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MAINT Make ArgKminClassMode accept sparse datasets #27018

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Merged
merged 6 commits into from
Aug 15, 2023
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MAINT Make ArgKminClassMode accept sparse datasets #27018

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a349f97
MAINT Make `ArgKminClassMode` accept sparse datasets
jjerphan Aug 5, 2023
876f21c
Override valid_metrics over is_usable_for
jjerphan Aug 8, 2023
4200088
docs: Add changelog entry
jjerphan Aug 13, 2023
72d1945
Add a smol comma
jjerphan Aug 13, 2023
0e8ee6b
DOC Shorten long line comment
jjerphan Aug 14, 2023
b47680c
Merge branch 'main' into maint/make-ArgKminClassMode-accept-sparse-da…
jjerphan Aug 14, 2023
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5 changes: 5 additions & 0 deletions doc/whats_new/v1.4.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -170,6 +170,11 @@ Changelog
:mod:`sklearn.neighbors`
........................

- |Efficiency| :meth:`sklearn.neighbors.KNeighborsRegressor.predict` and
:meth:`sklearn.neighbors.KNeighborsRegressor.predict_proba` now efficiently support
pairs of dense and sparse datasets.
:pr:`27018` by :user:`Julien Jerphanion <jjerphan>`.

- |Fix| Neighbors based estimators now correctly work when `metric="minkowski"` and the
metric parameter `p` is in the range `0 < p < 1`, regardless of the `dtype` of `X`.
:pr:`26760` by :user:`Shreesha Kumar Bhat <Shreesha3112>`.
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38 changes: 9 additions & 29 deletions sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py
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Expand Up @@ -451,35 +451,15 @@ class ArgKminClassMode(BaseDistancesReductionDispatcher):
"""

@classmethod
def is_usable_for(cls, X, Y, metric) -> bool:
"""Return True if the dispatcher can be used for the given parameters.

Parameters
----------
X : ndarray of shape (n_samples_X, n_features)
The input array to be labelled.

Y : ndarray of shape (n_samples_Y, n_features)
The input array whose labels are provided through the `Y_labels`
parameter.

metric : str, default='euclidean'
The distance metric to use. For a list of available metrics, see
the documentation of :class:`~sklearn.metrics.DistanceMetric`.
Currently does not support `'precomputed'`.

Returns
-------
True if the PairwiseDistancesReduction can be used, else False.
"""
return (
ArgKmin.is_usable_for(X, Y, metric)
# TODO: Support CSR matrices.
and not issparse(X)
and not issparse(Y)
# TODO: implement Euclidean specialization with GEMM.
and metric not in ("euclidean", "sqeuclidean")
)
def valid_metrics(cls) -> List[str]:
excluded = {
# Euclidean is technically usable for ArgKminClassMode
# but its current implementation would not be competitive.
# TODO: implement Euclidean specialization using GEMM.
"euclidean",
"sqeuclidean",
}
return list(set(BaseDistancesReductionDispatcher.valid_metrics()) - excluded)

@classmethod
def compute(
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