[MRG] Sparse multilabel target support in metrics

This introduces a series of helper classes that abstract away aggregation over multilabel structures. This enables efficient calculation in sparse and dense binary indicator matrices, while maintaining support for the deprecated sequences format.
Indeed, from the reviews I've been seeing, it seems the sprint was very
exhausting. Go take a long nap!


On 21 July 2014 17:09, Arnaud Joly <notifications@github.com> wrote:

>  In sklearn/metrics/tests/test_common.py:
>
> > @@ -636,7 +637,6 @@ def test_multilabel_representation_invariance():
> >      y2_shuffle = [shuffled(x) for x in y2]
> >
> >      # Let's have redundant labels
> > -    y1_redundant = [x * rng.randint(1, 4) for x in y1]
> >      y2_redundant = [x * rng.randint(1, 4) for x in y2]
>
> Sorry, lagging brain.
>
> —
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> <https://github.com/scikit-learn/scikit-learn/pull/3395/files#r15156785>.
>