BUG: bugfix for #28589 (quantile should error when weights are all zeros) #28594
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…eights are all zeros #28589 Previously, np.quantile with all weights set to zero returned the first sample. This is fixed now via adding an explicit check that raises a ValueError when all weights are zero, preventing division by zero in the CDF. - Added the weight check in `_quantile` (line 4874 ff) to detect all-zero weights and raise an error. - Added a test in `test_function_base.py` to verify this behavior. - Added a second test to ensure existing quantile functionality remains unchanged. This should close #28589
| result = np.quantile(arr, quantile, weights=weights, method='inverted_cdf') | ||
| expected = 2.5 | ||
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| assert np.isclose(result, expected) |
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Hopefully this part of the diff isn't really needed--presumably we have sufficient testing that if you broke the numerical behavior of quantile an existing test would fail.
That said, this test actually does fail--maybe there is a problem with the test design?
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As an aside, assert_allclose() is generally preferable in testing--it tends to give better feedback when something fails, etc.
| weights = np.array([0, 0, 0, 0]) | ||
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| with pytest.raises(ValueError, match="All weights are zero, cannot compute quantile."): | ||
| np.quantile(arr, quantile, weights=weights, method='inverted_cdf') |
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Note for other NumPy devs--this/this PR is very similar to gh-28597
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This commit addresses the following BUG: quantile should error when weights are all zeros #28589
Previously, np.quantile with all weights set to zero returned the first sample. This is fixed now via adding an explicit check that raises a ValueError when all weights are zero, preventing division by zero in the CDF.
_quantile(line 4874 ff) to detect all-zero weights and raise an error.test_function_base.pyto verify this behavior.This should close #28589