SKLL 1.1.0

dan-blanchard released this 20 Jul 13:35

The biggest changes in this release are that the required version of scikit-learn has been bumped up to 0.16.1 and config file parsing is much more robust and gives much better error messages when users make mistakes.

Implemented enhancements

Base estimators other than the defaults are now supported for AdaBoost classifiers and regressors (#238)
User can now specify number of cross-validation folds to use in the config file (#222)
Decision Trees and Random Forests no longer need dense inputs (#207)
Stratification during cross-validation is now optional (#160)

Fixed bugs

Bug when checking if hasher_features is a valid option (#234)
Invalid/missing/duplicate options in configuration are now detected (#223)
Stop modifying global numpy random seed (#220)
Relative paths specified in the config file are now relative to the config file location instead of to the current directory (#213)

Closed issues

Incompatibility with the latest version of scikit-learn (v0.16.1) (#235, #241, #233)
Learner.model_params will return weights with the wrong sign if sklearn is fixed (#111)

Merged pull requests

Overhaul configuration file parsing (@desilinguist, #246)
Several minor bugfixes (@desilinguist, #245)
Compatibility with scikit-learn v0.16.1 (@desilinguist, #243)
Expose cv_folds and stratified (@aoifecahill, #240)
Adding Report tests (@brianray, #237)

Assets 2