Given that we'd love to get rid of our libsvm/liblinear biddings, I would be more in favor of improving our matrix factorization code rather than including this code.
That said, +1 for missing data imputation with matrix factorization, once we're done with the current PRs on missing data. Gaël On Wed, Nov 02, 2016 at 12:10:40PM -0400, Andy wrote: > -------- Forwarded Message -------- > Subject: libmf bindings > Date: Wed, 2 Nov 2016 11:38:00 -0400 > From: sam royston <[email protected]> > To: [email protected] > Hi, > Thanks for all your hard work on this useful tool! I'm hoping to contribute > bindings to Chih-Jen Lin's libmf: https://www.csie.ntu.edu.tw/~cjlin/libmf/. > It > looks like you guys have functionality for NMF, but used only in the > decomposition/ dimensionality reduction setting (and obviously only with > non-negative values). Id like to add functionality in the form python wrappers > for libmf, much like you have for Chih-Jen Lin's other libraries libsvm and > liblinear. > Libmf is very efficient and offers great functionality for missing data > imputation, recommendation systems and more. > I have already written bindings using ctypes, but I see that you have you > Cython for libsvm and liblinear - is it necessary that I switch to that > interface? > Let me know what you think of a contribution like this. > Thanks, > Sam > _______________________________________________ > scikit-learn mailing list > [email protected] > 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 [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
