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TST use global_random_seed in sklearn/cluster/tests/test_feature_agglomeration.py #23700

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TST use global_random_seed in sklearn/cluster/tests/test_feature_agglomeration.py #23700

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TST use global_random_seed in sklearn/cluster/tests/test_feature_aggl…
Jun 20, 2022
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Merge branch 'scikit-learn:main' into test_feature_agglomeration
marenwestermann Jun 20, 2022
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4 changes: 2 additions & 2 deletions sklearn/cluster/tests/test_feature_agglomeration.py
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Expand Up @@ -42,9 +42,9 @@ def test_feature_agglomeration():
assert_array_almost_equal(agglo_median.transform(X_full_median), Xt_median)


def test_feature_agglomeration_feature_names_out():
def test_feature_agglomeration_feature_names_out(global_random_seed):
"""Check `get_feature_names_out` for `FeatureAgglomeration`."""
X, _ = make_blobs(n_features=6, random_state=0)
X, _ = make_blobs(n_features=6, random_state=global_random_seed)
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Actually this is one of the case that I don't think that it is necessary to test with several random_state. I think that we want mainly to check mathematical and numerical stability while this test is about the feature names. So it does not really matter the variation in X itself.

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True, I didn't pay attention properly here. Thanks for reviewing @glemaitre!

agglo = FeatureAgglomeration(n_clusters=3)
agglo.fit(X)
n_clusters = agglo.n_clusters_
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