- Future Releases
- Enhancements
- Added verbose parameter to load_fraud
560
- Added verbose parameter to load_fraud
- Fixes
- Changes
- Documentation Changes
- Testing Changes
- Matched install commands of check_latest_dependencies test and it's GitHub action
578
- Added Github app to auto assign PR author as assignee
477
- Matched install commands of check_latest_dependencies test and it's GitHub action
- v0.8.0 Apr. 1, 2020
- Enhancements
- Add normalization option and information to confusion matrix
484
- Add util function to drop rows with NaN values
487
- Renamed PipelineBase.name as PipelineBase.summary and redefined PipelineBase.name as class property
491
- Added access to parameters in Pipelines with PipelineBase.parameters (used to be return of PipelineBase.describe)
501
- Added fill_value parameter for SimpleImputer
509
- Added functionality to override component hyperparemeters and made pipelines take hyperparemeters from components
516
- Allow numpy.random.RandomState for random_state parameters
556
- Clarified how random seeds can be set for each component. Changed xgboost seed bounds
583
- Add normalization option and information to confusion matrix
- Fixes
- Removed unused dependency matplotlib, and move category_encoders to test reqs
572
- Removed unused dependency matplotlib, and move category_encoders to test reqs
- Changes
- Undo version cap in XGBoost placed in
402
and allowed all released of XGBoost407
- Support pandas 1.0.0
486
- Made all references to the logger static
503
- Refactored model_type parameter for components and pipelines to model_family
507
- Refactored problem_types for pipelines and components into supported_problem_types
515
- Moved pipelines/utils.save_pipeline and pipelines/utils.load_pipeline to PipelineBase.save and PipelineBase.load
526
- Limit number of categories encoded by OneHotEncoder
517
- Undo version cap in XGBoost placed in
- Documentation Changes
- Updated API reference to remove PipelinePlot and added moved PipelineBase plotting methods
483
- Add code style and github issue guides
463
512
- Updated API reference for to surface class variables for pipelines and components
537
- Fixed README documentation link
535
- Limit readthedocs formats to html and pdf, not epub
594
- Updated API reference to remove PipelinePlot and added moved PipelineBase plotting methods
- Testing Changes
- Added automated dependency check PR
482
,505
- Updated automated dependency check comment
497
- Have build_docs job use python executor, so that env vars are set properly
547
- Run windows unit tests on PRs
557
- Added automated dependency check PR
Warning
Breaking Changes
- AutoClassificationSearch and AutoRegressionSearch's model_types parameter has been refactored into allowed_model_families
- ModelTypes enum has been changed to ModelFamily
- Components and Pipelines now have a model_family field instead of model_type
- get_pipelines utility function now accepts model_families as an argument instead of model_types
- PipelineBase.name no longer returns structure of pipeline and has been replaced by PipelineBase.summary
- PipelineBase.problem_types and Estimator.problem_types has been renamed to supported_problem_types
- pipelines/utils.save_pipeline and pipelines/utils.load_pipeline moved to PipelineBase.save and PipelineBase.load
- 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
- Added emacs buffers to .gitignore
- Fixes
- Fixed ROC and confusion matrix plots not being calculated if user passed own additional_objectives
276
- Fixed ReadtheDocs FileNotFoundError exception for fraud dataset
439
- Fixed ROC and confusion matrix plots not being calculated if user passed own additional_objectives
- 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
- Refactored PipelineBase to take in parameter dictionary and moved pipeline metadata to class attribute
421
- Dropped support for Python 3.5
438
- Removed unused apply.py file
449
- Clean up requirements.txt to remove unused deps
451
- Support installation without all required dependencies
459
- Added n_estimators as a tunable parameter for XGBoost
- Documentation Changes
- Update release.md with instructions to release to internal license key
354
- Update release.md with instructions to release to internal license key
- 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
- Added tests for utils (and moved current utils to gen_utils)
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
- Added ability to create a plot of feature importances
- Fixes
- Lower botocore requirement
235
- Fixed decision_function calculation for FraudCost objective
254
- Fixed return value of Recall metrics
264
- Components return self on fit
289
- Lower botocore requirement
- 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
- Added Random and Grid Search Tuners
240
- Renamed automl classes to AutoRegressionSearch and AutoClassificationSearch
- Documentation Changes
- Adding class properties to API reference
244
- Fix and filter FutureWarnings from scikit-learn
249
,257
- Adding Linear Regression to API reference and cleaning up some Sphinx warnings
227
- Adding class properties to API reference
- Testing Changes
- Added support for testing on Windows with CircleCI
226
- Added support for doctests
233
- Added support for testing on Windows with CircleCI
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
- Enhancements
- Adding basic pipeline structure visualization
211
- Adding basic pipeline structure visualization
- Documentation Changes
- Added notebooks to build process
212
- Added notebooks to build process
- v0.5.1 Nov. 15, 2019
- Enhancements
- Added basic outlier detection guardrail
151
- Added basic ID column guardrail
135
- Added support for unlimited pipelines with a max_time limit
70
- Updated .readthedocs.yaml to successfully build
188
- Added basic outlier detection guardrail
- Fixes
- Removed MSLE from default additional objectives
203
- Fixed random_state passed in pipelines
204
- Fixed slow down in RFRegressor
206
- Removed MSLE from default additional objectives
- Changes
- Pulled information for describe_pipeline from pipeline's new describe method
190
- Refactored pipelines
108
- Removed guardrails from Auto(*)
202
,208
- Pulled information for describe_pipeline from pipeline's new describe method
- Documentation Changes
- Updated documentation to show max_time enhancements
189
- Updated release instructions for RTD
193
- Added notebooks to build process
212
- Added contributing instructions
213
- Added new content
222
- Updated documentation to show max_time enhancements
- 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
- Added basic one hot encoding
- Fixes
- Reordered describe_pipeline
94
- Added type check for model_type
109
- Fixed s units when setting string max_time
132
- Fix objectives not appearing in API documentation
150
- Reordered describe_pipeline
- Changes
- Reorganized tests
93
- Moved logging to its own module
119
- Show progress bar history
111
- Using cloudpickle instead of pickle to allow unloading of custom objectives
113
- Removed render.py
154
- Reorganized tests
- Documentation Changes
- Update release instructions
140
- Include additional_objectives parameter
124
- Added Changelog
136
- Update release instructions
- Testing Changes
- Code coverage
90
- Added CircleCI tests for other Python versions
104
- Added doc notebooks as tests
139
- Test metadata for CircleCI and 2 core parallelism
137
- Code coverage
- 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
- Added AutoML for classification and regressor using Autobase and Skopt
- Fixes
- Fixed feature selection in pipelines
13
- Made random_seed usage consistent
45
- Fixed feature selection in pipelines
- Documentation Changes
- Documentation Changes
- Added docstrings
6
- Created notebooks for docs
6
- Initialized readthedocs EvalML
6
- Added favicon
38
- Testing Changes
- Added testing for loading data
39
- Added testing for loading data
- v0.2.0 Aug. 13, 2019
- Enhancements
- Created fraud detection objective
4
- Created fraud detection objective
- v0.1.0 July. 31, 2019
- First Release
- Enhancements
- Added lead scoring objecitve
1
- Added basic classifier
1
- Added lead scoring objecitve
- Documentation Changes
- Initialized Sphinx for docs
1
- Initialized Sphinx for docs