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BUG: bugfix for #28589 (quantile should error when weights are all zeros)#28594
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…eights are all zerosnumpy#28589Previously, np.quantile with all weights set to zero returned the first sample. This is fixed now via adding an explicit check that raises aValueError 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 shouldclosenumpy#28589
| result = np.quantile(arr, quantile, weights=weights, method='inverted_cdf') | ||
| expected = 2.5 | ||
| 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 ofquantile 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]) | ||
| 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 togh-28597
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 shouldclose#28589