From d15aa5b4eae1a1c0d08af64ed5ec553e3708766e Mon Sep 17 00:00:00 2001
From: Meekail Zain <zainmeekail@gmail.com>
Date: Wed, 31 May 2023 10:16:03 -0400
Subject: [PATCH 1/5] Updated DistanceMetric API w/ new ABC/interface

---
 sklearn/cluster/_agglomerative.py             |  4 +--
 sklearn/cluster/_hierarchical_fast.pyx        |  8 ++---
 sklearn/metrics/_dist_metrics.pxd.tp          |  7 +++--
 sklearn/metrics/_dist_metrics.pyx.tp          | 29 +++++++++++++++--
 .../_datasets_pair.pxd.tp                     |  4 +--
 .../_datasets_pair.pyx.tp                     |  6 ++--
 .../_dispatcher.py                            |  4 +--
 sklearn/metrics/tests/test_dist_metrics.py    | 31 +++++--------------
 sklearn/neighbors/__init__.py                 |  2 --
 sklearn/neighbors/_ball_tree.pyx              | 30 +++++++++---------
 sklearn/neighbors/_binary_tree.pxi            | 19 ++++++------
 sklearn/neighbors/_distance_metric.py         | 22 -------------
 sklearn/neighbors/_kd_tree.pyx                |  4 +--
 sklearn/neighbors/tests/test_neighbors.py     | 12 -------
 sklearn/tests/test_common.py                  |  1 +
 15 files changed, 79 insertions(+), 104 deletions(-)
 delete mode 100644 sklearn/neighbors/_distance_metric.py

diff --git a/sklearn/cluster/_agglomerative.py b/sklearn/cluster/_agglomerative.py
index 77ac981934468..059056275ef3d 100644
--- a/sklearn/cluster/_agglomerative.py
+++ b/sklearn/cluster/_agglomerative.py
@@ -19,7 +19,7 @@
 from ..metrics.pairwise import paired_distances
 from ..metrics.pairwise import _VALID_METRICS
 from ..metrics import DistanceMetric
-from ..metrics._dist_metrics import METRIC_MAPPING
+from ..metrics._dist_metrics import METRIC_MAPPING64
 from ..utils import check_array
 from ..utils._fast_dict import IntFloatDict
 from ..utils.graph import _fix_connected_components
@@ -543,7 +543,7 @@ def linkage_tree(
             linkage == "single"
             and affinity != "precomputed"
             and not callable(affinity)
-            and affinity in METRIC_MAPPING
+            and affinity in METRIC_MAPPING64
         ):
             # We need the fast cythonized metric from neighbors
             dist_metric = DistanceMetric.get_metric(affinity)
diff --git a/sklearn/cluster/_hierarchical_fast.pyx b/sklearn/cluster/_hierarchical_fast.pyx
index 4ba895ddcf352..55e2b67030e13 100644
--- a/sklearn/cluster/_hierarchical_fast.pyx
+++ b/sklearn/cluster/_hierarchical_fast.pyx
@@ -3,7 +3,7 @@
 import numpy as np
 cimport cython
 
-from ..metrics._dist_metrics cimport DistanceMetric
+from ..metrics._dist_metrics cimport DistanceMetric64
 from ..utils._fast_dict cimport IntFloatDict
 from ..utils._typedefs cimport float64_t, intp_t, uint8_t
 
@@ -432,7 +432,7 @@ def single_linkage_label(L):
 # Implements MST-LINKAGE-CORE from https://arxiv.org/abs/1109.2378
 def mst_linkage_core(
         const float64_t [:, ::1] raw_data,
-        DistanceMetric dist_metric):
+        DistanceMetric64 dist_metric):
     """
     Compute the necessary elements of a minimum spanning
     tree for computation of single linkage clustering. This
@@ -449,8 +449,8 @@ def mst_linkage_core(
     raw_data: array of shape (n_samples, n_features)
         The array of feature data to be clustered. Must be C-aligned
 
-    dist_metric: DistanceMetric
-        A DistanceMetric object conforming to the API from
+    dist_metric: DistanceMetric64
+        A DistanceMetric64 object conforming to the API from
         ``sklearn.metrics._dist_metrics.pxd`` that will be
         used to compute distances.
 
diff --git a/sklearn/metrics/_dist_metrics.pxd.tp b/sklearn/metrics/_dist_metrics.pxd.tp
index 511e0941a7098..34ca704ab68bf 100644
--- a/sklearn/metrics/_dist_metrics.pxd.tp
+++ b/sklearn/metrics/_dist_metrics.pxd.tp
@@ -16,7 +16,7 @@ implementation_specific_values = [
     # The metric mapping is adapted accordingly to route to the correct
     # implementations.
     #
-    ('', 'float64_t', 'np.float64'),
+    ('64', 'float64_t', 'np.float64'),
     ('32', 'float32_t', 'np.float32')
 ]
 
@@ -25,6 +25,9 @@ from libc.math cimport sqrt, exp
 
 from ..utils._typedefs cimport float64_t, float32_t, int32_t, intp_t
 
+cdef class DistanceMetric:
+    pass
+
 {{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
 
 ######################################################################
@@ -68,7 +71,7 @@ cdef inline float64_t euclidean_rdist_to_dist{{name_suffix}}(const {{INPUT_DTYPE
 
 ######################################################################
 # DistanceMetric{{name_suffix}} base class
-cdef class DistanceMetric{{name_suffix}}:
+cdef class DistanceMetric{{name_suffix}}(DistanceMetric):
     # The following attributes are required for a few of the subclasses.
     # we must define them here so that cython's limited polymorphism will work.
     # Because we don't expect to instantiate a lot of these objects, the
diff --git a/sklearn/metrics/_dist_metrics.pyx.tp b/sklearn/metrics/_dist_metrics.pyx.tp
index 7e960c4e97934..a3f004abfd038 100644
--- a/sklearn/metrics/_dist_metrics.pyx.tp
+++ b/sklearn/metrics/_dist_metrics.pyx.tp
@@ -16,7 +16,7 @@ implementation_specific_values = [
     # The metric mapping is adapted accordingly to route to the correct
     # implementations.
     #
-    ('', 'float64_t', 'np.float64'),
+    ('64', 'float64_t', 'np.float64'),
     ('32', 'float32_t', 'np.float32')
 ]
 
@@ -73,9 +73,32 @@ def get_valid_metric_ids(L):
     >>> sorted(L)
     ['cityblock', 'euclidean', 'l1', 'l2', 'manhattan']
     """
-    return [key for (key, val) in METRIC_MAPPING.items()
+    return [key for (key, val) in METRIC_MAPPING64.items()
             if (val.__name__ in L) or (val in L)]
 
+cdef class DistanceMetric:
+    @classmethod
+    def get_metric(cls, metric, dtype=np.float64, **kwargs):
+        """Get the given distance metric from the string identifier.
+
+        See the docstring of DistanceMetric for a list of available metrics.
+
+        Parameters
+        ----------
+        metric : str or class name
+            The distance metric to use
+        dtype : {np.float32, np.float64}, default=np.float64
+            The dtype of the data on which the metric will be applied
+        **kwargs
+            additional arguments will be passed to the requested metric
+        """
+        if dtype == np.float32:
+            specialized_class = DistanceMetric32
+        else:
+            specialized_class = DistanceMetric64
+
+        return specialized_class.get_metric(metric, **kwargs)
+    pass
 
 {{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
 
@@ -126,7 +149,7 @@ cdef {{INPUT_DTYPE_t}} INF{{name_suffix}} = np.inf
 
 ######################################################################
 # Distance Metric Classes
-cdef class DistanceMetric{{name_suffix}}:
+cdef class DistanceMetric{{name_suffix}}(DistanceMetric):
     """DistanceMetric class
 
     This class provides a uniform interface to fast distance metric
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp
index 23337cb2b59d6..98123235d937c 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp
@@ -7,13 +7,13 @@ implementation_specific_values = [
     #
     # We use DistanceMetric for float64 for backward naming compatibility.
     #
-    ('64', 'DistanceMetric', 'float64_t'),
+    ('64', 'DistanceMetric64', 'float64_t'),
     ('32', 'DistanceMetric32', 'float32_t')
 ]
 
 }}
 from ...utils._typedefs cimport float64_t, float32_t, int32_t, intp_t
-from ...metrics._dist_metrics cimport DistanceMetric, DistanceMetric32
+from ...metrics._dist_metrics cimport DistanceMetric64, DistanceMetric32, DistanceMetric
 
 {{for name_suffix, DistanceMetric, INPUT_DTYPE_t in implementation_specific_values}}
 
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp
index 5569c1f231d62..befbe48e59996 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp
@@ -7,7 +7,7 @@ implementation_specific_values = [
     #
     # We use DistanceMetric for float64 for backward naming compatibility.
     #
-    ('64', 'DistanceMetric', 'float64_t', 'np.float64'),
+    ('64', 'DistanceMetric64', 'float64_t', 'np.float64'),
     ('32', 'DistanceMetric32', 'float32_t', 'np.float32')
 ]
 
@@ -17,7 +17,6 @@ import numpy as np
 from cython cimport final
 
 from ...utils._typedefs cimport float64_t, float32_t, intp_t
-from ...metrics._dist_metrics cimport DistanceMetric
 
 from scipy.sparse import issparse, csr_matrix
 
@@ -96,8 +95,9 @@ cdef class DatasetsPair{{name_suffix}}:
         if metric_kwargs is not None:
             metric_kwargs.pop("Y_norm_squared", None)
         cdef:
-            {{DistanceMetric}} distance_metric = {{DistanceMetric}}.get_metric(
+            {{DistanceMetric}} distance_metric = DistanceMetric.get_metric(
                 metric,
+                {{INPUT_DTYPE}},
                 **(metric_kwargs or {})
             )
 
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py b/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py
index f72861bcbeb8c..5f4325af3a09f 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py
+++ b/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py
@@ -6,7 +6,7 @@
 
 from scipy.sparse import isspmatrix_csr, issparse
 
-from .._dist_metrics import BOOL_METRICS, METRIC_MAPPING
+from .._dist_metrics import BOOL_METRICS, METRIC_MAPPING64
 
 from ._base import _sqeuclidean_row_norms32, _sqeuclidean_row_norms64
 from ._argkmin import (
@@ -76,7 +76,7 @@ def valid_metrics(cls) -> List[str]:
             "hamming",
             *BOOL_METRICS,
         }
-        return sorted(({"sqeuclidean"} | set(METRIC_MAPPING.keys())) - excluded)
+        return sorted(({"sqeuclidean"} | set(METRIC_MAPPING64.keys())) - excluded)
 
     @classmethod
     def is_usable_for(cls, X, Y, metric) -> bool:
diff --git a/sklearn/metrics/tests/test_dist_metrics.py b/sklearn/metrics/tests/test_dist_metrics.py
index 7cde0397d70be..c6ec517a10be2 100644
--- a/sklearn/metrics/tests/test_dist_metrics.py
+++ b/sklearn/metrics/tests/test_dist_metrics.py
@@ -9,11 +9,7 @@
 from scipy.spatial.distance import cdist
 from sklearn.metrics import DistanceMetric
 
-from sklearn.metrics._dist_metrics import (
-    BOOL_METRICS,
-    # Unexposed private DistanceMetric for 32 bit
-    DistanceMetric32,
-)
+from sklearn.metrics._dist_metrics import BOOL_METRICS
 
 from sklearn.utils import check_random_state
 from sklearn.utils._testing import assert_allclose, create_memmap_backed_data
@@ -78,9 +74,6 @@ def dist_func(x1, x2, p):
 )
 @pytest.mark.parametrize("X, Y", [(X64, Y64), (X32, Y32), (X_mmap, Y_mmap)])
 def test_cdist(metric_param_grid, X, Y):
-    DistanceMetricInterface = (
-        DistanceMetric if X.dtype == Y.dtype == np.float64 else DistanceMetric32
-    )
     metric, param_grid = metric_param_grid
     keys = param_grid.keys()
     X_csr, Y_csr = sp.csr_matrix(X), sp.csr_matrix(Y)
@@ -105,7 +98,7 @@ def test_cdist(metric_param_grid, X, Y):
         else:
             D_scipy_cdist = cdist(X, Y, metric, **kwargs)
 
-        dm = DistanceMetricInterface.get_metric(metric, **kwargs)
+        dm = DistanceMetric.get_metric(metric, X.dtype, **kwargs)
 
         # DistanceMetric.pairwise must be consistent for all
         # combinations of formats in {sparse, dense}.
@@ -165,9 +158,6 @@ def test_cdist_bool_metric(metric, X_bool, Y_bool):
 )
 @pytest.mark.parametrize("X", [X64, X32, X_mmap])
 def test_pdist(metric_param_grid, X):
-    DistanceMetricInterface = (
-        DistanceMetric if X.dtype == np.float64 else DistanceMetric32
-    )
     metric, param_grid = metric_param_grid
     keys = param_grid.keys()
     X_csr = sp.csr_matrix(X)
@@ -195,7 +185,7 @@ def test_pdist(metric_param_grid, X):
         else:
             D_scipy_pdist = cdist(X, X, metric, **kwargs)
 
-        dm = DistanceMetricInterface.get_metric(metric, **kwargs)
+        dm = DistanceMetric.get_metric(metric, X.dtype, **kwargs)
         D_sklearn = dm.pairwise(X)
         assert D_sklearn.flags.c_contiguous
         assert_allclose(D_sklearn, D_scipy_pdist, **rtol_dict)
@@ -226,8 +216,8 @@ def test_distance_metrics_dtype_consistency(metric_param_grid):
 
     for vals in itertools.product(*param_grid.values()):
         kwargs = dict(zip(keys, vals))
-        dm64 = DistanceMetric.get_metric(metric, **kwargs)
-        dm32 = DistanceMetric32.get_metric(metric, **kwargs)
+        dm64 = DistanceMetric.get_metric(metric, np.float64, **kwargs)
+        dm32 = DistanceMetric.get_metric(metric, np.float32, **kwargs)
 
         D64 = dm64.pairwise(X64)
         D32 = dm32.pairwise(X32)
@@ -269,9 +259,6 @@ def test_pdist_bool_metrics(metric, X_bool):
 )
 @pytest.mark.parametrize("X", [X64, X32])
 def test_pickle(writable_kwargs, metric_param_grid, X):
-    DistanceMetricInterface = (
-        DistanceMetric if X.dtype == np.float64 else DistanceMetric32
-    )
     metric, param_grid = metric_param_grid
     keys = param_grid.keys()
     for vals in itertools.product(*param_grid.values()):
@@ -281,7 +268,7 @@ def test_pickle(writable_kwargs, metric_param_grid, X):
                 if isinstance(val, np.ndarray):
                     val.setflags(write=writable_kwargs)
         kwargs = dict(zip(keys, vals))
-        dm = DistanceMetricInterface.get_metric(metric, **kwargs)
+        dm = DistanceMetric.get_metric(metric, X.dtype, **kwargs)
         D1 = dm.pairwise(X)
         dm2 = pickle.loads(pickle.dumps(dm))
         D2 = dm2.pairwise(X)
@@ -302,10 +289,6 @@ def test_pickle_bool_metrics(metric, X_bool):
 
 @pytest.mark.parametrize("X, Y", [(X64, Y64), (X32, Y32), (X_mmap, Y_mmap)])
 def test_haversine_metric(X, Y):
-    DistanceMetricInterface = (
-        DistanceMetric if X.dtype == np.float64 else DistanceMetric32
-    )
-
     # The Haversine DistanceMetric only works on 2 features.
     X = np.asarray(X[:, :2])
     Y = np.asarray(Y[:, :2])
@@ -327,7 +310,7 @@ def haversine_slow(x1, x2):
         for j, yj in enumerate(Y):
             D_reference[i, j] = haversine_slow(xi, yj)
 
-    haversine = DistanceMetricInterface.get_metric("haversine")
+    haversine = DistanceMetric.get_metric("haversine", X.dtype)
 
     D_sklearn = haversine.pairwise(X, Y)
     assert_allclose(
diff --git a/sklearn/neighbors/__init__.py b/sklearn/neighbors/__init__.py
index 12824e9cb684e..8223c20991904 100644
--- a/sklearn/neighbors/__init__.py
+++ b/sklearn/neighbors/__init__.py
@@ -5,7 +5,6 @@
 
 from ._ball_tree import BallTree
 from ._kd_tree import KDTree
-from ._distance_metric import DistanceMetric
 from ._graph import kneighbors_graph, radius_neighbors_graph
 from ._graph import KNeighborsTransformer, RadiusNeighborsTransformer
 from ._unsupervised import NearestNeighbors
@@ -20,7 +19,6 @@
 
 __all__ = [
     "BallTree",
-    "DistanceMetric",
     "KDTree",
     "KNeighborsClassifier",
     "KNeighborsRegressor",
diff --git a/sklearn/neighbors/_ball_tree.pyx b/sklearn/neighbors/_ball_tree.pyx
index cf02bd37c5829..0bc65fdd36ec8 100644
--- a/sklearn/neighbors/_ball_tree.pyx
+++ b/sklearn/neighbors/_ball_tree.pyx
@@ -5,15 +5,15 @@ __all__ = ['BallTree']
 
 DOC_DICT = {'BinaryTree': 'BallTree', 'binary_tree': 'ball_tree'}
 
-VALID_METRICS = ['EuclideanDistance', 'SEuclideanDistance',
-                 'ManhattanDistance', 'ChebyshevDistance',
-                 'MinkowskiDistance', 'WMinkowskiDistance',
-                 'MahalanobisDistance', 'HammingDistance',
-                 'CanberraDistance', 'BrayCurtisDistance',
-                 'JaccardDistance', 'DiceDistance',
-                 'RogersTanimotoDistance', 'RussellRaoDistance',
-                 'SokalMichenerDistance', 'SokalSneathDistance',
-                 'PyFuncDistance', 'HaversineDistance']
+VALID_METRICS = ['EuclideanDistance64', 'SEuclideanDistance64',
+                 'ManhattanDistance64', 'ChebyshevDistance64',
+                 'MinkowskiDistance64', 'WMinkowskiDistance64',
+                 'MahalanobisDistance64', 'HammingDistance64',
+                 'CanberraDistance64', 'BrayCurtisDistance64',
+                 'JaccardDistance64', 'DiceDistance64',
+                 'RogersTanimotoDistance64', 'RussellRaoDistance64',
+                 'SokalMichenerDistance64', 'SokalSneathDistance64',
+                 'PyFuncDistance64', 'HaversineDistance64']
 
 
 include "_binary_tree.pxi"
@@ -129,7 +129,7 @@ cdef inline float64_t min_rdist(BinaryTree tree, intp_t i_node,
                                 float64_t* pt) except -1 nogil:
     """Compute the minimum reduced-distance between a point and a node"""
     if tree.euclidean:
-        return euclidean_dist_to_rdist(min_dist(tree, i_node, pt))
+        return euclidean_dist_to_rdist64(min_dist(tree, i_node, pt))
     else:
         return tree.dist_metric._dist_to_rdist(min_dist(tree, i_node, pt))
 
@@ -138,7 +138,7 @@ cdef inline float64_t max_rdist(BinaryTree tree, intp_t i_node,
                                 float64_t* pt) except -1:
     """Compute the maximum reduced-distance between a point and a node"""
     if tree.euclidean:
-        return euclidean_dist_to_rdist(max_dist(tree, i_node, pt))
+        return euclidean_dist_to_rdist64(max_dist(tree, i_node, pt))
     else:
         return tree.dist_metric._dist_to_rdist(max_dist(tree, i_node, pt))
 
@@ -167,8 +167,8 @@ cdef inline float64_t min_rdist_dual(BinaryTree tree1, intp_t i_node1,
                                      BinaryTree tree2, intp_t i_node2) except -1:
     """compute the minimum reduced distance between two nodes"""
     if tree1.euclidean:
-        return euclidean_dist_to_rdist(min_dist_dual(tree1, i_node1,
-                                                     tree2, i_node2))
+        return euclidean_dist_to_rdist64(min_dist_dual(tree1, i_node1,
+                                                       tree2, i_node2))
     else:
         return tree1.dist_metric._dist_to_rdist(min_dist_dual(tree1, i_node1,
                                                               tree2, i_node2))
@@ -178,8 +178,8 @@ cdef inline float64_t max_rdist_dual(BinaryTree tree1, intp_t i_node1,
                                      BinaryTree tree2, intp_t i_node2) except -1:
     """compute the maximum reduced distance between two nodes"""
     if tree1.euclidean:
-        return euclidean_dist_to_rdist(max_dist_dual(tree1, i_node1,
-                                                     tree2, i_node2))
+        return euclidean_dist_to_rdist64(max_dist_dual(tree1, i_node1,
+                                                       tree2, i_node2))
     else:
         return tree1.dist_metric._dist_to_rdist(max_dist_dual(tree1, i_node1,
                                                               tree2, i_node2))
diff --git a/sklearn/neighbors/_binary_tree.pxi b/sklearn/neighbors/_binary_tree.pxi
index 36fb03ce2afe5..7d4d08b2703a4 100644
--- a/sklearn/neighbors/_binary_tree.pxi
+++ b/sklearn/neighbors/_binary_tree.pxi
@@ -153,9 +153,10 @@ import warnings
 
 from ..metrics._dist_metrics cimport (
     DistanceMetric,
-    euclidean_dist,
-    euclidean_rdist,
-    euclidean_dist_to_rdist,
+    DistanceMetric64,
+    euclidean_dist64,
+    euclidean_rdist64,
+    euclidean_dist_to_rdist64,
 )
 
 from ._partition_nodes cimport partition_node_indices
@@ -231,7 +232,7 @@ leaf_size : positive int, default=40
     satisfy ``leaf_size <= n_points <= 2 * leaf_size``, except in
     the case that ``n_samples < leaf_size``.
 
-metric : str or DistanceMetric object, default='minkowski'
+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
@@ -782,7 +783,7 @@ cdef class BinaryTree:
     cdef intp_t n_levels
     cdef intp_t n_nodes
 
-    cdef DistanceMetric dist_metric
+    cdef DistanceMetric64 dist_metric
     cdef int euclidean
 
     # variables to keep track of building & querying stats
@@ -832,7 +833,7 @@ cdef class BinaryTree:
 
         self.dist_metric = DistanceMetric.get_metric(metric, **kwargs)
         self.euclidean = (self.dist_metric.__class__.__name__
-                          == 'EuclideanDistance')
+                          == 'EuclideanDistance64')
 
         metric = self.dist_metric.__class__.__name__
         if metric not in VALID_METRICS:
@@ -922,7 +923,7 @@ cdef class BinaryTree:
         sample_weight = state[12]
 
         self.euclidean = (self.dist_metric.__class__.__name__
-                          == 'EuclideanDistance')
+                          == 'EuclideanDistance64')
         n_samples = self.data.shape[0]
         self._update_sample_weight(n_samples, sample_weight)
 
@@ -996,7 +997,7 @@ cdef class BinaryTree:
         """Compute the distance between arrays x1 and x2"""
         self.n_calls += 1
         if self.euclidean:
-            return euclidean_dist(x1, x2, size)
+            return euclidean_dist64(x1, x2, size)
         else:
             return self.dist_metric.dist(x1, x2, size)
 
@@ -1011,7 +1012,7 @@ cdef class BinaryTree:
         """
         self.n_calls += 1
         if self.euclidean:
-            return euclidean_rdist(x1, x2, size)
+            return euclidean_rdist64(x1, x2, size)
         else:
             return self.dist_metric.rdist(x1, x2, size)
 
diff --git a/sklearn/neighbors/_distance_metric.py b/sklearn/neighbors/_distance_metric.py
deleted file mode 100644
index 9bfd131c482d8..0000000000000
--- a/sklearn/neighbors/_distance_metric.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# TODO: Remove this file in 1.3
-import warnings
-
-from ..metrics import DistanceMetric as _DistanceMetric
-
-
-class DistanceMetric(_DistanceMetric):
-    @classmethod
-    def _warn(cls):
-        warnings.warn(
-            (
-                "sklearn.neighbors.DistanceMetric has been moved "
-                "to sklearn.metrics.DistanceMetric in 1.0. "
-                "This import path will be removed in 1.3"
-            ),
-            category=FutureWarning,
-        )
-
-    @classmethod
-    def get_metric(cls, metric, **kwargs):
-        DistanceMetric._warn()
-        return _DistanceMetric.get_metric(metric, **kwargs)
diff --git a/sklearn/neighbors/_kd_tree.pyx b/sklearn/neighbors/_kd_tree.pyx
index ece293229fa10..f5cd2617be147 100644
--- a/sklearn/neighbors/_kd_tree.pyx
+++ b/sklearn/neighbors/_kd_tree.pyx
@@ -6,8 +6,8 @@ __all__ = ['KDTree']
 
 DOC_DICT = {'BinaryTree': 'KDTree', 'binary_tree': 'kd_tree'}
 
-VALID_METRICS = ['EuclideanDistance', 'ManhattanDistance',
-                 'ChebyshevDistance', 'MinkowskiDistance']
+VALID_METRICS = ['EuclideanDistance64', 'ManhattanDistance64',
+                 'ChebyshevDistance64', 'MinkowskiDistance64']
 
 
 include "_binary_tree.pxi"
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
index 092b85ad9dcd0..6fd3db9b7b509 100644
--- a/sklearn/neighbors/tests/test_neighbors.py
+++ b/sklearn/neighbors/tests/test_neighbors.py
@@ -2158,18 +2158,6 @@ def test_auto_algorithm(X, metric, metric_params, expected_algo):
     assert model._fit_method == expected_algo
 
 
-# TODO: Remove in 1.3
-def test_neighbors_distance_metric_deprecation():
-    from sklearn.neighbors import DistanceMetric
-    from sklearn.metrics import DistanceMetric as ActualDistanceMetric
-
-    msg = r"This import path will be removed in 1\.3"
-    with pytest.warns(FutureWarning, match=msg):
-        dist_metric = DistanceMetric.get_metric("euclidean")
-
-    assert isinstance(dist_metric, ActualDistanceMetric)
-
-
 # TODO: Remove filterwarnings in 1.3 when wminkowski is removed
 @pytest.mark.filterwarnings("ignore:WMinkowskiDistance:FutureWarning:sklearn")
 @pytest.mark.parametrize(
diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index 176a2d463d162..b3c6391729698 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -221,6 +221,7 @@ def test_import_all_consistency():
             or "feature_extraction._hashing_fast" in modname
         ):
             continue
+        print(f"DEBUG *** {modname=}")
         package = __import__(modname, fromlist="dummy")
         for name in getattr(package, "__all__", ()):
             assert hasattr(package, name), "Module '{0}' has no attribute '{1}'".format(

From 9d1e26caa91965be6ab888f12e82dc0353a40f27 Mon Sep 17 00:00:00 2001
From: Meekail Zain <zainmeekail@gmail.com>
Date: Thu, 1 Jun 2023 18:18:39 -0400
Subject: [PATCH 2/5] Updated hdbscan linkage for new DistanceMetric64

---
 sklearn/cluster/_hdbscan/_linkage.pyx | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sklearn/cluster/_hdbscan/_linkage.pyx b/sklearn/cluster/_hdbscan/_linkage.pyx
index 0cb51a7b42992..03e91ac8d6833 100644
--- a/sklearn/cluster/_hdbscan/_linkage.pyx
+++ b/sklearn/cluster/_hdbscan/_linkage.pyx
@@ -35,7 +35,7 @@ cimport numpy as cnp
 from libc.float cimport DBL_MAX
 
 import numpy as np
-from ...metrics._dist_metrics cimport DistanceMetric
+from ...metrics._dist_metrics cimport DistanceMetric64
 from ...cluster._hierarchical_fast cimport UnionFind
 from ...cluster._hdbscan._tree cimport HIERARCHY_t
 from ...cluster._hdbscan._tree import HIERARCHY_dtype
@@ -111,7 +111,7 @@ cpdef cnp.ndarray[MST_edge_t, ndim=1, mode='c'] mst_from_mutual_reachability(
 cpdef cnp.ndarray[MST_edge_t, ndim=1, mode='c'] mst_from_data_matrix(
     const float64_t[:, ::1] raw_data,
     const float64_t[::1] core_distances,
-    DistanceMetric dist_metric,
+    DistanceMetric64 dist_metric,
     float64_t alpha=1.0
 ):
     """Compute the Minimum Spanning Tree (MST) representation of the mutual-

From d9e49e7551fddffd013a686980bc4df16358af1a Mon Sep 17 00:00:00 2001
From: Meekail Zain <zainmeekail@gmail.com>
Date: Thu, 1 Jun 2023 18:19:45 -0400
Subject: [PATCH 3/5] Removed old debug print

---
 sklearn/tests/test_common.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/sklearn/tests/test_common.py b/sklearn/tests/test_common.py
index b3c6391729698..176a2d463d162 100644
--- a/sklearn/tests/test_common.py
+++ b/sklearn/tests/test_common.py
@@ -221,7 +221,6 @@ def test_import_all_consistency():
             or "feature_extraction._hashing_fast" in modname
         ):
             continue
-        print(f"DEBUG *** {modname=}")
         package = __import__(modname, fromlist="dummy")
         for name in getattr(package, "__all__", ()):
             assert hasattr(package, name), "Module '{0}' has no attribute '{1}'".format(

From 2bdc44b6f2002316999cd88e2ee1f62d1bc82e87 Mon Sep 17 00:00:00 2001
From: Meekail Zain <zainmeekail@gmail.com>
Date: Fri, 2 Jun 2023 17:41:20 -0400
Subject: [PATCH 4/5] Updated test

---
 sklearn/metrics/_dist_metrics.pyx.tp       |  8 ++++--
 sklearn/metrics/tests/test_dist_metrics.py | 29 +++++++++++++++++++++-
 2 files changed, 34 insertions(+), 3 deletions(-)

diff --git a/sklearn/metrics/_dist_metrics.pyx.tp b/sklearn/metrics/_dist_metrics.pyx.tp
index 8388a125ea9bd..d3e80d98ef3c8 100644
--- a/sklearn/metrics/_dist_metrics.pyx.tp
+++ b/sklearn/metrics/_dist_metrics.pyx.tp
@@ -94,11 +94,15 @@ cdef class DistanceMetric:
         """
         if dtype == np.float32:
             specialized_class = DistanceMetric32
-        else:
+        elif dtype == np.float64:
             specialized_class = DistanceMetric64
+        else:
+            raise ValueError(
+                f"Unexpected dtype {dtype} provided. Please select a dtype from"
+                " {np.float32, np.float64}"
+            )
 
         return specialized_class.get_metric(metric, **kwargs)
-    pass
 
 {{for name_suffix, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
 
diff --git a/sklearn/metrics/tests/test_dist_metrics.py b/sklearn/metrics/tests/test_dist_metrics.py
index 3c08ad32b8e2a..fc9b006e2fefd 100644
--- a/sklearn/metrics/tests/test_dist_metrics.py
+++ b/sklearn/metrics/tests/test_dist_metrics.py
@@ -9,7 +9,11 @@
 from scipy.spatial.distance import cdist
 from sklearn.metrics import DistanceMetric
 
-from sklearn.metrics._dist_metrics import BOOL_METRICS
+from sklearn.metrics._dist_metrics import (
+    BOOL_METRICS,
+    DistanceMetric32,
+    DistanceMetric64,
+)
 
 from sklearn.utils import check_random_state
 from sklearn.utils._testing import assert_allclose, create_memmap_backed_data
@@ -372,3 +376,26 @@ def test_minkowski_metric_validate_weights_size():
     )
     with pytest.raises(ValueError, match=msg):
         dm.pairwise(X64, Y64)
+
+
+@pytest.mark.parametrize("metric, metric_kwargs", METRICS_DEFAULT_PARAMS)
+@pytest.mark.parametrize("dtype", (np.float32, np.float64))
+def test_get_metric_dtype(metric, metric_kwargs, dtype):
+    specialized_cls = {
+        np.float32: DistanceMetric32,
+        np.float64: DistanceMetric64,
+    }[dtype]
+
+    # We don't need the entire grid, just one for a sanity check
+    metric_kwargs = {k: v[0] for k, v in metric_kwargs.items()}
+    generic_type = type(DistanceMetric.get_metric(metric, dtype, **metric_kwargs))
+    specialized_type = type(specialized_cls.get_metric(metric, **metric_kwargs))
+
+    assert generic_type is specialized_type
+
+
+def test_get_metric_bad_dtype():
+    dtype = np.int32
+    msg = r"Unexpected dtype .* provided. Please select a dtype from"
+    with pytest.raises(ValueError, match=msg):
+        DistanceMetric.get_metric("manhattan", dtype)

From 9b2e36c57845708bb129ca7fb745a34a365095bd Mon Sep 17 00:00:00 2001
From: Meekail Zain <zainmeekail@gmail.com>
Date: Fri, 2 Jun 2023 17:45:15 -0400
Subject: [PATCH 5/5] Removed now extraneous documentation and improved
 VALID_METRICS list

---
 sklearn/metrics/_dist_metrics.pxd.tp          | 14 +--------
 sklearn/metrics/_dist_metrics.pyx.tp          | 14 +--------
 .../_datasets_pair.pxd.tp                     |  5 +---
 .../_datasets_pair.pyx.tp                     |  5 +---
 sklearn/neighbors/_ball_tree.pyx              | 30 ++++++++++++-------
 5 files changed, 24 insertions(+), 44 deletions(-)

diff --git a/sklearn/metrics/_dist_metrics.pxd.tp b/sklearn/metrics/_dist_metrics.pxd.tp
index 34ca704ab68bf..60b8da3ecfa46 100644
--- a/sklearn/metrics/_dist_metrics.pxd.tp
+++ b/sklearn/metrics/_dist_metrics.pxd.tp
@@ -3,19 +3,7 @@
 implementation_specific_values = [
     # Values are the following ones:
     #
-    #       name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
-    #
-    # On the first hand, an empty string is used for `name_suffix`
-    # for the float64 case as to still be able to expose the original
-    # float64 implementation under the same API, namely `DistanceMetric`.
-    #
-    # On the other hand, '32' is used for `name_suffix` for the float32
-    # case to remove ambiguity and use `DistanceMetric32`, which is not
-    # publicly exposed.
-    #
-    # The metric mapping is adapted accordingly to route to the correct
-    # implementations.
-    #
+    # name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
     ('64', 'float64_t', 'np.float64'),
     ('32', 'float32_t', 'np.float32')
 ]
diff --git a/sklearn/metrics/_dist_metrics.pyx.tp b/sklearn/metrics/_dist_metrics.pyx.tp
index d3e80d98ef3c8..bc54e51a7511a 100644
--- a/sklearn/metrics/_dist_metrics.pyx.tp
+++ b/sklearn/metrics/_dist_metrics.pyx.tp
@@ -3,19 +3,7 @@
 implementation_specific_values = [
     # Values are the following ones:
     #
-    #       name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
-    #
-    # On the first hand, an empty string is used for `name_suffix`
-    # for the float64 case as to still be able to expose the original
-    # float64 implementation under the same API, namely `DistanceMetric`.
-    #
-    # On the other hand, '32' bit is used for `name_suffix` for the float32
-    # case to remove ambiguity and use `DistanceMetric32`, which is not
-    # publicly exposed.
-    #
-    # The metric mapping is adapted accordingly to route to the correct
-    # implementations.
-    #
+    # name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
     ('64', 'float64_t', 'np.float64'),
     ('32', 'float32_t', 'np.float32')
 ]
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp
index 98123235d937c..fc56a59cab16f 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pxd.tp
@@ -3,10 +3,7 @@
 implementation_specific_values = [
     # Values are the following ones:
     #
-    #       name_suffix, DistanceMetric, INPUT_DTYPE_t, INPUT_DTYPE
-    #
-    # We use DistanceMetric for float64 for backward naming compatibility.
-    #
+    # name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
     ('64', 'DistanceMetric64', 'float64_t'),
     ('32', 'DistanceMetric32', 'float32_t')
 ]
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp
index befbe48e59996..40a9a45e8b8e1 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx.tp
@@ -3,10 +3,7 @@
 implementation_specific_values = [
     # Values are the following ones:
     #
-    #       name_suffix, DistanceMetric, INPUT_DTYPE_t, INPUT_DTYPE
-    #
-    # We use DistanceMetric for float64 for backward naming compatibility.
-    #
+    # name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
     ('64', 'DistanceMetric64', 'float64_t', 'np.float64'),
     ('32', 'DistanceMetric32', 'float32_t', 'np.float32')
 ]
diff --git a/sklearn/neighbors/_ball_tree.pyx b/sklearn/neighbors/_ball_tree.pyx
index 0bc65fdd36ec8..d9b933cb43c66 100644
--- a/sklearn/neighbors/_ball_tree.pyx
+++ b/sklearn/neighbors/_ball_tree.pyx
@@ -5,16 +5,26 @@ __all__ = ['BallTree']
 
 DOC_DICT = {'BinaryTree': 'BallTree', 'binary_tree': 'ball_tree'}
 
-VALID_METRICS = ['EuclideanDistance64', 'SEuclideanDistance64',
-                 'ManhattanDistance64', 'ChebyshevDistance64',
-                 'MinkowskiDistance64', 'WMinkowskiDistance64',
-                 'MahalanobisDistance64', 'HammingDistance64',
-                 'CanberraDistance64', 'BrayCurtisDistance64',
-                 'JaccardDistance64', 'DiceDistance64',
-                 'RogersTanimotoDistance64', 'RussellRaoDistance64',
-                 'SokalMichenerDistance64', 'SokalSneathDistance64',
-                 'PyFuncDistance64', 'HaversineDistance64']
-
+VALID_METRICS = [
+    'BrayCurtisDistance64',
+    'CanberraDistance64',
+    'ChebyshevDistance64',
+    'DiceDistance64',
+    'EuclideanDistance64',
+    'HammingDistance64',
+    'HaversineDistance64',
+    'JaccardDistance64',
+    'MahalanobisDistance64',
+    'ManhattanDistance64',
+    'MinkowskiDistance64',
+    'PyFuncDistance64',
+    'RogersTanimotoDistance64',
+    'RussellRaoDistance64',
+    'SEuclideanDistance64',
+    'SokalMichenerDistance64',
+    'SokalSneathDistance64',
+    'WMinkowskiDistance64',
+]
 
 include "_binary_tree.pxi"