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KBinsDiscretizer efficiency improvement to 'kmeans' strategy #19934

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Merged
merged 4 commits into from
Apr 20, 2021
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KBinsDiscretizer efficiency improvement to 'kmeans' strategy #19934

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b5142a2
efficiency improvement
glevv Apr 20, 2021
fcd35ac
update doc
glevv Apr 20, 2021
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lint
glevv Apr 20, 2021
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glevv Apr 20, 2021
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4 changes: 4 additions & 0 deletions doc/whats_new/v1.0.rst
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Expand Up @@ -386,6 +386,10 @@ Changelog
supporting sparse matrix and raise the appropriate error message.
:pr:`19879` by :user:`Guillaume Lemaitre <glemaitre>`.

- |Efficiency| Changed ``algorithm`` argument for :class:`cluster.KMeans` in
:class:`preprocessing.KBinsDiscretizer` from ``auto`` to ``full``.
:pr:`19934` by :user:`Gleb Levitskiy <GLevV>`.

:mod:`sklearn.tree`
...................

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3 changes: 2 additions & 1 deletion sklearn/preprocessing/_discretization.py
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Expand Up @@ -205,7 +205,8 @@ def fit(self, X, y=None):
init = (uniform_edges[1:] + uniform_edges[:-1])[:, None] * 0.5

# 1D k-means procedure
km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1)
km = KMeans(n_clusters=n_bins[jj], init=init, n_init=1,
algorithm='full')
centers = km.fit(column[:, None]).cluster_centers_[:, 0]
# Must sort, centers may be unsorted even with sorted init
centers.sort()
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