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FIX BisectingKMeans crashes randomly #25563

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Merged
merged 2 commits into from
Feb 7, 2023
Merged

FIX BisectingKMeans crashes randomly #25563

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9302b29
ensure 2 bins
jeremiedbb Feb 7, 2023
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jeremiedbb Feb 7, 2023
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7 changes: 7 additions & 0 deletions doc/whats_new/v1.2.rst
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Expand Up @@ -27,6 +27,13 @@ Changes impacting all modules
Changelog
---------

:mod:`sklearn.cluster`
......................

- |Fix| Fixed a bug in :class:`cluster.BisectingKMeans`, preventing `fit` to randomly
fail due to a permutation of the labels when running multiple inits.
:pr:`25563` by :user:`Jérémie du Boisberranger <jeremiedbb>`.

:mod:`sklearn.isotonic`
.......................

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4 changes: 3 additions & 1 deletion sklearn/cluster/_bisect_k_means.py
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Expand Up @@ -337,7 +337,9 @@ def _bisect(self, X, x_squared_norms, sample_weight, cluster_to_bisect):
X, best_centers, best_labels, sample_weight
)
else: # bisecting_strategy == "largest_cluster"
scores = np.bincount(best_labels)
# Using minlength to make sure that we have the counts for both labels even
# if all samples are labelled 0.
scores = np.bincount(best_labels, minlength=2)
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Maybe add a comment above mentioning why we need minlength?

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@lesteve lesteve Feb 7, 2023 •
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Just to be sure: minlength=2 at this point we just have two possible labels (0 and 1) since this is BisectingKMeans right?

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yes


cluster_to_bisect.split(best_labels, best_centers, scores)

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