LabelBinarizer and LabelEncoder fit and transform signatures not compatible with Pipeline #3112
Description
I get this error when I try to use LabelBinarizer and LabelEncoder in a Pipeline:
sklearn/pipeline.pyc in fit_transform(self, X, y, **fit_params)
141 Xt, fit_params = self._pre_transform(X, y, **fit_params)
142 if hasattr(self.steps[-1][-1], 'fit_transform'):
--> 143 return self.steps[-1][-1].fit_transform(Xt, y, **fit_params)
144 else:
145 return self.steps[-1][-1].fit(Xt, y, **fit_params).transform(Xt)
TypeError: fit_transform() takes exactly 2 arguments (3 given)It seems like this is because the classes' fit and transform signatures are different from most other estimators and only accept a single argument.
I think this is a pretty easy fix (just change the signature to def(self, X, y=None)) that I'd be happy to send a pull request for, but I wanted to check if there were any other reasons that the signatures are the way they are that I didn't think of.