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TST Relax test_minibatch_sensible_reassign to avoid CI failures with single global random seed #29278

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Merged
merged 11 commits into from
Jun 20, 2024
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TST Relax test_minibatch_sensible_reassign to avoid CI failures with single global random seed #29278

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6e9d4f9
NOMRG Try to trigger test_minibatch_sensible_reassign
lesteve Jun 17, 2024
5648219
[free-threaded]
lesteve Jun 17, 2024
c0cab37
[free-threaded] trigger CI
lesteve Jun 17, 2024
6df6157
[free-threaded] warnings as error should do the trick right? right?
lesteve Jun 17, 2024
c0a9a6a
[free-threaded] use error rather than assertion
lesteve Jun 17, 2024
b141137
[free-threaded] oh well ...
lesteve Jun 17, 2024
7d4e1b3
[free-threaded] [all random seeds]
lesteve Jun 18, 2024
c09298b
[free-threaded] [azure parallel] [all random seeds]
lesteve Jun 18, 2024
38fc24c
[free-threaded] relax constraint [all random seeds]
lesteve Jun 18, 2024
162684a
[free-threaded] go back to assertions [all random seeds]
lesteve Jun 18, 2024
3abcd1c
[free-threaded] [azure parallel] trigger normal CI
lesteve Jun 18, 2024
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9 changes: 6 additions & 3 deletions sklearn/cluster/tests/test_k_means.py
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Original file line number Diff line number Diff line change
Expand Up @@ -437,21 +437,24 @@ def test_minibatch_sensible_reassign(global_random_seed):
n_clusters=20, batch_size=10, random_state=global_random_seed, init="random"
).fit(zeroed_X)
# there should not be too many exact zero cluster centers
assert km.cluster_centers_.any(axis=1).sum() > 10
num_non_zero_clusters = km.cluster_centers_.any(axis=1).sum()
assert num_non_zero_clusters > 9, f"{num_non_zero_clusters=} is too small"
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@lesteve lesteve Jun 18, 2024 •
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I added assertion string (i.e. second argument in the assert, not sure if there is a more exact name) because it seems like pytest assertion rewriting is a bit broken (needs a bit of investigation as to why). If this ever fails again, at least the message will tell how far we are from the threshold


# do the same with batch-size > X.shape[0] (regression test)
km = MiniBatchKMeans(
n_clusters=20, batch_size=200, random_state=global_random_seed, init="random"
).fit(zeroed_X)
# there should not be too many exact zero cluster centers
assert km.cluster_centers_.any(axis=1).sum() > 10
num_non_zero_clusters = km.cluster_centers_.any(axis=1).sum()
assert num_non_zero_clusters > 9, f"{num_non_zero_clusters=} is too small"

# do the same with partial_fit API
km = MiniBatchKMeans(n_clusters=20, random_state=global_random_seed, init="random")
for i in range(100):
km.partial_fit(zeroed_X)
# there should not be too many exact zero cluster centers
assert km.cluster_centers_.any(axis=1).sum() > 10
num_non_zero_clusters = km.cluster_centers_.any(axis=1).sum()
assert num_non_zero_clusters > 9, f"{num_non_zero_clusters=} is too small"


@pytest.mark.parametrize(
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