8000 MAINT pin numpydoc in arm for now by thomasjpfan · Pull Request #22292 · scikit-learn/scikit-learn · GitHub
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Merged
merged 1 commit into from
Jan 25, 2022

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Reference Issues/PRs

Follow up to #22286

What does this implement/fix? Explain your changes.

arm build needs to be pinned too.

@thomasjpfan thomasjpfan merged commit 3209f84 into scikit-learn:main Jan 25, 2022
@thomasjpfan thomasjpfan deleted the pin_numpydoc_arm branch January 25, 2022 03:43
jjerphan pushed a commit to jjerphan/scikit-learn that referenced this pull request Jan 25, 2022
lorentzenchr pushed a commit that referenced this pull request Feb 10, 2022
* Forward pairwise_dist_chunk_size in the configuration

* Flip finalized results for PairwiseDistancesArgKmin

The previous would have made the code more complex
by introducing some boilerplate for the interface plugs.

Having it this way actually simplifies the code.

This also removes the haversine branch for
test_pairwise_distances_argkmin

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* Plug PairwiseDistancesArgKmin as a back-end

* Adapt test accordingly

* Add whats_new entry

* Change input validation order for kneighbors

* Remove duplicated test_neighbors_distance_metric_deprecation

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* Adapt the documentation

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* Add mahalanobis case to test fixtures

* Correct whats_new entry

* CLN Remove unneeded private metric attribute

This was needed when 'fast_sqeuclidean' and 'fast_euclidean'
were present to choose the best implementation based on the user
specification.

Those metric have been removed since then, making this attribute
useless.

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* TST Assert FutureWarning instead of DeprecationWarning in
test_neighbors_metrics

* MAINT Add use_pairwise_dist_activate to scikit-learn config

* TST Add a test for the 'brute' backends' results' consistency

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* fixup! MAINT Add use_pairwise_dist_activate to scikit-learn config

* fixup! fixup! MAINT Add use_pairwise_dist_activate to scikit-learn config

* TST Filter FutureWarning for WMinkowskiDistance

* MAINT pin numpydoc in arm for now (#22292)

* fixup! TST Filter FutureWarning for WMinkowskiDistance

* Revert keywords arguments removal for the GEMM trick for 'euclidean'

* MAINT pin max numpydoc for now (#22286)

* Add 'haversine' to CDIST_PAIRWISE_DISTANCES_REDUCTION_COMMON_METRICS

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* fixup! Add 'haversine' to CDIST_PAIRWISE_DISTANCES_REDUCTION_COMMON_METRICS

* Apply suggestions from code review

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* MAINT Document some config parameters for maintenance

Also rename one of them.

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* FIX Support and test one of 'sqeuclidean' specification

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* FIX Various typos fix and correct haversine

'haversine' is not supported by cdist.

* Directly use get_config

* CLN Apply comments from review

Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Co-authored-by: Jérémie du Boisberranger
<jeremiedbb@users.noreply.github.com>

* Motivate swapped returned values

Co-authored-by: Jérémie du Boisberranger
<jeremiedbb@users.noreply.github.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* TST Remove mahalanobis from test fixtures

Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com>

* MNT Add comment regaduction functions' signatures

Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* TST Complete test for `pairwise_distance_{argmin,argmin_min}` (#22371)

* DOC Add sub-pull requests to the whats_new entry
lorentzenchr added a commit that referenced this pull request Feb 17, 2022
…min` (feature branch) (#22134)

* MAINT Introduce Pairwise Distances Reductions private submodule  (#22064)

* MAINT Introduce FastEuclideanPairwiseArgKmin  (#22065)

* fixup! Merge branch 'main' into pairwise-distances-argkmin

Remove duplicated Bunch

* MAINT Plug `PairwiseDistancesArgKmin` as a back-end (#22288)

* Forward pairwise_dist_chunk_size in the configuration

* Flip finalized results for PairwiseDistancesArgKmin

The previous would have made the code more complex
by introducing some boilerplate for the interface plugs.

Having it this way actually simplifies the code.

This also removes the haversine branch for
test_pairwise_distances_argkmin

* Plug PairwiseDistancesArgKmin as a back-end

* Adapt test accordingly

* Add whats_new entry

* Change input validation order for kneighbors

* Remove duplicated test_neighbors_distance_metric_deprecation

* Adapt the documentation

* Add mahalanobis case to test fixtures

* Correct whats_new entry

* CLN Remove unneeded private metric attribute

This was needed when 'fast_sqeuclidean' and 'fast_euclidean'
were present to choose the best implementation based on the user
specification.

Those metric have been removed since then, making this attribute
useless.

* TST Assert FutureWarning instead of DeprecationWarning in
test_neighbors_metrics

* MAINT Add use_pairwise_dist_activate to scikit-learn config

* TST Add a test for the 'brute' backends' results' consistency

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* fixup! MAINT Add use_pairwise_dist_activate to scikit-learn config

* fixup! fixup! MAINT Add use_pairwise_dist_activate to scikit-learn config

* TST Filter FutureWarning for WMinkowskiDistance

* MAINT pin numpydoc in arm for now (#22292)

* fixup! TST Filter FutureWarning for WMinkowskiDistance

* Revert keywords arguments removal for the GEMM trick for 'euclidean'

* MAINT pin max numpydoc for now (#22286)

* Add 'haversine' to CDIST_PAIRWISE_DISTANCES_REDUCTION_COMMON_METRICS

* fixup! Add 'haversine' to CDIST_PAIRWISE_DISTANCES_REDUCTION_COMMON_METRICS

* Apply suggestions from code review

* MAINT Document some config parameters for maintenance

Also rename one of them.

* FIX Support and test one of 'sqeuclidean' specification

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* FIX Various typos fix and correct haversine

'haversine' is not supported by cdist.

* Directly use get_config

* CLN Apply comments from review

* Motivate swapped returned values

* TST Remove mahalanobis from test fixtures

* MNT Add comment regaduction functions' signatures

* TST Complete test for `pairwise_distance_{argmin,argmin_min}` (#22371)

* DOC Add sub-pull requests to the whats_new entry

* DOC place comment inside functions

* DOC move up whatsnew entry

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com>
thomasjpfan added a commit to thomasjpfan/scikit-learn that referenced this pull request Mar 1, 2022
…min` (feature branch) (scikit-learn#22134)

* MAINT Introduce Pairwise Distances Reductions private submodule  (scikit-learn#22064)

* MAINT Introduce FastEuclideanPairwiseArgKmin  (scikit-learn#22065)

* fixup! Merge branch 'main' into pairwise-distances-argkmin

Remove duplicated Bunch

* MAINT Plug `PairwiseDistancesArgKmin` as a back-end (scikit-learn#22288)

* Forward pairwise_dist_chunk_size in the configuration

* Flip finalized results for PairwiseDistancesArgKmin

The previous would have made the code more complex
by introducing some boilerplate for the interface plugs.

Having it this way actually simplifies the code.

This also removes the haversine branch for
test_pairwise_distances_argkmin

* Plug PairwiseDistancesArgKmin as a back-end

* Adapt test accordingly

* Add whats_new entry

* Change input validation order for kneighbors

* Remove duplicated test_neighbors_distance_metric_deprecation

* Adapt the documentation

* Add mahalanobis case to test fixtures

* Correct whats_new entry

* CLN Remove unneeded private metric attribute

This was needed when 'fast_sqeuclidean' and 'fast_euclidean'
were present to choose the best implementation based on the user
specification.

Those metric have been removed since then, making this attribute
useless.

* TST Assert FutureWarning instead of DeprecationWarning in
test_neighbors_metrics

* MAINT Add use_pairwise_dist_activate to scikit-learn config

* TST Add a test for the 'brute' backends' results' consistency

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* fixup! MAINT Add use_pairwise_dist_activate to scikit-learn config

* fixup! fixup! MAINT Add use_pairwise_dist_activate to scikit-learn config

* TST Filter FutureWarning for WMinkowskiDistance

* MAINT pin numpydoc in arm for now (scikit-learn#22292)

* fixup! TST Filter FutureWarning for WMinkowskiDistance

* Revert keywords arguments removal for the GEMM trick for 'euclidean'

* MAINT pin max numpydoc for now (scikit-learn#22286)

* Add 'haversine' to CDIST_PAIRWISE_DISTANCES_REDUCTION_COMMON_METRICS

* fixup! Add 'haversine' to CDIST_PAIRWISE_DISTANCES_REDUCTION_COMMON_METRICS

* Apply suggestions from code review

* MAINT Document some config parameters for maintenance

Also rename one of them.

* FIX Support and test one of 'sqeuclidean' specification

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* FIX Various typos fix and correct haversine

'haversine' is not supported by cdist.

* Directly use get_config

* CLN Apply comments from review

* Motivate swapped returned values

* TST Remove mahalanobis from test fixtures

* MNT Add comment regaduction functions' signatures

* TST Complete test for `pairwise_distance_{argmin,argmin_min}` (scikit-learn#22371)

* DOC Add sub-pull requests to the whats_new entry

* DOC place comment inside functions

* DOC move up whatsnew entry

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com>
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