Actually, thinking a bit about this, the inconvenience with the pattern that I lay out below is that it adds an extra indirection in the parameter setting. One way to avoid this would be to have a subclass of the pipeline that includes memoizing. It would call a memoized version of fit.
I think that it would be quite handy :). Should I open an issue on that? G On Mon, Nov 28, 2016 at 07:51:21PM +0100, Gael Varoquaux wrote: > On Mon, Nov 28, 2016 at 01:46:08PM -0500, Andreas Mueller wrote: > > I guess so. You'd handle parameters using an estimator_params dict in init > > and pass that to the caching function? > I'd try to set on the estimator, before passing them to the function, as we > do in standard scikit-learn, and joblib is clever enough to take that in > account when given the estimator as a function of the function that is > memoized. > G > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn -- Gael Varoquaux Researcher, INRIA Parietal NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
