Changelog¶
- Future Releases
Enhancements
Fixes
Changes
Documentation Changes
Testing Changes
- v0.7.0 Mar. 9, 2020
- Enhancements
Added emacs buffers to .gitignore #350
Add CatBoost (gradient-boosted trees) classification and regression components and pipelines #247
Added Tuner abstract base class #351
Added n_jobs as parameter for AutoClassificationSearch and AutoRegressionSearch #403
Changed colors of confusion matrix to shades of blue and updated axis order to match scikit-learn’s #426
Added PipelineBase graph and feature_importance_graph methods, moved from previous location #423
Added support for python 3.8 #462
- Changes
Added n_estimators as a tunable parameter for XGBoost #307
Remove unused parameter ObjectiveBase.fit_needs_proba #320
Remove extraneous parameter component_type from all components #361
Remove unused rankings.csv file #397
Downloaded demo and test datasets so unit tests can run offline #408
Remove _needs_fitting attribute from Components #398
Changed plot.feature_importance to show only non-zero feature importances by default, added optional parameter to show all #413
Dropped support for Python 3.5 #438
Removed unused apply.py file #449
Clean up requirements.txt to remove unused deps #451
- Documentation Changes
Update release.md with instructions to release to internal license key #354
- Testing Changes
Added tests for utils (and moved current utils to gen_utils) #297
Moved XGBoost install into it’s own separate step on Windows using Conda #313
Rewind pandas version to before 1.0.0, to diagnose test failures for that version #325
Added dependency update checkin test #324
Rewind XGBoost version to before 1.0.0 to diagnose test failures for that version #402
Update dependency check to use a whitelist #417
Update unit test jobs to not install dev deps #455
Warning
Breaking Changes
Python 3.5 will not be actively supported.
- v0.6.0 Dec. 16, 2019
- Enhancements
Added ability to create a plot of feature importances #133
Add early stopping to AutoML using patience and tolerance parameters #241
Added ROC and confusion matrix metrics and plot for classification problems and introduce PipelineSearchPlots class #242
Enhanced AutoML results with search order #260
- Changes
Renamed automl classes to AutoRegressionSearch and AutoClassificationSearch #287
Updating demo datasets to retain column names #223
Moving pipeline visualization to PipelinePlots class #228
Standarizing inputs as pd.Dataframe / pd.Series #130
Enforcing that pipelines must have an estimator as last component #277
Added ipywidgets as a dependency in requirements.txt #278
Warning
Breaking Changes
The
fit()
method forAutoClassifier
andAutoRegressor
has been renamed tosearch()
.AutoClassifier
has been renamed toAutoClassificationSearch
AutoRegressor
has been renamed toAutoRegressionSearch
AutoClassificationSearch.results
andAutoRegressionSearch.results
now is a dictionary withpipeline_results
andsearch_order
keys.pipeline_results
can be used to access a dictionary that is identical to the old.results
dictionary. Whereas,``search_order`` returns a list of the search order in terms of pipeline id.Pipelines now require an estimator as the last component in component_list. Slicing pipelines now throws an NotImplementedError to avoid returning Pipelines without an estimator.
- v0.5.2 Nov. 18, 2019
- v0.5.1 Nov. 15, 2019
- v0.5.0 Oct. 29, 2019
- Enhancements
Added basic one hot encoding #73
Use enums for model_type #110
Support for splitting regression datasets #112
Auto-infer multiclass classification #99
Added support for other units in max_time #125
Detect highly null columns #121
Added additional regression objectives #100
Show an interactive iteration vs. score plot when using fit() #134
- v0.4.1 Sep. 16, 2019
- Enhancements
Added AutoML for classification and regressor using Autobase and Skopt #7 #9
Implemented standard classification and regression metrics #7
Added logistic regression, random forest, and XGBoost pipelines #7
Implemented support for custom objectives #15
Feature importance for pipelines #18
Serialization for pipelines #19
Allow fitting on objectives for optimal threshold #27
Added detect label leakage #31
Implemented callbacks #42
Allow for multiclass classification #21
Added support for additional objectives #79
- Testing Changes
Added testing for loading data #39
- v0.2.0 Aug. 13, 2019
- Enhancements
Created fraud detection objective #4
- v0.1.0 July. 31, 2019