Hi Andreas, Thanks a lot for the information.
Yoann Date: Tue, 13 Sep 2016 11:56:45 -0400 From: Andreas Mueller <[email protected]> To: Scikit-learn user and developer mailing list <[email protected]> Subject: Re: [scikit-learn] Use of Scaler with LassoCV, RidgeCV Message-ID: <[email protected]> Content-Type: text/plain; charset=windows-1252; format=flowed There is no way to use the "efficient" EstimatorCV objects with pipelines. This is an API bug and there's an open issue and maybe even a PR for that. On 09/13/2016 08:45 AM, Brenet, Yoann wrote: > Hi Sebastian, > > Many thanks, that's what I was thinking I should be doing, so thanks a lot > for confirming that was the way to go. > > Really appreciate the help, > Yoann > > Date: Tue, 13 Sep 2016 08:33:52 -0400 > From: Sebastian Raschka <[email protected]> > To: Scikit-learn user and developer mailing list > <[email protected]> > Subject: Re: [scikit-learn] Use of Scaler with LassoCV, RidgeCV > Message-ID: <[email protected]> > Content-Type: text/plain; charset=utf-8 > > Hi, Yoann, > > when I understand correctly, you want to apply the scaling to each iteration > in cross-validation (i.e., the recommended way to do it)? Here, you could use > the make_pipeline function, which will call fit on each training fold and > call transform on each test fold: > > > from sklearn.preprocessing import StandardScaler from sklearn.pipeline > import make_pipeline from sklearn.cross_validation import > cross_val_score from sklearn.linear_model import Ridge > > pipe = make_pipeline(StandardScaler(), Ridge()) cross_val_score(pipe, > X, y) > > You can think of ?pipe? as an Ridge estimator with a StandardScaler attached > to it. > > Best, > Sebastian > > >> On Sep 13, 2016, at 8:16 AM, Brenet, Yoann <[email protected]> wrote: >> >> Hi all, >> >> I was trying to use scikit-learn LassoCV/RidgeCV while applying a >> 'StandardScaler' on each fold set. I do not want to apply the scaler before >> the cross-validation to avoid leakage but I cannot figure out how I am >> supposed to do that with LassoCV/RidgeCV. >> >> Is there a way to do this ? Or should I create a pipeline with Lasso/Ridge >> and 'manually' search for the hyper-parameters (using GridSearchCV for >> instance) ? >> >> Many thanks. >> >> Yoann >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn > > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > > > ------------------------------ > > End of scikit-learn Digest, Vol 6, Issue 15 > ******************************************* > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn ------------------------------ Subject: Digest Footer _______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn ------------------------------ End of scikit-learn Digest, Vol 6, Issue 17 ******************************************* _______________________________________________ scikit-learn mailing list [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
