8000 DOC specify the meaning of W=None and H=None in sklearn.decomposition.non_negative_factorization by marenwestermann · Pull Request #25770 · scikit-learn/scikit-learn · GitHub
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DOC specify the meaning of W=None and H=None in sklearn.decomposition.non_negative_factorization #25770

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Merged
merged 3 commits into from
Mar 8, 2023
Merged

DOC specify the meaning of W=None and H=None in sklearn.decomposition.non_negative_factorization #25770

merged 3 commits into from
Mar 8, 2023

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marenwestermann
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@marenwestermann marenwestermann commented Mar 6, 2023 •
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Reference Issues/PRs

towards #17295

What does this implement/fix? Explain your changes.

Adds documentation to non_negative_factorization and _fit_transform in sklearn/decomposition/_nmf.py for cases W=None and H=None. Also adds documentation for W when update_H=False.

Any other comments?

#PyladiesBerlin sprint

jeremiedbb
jeremiedbb reviewed Mar 7, 2023
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Thanks for the PR @marenwestermann.

I think it's worth updating the docstrings of the public fit_transform as well (the not update_H does not apply there though). Can you also update the docstring of fit_transform in MiniBatchNMF.

Another thing that is worth mentioning imo is that when init="custom", both W and H must be provided, in the description of the init parameter in both classes and in non_negative_factorization.

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Thanks for your review @jeremiedbb. I updated the PR.

jeremiedbb
jeremiedbb approved these changes Mar 8, 2023
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LGTM, just a few nitpicks left

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Done. I also added back ticks to lines 969 and 970.

@jeremiedbb jeremiedbb merged commit a70954d into scikit-learn:main Mar 8, 2023
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Thanks

@marenwestermann marenwestermann deleted the non-negative-factorization branch March 9, 2023 08:12
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