TST use global_random_seed in sklearn/utils/tests/test_arrayfuncs.py - #34158
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jeremiedbb merged 2 commits intoJun 3, 2026
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Convert `test_min_pos` to use the `global_random_seed` fixture instead of a hardcoded `RandomState(0)`, so the assertion that `min_pos` agrees between float32 and float64 is exercised against the full seed range rather than a single fixed sample. Verified with `SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all"`: 100 / 100 seeds pass. Ref scikit-learn#22827
test_min_pos
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Sep 2, 2026
…cikit-learn#34158) Co-authored-by: Jérémie du Boisberranger <[email protected]>
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Reference: #22827.
Convert
test_min_posto use theglobal_random_seedfixture instead of a hardcodedRandomState(0). The test asserts thatmin_posagrees between float32 and float64 on a random input, so it is genuinely independent of the seed and benefits from being exercised across the full seed range.The other two tests in the file (
test_min_pos_no_positive,test_all_with_any_reduction_axis_1) use deterministic inputs and don't need a fixture.Verification
SKLEARN_TESTS_GLOBAL_RANDOM_SEED="all" pytest sklearn/utils/tests/test_arrayfuncs.py— 100 / 100 seeds pass fortest_min_pos; 117 / 117 tests pass for the whole file.