On 28 March 2013 23:24, Lars Buitinck <[email protected]> wrote:

> On Thu, Mar 28, 2013 at 2:35 PM, Nelle Varoquaux
> <[email protected]> wrote:
> > But in general, I don't think we can "force" the user to use sparse
> > matrices. They are an absolute pain to work with because of the
> > inconsistencies of interface with ndarray and conversion between sparse
> and
> > dense can be time consuming.
>
> True. What is the use case we're thinking of? Most data missing or
> most data zero and only some of it missing? The latter can be easily
> handled by sparse matrices and ndarrays in the same way, but the
> former is tricky.
>

I think this really depends on the use case and the application. This is
what makes dealing with missing data so complicated. In the case of the
MDS, missing data is explicitely not taken in account in the objective
function: I'm not sure how much this extends to other algorithms.
Also in the case of the classical MDS, having 0 entries in the distance
matrix doesn't make much sense, so I'd have a tendancy to think 0 should be
considered as missing data. And you can have both the case of a lot of
missing data (for example if you have to measure the distances between two
points, and this is costly or time consuming, you would end up with a lot
of missing data), and no missing data at all.


>
> 2013/3/28 Kenneth C. Arnold <[email protected]>:
> > Every once in a while I think of refactoring SciPy's sparse matrices into
> > sparse arrays (with ndarray-like semantics) with the matrix interface as
> a
> > wrapper like np.matrix is, but it would be a big undertaking and so far
> > nobody else has seemed excited about it.
>
> I know the "need to rewrite scipy.sparse" itch. I for one would be
> very excited if you were to volunteer ;)
>

Me too ! I could also give a hand.


>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
>
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