On Thu, Mar 28, 2013 at 10:10 AM, Lars Buitinck <[email protected]> wrote:
> 2013/3/28 Mathieu Blondel <[email protected]>:
> > Encoding missing values with np.nan doesn't scale to very
> > high-dimensional problems with mostly missing values.
> > Personally, for encoding missing data, I just use sparse matrices.
> > Values which are actually zero can be stored explicitly in the .data
> > attribute.
>
> +1 for not storing missing values, but will scipy.sparse matrices work
> correctly when .data has zeros, and will conversion between formats
> retain them?
>
It depends on the conversion. Some sparse matrix conversions keep explicit
zeros, some don't. We dealt with this in scipy.sparse.csgraph: there are
some utilities there that do the matrix conversions and make sure missing
entries & zero entries are distinguished correctly. The functions are a
bit graph-specific, but it might be useful to look at for some ideas.
Jake
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
>
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