HIERARCHY_dtype, labelling_at_cut, tree_to_labels
 
-FAST_METRICS = set(KDTree.valid_metrics() + BallTree.valid_metrics())
+FAST_METRICS = set(KDTree.valid_metrics + BallTree.valid_metrics)
 
 # Encodings are arbitrary but must be strictly negative.
 # The current encodings are chosen as extensions to the -1 noise label.
@@ -768,14 +768,12 @@ def fit(self, X, y=None):
             n_jobs=self.n_jobs,
             **self._metric_params,
         )
-        if self.algorithm == "kdtree" and self.metric not in KDTree.valid_metrics():
+        if self.algorithm == "kdtree" and self.metric not in KDTree.valid_metrics:
             raise ValueError(
                 f"{self.metric} is not a valid metric for a KDTree-based algorithm."
                 " Please select a different metric."
             )
-        elif (
-            self.algorithm == "balltree" and self.metric not in BallTree.valid_metrics()
-        ):
+        elif self.algorithm == "balltree" and self.metric not in BallTree.valid_metrics:
             raise ValueError(
                 f"{self.metric} is not a valid metric for a BallTree-based algorithm."
                 " Please select a different metric."
@@ -805,7 +803,7 @@ def fit(self, X, y=None):
                 # We can't do much with sparse matrices ...
                 mst_func = _hdbscan_brute
                 kwargs["copy"] = self.copy
-            elif self.metric in KDTree.valid_metrics():
+            elif self.metric in KDTree.valid_metrics:
                 # TODO: Benchmark KD vs Ball Tree efficiency
                 mst_func = _hdbscan_prims
                 kwargs["algo"] = "kd_tree"

diff --git a/sklearn/cluster/tests/test_hdbscan.py b/sklearn/cluster/tests/test_hdbscan.py
@@ -165,7 +165,7 @@ def test_hdbscan_algorithms(algo, metric):
         metric_params=metric_params,
     )
 
-    if metric not in ALGOS_TREES[algo].valid_metrics():
+    if metric not in ALGOS_TREES[algo].valid_metrics:
         with pytest.raises(ValueError):
             hdb.fit(X)
     elif metric == "wminkowski":
@@ -424,7 +424,7 @@ def test_hdbscan_tree_invalid_metric():
 
     # The set of valid metrics for KDTree at the time of writing this test is a
     # strict subset of those supported in BallTree
-    metrics_not_kd = list(set(BallTree.valid_metrics()) - set(KDTree.valid_metrics()))
+    metrics_not_kd = list(set(BallTree.valid_metrics) - set(KDTree.valid_metrics))
     if len(metrics_not_kd) > 0:
         with pytest.raises(ValueError, match=msg):
             HDBSCAN(algorithm="kdtree", metric=metrics_not_kd[0]).fit(X)

diff --git a/sklearn/neighbors/_base.py b/sklearn/neighbors/_base.py
@@ -65,8 +65,8 @@
     SCIPY_METRICS += ["matching"]
 
 VALID_METRICS = dict(
-    ball_tree=BallTree._valid_metrics,
-    kd_tree=KDTree._valid_metrics,
+    ball_tree=BallTree.valid_metrics,
+    kd_tree=KDTree.valid_metrics,
     # The following list comes from the
     # sklearn.metrics.pairwise doc string
     brute=sorted(set(PAIRWISE_DISTANCE_FUNCTIONS).union(SCIPY_METRICS)),

diff --git a/sklearn/neighbors/_binary_tree.pxi b/sklearn/neighbors/_binary_tree.pxi
@@ -236,9 +236,10 @@ metric : str or DistanceMetric64 object, default='minkowski'
     Metric to use for distance computation. Default is "minkowski", which
     results in the standard Euclidean distance when p = 2.
     A list of valid metrics for {BinaryTree} is given by
-    :meth:`{BinaryTree}.valid_metrics`.
+    :attr:`{BinaryTree}.valid_metrics`.
     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
+    <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
+    the metrics listed in :class:`~sklearn.metrics.pairwise.distance_metrics` for
     more information on any distance metric.
 
 Additional keywords are passed to the distance metric class.
@@ -249,6 +250,8 @@ Attributes
 ----------
 data : memory view
     The training data
+valid_metrics: list of str
+    List of valid distance metrics.
 
 Examples
 --------
@@ -792,7 +795,7 @@ cdef class BinaryTree:
     cdef int n_splits
     cdef int n_calls
 
-    _valid_metrics = VALID_METRIC_IDS
+    valid_metrics = VALID_METRIC_IDS
 
     # Use cinit to initialize all arrays to empty: this will prevent memory
     # errors and seg-faults in rare cases where __init__ is not called
@@ -979,19 +982,6 @@ cdef class BinaryTree:
             self.node_bounds.base,
         )
 
-    @classmethod
-    def valid_metrics(cls):
-        """Get list of valid distance metrics.
-
-        .. versionadded:: 1.3
-
-        Returns
-        -------
-        valid_metrics: list of str
-            List of valid distance metrics.
-        """
-        return cls._valid_metrics
-
     cdef inline float64_t dist(self, float64_t* x1, float64_t* x2,
                                intp_t size) except -1 nogil:
         """Compute the distance between arrays x1 and x2"""

diff --git a/sklearn/neighbors/_kde.py b/sklearn/neighbors/_kde.py
@@ -173,12 +173,12 @@ def _choose_algorithm(self, algorithm, metric):
         # algorithm to compute the result.
         if algorithm == "auto":
             # use KD Tree if possible
-            if metric in KDTree.valid_metrics():
+            if metric in KDTree.valid_metrics:
                 return "kd_tree"
-            elif metric in BallTree.valid_metrics():
+            elif metric in BallTree.valid_metrics:
                 return "ball_tree"
         else:  # kd_tree or ball_tree
-            if metric not in TREE_DICT[algorithm].valid_metrics():
+            if metric not in TREE_DICT[algorithm].valid_metrics:
                 raise ValueError(
                     "invalid metric for {0}: '{1}'".format(TREE_DICT[algorithm], metric)
                 )

diff --git a/sklearn/neighbors/tests/test_kde.py b/sklearn/neighbors/tests/test_kde.py
@@ -113,7 +113,7 @@ def test_kde_algorithm_metric_choice(algorithm, metric):
 
     kde = KernelDensity(algorithm=algorithm, metric=metric)
 
-    if algorithm == "kd_tree" and metric not in KDTree.valid_metrics():
+    if algorithm == "kd_tree" and metric not in KDTree.valid_metrics:
         with pytest.raises(ValueError, match="invalid metric"):
             kde.fit(X)
     else:
@@ -164,7 +164,7 @@ def test_kde_sample_weights():
         test_points = rng.rand(n_samples_test, d)
         for algorithm in ["auto", "ball_tree", "kd_tree"]:
             for metric in ["euclidean", "minkowski", "manhattan", "chebyshev"]:
-                if algorithm != "kd_tree" or metric in KDTree.valid_metrics():
+                if algorithm != "kd_tree" or metric in KDTree.valid_metrics:
                     kde = KernelDensity(algorithm=algorithm, metric=metric)
 
                     # Test that adding a constant sample weight has no effect