MNT: Simplify unknown handling in encoders/_encode.py - #34452
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_encode.py
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Marking this as ready and cc @thomasjpfan, it seems you have a good knowledge of encoders :) |
cakedev0
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July 10, 2026 08:05
thomasjpfan
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Jul 15, 2026
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Rechecked after recent iterations, it's still the case 👍 |
This was referenced Jul 22, 2026
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This is great @cakedev0! May I ask how did you measure that? |
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With benchmark code from #34394 (but maybe a bit different, I have a local version so it's kept around from branch to branch 😅 ) |
ogrisel
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Aug 5, 2026
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LGTM besides the following comment suggestions.
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Olivier Grisel <[email protected]>
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@cakedev0 Nice PR! |
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…#34452) Co-authored-by: Olivier Grisel <[email protected]>
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Reference Issues/PRs
When I was working on #34393 I started to refactor a bit some things in the encoders. I isolated the refactor in this PR.
What does this implement/fix? Explain your changes.
In
sklearn/utils/_encode.py:_check_unknownwhich had a fairly complex logic in favor of the slightly more powerful_encode(encodes unknown as -1)._encode_labels: a simple . This avoids having a multi purposes with error messages aboutywhen it's also used for features...Thanks to those changes, I can simplify
_BaseEncoder._transform: no need to replace unknow values by a valid category before calling encode (this was a fairly dirty trick IMO).Those changes are also helpful for #34393
AI usage: no
In my experience, AI is terrible at this kind of refactor.
Any other comments?
This also makes
transformslightly faster for both the numeric and object path, somethings like 10-20%. I don't think it's worth publishing detailed benchmark results. But I can if needed.