On Fri, Jun 21, 2013 at 1:28 PM, Lars Buitinck <l.j.buiti...@uva.nl> wrote:
> Besides, scipy.sparse is hard to update in-place, is a very wasteful
> representation for dense data and is harder to work with than np.array
> (for us, but more importantly for users).
>
Dense formats like masked arrays or arrays with missing-values encoded by
NaN won't work for recommender datasets (unless you can fit a n_users x
n_items dense matrix in memory). So, we do need to find a suitable sparse
format to work with, since the second half of Nicolas's GSOC is about
matrix completion.
In my research, I have used CSR matrices, since they can be processed very
efficiently in Cython. Since CSR matrices are indeed non-trivial to
construct, we could provide an utility function which takes an iterator of
(feature_index, feature_value) pairs and produces a CSR matrix (with
hardcoded zero-values safely preserved).
Mathieu
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