SciKit-Learn Laboratory (SKLL) 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.