Implement sample_weight functionality in pLSA #8
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_check_sample_weight was not added to sklearn.utils.validation until v0.22
minor cleanup of imports
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@lmcinnes - what do you think about adding sample weights to pLSA? I know this isn't part of the TransformerMixin interface, or in the other sklearn.decomposition methods, but there are lots of settings where this is useful (and also more elegant than over/under-sampling). |
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This looks great -- it certainly makes sense, and I agree that it doesn't have much overhead. |
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I've added sample_weight as a parameter to fit and fit_transform. In my experiments on both my data and on the newsgroups notebook, it added very little runtime overhead. Please let me know if further testing or documentation in a notebook is needed!