enforce float32 dtype for target encoded features. #39
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Ludecan
requested changes
Jan 24, 2025
| """Convert the columns handled by any TargetEncoder in the given encoder to float32.""" | ||
| for name, transformer, cols in encoder.transformers: | ||
| # Direct TargetEncoder | ||
| if type(transformer).__name__ == "TargetEncoder": |
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Maybe we can use isinstance(transformer, TargetEncoder) here?
| encoded_data[col] = encoded_data[col].astype("float32") | ||
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| # Nested transformers (if inside pipelines or additional ColumnTransformers) | ||
| elif hasattr(transformer, "transformers"): |
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Did we have cases like these?
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Description
Adds a step to enforce target encoded columns to float32 dtype.
This fixes an issue when in some datasets the target encoded categorical features keeps objet dtype and makes XGBoostClassifier rise a ValueError.