auto-sklearn - Version 0.15.0

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 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 returnvalue or runvalue 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.

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.

- Python
Published by aron-bram over 3 years ago

auto-sklearn - v0.14.7

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

Note, this release was generated later but has been on PyPI for a while

- Python
Published by eddiebergman almost 4 years ago

auto-sklearn - 0.14.6

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

- Python
Published by eddiebergman over 4 years ago

auto-sklearn - v0.14.5

- Python
Published by eddiebergman over 4 years ago

auto-sklearn - v0.14.4

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

- Python
Published by eddiebergman over 4 years ago

auto-sklearn - v0.14.3

- Python
Published by eddiebergman over 4 years ago

auto-sklearn - Version 0.14.1

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

Edward Bergman
Michael Becker
Katharina Eggensperger

- Python
Published by mfeurer over 4 years ago

auto-sklearn - Version 0.14

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

- Python
Published by mfeurer over 4 years ago

auto-sklearn - Version 0.13

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 buiding 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

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

- Python
Published by mfeurer almost 5 years ago

auto-sklearn - Version 0.12.7

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

- Python
Published by mfeurer almost 5 years ago

auto-sklearn - 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

- Python
Published by mfeurer about 5 years ago

auto-sklearn - 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

- Python
Published by mfeurer about 5 years ago

auto-sklearn - Version 0.12.5

Version 0.12.5

MAINT: Remove Cython and numpy as installation requirements.

Contributors 0.12.5

Matthias Feurer

- Python
Published by mfeurer about 5 years ago

auto-sklearn - 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

- Python
Published by mfeurer about 5 years ago

auto-sklearn - 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