Version 0.10.0

Version 0.10.0

• ADD #325: Allow to separately optimize metrics for metadata generation.
• ADD #946: New dask backend for parallel Auto-sklearn.
• BREAKING #947: Drop Python3.5 support.
• BREAKING #946: Remove shared model mode for parallel Auto-sklearn.
• FIX #351: No longer pass un-picklable logger instances to the target function.
• FIX #840: Fixes a bug which prevented computing metadata for regression datasets. Also adds a unit test for regression metadata computation.
• FIX #897: Allow custom splitters to be used with multi-ouput regression.
• FIX #951: Fixes a lot of bugs in the regression pipeline that caused bad performance for regression datasets.
• FIX #953: Re-add liac-arff as a dependency.
• FIX #956: Fixes a bug which could cause Auto-sklearn not to find a model on disk which is part of the ensemble.
• FIX #961: Fixes a bug which caused Auto-sklearn to load bad meta-data for metrics which cannot be computed on multiclass datasets (especially ROC_AUC).
• DOC #498: Improve the example on resampling strategies by showing how to pass scikit-learn's splitter objects to Auto-sklearn.
• DOC #670: Demonstrate how to give access to training accuracy.
• DOC #872: Improve an example on how obtain the best model.
• DOC #940: Improve documentation of the docker image.
• MAINT: Improve the docker file by setting environment variable that restrict BLAS and OMP to only use a single core.
• MAINT #949: Replace pip by pip3 in the installation guidelines.
• MAINT #280, #535, #956: Update meta-data and include regression meta-data again.

Contributors v0.10.0

• Francisco Rivera
• Matthias Feurer
• felixleungsc
• Chu-Cheng Fu
• Francois Berenger

Version 0.8.0

Version 0.8.0

• ADD #803: multi-output regression
• ADD #893: new Auto-sklearn mode Auto-sklearn 2.0

Contributors

• Chu-Cheng Fu
• Matthias Feurer

Version 0.7.1

Version 0.7.1

• ADD #764: support for automatic per_run_time_limit selection
• ADD #864: add the possibility to predict with cross-validation
• ADD #874: support to limit the disk space consumption
• MAINT #862: improved documentation and render examples in web page
• MAINT #869: removal of competition data manager support
• MAINT #870: memory improvements when building ensemble
• MAINT #882: memory improvements when performing ensemble selection
• FIX #701: scaling factors for metafeatures should not be learned using test data
• FIX #715: allow unlimited ML memory
• FIX #771: improved worst possible result calculation
• FIX #843: default value for SelectPercentileRegression
• FIX #852: clip probabilities within [0-1]
• FIX #854: improved tmp file naming
• FIX #863: SMAC exceptions also registered in log file
• FIX #876: allow Auto-sklearn model to be cloned
• FIX #879: allow 1-D binary predictions

Contributors

• Matthias Feurer
• Xiaodong DENG
• Francisco Rivera

Version 0.7.0

Version 0.7.0

• ADD #785: user control to reduce the hard drive memory required to store ensembles
• ADD #794: iterative fit for gradient boosting
• ADD #795: add successive halving evaluation strategy
• ADD #814: new sklearn.metrics.balanced_accuracy_score instead of custom metric
• ADD #815: new experimental evaluation mode called iterative_cv
• MAINT #774: move from scikit-learn 0.21.X to 0.22.X
• MAINT #791: move from smac 0.8 to 0.12
• MAINT #822: make autosklearn modules PEP8 compliant
• FIX #733: fix for n_jobs=-1
• FIX #739: remove unnecessary warning
• FIX ##769: fixed error in calculation of meta features
• FIX #778: support for python 3.8
• FIX #781: support for pandas 1.x

Contributors

• Andrew Nader
• Gui Miotto
• Julian Berman
• Katharina Eggensperger
• Matthias Feurer
• Maximilian Peters
• Rong-Inspur
• Valentin Geffrier
• Francisco Rivera

Version 0.6.0

Version 0.6.0

• MAINT: move from scikit-learn 0.19.X to 0.21.X
• MAINT #688: allow for pyrfr version 0.8.X
• FIX #680: Remove unnecessary print statement
• FIX #600: Remove unnecessary warning

Contributors

• Guilherme Miotto
• Matthias Feurer
• Jin Woo Ahn

Version 0.5.2

Version 0.5.2

• FIX #669: Correctly handle arguments to the AutoMLRegressor
• FIX #667: Auto-sklearn works with numpy 1.16.3 again.
• ADD #676: Allow brackets [ ] inside the temporary and output directory paths.
• ADD #424: (Experimental) scripts to reproduce the results from the original Auto-sklearn paper.

Contributors

• Jin Woo Ahn (@ahn1340)
• Herilalaina Rakotoarison (@herilalaina)
• Matthias Feurer (@mfeurer)
• yazanobeidi (@yazanobeidi)

Version 0.4.2

Version 0.4.2

• Fixes #538: Remove rounding errors when giving a training set fraction for holdout.
• Fixes #558: Ensemble script now uses less memory and the memory limit can be given to Auto-sklearn.
• Fixes #585: Auto-sklearn’s ensemble script produced wrong results when called directly (and not via one of Auto-sklearn’s estimator classes).
• Fixes an error in the ensemble script which made it non-deterministic.
• MAINT #569: Rename hyperparameter to have a different name than a scikit-learn hyperparameter with different meaning.
• MAINT #592: backwards compatible requirements.txt
• MAINT #588: Fix SMAC version to 0.8.0
• MAINT: remove dependency on the six package
• MAINT: upgrade to XGBoost 0.80

Contributors

• Taneli Mielikäinen (@tmielika)
• Matthias Feurer (@mfeurer)
• Diogo Bastos (@diogo-bastos)
• Zeyi Wen (@zeyiwen)
• Teresa Conceição (@teresaconc)
• Jin Woo Ahn (@ahn1340)

Version 0.4.1

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 of sklearn.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

Version 0.4.0

Merge pull request #495 from automl/development

Development

Version 0.3.0

Merge pull request #406 from automl/development

Development