Comments (2)
wdm0006
commented on July 4, 2024
Hm, yeah that looks like a bug. -1 is for unknown categories (so a category showing up in transform that wasnt seen in fit), which clearly, it shouldn't have been. I think you're correct that root cause here is the None vs. nan mixup.
Not sure the easiest way to resolve this, since we cast to pandas always, we can treat Nonetypes in mappings special maybe? I'll have to think on that some.
Great catch, btw.
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wdm0006
commented on July 4, 2024
see #56
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Related Issues (20)
- Sklearn pipeline compatibility and pandas dependencies
- Quadratic time intersection on Pandas categories HOT 2
- get_feature_names_out is incompatible with sklearn estimators and eli5, consequently HOT 3
- Equivalent method to sklearn's partial_fit? HOT 1
- CountEncoder incorrectly counts Timestamp columns HOT 3
- Target encoding categories with a single training example HOT 1
- DOC: one of the source links is dead HOT 1
- Missing text in documentation HOT 2
- Support Pandas 2.1 HOT 1
- Feature Request: Count-Based Target Encoder (Dracula)? HOT 1
- Pandas' string columns are not recognized HOT 3
- Pandas copy-on-write doesn't work properly HOT 2
- pd.NA should behave as np.nan HOT 5
- Multidimensional/composite target encoding HOT 4
- FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. HOT 2
- Support for Spark HOT 1
- EOF Error Raised while Calling HashingEncoders function HOT 6
- why we combine this library with main sklearn ? HOT 1
- catboost encoder get different result with catboost HOT 8
- Combining with set_output can produce errors HOT 1
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