Allow float categories to be passed into CatBoost estimators #3966
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## main #3966 +/- ##
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+ Coverage 99.7% 99.7% +0.1%
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Files 347 347
Lines 36801 36892 +91
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+ Hits 36681 36772 +91
Misses 120 120
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evalml/pipelines/components/utils.py
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| # --> there has to be a better way | ||
| # - just string raises TypeError: Cannot convert StringArray to numpy.ndarray | ||
| # - just object raises the original cat_features must be integer or string, real number values error | ||
| X_float_cats = X[float_cols].astype("string").astype("object").astype("category") |
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There is a lot of bugginess with making the float categories usable as strings, and based on the CatBoost FAQ on why they don't support float categories in the first place, I'm not fully convinced we should support this (note this is different from the case where we can just convert to Integer without truncating values, which is the main thing we're adding support for here).
- If we convert to just
string, we getTypeError: Cannot convert StringArray to numpy.ndarrayfor which there's an open catboost ticket - If we convert to just
object, that doesn't actually change the underlying float values, so we get the original error. But the combination ofstringandobjectconversions does indeed work (only in that order, though) - If you try and change type in one fell swoop by creating a
pd.CategoricalDtype(categories=col_categories.astype("string").astype("object"))dtype that then gets passed into a singleastypecall, it just nulls out the whole column (pandas ticket for this bug: BUG: SettingCategoricalDtypecategories as string objects nulls out data pandas-dev/pandas#51074)
The result is this ugly series of chained astype calls . Given the messiness introduced by handling the "true float" case and the fact that this is the exact thing CatBoost didn't want to handle, I'm going to suggest we raise our own error for now. I'll add comments with the tickets tracking the bugs described above so that if a need for these string floats arises, we may be able to implement it nicely if any of the bugs are fixed.
| self._component_obj.fit(X, y, silent=True, cat_features=cat_cols) | ||
| return self | ||
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| def predict(self, X): |
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Figured if I was going to handle categories for the regressor (and I did double check it was necessary for predict!) I'd have to add an explicit predict method -- let me know if there's a better way that I missed though!
| evalml_pipeline = MulticlassClassificationPipeline([RandomForestClassifier]) | ||
| evalml_pipeline = MulticlassClassificationPipeline( | ||
| [RandomForestClassifier], | ||
| ) |
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I might be in a weird state in VSCode with the new ruff linting, so idk if this and the above change are expected or indicative of my own weird environment. Doesn't seem like I have any linting issues in this PR so I'm fine leaving as is.
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| self._component_obj.fit(X, y, silent=True, cat_features=cat_cols) | ||
| return self | ||
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| def predict(self, X): |
tamargrey
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fixes #3965
Since catboost cannot handle float categories, converts categories that are really integers to be
int64