Changes
- Added examples on how to extend Auto-sklearn with a custom classifier, regressor, and preprocessor.
- Auto-sklearn now requires numpy version between 1.9.0 and 1.14.5, due to higher versions causing travis failure.
- Examples now use
sklearn.datasets.load_breast_cancer()instead ofsklearn.datasets.load_digits()to reduce memory usage for travis build. - Fixes future warnings on non-tuple sequence for indexing.
- Fixes #500: fixes ensemble builder to correctly evaluate model score with any metrics. See PR #522.
- Fixes #482 and #491: Users can now set up custom logger configuration by passing a dictionary created by a yaml file to
logging_config. - Fixes #566: ensembles are now sorted correctly.
- Fixes #293: Auto-sklearn checks if appropriate target type was given for classification and regression before call to
fit(). - Travis-ci now runs flake8 to enforce pep8 style guide, and uses travis-ci instead of circle-ci for deployment.
Contributors
- Matthias Feurer
- Manuel Streuhofer
- Taneli Mielikäinen
- Katharina Eggensperger
- Jin Woo Ahn