But where it is the case, an index into the results (so that you can use
np.asarray(results)[grid.build_index()] in the desired manner) is possible.
https://github.com/scikit-learn/scikit-learn/pull/1842
On the other hand, as long as you can get an array of parameter values for
each parameter name (e.g.
https://github.com/jnothman/scikit-learn/tree/parameters_mrecarray), even
if not a grid, you can transform your data with pandas.DataFrame or similar.
And Olivier, I'm looking forward to reading your response, but will need a
moment more than I have right now!
- Joel
On Sat, Jun 8, 2013 at 12:00 AM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
> > It would be great if there was a way to access the parameter search
> > results as a numpy ndarray, with one axis for each parameter and one
> > additional axis for the cross-validation folds. This would make it easy
> > to visualise the grid search results, compute the mean, median or
> > variance for each grid point, etc.
>
> That only works if these parameters are on the nD grid, which is not at
> all garanteed.
>
>
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