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BUG: quantile should error when weights are all zeros #28595

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BUG: quantile should error when weights are all zeros #28595

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293ea24
added err messages and tests
Tontonio3 Mar 27, 2025
ae8cd54
Modified tests and added release note
Tontonio3 Mar 27, 2025
565fc75
Fixed tests
Tontonio3 Mar 27, 2025
8309d16
Fixed bug to handle object dtypes
Tontonio3 Mar 27, 2025
04d05f6
Fixed bug to handle object dtypes
Tontonio3 Mar 27, 2025
c76b5ad
Streamlined testing, improved error handling capabilities
Tontonio3 Mar 28, 2025
bfcec09
Changed infinite error message
Tontonio3 Mar 28, 2025
f06a1f8
Bug fix
Tontonio3 Mar 28, 2025
1303b3c
Fixed lint test
Tontonio3 Mar 28, 2025
ad95df2
Improved testing
Tontonio3 Apr 2, 2025
dc14e6b
Changed error handling, made it faster, removed dtype=object special …
Tontonio3 Apr 2, 2025
8eeed6a
More comprehensive testing
Tontonio3 Apr 2, 2025
4fe3444
More comprehensive testing
Tontonio3 Apr 3, 2025
ba2c398
Fixed lint
Tontonio3 Apr 3, 2025
2e8e2ea
Fixed tests
Tontonio3 Apr 3, 2025
3a20796
Fixed CircleCI test
Tontonio3 Apr 7, 2025
40075b3
streamlined checks
Tontonio3 Apr 24, 2025
a83887b
lint fix
Tontonio3 Apr 28, 2025
3d1c7b0
lint fix
Tontonio3 Apr 28, 2025
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Changed infinite error message
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@Tontonio3
Tontonio3 committed Mar 28, 2025
commit bfcec095a681646491c5640f1c852247c8e7e373
2 changes: 1 addition & 1 deletion 2 numpy/lib/_function_base_impl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4537,7 +4537,7 @@ def quantile(a,
weights = _weights_are_valid(weights=weights, a=a, axis=axis)
if weights.dtype != object:
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A general comment: I think these checks should happen inside _weights_ar_valid - this will ensure they are used for percentile as well.

if np.any(np.isinf(weights)):
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Another general comment: as written, the common case has to go through a lot of checks. I think it would be better to optimize for the common case, and not worry too much about distinguishing failure cases. E.g., you can do just one evaluation with:

if not np.all(np.isfinite(weights)):
    raise ValueError("weights must be finite.")

raise ValueError("Weights must be non-infinite.")
raise ValueError("Weights must be finite.")
elif np.any(np.isnan(weights)):
raise ValueError("At least one weight is nan.")
# Since np.isinf and np.isnan do not work in dtype object arrays
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10 changes: 5 additions & 5 deletions 10 numpy/lib/tests/test_function_base.py
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Expand Up @@ -4144,11 +4144,11 @@ def test_closest_observation(self):


@pytest.mark.parametrize(["err_msg", "weight"],
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I'd parametrize over np.quantile and np.percentile as well - they should have the same errors.

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Also, if you pass in a list rather than an array, you could parametrize over dtype=float and dtype=object, to make this a little more readable.

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Done

[("Weights must be non-infinite.", np.array([1,np.inf, 1, 1])),
("Weights must be non-infinite.", np.array([1,np.inf, 1, 1], dtype=object)),
("Weights must be non-infinite.", np.array([1,-np.inf, 1, 1])),
("Weights must be non-infinite.", np.array([1,-np.inf, 1, 1], dtype=object)),
("Weights must be non-infinite.", np.array([1,np.inf, 1, np.inf])),
[("Weights must be finite.", np.array([1,np.inf, 1, 1])),
("Weights must be finite.", np.array([1,np.inf, 1, 1], dtype=object)),
("Weights must be finite.", np.array([1,-np.inf, 1, 1])),
("Weights must be finite.", np.array([1,-np.inf, 1, 1], dtype=object)),
("Weights must be finite.", np.array([1,np.inf, 1, np.inf])),
("At least one weight is nan.", np.array([1,np.nan, 1, 1])),
("At least one weight is nan.", np.array([1,np.nan, 1, 1], dtype=object)),
("At least one weight is nan.", np.array([1,np.nan, np.nan, 1])),
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