Hi Manuel, thanks for your reply, before trying an alternative as PipeGraph, or implementing the class as you propose, I would prefer to include some code in the _fit method of BaggingClassifier, so the correct value of X can be passed to the base_estimator (the dataframe or its array of values). Many thanks in advance, Roxna
On Fri, Jun 28, 2019 at 2:39 PM Manuel CASTEJÓN LIMAS via scikit-learn < scikit-learn@python.org> wrote: > You can always add a first step that turns you numpy array into a > DataFrame such as the one required afterwards. > A bit of object oriented programming might be required though, for > deriving you class from BaseTransformer and writing you particular code for > fit and transform method. > Alternatively you can try the PipeGraph library for dealing with those > complex routes. > Best > Manuel > Disclaimer: yes, I'm a coauthour of the PipeGraph library. > > El vie., 28 jun. 2019 7:28, Roxana Danger <roxana.dan...@gmail.com> > escribió: > >> Hello, >> I would like to use the BaggingClassifier whose base estimator is a >> pipeline with multiple transformations including a DataFrameMapper from >> sklearn_pandas. >> I am getting an error during the fitting the DataFrameMapper as the first >> step of the BaggingClassifier is to convert the DataFrame to an array (see >> in BaseBagging._fit method). Similar problem happen using directly >> sklearn.Pipeline instead of the DataFrameMapper. in both cases, a DataFrame >> is expected as input, but, instead, an array is provided to the Pipeline. >> >> Is there anyway I can overcome this problem? >> >> Many thanks, >> Roxana >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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