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MAINT Make ArgKminClassMode accept sparse datasets #27018

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merged 6 commits into from
Aug 15, 2023
Merged

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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Override valid_metrics over is_usable_for 
Signed-off-by: Julien Jerphanion <git@jjerphan.xyz>

Co-authored-by: Meekail Zain <Micky774@users.noreply.github.com>
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@jjerphan @Micky774
jjerphan and Micky774 committed Aug 8, 2023
commit 876f21c30344cace1a058c02ad65f255f6a55010
34 changes: 8 additions & 26 deletions sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py
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Expand Up @@ -451,32 +451,14 @@ 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: 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 it 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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