[scikit-learn] Fwd: libmf bindings

[Raphael C]

(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 <t3kcit at gmail.com> wrote:

>
>
>
> -------- Forwarded Message --------
> Subject: libmf bindings
> Date: Wed, 2 Nov 2016 11:38:00 -0400
> From: sam royston <sfoxroyston at gmail.com> <sfoxroyston at gmail.com>
> To: scikit-learn-owner at python.org
>
> 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
>
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