Release 0.27.0

The main new feature in this release is that `.libsvm` files are now fully supported by `skll_convert` and `run_experiment`. Because of this change, we've removed `megam_to_libsvm`.

Other changes include:
- Integer keys are now allowed in `fixed_parameters` and `param_grids` (#134).  Therefore, SKLL now requires PyYAML to function properly.
- Added documentation about using `class_weights` to manage imbalanced datasets (#132)
- Added information about pre-specified folds (via `cv_folds_location) to results JSON and plain-text files. (#108)
- Added warning when encountering classes that are not in `class_map`. (#114)
- Fixed issue where sampler random_state parameter would be overridden.
- Fixed license headers in CLI package. They were still GPL for some reason.
- Fixed issue #112 by switching to `joblib.pool.MemmappingPool` for handling parallel file loading. SKLL now requires joblib 0.8 to function properly.
- Fixed issue #104 by making result formatting more consistent.
- `compute_eval_from_predictions` now supports string-valued classes, as it should have. (#135)
- We now raise an exception instead of allowing you to overwrite your results by including the same learner in the `learners` list in your config file twice (#140).
- Fixed warning about files being left open in Python 3.4 (by not leaving them open anymore).
- Short names for learners have been deprecated and will be removed in SKLL 1.0.