[WIP] Sparse label_binarizer

This is a continuation of a part of PR #2458 based on the code already written by @rsivapr and @arjoly from the branch https://github.com/arjoly/scikit-learn/tree/sparse-label_binarizer.

The first steps are to write tests to motivate correctness and then implement the appropriate changes in label.py. Validation of the input has been abstracted to a global private function to avoid duplication when using the two different APIs. 

Initial Checklist:

- [x] label validation on ```LabelBinarizer()``` - with testing
- [x] label validation on ```label_binarizer()``` - with testing
- [x] Import changes for label.py from arjolys branch sparse-label_binarizer
- [x] Import changes for test_label.py from arjolys branch sparse-label_binarizer
- [x] Handle case where type_of_target gets a sparse matrix input
- [x] Debug: One Label Tests in sklearn/tests/common.py
- [x] Test ```is_label_indicator_matrix``` with sparse input
- [x] Test ```label_binarize``` with binary one class case 
- [x] Break down _inverse_label_binarize

Rebase Checklist:

- [x] Rebase on top of PR #2657 changes in label.py
- [x] Rebase on top of PR #2657 changes utils/multiclass.py
- [x] Reintegrate sparse_output tests in test_label.py
- [x] Reintegrate sparse_output tests in utils/test_multiclass.py
- [x] Reintegrate coverage boosting tests in test_label.py, test_multiclass.py
- [x] Include changes to Misc. files guided by make errors
- [ ] Documentation updates
- [ ] Deprecate parameters in lable.py as in previous changes
- [ ] Figure out behavior for sequence of sequences passed to label_binarize. fail or warning?
here, you can do `Y.data = pos_label`