[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~~
If you plan to add support for `sample_weight` for those losses at some point in the future, I would rather raise `NotImplementedError` to state those are not mathematically invalid constructor parameters but could be implemented in a future version of scikit-learn.