:orphan:

..
    The following command allows to retrieve all commiters since a specified
    commit. From https://stackoverflow.com/questions/6482436/list-of-authors-in-git-since-a-given-commit
    git log 6cc8bb179fcb023d1c341cf33d2958a16a6935be.. --format="%aN <%aE>" --reverse | perl -e 'my %dedupe; while (<STDIN>) { print unless $dedupe{$_}++}'

========
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](https://github.com/scipy/scipy/issues/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 <https://github.com/automl/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 <https://github.com/automl/auto-sklearn/pull/510>`_
  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 <https://github.com/automl/auto-sklearn/issues/500>`_: fixes
  ensemble builder to correctly evaluate model score with any metrics.
  See this `PR <https://github.com/automl/auto-sklearn/pull/522>`_.
* Fixes `#482 <https://github.com/automl/auto-sklearn/issues/482>`_ and
  `#491 <https://github.com/automl/auto-sklearn/issues/491>`_: Users can now set up
  custom logger configuration by passing a dictionary created by a yaml file to
  ``logging_config``.
* Fixes `#566 <https://github.com/automl/auto-sklearn/issues/566>`_: ensembles are now sorted correctly.
* Fixes `#293 <https://github.com/automl/auto-sklearn/issues/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 <https://github.com/automl/auto-sklearn/issues/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
  <https://scikit-learn.org/stable/developers/develop.html#fitting>`_.
* Fixes the usage of the shrinkage parameter in LDA.
* Fixes `#410 <https://github.com/automl/auto-sklearn/issues/410>`_ and changes
  the SGD hyperparameters
* Fixes `#425 <https://github.com/automl/auto-sklearn/issues/425>`_ which
  caused the non-linear support vector machine to always crash on OSX.
* Implements `#149 <https://github.com/automl/auto-sklearn/issues/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 <https://github.com/automl/auto-sklearn/issues/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 <https://github.com/automl/auto-sklearn/issues/271>`_:
  XGBoost is available again, even configuring the new dropout functionality.
* New documentation section :ref:`inspect`.
* Fixes `#444 <https://github.com/automl/auto-sklearn/issues/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 <https://github.com/automl/auto-sklearn/issues/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 <https://github.com/automl/auto-sklearn/issues/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 <https://github.com/lsorber>`_.

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