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MAINT Do not compute distances for uniform weighting for knn-based estimators' predictions #22280

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Merged
merged 1 commit into from
Jan 24, 2022
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MAINT Do not compute distances for uniform weighting for knn-based estimators' predictions #22280

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17 changes: 15 additions & 2 deletions sklearn/neighbors/_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,7 +213,14 @@ def predict(self, X):
y : ndarray of shape (n_queries,) or (n_queries, n_outputs)
Class labels for each data sample.
"""
neigh_dist, neigh_ind = self.kneighbors(X)
if self.weights == "uniform":
# In that case, we do not need the distances to perform
# the weighting so we do not compute them.
neigh_ind = self.kneighbors(X, return_distance=False)
neigh_dist = None
else:
neigh_dist, neigh_ind = self.kneighbors(X)

classes_ = self.classes_
_y = self._y
if not self.outputs_2d_:
Expand Down Expand Up @@ -255,7 +262,13 @@ def predict_proba(self, X):
The class probabilities of the input samples. Classes are ordered
by lexicographic order.
"""
neigh_dist, neigh_ind = self.kneighbors(X)
if self.weights == "uniform":
# In that case, we do not need the distances to perform
# the weighting so we do not compute them.
neigh_ind = self.kneighbors(X, return_distance=False)
neigh_dist = None
else:
neigh_dist, neigh_ind = self.kneighbors(X)

classes_ = self.classes_
_y = self._y
Expand Down
8 changes: 7 additions & 1 deletion sklearn/neighbors/_regression.py
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Expand Up @@ -228,7 +228,13 @@ def predict(self, X):
y : ndarray of shape (n_queries,) or (n_queries, n_outputs), dtype=int
Target values.
"""
neigh_dist, neigh_ind = self.kneighbors(X)
if self.weights == "uniform":
# In that case, we do not need the distances to perform
# the weighting so we do not compute them.
neigh_ind = self.kneighbors(X, return_distance=False)
neigh_dist = None
else:
neigh_dist, neigh_ind = self.kneighbors(X)

weights = _get_weights(neigh_dist, self.weights)

Expand Down