releases.rst

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Releases

Version 0.15.0

• ADD #1317, #1455, #1485, #1501, #1518, #1523: Initial support for multi-objective Auto-sklearn.
• ADD #1300, #1410, #1414, #1415, #1420, #1468, #1500: Intial support for text features Auto-sklearn. You can now pass in columns identified as "string" columns which will be tokenized using pure sklearn methods.
• ADD #1475: Support for passing X data to metrics, as required by [fairlearn](https://github.com/fairlearn/fairlearn) metrics
• ADD #1341, #1250: Expose interface to interact with how auto-sklearn performs dataset compression when required
• DOC #1304: This adds documentation for SMAC callbacks that can be used by Auto-sklearn.
• DOC #1476: Example on how to interupt autosklearn with a callback, implementing a very naive early stopping
• MAINT #1364, #1473: Improve import time of Auto-sklearn 2 by moving the construction of the selector model from import time to construction time.
• MAINT #1425: Update StopWatch to be context manager.
• MAINT #1454: Rename interal bool parameters categorical to feat_type to reflect the use of different feature types
• MAINT #1474: remove left-overs of a "public test set" from the code. This has no influence on any user-facing code.
• MAINT #1487: Replace deprecated of DataFrame.append
• MAINT #1504: Rename rval to return_value or run_value to remove ambiguity
• MAINT #1506: Increase the time given to meta-learning-related unit tests to decrease the amount of timeouts on github.
• MAINT #1527: Relax MLPRegressor unit tests precision.
• MAINT #1545: Add explicit lower bound subsample check in the train evaluator
• MAINT #1551: Fix issue with updated scipy skew see [here](scipy/scipy#16765).
• MAINT #1434: Refactor the ensemble building process
• MAINT #1464: Improve testing, with caching (#1464), modularity (#1417)
• MAINT #1358: Add tooling Mypy, Flake8, isort, black
• FIX #741: Disable hyperparameters for a special data modality if it is not present, for example disable one hot encoding if no categorical features are present.
• FIX #1365, #1369: Fix an issue with ensemble_size == 0.
• FIX #1374: Pass random state to all components of a pipeline.
• FIX #1432: Fixes an issue in which the AutoSklearnClassifier.leaderboard() or AutoSklearnRegressor.leaderboard() could fail to display results.
• FIX #1480: Properly terminate Auto-sklearn on an exception or a keyboard interrupt.
• FIX #1532: Removes exception printing at shutdown for latest dask versions. The printed exceptions did not impact performance at all and were only confusing as they suggested failures of Auto-sklearn.
• FIX #1547: Fixes a bug in Auto-sklearn 2 that could silently break it when passing in pandas DataFrames.
• FIX #1550: Fix recent bug when performing evaluations with pandas Y.

Contributors v0.15.0

• Matthias Feurer
• Eddie Bergman
• Katharina Eggensperger
• Sagar Kaushik
• partev
• Lukas Strack
• Basavasagar K Patil
• Eric Pedley
• Aseem Kannal
• SkBlaz

Version 0.14.7

• HOTFIX #1445: Locks ConfigSpace to <0.5.0 and smac to <1.3. Adds upper bounds on automl packages to help prevent further issues.

Contributors v0.14.7

• Eddie Bergman

Version 0.14.6

• HOTFIX #1407: Catches keyword arguments in SingleThreadedClient so they don't get passed to it's executing func.

Contributors v0.14.6

• Eddie Bergman

Version 0.14.5

• HOTFIX: Release PyPi package with automl_common included

Contributors v0.14.5

• Eddie Bergman

Version 0.14.4

• Fix #1356: SVR degree hyperparameter now only active with "poly" kernel.
• Add #1311: Black format checking (non-strict).
• Maint #1306: Run history is now saved every iteration
• Doc #1309: Updated the doc faqs to include many use cases and the manual for early introductions
• Doc #1322: Fix typo in contribution guide
• Maint #1326: Add isort checker (non-strict)
• Maint #1238, #1346, #1368, #1370: Update warnings in tests
• Maint #1325: Test workflow can now be manually triggered
• Maint #1332: Update docstring and typing of include and exclude params
• Add #1260: Support for Python 3.10
• Add #1318: First update to use the shared backend in a new submodule automl_common
• Fix #1339: Resolve dependancy issues with sphinx_toolbox
• Fix #1335: Fix issue where some regression algorithm gave incorrect output dimensions as raised in #1297
• Doc #1340: Update example for predefined splits
• Fix #1329: Fix random state not being passed to the ConfigurationSpace
• Maint #1348: Stop double triggering of github workflows
• Doc #1349: Rename OSX to macOS in docs
• Add #1321: Change show_models() to produce actual pipeline objects and not a str
• Maint #1361: Remove flaky dependency
• Maint #1366: Make SimpleClassificationPipeline tests more deterministic
• Maint #1367: Update test values for MLPRegressor with newer numpy

Contributors v0.14.4

• Eddie Bergman
• Matthias Feurer
• Katharina Eggensperger
• UserFindingSelf
• partev

Version 0.14.3

• HOTFIX #1356: Updates dask to dask.distributed >=2012.12.

Contributors v0.14.3

• Eddie Bergman

Version 0.14.2

• FIX #1290: Fixes a bug where it was not possible to extend Auto-sklearn and run it in parallel.

Contributors v0.14.2

• Matthias Feurer

Version 0.14.1

• FIX #1248: Allow for sparse y_test.
• FIX #1259: Fix an issue that could result in setup.py not working due to relative paths being chosen.
• MAINT #1261: Include a CITATION.cff file
• MAINT #1263: Make unit test deterministic.
• DOC #1269: Fix example on extending data preprocessing.
• DOC #1270: Remove >>> from code examples in the documentation.
• DOC #1271: Fix a typo in an example in the documentation.
• DOC #1282: Add a contribution guide.

Contributors v0.14.1

• Eddie Bergman
• Michael Becker
• Katharina Eggensperger

Version 0.14.0

• ADD #900: Make data preprocessing more configurable, for example allow to completely disable it.
• ADD #1128: Adds new functionality to retrieve data for an accuracy over time plot from Auto-sklearn without additional code.
• FIX #1149: Stops Auto-sklearn from printing weird warnings (Exception ignored in [...]) at shutdown.
• FIX #1169: Fixes a bug which made cross-validation and multi-output regression incompatible.
• FIX #1170: Make all preprocessing techniques deterministic.
• FIX #1190: Fixes a bug which could make predictive probabilities contain too few classes in case one class was only present a single time.
• FIX #1209: Pass random states to pipeline objects.
• FIX #1204: Add support for sparse data in Auto-sklearn 2.0.
• FIX #1210: Add support for sparse y labels.
• FIX #1245: Fixes a bug which could result in Auto-sklearn crashing in case a class was present only once.
• DOC #532,#1242: Simplify installation instructions.
• DOC #1144: Document installation via conda
• DOC #1195,#1201,#1214: Fix a few typos and links. Make some http links https links.
• DOC #1200: Fixes variable name in an example.
• DOC #1229: Improve code formatting in the documentation.
• DOC #1235: Improve docker startup command so it also work on Windows.
• MAINT #1198: Use latest Ubuntu LTS (20:04) for github actions.
• MAINT #1231: The command make linkcheck no longer builds the documentation, speeding up link-checking.
• MAINT #1233: Enable regression testing with 3 classification and 3 regression datasets on github actions.
• MAINT #1239: Increase the timeout for github actions to 60 minutes.

Contributors v0.14.0

• Pieter Gijsbers
• Taneli Mielikäinen
• Rohit Agarwal
• hnishi
• Francisco Rivera Valverde
• Eddie Bergman
• Satyam Jha
• Joel Jose
• Oli
• Matthias Feurer

Version 0.13.0

• ADD #1100: Provide access to the callbacks of SMAC.
• ADD #1185: New leaderboard functionality to visualize models
• FIX #1133: Refer to the correct attribute in an error message.
• FIX #1154: Allow running Auto-sklearn on a 32-bit system.
• MAINT #924: Instead of passing classes for the resampling strategy one has now to pass objects.
• MAINT #1108: Limit the number of threads used by numpy and/or scikit-learn via threadpoolctl.
• MAINT #1135: Simplify internal workflow of pandas handling. This results in pandas being passed directly passed to scikit-learn models instead of being internally converted into a numpy array. However, this should neither impact the behavior nor the performance of Auto-sklearn.
• MAINT #1157: Drop support for Python 3.6, enable support for Python 3.9.
• MAINT #1159: Remove the output directory argument to the classifier and regressor. Despite the name, the output directory was not used and was a leftover from participating in the AutoML challenges.
• MAINT #1187: Bump requires SMAC version to at least 0.14.
• DOC #1109: Add an FAQ.
• DOC #1126: Add new examples on how to use scikit-learn's inspect module.
• DOC #1136: Add a new example on how to perform multi-output regression.
• DOC #1152: Enable link checking when building the documentation.
• DOC #1158: New example on how to configure the logger for Auto-sklearn.
• DOC #1165: Improve the readme page.

Contributors v0.13.0

• Matthias Feurer
• Eddie Bergman
• bitsbuffer
• Francisco Rivera Valverde

Version 0.12.8

• MAINT #1183: Introduce an upper bound on the dask version to retain compatibility with SMAC3.

Contributors v0.12.8

• Eddie Bergman

Version 0.12.7

• ADD #1178: Reduce precision if dataset is too large for given memory limit.
• ADD #1179: Improve Auto-sklearn 2.0 meta-data by providing new meta-data for the metrics roc_auc and logloss.
• DOC: Fix reference to arXiv paper
• MAINT #1134,#1142,#1143: Improvements to the stale bot - the stale bot now marks issues labeled with feedback required as stale if there is nothing happening for 30 days. After another 7 days it then closes the issue.
• MAINT: Added a new issue template for questions.
• MAINT #1168: Upper-bound scipy to 1.6.3 as 1.7.0 is incompatible with SMAC.
• MAINT #1173: Update the license files to be recognized by github.

Contributors v0.12.7

• Francisco Rivera Valverde
• Matthias Feurer
• JJ Ben-Joseph
• Isaac Chung
• Katharina Eggensperger
• bitsbuffer
• Eddie Bergman
• olehb007

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

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

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 :ref:`inspect`.
• 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: :ref:`examples`
• 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