Version 0.12.6

Version 0.12.6

• ADD #886: Provide new function which allows fitting only a single configuration.
• DOC #1070: Clarify example on how successive halving and Bayesian optimization play together.
• DOC #1112: Fix type.
• DOC #1122: Add Python 3 to the installation command for Ubuntu.
• FIX #1114: Fix a bug which made printing dummy models fail.
• FIX #1117: Fix a bug previously made memory_limit=None fail.
• FIX #1121: Fix an edge case which could decrease performance in Auto-sklearn 2.0 when using cross-validation with iterative fitting.
• FIX #1123: Fix a bug autosklearn.metrics.calculate_score for metrics/scores which need to be minimized where the function previously returned the loss and not the score.
• FIX #1115/#1124: Fix a bug which would prevent Auto-sklearn from computing meta-features in the multiprocessing case.

Contributors v0.12.6

• Francisco Rivera Valverde
• stock90975
• Lucas Nildaimon dos Santos Silva
• Matthias Feurer
• Rohit Agarwal

Version 0.12.5

Version 0.12.5

• MAINT: Remove Cython and numpy as installation requirements.

Contributors 0.12.5

• Matthias Feurer

Version 0.12.4

Version 0.12.4

• ADD #660: Enable scikit-learn's power transformation for input features.
• MAINT: Bump the pyrfr minimum dependency to 0.8.1 to automatically download wheels from pypi if possible.
• FIX #732: Add a missing size check into the GMEANS clustering used for the NeurIPS 2015 paper.
• FIX #1050: Add missing arguments to the AutoSklearn2Classifier signature.
• FIX #1072: Fixes a bug where the AutoSklearn2Classifier could not be created due to trying to cache to the wrong directory.

Contributors v0.12.4

• Matthias Feurer
• Francisco Rivera
• Maximilian Greil
• Pepe Berba

Version 0.12.3

Version 0.12.3

• FIX #1061: Fixes a bug where the model could not be printed in a jupyter notebook.
• FIX #1075: Fixes a bug where the ensemble builder would wrongly prune good models for loss functions (i.e. functions that need to be minimized such as logloss or mean_squared_error.
• FIX #1079: Fixes a bug where AutoMLClassifier.cv_results and AutoMLRegressor.cv_results could rank results in opposite order for loss functions (i.e. functions that need to be minimized such as logloss or mean_squared_error.
• FIX: Fixes a bug in offline meta-data generation that could lead to a deadlock.
• MAINT #1076: Uses the correct multiprocessing context for computing meta-features
• MAINT: Cleanup readme and main directory

Contributors v0.12.3

• Matthias Feurer
• ROHIT AGARWAL
• Francisco Rivera

v0.12.2: Merge pull request #1068 from automl/development

Version 0.12.2

• ADD #1045: New example demonstrating how to log multiple metrics during a run of Auto-sklearn.
• DOC #1052: Add links to mybinder
• DOC #1059: Improved the example on manually starting workers for Auto-sklearn.
• FIX #1046: Add the final result of the ensemble builder to the ensemble builder trajectory.
• MAINT: Two log outputs of level warning about metadata were turned reduced to the info loglevel as they are not actionable for the user.
• MAINT #1062: Use threads for local dask workers and forkserver to start subprocesses to reduce overhead.
• MAINT #1053: Remove the restriction to guard single-core Auto-sklearn by __main__ == "__name__" again.

Contributors v0.12.2

• Matthias Feurer
• ROHIT AGARWAL
• Francisco Rivera
• Katharina Eggensperger

Version 0.12.1

Version 0.12.1

• ADD: A new heuristic which gives a warning and subsamples the data if it is too large for the given memory_limit.
• ADD #1024: Tune scikit-learn's MLPClassifier and MLPRegressor.
• MAINT #1017: Improve the logging server introduced in release 0.12.0.
• MAINT #1024: Move to scikit-learn 0.24.X.
• MAINT #1038: Use new datasets for regression and classification and also update the metadata used for Auto-sklearn 1.0.
• MAINT #1040: Minor speed improvements in the ensemble selection algorithm.

Contributors v0.12.1

• Matthias Feurer
• Katharina Eggensperger
• Francisco Rivera

Version 0.12.1 Release Candidate 1

Version 0.12.1

• ADD: A new heuristic which gives a warning and subsamples the data if it is too large for the given memory_limit.
• ADD #1024: Tune scikit-learn's MLPClassifier and MLPRegressor.
• MAINT #1017: Improve the logging server introduced in release 0.12.0.
• MAINT #1024: Move to scikit-learn 0.24.X.
• MAINT #1038: Use new datasets for regression and classification and also update the metadata used for Auto-sklearn 1.0.
• MAINT #1040: Minor speed improvements in the ensemble selection algorithm.

Contributors v0.12.1

• Matthias Feurer
• Katharina Eggensperger
• Francisco Rivera

Version 0.12.0

Version 0.12.0

• BREAKING: Auto-sklearn must now be guarded by __name__ == "__main__" due to the use of the spawn multiprocessing context.
• ADD #1026: Adds improved meta-data for Auto-sklearn 2.0 which results in strong improved performance.
• MAINT #984 and #1008: Move to scikit-learn 0.23.X
• MAINT #1004: Move from travis-ci to github actions.
• MAINT 8b67af6: drop the requirement to the lockfile package.
• FIX #990: Fixes a bug that made Auto-sklearn fail if there are missing values in a pandas DataFrame.
• FIX #1007, #1012 and #1014: Log multiprocessing output via a new log server. Remove several potential deadlocks related to the joint use of multi-processing, multi-threading and logging.

Contributors v0.12.0

• Matthias Feurer
• ROHIT AGARWAL
• Francisco Rivera

Version 0.11.1

Version 0.11.1

• FIX #989: Fixes a bug where y was not passed to all data preprocessors which made 3rd party category encoders fail.
• FIX #1001: Fixes a bug which could make Auto-sklearn fail at random.
• MAINT #1000: Introduce a minimal version for dask.distributed.

Version 0.11.0

Version 0.11.0

• ADD #992: Move ensemble building from being a separate process to a job submitted to the dask cluster. This allows for better control of the memory used in multiprocessing settings. This change also removes the arguments ensemble_memory_limit and ml_memory_limit and replaces them by the single argument memory_limit.
• FIX #905: Make AutoSklearn2Classifier picklable.
• FIX #970: Fix a bug where Auto-sklearn would fail if categorical features are passed as a Pandas Dataframe.
• MAINT #772: Improve error message in case of dummy prediction failure.
• MAINT #948: Finally use Pandas >= 1.0.
• MAINT #973: Improve meta-data by running meta-data generation for more time and separately for important metrics.
• MAINT #997: Improve memory handling in the ensemble building process. This allows building ensembles for larger datasets.

Contributors v0.11.0

• Matthias Feurer
• Francisco Rivera
• Karl Leswing
• ROHIT AGARWAL