[MRG] ENH add coverage multilabel ranking metric

This pr adds the coverage multilabel ranking metric in scikit-learn.

It's very useful as it carries a lot of intuition on your estimator performance. This is the average number of ~~true~~ labels that you need to predict in order to avoid missing any true one given your y_score matrix.

@jnothman Do you see an easy way to make this works with sparse csc `y_true`?
I can you link to a ton of papers. But they never defined the rank transformation. 
If I remember correctly in the mulan implementation, the ranking is broken randomly and is based on the sorting algorithm (which is stable).

At the moment, I have used a tie breaker that is sensible given the definition (maximal rank tie) as in the label ranking average precision metric.