They look like some good comments, Andy. Sorry, Alexander that I haven't
been able to look in detail at your changes to grid search yet. As I said,
it would be nice if this could all be refactored with cross_val_score at
some point soon.

On this point, I include a `get_scorer` in
#<https://github.com/scikit-learn/scikit-learn/pull/2676>2676,
as it is needed to deprecate the old implicit-averaging `f1` scorer. I
would very much like to see that PR (and #2679) merged for the upcoming
release, but it needs reviews.


On Tue, Jan 7, 2014 at 5:26 AM, Andreas Mueller <notifications@github.com>wrote:

>  In sklearn/learning_curve.py:
>
> > +        new_cv = []
> > +        for i in range(len(cv)):
> > +            new_cv.append((np.nonzero(cv[i][0])[0], np.nonzero(cv[i][1])[0]))
> > +        cv = new_cv
> > +
> > +    n_max_training_samples = len(cv[0][0])
> > +    n_samples_range, n_unique_ticks = _translate_n_samples_range(
> > +        n_samples_range, n_max_training_samples)
> > +    # Because the lengths of folds can be significantly different, it is
> > +    # not guaranteed that we use all of the available training data when we
> > +    # use the first 'n_max_training_samples' samples.
> > +    if verbose > 0:
> > +        print("[learning_curve] Training set sizes: " + str(n_samples_range))
> > +
> > +    _check_scorable(estimator, scoring=scoring)
> > +    scorer = _deprecate_loss_and_score_funcs(scoring=scoring)
>
> As this is new functionality, you don't need to deprecate anything, right?
> If you want to use the last "if" please factor it out and rename it. Maybe
> at a get_scoring or validate_scoring function to the scoring file.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/scikit-learn/scikit-learn/pull/2701/files#r8672523>
> .
>