Refactoring feature selection #19
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- Store target and cross NMI inside MODData by default - Loop unique over pairs rather than rows and columns - Fix edgecase when providing both df_featurized and structures - Use same random seed when computing NMI for all features - For constant features, explicitly set diagonal to NaN to avoid overflow elsewhere
Tweaked setup.py
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ml-evs
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Nov 13, 2020
| @@ -7,17 +7,19 @@ | |||
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| from __future__ import annotations | |||
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Just found out about this, it lets you defer type annotation evaluation so e.g. methods of MODData can use MODData as a type-hint
ppdebreuck
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Nov 18, 2020
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Refactors feature selection with a multiple changes:
MODData.split()method for creating newMODDataobjects for test/train splitspytest -m "not slow".