[MRG] Sparse One vs. Rest

This is a PR for the other parts in PR #2458 concerning sparse output support for the ovr classier. This branch was made off of the sprs-lbl-bin branch from PR #3203 and will be rebased to remove all the sparse label binarizer additions from the diff once the pull request is merged.

Benchmark Experiments using PR #2828 for data generation:
- [x] ```fit_ovr```: ```sparse_output=True``` VS. ```sparse_output=sp.issparse(y)```
- [x] ```fit_ovr``` With input as CSR matrix: Cast to CSC before column extraction VS. Leave as CSR
- [x] Memory profiling for sparse vs. dense target data
- [x] Test the changes to label.py
- [x] Test probability and desicion functions after fitting on sparse target data
- [x] Memory benchmark with ```njobs != 1```
By this small sample it looks like ```X_test``` will not become square

```
>>> np.eye(10)[4:]
array([[ 0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]])
```