v0.3.1

v0.3.1 is a hotfix release which fixes several major bugs as well as including several model quality improvements.

This release is non-breaking when upgrading from v0.3.0. As always, only load previously trained models using the same version of AutoGluon that they were originally trained on. Loading models trained in different versions of AutoGluon is not supported.

This release contains 9 commits from 4 contributors.

See the full commit change-log here: v0.3.0...v0.3.1

Thanks to the 4 contributors that contributed to the v0.3.1 release!

Special thanks to @yinweisu who is a first time contributor to AutoGluon and fixed a major bug in ImagePredictor HPO!

Full Contributor List (ordered by # of commits):

@Innixma, @gradientsky, @yinweisu, @sackoh

Changes

Tabular

• AutoGluon v0.3.1 has a 58% win-rate vs AutoGluon v0.3.0 for best_quality preset.
• AutoGluon v0.3.1 has a 75% win-rate vs AutoGluon v0.3.0 for high and good quality presets.
• Fixed major bug introduced in v0.3.0 with models trained in refit_full causing weighted ensembles to incorrectly weight models. This severely impacted accuracy and caused worse results for high and good quality presets. @Innixma (#1293)
• Removed KNN from stacker models, resulting in stack quality improvement. @Innixma (#1294)
• Added automatic detection and optimized usage of boolean features. @Innixma (#1286)
• Improved handling of time limit in FastAI NN model to avoid edge cases where the model would use the entire time budget but fail to train. @Innixma (#1284)
• Updated XGBoost to use -1 as n_jobs value instead of using os.cpu_count(). @sackoh (#1289)

Vision

• Fixed major bug that caused HPO with time limits specified to return very poor models. @yinweisu (#1282)

General

• Minor doc updates. @gradientsky (#1288, #1290)