WIP Overhaul of feature_selection #1939
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| 'stds': _scale_standard_deviations, | ||
| 'percentile': _scale_percentile, | ||
| 'incrank': _scale_rank, | ||
| 'decrank': lambda scores, n_samples: _scale_rank(-scores, n_samples), |
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Yes, I expect it should: it's not part of a __dict__, and never intends to be. As it is, I'm not sure it's such a useful feature, so I don't mind removing it.
Now can be pickled Now can set est params
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It seems to me there are commonalities between feature selection processes that are not being exploited, and places where ad-hoc code are used instead of a library.
Some of these things may be split off into a separate PR, especially on request, and all are up for discussion.
create a mixin to providesplit off into MRG Centralise feature selection transformations in a mixin #1962[inverse_]transformgiven_get_support_mask()in all feature selectors (but what to call it, given thatSelectorMixinis taken by something else?)SelectByScore (score_func, minimum, maximum, scaling_func, limit)for more generic score thresholding, e.g. supporting two-sided document frequency cutoffs. This implementation also reduces work attransformtime by pre-transforming/scaling scores to match the threshold.nannans?SelectByScoreforSelect{KBest,Percentile,Fdr,Fpr,Fwe,orMixin}, closing Retrieve the mask in SelectFromModel #1459SelectByScoreto handle thresholding infeature_extraction.text.CountVectorizerrandomized_l1chi2,f_classify, etc. tosklearn.metrics.feature_scoresor similar (and add document frequency ==count_nonzero, what else?)