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OPTIM use pairwise_distances_argmin in NearestCentroid.predict #24645

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Merged
merged 3 commits into from
Oct 13, 2022
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OPTIM use pairwise_distances_argmin in NearestCentroid.predict #24645

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OPTIM use pairwise_distances_argmin in NearestCentroid.predict
ogrisel Oct 12, 2022
9b2a548
Changlog
ogrisel Oct 12, 2022
21ce8ee
Update doc/whats_new/v1.2.rst
ogrisel Oct 13, 2022
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4 changes: 4 additions & 0 deletions doc/whats_new/v1.2.rst
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Expand Up @@ -456,6 +456,10 @@ Changelog
:pr:`10468` by :user:`Ruben <icfly2>` and :pr:`22993` by
:user:`Jovan Stojanovic <jovan-stojanovic>`.

- |Efficiency| :class:`neighbors.NearestCentroid` is faster and requires
less memory as it better leverages CPUs' caches to compute predictions.
:pr:`24645` by :user:`Olivier Grisel <ogrisel>`.

- |Feature| Adds new function :func:`neighbors.sort_graph_by_row_values` to
sort a CSR sparse graph such that each row is stored with increasing values.
This is useful to improve efficiency when using precomputed sparse distance
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4 changes: 2 additions & 2 deletions sklearn/neighbors/_nearest_centroid.py
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Expand Up @@ -13,7 +13,7 @@
from scipy import sparse as sp

from ..base import BaseEstimator, ClassifierMixin
from ..metrics.pairwise import pairwise_distances
from ..metrics.pairwise import pairwise_distances_argmin
from ..preprocessing import LabelEncoder
from ..utils.validation import check_is_fitted
from ..utils.sparsefuncs import csc_median_axis_0
Expand Down Expand Up @@ -234,5 +234,5 @@ def predict(self, X):

X = self._validate_data(X, accept_sparse="csr", reset=False)
return self.classes_[
pairwise_distances(X, self.centroids_, metric=self.metric).argmin(axis=1)
pairwise_distances_argmin(X, self.centroids_, metric=self.metric)
]