On Wed, Jul 31, 2013 at 4:08 PM, Lars Buitinck <[email protected]> wrote:
> 2013/7/31 Joel Nothman <[email protected]>:
> > I am wondering why there is a need to support the indices=False case in
> > cross_validation. Indices are superior in that they can be used with
> np.take
> > and with sparse matrices. And most of the standard cv implementations
> output
> > indices that are converted into boolean masks and back to indices.
> >
> > Moreover, building generic tools that take cv implementations as input
> need
> > to handle both cases (or make assumptions).
> >
> > What is the intention behind indices=False; why not deprecate it and
> > simplify the API and code? (And speed up indexing by using np.take.)
>
> Funny, I was wondering the same thing yesterday. IIRC, we originally
> used only masks and indices were added to please the sparse
> matrix-pushing crowd (yours truly). Then safe_mask got introduced to
> accept both at the consumer side.
>
> Arguably, masks are easier to interpret, though, esp. in feature
> selection code; you can multiply them with your coef_ before plotting
> it to see which features are deactivated.
>
But that isn't really meaningful for cv.
Do you have any timings for np.take?
>
See http://wesmckinney.com/blog/?p=215
Ideally this is a bug that will disappear from numpy anyway -- for all I
know it already has -- so it should be less of the focus than a simplified
API.
- Joel
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