8000 FIX np.divide undefined behaviour with where in gaussian processes by lesteve · Pull Request #24245 · scikit-learn/scikit-learn · GitHub
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FIX np.divide undefined behaviour with where in gaussian processes #24245

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Merged
merged 2 commits into from
Sep 5, 2022
Merged

FIX np.divide undefined behaviour with where in gaussian processes #24245

merged 2 commits into from
Sep 5, 2022

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lesteve
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@lesteve lesteve commented Aug 24, 2022 •
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Triggered by investigating #24221.

We are using code like:

result = np.divide(numerator, denominator, where=denominator!=0)

result[denominator == 0] values are undefined when using this. For CPython it seems like the np.empty allocated for the return value reuses a temporary array created in a previous line that computes (X[:, np.newaxis, :] - X[np.newaxis, :, :]) ** 2 so that result[denominator == 0] only contains 0. For PyPy this is not the case and at one point we get 8000 a 4. rather than a 0. (don't ask me where the 4. comes from ...).

A snippet to reproduce a similar behaviour:

import numpy as np

numerator = np.array([1., 1., 1.])
denominator = np.array([0., 1., 1.])
where = denominator != 0

tmp = np.array([-999., -999., -999.])
print(f"tmp address: {tmp.__array_interface__['data'][0]}")
del tmp
divide_result = np.divide(numerator, denominator, where=where)
print(f"div address: {divide_result.__array_interface__['data'][0]}")
print(f"{divide_result}")

Output with CPython (address is the same so the -999. of the tmp array is reused):

tmp address: 94515367946736
div address: 94515367946736
[-999.    1.    1.]

Output with pypy (address is not the same first value is random):

tmp address: 94369592900336
div address: 94369602251488
[4.66247785e-310 1.00000000e+000 1.00000000e+000]

@lesteve lesteve mentioned this pull request Aug 24, 2022
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thomasjpfan
thomasjpfan approved these changes Aug 27, 2022
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Thanks for the PR and the explanation!

Besides a minor comment on the whats new placement, LGTM

doc/whats_new/v1.2.rst
@@ -198,6 +198,13 @@ Changelog
:mod:`sklearn.feature_selection`
................................

:mod:`sklearn.gaussian_process`
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This section seems to be out of place in the whats new.

@thomasjpfan thomasjpfan added the Quick Review For PRs that are quick to review label Aug 27, 2022
jeremiedbb
jeremiedbb approved these changes Sep 5, 2022
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LGTM, thanks @lesteve. We should almost always be using the out arg of divide.

@jeremiedbb jeremiedbb merged commit 056d5cf into scikit-learn:main Sep 5, 2022
@lesteve lesteve deleted the np-divide-undefined branch September 6, 2022 07:39
@lesteve lesteve mentioned this pull request Sep 7, 2022
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glemaitre pushed a commit to glemaitre/scikit-learn that referenced this pull request Sep 12, 2022
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FIX np.divide undefined behaviour with where in gaussian processes (s…
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…cikit-learn#24245)
@jeremiedbb jeremiedbb mentioned this pull request Feb 28, 2023
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