[MRG] Sparse input support for decision tree and forest

This pull request aims to finish the work in #2984.
For reference: #655, #2848 

TODO list:

- [x] clean rebase (remove unwanted generated file, github merge commit, ...)
- [x] simpler input validation for tree base method (safe_array, check_array?)
- [x] reduce testing times and rationalize tests
- [x] use the new safe_realloc
- [x] document fit, ... with the sparse format support
- [x] fix bug in algorithm selection binary_search
- [x] raise error if int64 sparse index matrix
- [x] test oob scores
- [x] test random tree embedding with sparse input data
- [x] ensure that X is 2d
- [x] test forest apply (sparse)
- [x] test error raised if bootstrap=False and oob_score=True
- [x] test not fitted and `feature_importances_`
- [x] Check warning oob_score
- [x] Let tree estimator handle int64 based sparse matrices
- [x] switch xrange -> range
- [ ] Update adaboost for dtype

Todo later:
- update what's new


Also from here to the end of the `best` update could be refactored with the dense `RandomSplitter`: the code is identical except for the partition, which can be factored out. I imagine that Cython will repeat the implementation anyway as part of the inheritance (which is good for inlining, but bad for number of lines of C code). The most annoying part is that the dense version would need to include dummy values for `start_positive` and `end_negative` because C doesn't do closures.

So I'm not certain this is worthwhile, but it may be worth considering. Once `_partition` is factored out, it might be no big deal.