scikit-learn
diff --git a/‎sklearn/neighbors/_base.py
Copy file name to clipboardExpand all lines: sklearn/neighbors/_base.py
+36-11Lines changed: 36 additions & 11 deletions b/‎sklearn/neighbors/_base.py
Copy file name to clipboardExpand all lines: sklearn/neighbors/_base.py
+36-11Lines changed: 36 additions & 11 deletions
@@ -22,6 +22,7 @@
 from ..base import BaseEstimator, MultiOutputMixin
 from ..base import is_classifier
 from ..metrics import pairwise_distances_chunked
+from ..metrics._pairwise_distances_reduction import PairwiseDistancesRadiusNeighborhood
 from ..metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS
 from ..utils import (
     check_array,
@@ -1061,25 +1062,53 @@ class from an array representing our data set and ask who's
         """
         check_is_fitted(self)
 
-        if X is not None:
-            query_is_train = False
+        if sort_results and not return_distance:
+            raise ValueError("return_distance must be True if sort_results is True.")
+
+        query_is_train = X is None
+        if query_is_train:
+            X = self._fit_X
+        else:
             if self.metric == "precomputed":
                 X = _check_precomputed(X)
             else:
-                X = self._validate_data(X, accept_sparse="csr", reset=False)
-        else:
-            query_is_train = True
-            X = self._fit_X
+                X = self._validate_data(X, accept_sparse="csr", reset=False, order="C")
 
         if radius is None:
             radius = self.radius
 
-        if self._fit_method == "brute" and self.metric == "precomputed" and issparse(X):
+        use_pairwise_distances_reductions = (
+            self._fit_method == "brute"
+            and PairwiseDistancesRadiusNeighborhood.is_usable_for(
+                X if X is not None else self._fit_X, self._fit_X, self.effective_metric_
+            )
+        )
+
+        if use_pairwise_distances_reductions:
+            results = PairwiseDistancesRadiusNeighborhood.compute(
+                X=X,
+                Y=self._fit_X,
+                radius=radius,
+                metric=self.effective_metric_,
+                metric_kwargs=self.effective_metric_params_,
+                n_threads=self.n_jobs,
+                strategy="auto",
+                return_distance=return_distance,
+                sort_results=sort_results,
+            )
+
+        elif (
+            self._fit_method == "brute" and self.metric == "precomputed" and issparse(X)
+        ):
             results = _radius_neighbors_from_graph(
                 X, radius=radius, return_distance=return_distance
             )
 
         elif self._fit_method == "brute":
+            # TODO: should no longer be needed once we have Cython-optimized
+            # implementation for radius queries, with support for sparse and/or
+            # float32 inputs.
+
             # for efficiency, use squared euclidean distances
             if self.effective_metric_ == "euclidean":
                 radius *= radius
@@ -1113,10 +1142,6 @@ class from an array representing our data set and ask who's
                 results = _to_object_array(neigh_ind_list)
 
             if sort_results:
-                if not return_distance:
-                    raise ValueError(
-                        "return_distance must be True if sort_results is True."
-                    )
                 for ii in range(len(neigh_dist)):
                     order = np.argsort(neigh_dist[ii], kind="mergesort")
                     neigh_ind[ii] = neigh_ind[ii][order]