Skll

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.

- Added AdaBoost and KNeighbors classifiers and regressors (finally closing 7).
- Added support for [kernel approximation samplers](http://scikit-learn.org/stable/modules/kernel_approximation.html). (Thanks nineil)
- All linear models are now supported by `print_model_weights` (issue 119).
- Added `f1_score_weighted` metric so that weighted F1 will be calculated even for binary classification tasks.
- Modified `f1_score_micro` and `f1_score_macro` to also always return average for binary classification tasks (instead of previous behavior where only performance on positive class was returned).

This release includes a long-standing request being finally fulfilled (part of 7). We now support Stochastic Gradient Descent!

Full changelog:
- Added support for `SGDClassifier` and `SGDRegressor`
- Added option to use FeatureHasher instead of DictVectorizer to make learning with feature sets that have millions of features possible.
- Minor documentation fix for `generate_predictions`.

All the credit for this release goes to nineil. Thanks Nils!

- Added `compute_eval_from_predictions` utility for computing evaluation metrics after experiments have been run.
- Made rounding consistent in Python 2 `kappa` code use banker's rounding, just like Python 3 does.
- Added support for printing model weights for linear SVR (110)
- Made `print_model_weights` only print all negative or all positive weights (105)
- Little PEP8 and documentation tweaks.

Fixed issue where some models would be different depending on order of feature files specified in config file (101).

- Add `--resume` option to `run_experiment` for resuming large experiments in the event of a crash.
- Fix issue where `grid_scores` was undefined when using `--keep-models`.
- Automatically generated feature set names now have sorted features to ensure they will always be generated in the same fashion.