scikit-learn · jjerphan · Feb 17, 2023 · Jan 25, 2023 · Jan 30, 2023 · Jan 31, 2023
diff --git a/doc/modules/neighbors.rst b/doc/modules/neighbors.rst
@@ -136,9 +136,13 @@ have the same interface; we'll show an example of using the KD Tree here:
 Refer to the :class:`KDTree` and :class:`BallTree` class documentation
 for more information on the options available for nearest neighbors searches,
 including specification of query strategies, distance metrics, etc. For a list
-of available metrics, see the documentation of the :class:`DistanceMetric` class
-and the metrics listed in `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`.
-Note that the "cosine" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`.
+of valid metrics use :meth:`KDTree.valid_metrics` and :meth:`BallTree.valid_metrics`:
+
+    >>> from sklearn.neighbors import KDTree, BallTree
+    >>> KDTree.valid_metrics()
+    ['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev', 'infinity']
+    >>> BallTree.valid_metrics()
+    ['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev', 'infinity', 'seuclidean', 'mahalanobis', 'wminkowski', 'hamming', 'canberra', 'braycurtis', 'matching', 'jaccard', 'dice', 'rogerstanimoto', 'russellrao', 'sokalmichener', 'sokalsneath', 'haversine', 'pyfunc']
 
 .. _classification:
 
@@ -476,7 +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/sklearn/neighbors/_base.py b/sklearn/neighbors/_base.py
@@ -66,8 +66,8 @@
     SCIPY_METRICS += ["kulsinski"]
 
 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
@@ -234,11 +234,11 @@ leaf_size : positive int, default=40
 metric : str or DistanceMetric object, default='minkowski'
     Metric to use for distance computation. Default is "minkowski", which
     results in the standard Euclidean distance when p = 2.
-    {binary_tree}.valid_metrics gives a list of the metrics which are valid for
-    {BinaryTree}. 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
-    more information.
+    A list of valid metrics for {BinaryTree} is given by
+    :meth:`{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
+    more information on any distance metric.
 
 Additional keywords are passed to the distance metric class.
 Note: Callable functions in the metric parameter are NOT supported for KDTree
@@ -791,7 +791,7 @@ 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
@@ -979,6 +979,19 @@ 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 DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
                              ITYPE_t size) nogil except -1:
         """Compute the distance between arrays x1 and x2"""

diff --git a/sklearn/neighbors/_kde.py b/sklearn/neighbors/_kde.py
@@ -174,12 +174,12 @@ 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
@@ -114,7 +114,7 @@ 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:
@@ -165,7 +165,7 @@ 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