[MRG] Sample weights for gradient boosting

and piggy-packed ``'exponential'`` loss for binary classification (= AdaBoost).

I didn't implement sample weights for ``'lad'``, ``'huber'``, and ``'quantile'`` since it would require weighted median / percentiles. I will either raise a warning or exception in this case: what do you prefer?

cc @glouppe

TODO: ~~benchmark benchmark and some regression tests~~
Note: at some point we could use `sklearn.utils.validation.check_consistent_length` but I rather use your manual check in the mean time as the error messages are more informative.