OrdinalEncoder conversion fixes #1044
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xadupre
reviewed
Nov 13, 2023
…lEncoder is determined by the type of input variable, and not by the type of categories in OrdinalEncoder - Fixed FunctionTransformer converter. Added axis to Concat operator - Fixed Imputer shapes calculator. Now number of inputs can be >1 - Fixed SklearnMultiply converter. Now initializer type is equal to input type - Fixed Pipeline converter. Now Cast operator applies if pipeline outputs are different with last stage outputs - Changed VotingClassifier and VotingRegressor converter. Now VotingClassifier can accept number of inputs >1
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@xadupre I have refactored the code via black |
xadupre
approved these changes
Dec 7, 2023
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Hello! Thank you for this awesome library that helps me to use sklearn models in highly loaded systems.
I think I found some problems for OrdinalEncoder conversion. Current implementation doesn't work with encoder that was fitted on data with different column datatypes. For example, if I pass to fit() function pandas dataframe with different column datatypes (np.int64, object), I get error when I try run ONNX model using onnxruntime framework because ONNX model contains inconsistent attributes for LabelEncoder operator.
My suggestion is removing cast to string for each input variable and pay attention not only to the
categories_array dtype but also to the type ofcategories_values because in yourtest_ordinal_encoder_mixed_string_int_drop()functioncategories_array is object dtype, but values type is int.In the end I want attach demo jupyter notebook and ONNX models with old and new conversions for problem demonstration.
ordinal_encoder.zip
I will be waiting for comments on my pull request