[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


It looks like this passage of code is repeated four times in `extra_nnz_index_to_samples` and `extract_nnz_binary_search`, sometimes with formatting inconsistencies. A private inline function like `_swap_samples(k_, start_positive_, X_indices, X_data, Xf, samples, index_to_samples)` might be helpful, although many of those arguments could be dropped if `extract_nnz` and its subsidiaries were members of `SparseSplitter` (with tiny-if-any overhead for dereferencing `self->member`).