TST use global_random_seed in sklearn/utils/tests/test_stats.py #30857
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test_averaged_weighted_percentile test_weighted_median_equal_weights test_weighted_median_integer_weights test_weighted_percentile_2d
OmarManzoor
reviewed
Mar 2, 2025
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Thanks for the PR @Rishab260
Please also add a commit with all global seeds for the respective tests on this PR too.
test_averaged_weighted_percentile test_weighted_median_equal_weights test_weighted_median_integer_weights test_weighted_percentile_2d
test_averaged_weighted_percentile test_weighted_median_equal_weights test_weighted_median_integer_weights test_weighted_percentile_2d
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Currently blocked by Will fix this ASAP. |
test_averaged_weighted_percentile test_weighted_median_equal_weights test_weighted_median_integer_weights test_weighted_percentile_2d
OmarManzoor
approved these changes
Mar 4, 2025
sklearn/utils/tests/test_stats.py
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| x = rng.randint(20, size=1000) | ||
| weights = rng.choice(5, size=1000) |
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Would size=100 work or does that still fail? I think 1000 is fine anyways.
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Hi, Size 100 failed, we can probably relax the tolerance but since size 1000 worked, I went with that one.
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The test was not correct. It is not equivalent to np.median when the number of effective samples is even. _averaged_weighted_percentile should be equivalent instead. I modified the test accordingly.
test_averaged_weighted_percentile test_weighted_median_equal_weights test_weighted_median_integer_weights test_weighted_percentile_2d test_weighted_percentile_array_api_consistency test_weighted_percentile_nan_filtered test_weighted_percentile_like_numpy_quantile test_weighted_percentile_like_numpy_nanquantile
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Reference Issues/PRs
towards #22827
What does this implement/fix? Explain your changes.
I added the global_random_seed fixture to the following tests:-
Any other comments?
Feedback and guidance is highly appreciated. Looking forward to contributing to this project more and more in the coming days. Thanks in advance!