[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
- [x] Update adaboost for dtype

Todo later:
- update what's new


On 07/16/2014 03:07 PM, Arnaud Joly wrote:
>
> In sklearn/ensemble/weight_boosting.py:
>
> >
> >  from .base import BaseEnsemble
> >  from ..base import ClassifierMixin, RegressorMixin
> >  from ..externals import six
> > -from ..externals.six.moves import xrange, zip
> > +from ..externals.six.moves import zip
> > +from ..external.six.moves import xrange as range
>
> indeed.
>
I just checked this out and ran it.... and it went through... kinda 
puzzling...