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

  • MAINT: Remove Cython and numpy as installation requirements.

Contributors v0.12.5

  • Matthias Feurer

Releases

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

  • 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

Releases

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

  • 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

  • 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

  • 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.

Contributors v0.11.1

  • Matthias Feurer

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.

  • 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

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 0.9.0

  • ADD #157,#889: Improve handling of pandas dataframes, including the possibility to use pandas’ categorical column type.

  • ADD #375: New SelectRates feature preprocessing component for regression.

  • ADD #891: Improve the robustness of Auto-sklearn by using the single best model if no ensemble is found.

  • ADD #902: Track performance of the ensemble over time.

  • ADD #914: Add an example on using pandas dataframes as input to Auto-sklearn.

  • ADD #919: Add an example for multilabel classification.

  • MAINT #909: Fix broken links in the documentation.

  • MAINT #907,#911: Add initial support for mypy.

  • MAINT #881,#927: Automatically build docker images on pushes to the master and development branch and also push them to dockerhub and the github docker registry.

  • MAINT #918: Remove old dependencies from requirements.txt.

  • MAINT #931: Add information about the host system and installed packages to the log file.

  • MAINT #933: Reduce the number of warnings raised when building the documentation by sphinx.

  • MAINT #936: Completely restructure the examples section.

  • FIX #558: Provide better error message when the ensemble process fails due to a memory issue.

  • FIX #901: Allow custom resampling strategies again (was broken due to an upgrade of SMAC).

  • FIX #916: Fixes a bug where the data preprocessing configurations were ignored.

  • FIX #925: make internal data preprocessing objects clonable.

Contributors v0.9.0

  • Francisco Rivera

  • Matthias Feurer

  • felixleungsc

  • Vladislav Skripniuk

Version 0.8

  • ADD #803: multi-output regression

  • ADD #893: new Auto-sklearn mode Auto-sklearn 2.0

Contributors v0.8.0

  • Chu-Cheng Fu

  • Matthias Feurer

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 v0.7.1

  • Matthias Feurer

  • Xiaodong DENG

  • Francisco Rivera

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 v0.7.0

  • Andrew Nader

  • Gui Miotto

  • Julian Berman

  • Katharina Eggensperger

  • Matthias Feurer

  • Maximilian Peters

  • Rong-Inspur

  • Valentin Geffrier

  • Francisco Rivera

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 v0.6.0

  • Guilherme Miotto

  • Matthias Feurer

  • Jin Woo Ahn

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 v0.5.2

  • Jin Woo Ahn

  • Herilalaina Rakotoarison

  • Matthias Feurer

  • yazanobeidi

Version 0.5.1

  • ADD #650: Auto-sklearn will immediately stop if prediction using scikit-learn’s dummy predictor fail.

  • ADD #537: Auto-sklearn will no longer start for time limits less than 30 seconds.

  • FIX #655: Fixes an issue where predictions using models from parallel Auto-sklearn runs could be wrong.

  • FIX #648: Fixes an issue with custom meta-data directories.

  • FIX #626: Fixes an issue where losses were not minimized, but maximized.

  • MAINT #646: Do no longer restrict the numpy version to be less than 1.14.5.

Contributors v0.5.1

  • Jin Woo Ahn

  • Taneli Mielikäinen

  • Matthias Feurer

  • jianswang

Version 0.5.0

  • ADD #593: Auto-sklearn supports the n_jobs argument for parallel computing on a single machine.

  • DOC #618: Added links to several system requirements.

  • Fixes #611: Improved installation from pip.

  • TEST #614: Test installation with clean Ubuntu on travis-ci.

  • MAINT: Fixed broken link and typo in the documentation.

Contributors v0.5.0

  • Mohd Shahril

  • Adrian

  • Matthias Feurer

  • Jirka Borovec

  • Pradeep Reddy Raamana

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 v0.4.2

  • Taneli Mielikäinen

  • Matthias Feurer

  • Diogo Bastos

  • Zeyi Wen

  • Teresa Conceição

  • Jin Woo Ahn

Version 0.4.1

  • Added documentation on how to extend Auto-sklearn with 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 this PR.

  • 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 v0.4.1

  • Matthias Feurer

  • Manuel Streuhofer

  • Taneli Mielikäinen

  • Katharina Eggensperger

  • Jin Woo Ahn

Version 0.4.0

  • Fixes #409: fixes predict_proba to no longer raise an AttributeError.

  • Improved documentation of the parallel example.

  • Classifiers are now tested to be idempotent as required by scikit-learn.

  • Fixes the usage of the shrinkage parameter in LDA.

  • Fixes #410 and changes the SGD hyperparameters

  • Fixes #425 which caused the non-linear support vector machine to always crash on OSX.

  • Implements #149: it is now possible to pass a custom cross-validation split following scikit-learn’s model_selection module.

  • It is now possible to decide whether or not to shuffle the data in Auto-sklearn by passing a bool shuffle in the dictionary of resampling_strategy_arguments.

  • Added functionality to track the test performance over time.

  • Re-factored the ensemble building to be faster, read less data from the hard drive and perform random tie breaking in case of equally well-performing models.

  • Implements #438: To be consistent with the output of SMAC (which minimizes the loss of a target function), the output of the ensemble builder is now also the output of a minimization problem.

  • Implements #271: XGBoost is available again, even configuring the new dropout functionality.

  • New documentation section inspecting the results.

  • Fixes #444: Auto-sklearn now only loads models for refit which are actually relevant for the ensemble.

  • Adds an operating system check at import and installation time to make sure to not accidentaly run on a Windows machine.

  • New examples gallery using sphinx gallery: http://automl.github.io/auto-sklearn/stable/examples/index.html

  • Safeguard Auto-sklearn against deleting directories it did not create (Issue #317.

Contributors v0.4.0

  • Matthias Feurer

  • kaa

  • Josh Mabry

  • Katharina Eggensperger

  • Vladimir Glazachev

  • Jesper van Engelen

  • Jin Woo Ahn

  • Enrico Testa

  • Marius Lindauer

  • Yassine Morakakam

Version 0.3.0

  • Upgrade to scikit-learn 0.19.1.

  • Do not use the DummyClassifier or DummyRegressor as part of an ensemble. Fixes #140.

  • Fixes #295 by loading the data in the subprocess instead of the main process.

  • Fixes #326: refitting could result in a type error. This is now fixed by better type checking in the classification components.

  • Updated search space for RandomForestClassifier, ExtraTreesClassifier and GradientBoostingClassifier (fixes #358).

  • Removal of constant features is now a part of the pipeline.

  • Allow passing an SMBO object into the AutoSklearnClassifier and AutoSklearnRegressor.

Contributors v0.3.0

  • Matthias Feurer

  • Jesper van Engelen

Version 0.2.1

  • Allows the usage of scikit-learn 0.18.2.

  • Upgrade to latest SMAC version (0.6.0) and latest random forest version (0.6.1).

  • Added a Dockerfile.

  • Added the possibility to change the size of the holdout set when using holdout resampling strategy.

  • Fixed a bug in QDA’s hyperparameters.

  • Typo fixes in print statements.

  • New method to retrieve the models used in the final ensemble.

Contributors v0.2.1

  • Matthias Feurer

  • Katharina Eggensperger

  • Felix Leung

  • caoyi0905

  • Young Ryul Bae

  • Vicente Alencar

  • Lukas Großberger

Version 0.2.0

  • auto-sklearn supports custom metrics and all metrics included in scikit-learn. Different metrics can now be passed to the fit()-method estimator objects, for example AutoSklearnClassifier.fit(metric='roc_auc').

  • Upgrade to scikit-learn 0.18.1.

  • Drop XGBoost as the latest release (0.6a2) does not work when spawned by the pyninsher.

  • auto-sklearn can use multiprocessing in calls to predict() and predict_proba. By Laurent Sorber.

Contributors v0.2.0

  • Matthias Feurer

  • Katharina Eggensperger

  • Laurent Sorber

  • Rafael Calsaverini

Version 0.1.x

There are no release notes for auto-sklearn prior to version 0.2.0.

Contributors v0.1.x

  • Matthias Feurer

  • Katharina Eggensperger

  • Aaron Klein

  • Jost Tobias Springenberg

  • Anatolii Domashnev

  • Stefan Falkner

  • Alexander Sapronov

  • Manuel Blum

  • Diego Kobylkin

  • Jaidev Deshpande

  • Jongheon Jeong

  • Hector Mendoza

  • Timothy J Laurent

  • Marius Lindauer

  • _329_

  • Iver Jordal