(I am not a scikit learn dev.) This is a great idea and I for one look forward to using it.
My understanding is that libmf optimises only over the observed values (that is the explicitly given values in a sparse matrix) as is typically needed for recommender system whereas the scikit learn NMF code assumes that any non-specified value in a sparse matrix is zero. It is worth bearing that in mind in any comparison that is carried out. Raphael On 2 November 2016 at 16:10, Andy <[email protected]> wrote: > > > > -------- Forwarded Message -------- > Subject: libmf bindings > Date: Wed, 2 Nov 2016 11:38:00 -0400 > From: sam royston <[email protected]> <[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 > >
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