Dear all, Kudos to scikit-learn! Having said that, Pipeline is killing me not being able to transform anything other than X.
My current study case would need: - Transformers being able to handle both X and y, e.g. clustering X and y concatenated - Pipeline being able to change other params, e.g. sample_weight Currently, I'm augmenting X through every step with the extra information which seems to work ok for my_pipe.fit_transform(X_train,y_train) but breaks on my_pipe.transform(X_test) for the lack of the y parameter. Ok, I can inherit and modify a descendant from Pipeline class to allow the y parameter which is not ideal but I guess it is an option. The gritty part comes when having to adapt every regressor at the end of the ladder in order to split the extra information from the raw data in X and not being able to generate more than one subproduct from each preprocessing step My current research involves clustering the data and using that classification along with X in order to predict outliers which generates sample_weight info and I would love to use that on the final regressor. Currently there seems not to be another option than pasting that info on X. All in all, I'm stuck with this API limitation and I would love to learn some tricks from you if you could enlighten me. Thanks in advance! Manuel Castejón-Limas
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