DOC Clarify how initial conditions are chosen for NMF.#17369
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NicolasHug merged 2 commits intoscikit-learn:masterfrom Jun 3, 2020
Merged
DOC Clarify how initial conditions are chosen for NMF.#17369NicolasHug merged 2 commits intoscikit-learn:masterfrom
NicolasHug merged 2 commits intoscikit-learn:masterfrom
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When update_H is False, any given initial value of W is silently ignored, as are all of the initialization procedures. This commit fixes that by clarifying this in the non_negative_factorization function docstring.
thomasjpfan
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Thank you for the PR @bsmith89 !
sklearn/decomposition/_nmf.py
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| - 'custom': use custom matrices W and H | ||
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| Ignored if update_H is False, in which case the initial value of W | ||
| is set automatically. |
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This may be clearer:
- 'custom': use custom matrices W and H if `update_H=True`. If `update_H=False`, then
only custom matrix H is used.
The init=='custom' and update_H=False will down the following code path:
scikit-learn/sklearn/decomposition/_nmf.py
Lines 1040 to 1050 in f27adc5
Revised as suggested by @thomasjpfan.
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Reference Issues/PRs
None yet. I can submit an issue if that's important.
What does this implement/fix? Explain your changes.
For
sklearn.decomposition.non_negative_factorization, when update_H is False, any given initial value of W is silently ignored, as are all of the initialization procedures.See: sklearn/decomposition/_nmf.py#L1032-L1053
This commit fixes that by clarifying this in the non_negative_factorization function docstring.
Any other comments?
Nope. Simple problem, simple fix.