- Future Releases
- Enhancements
- Added accuracy as an standard objective
624
- Added verbose parameter to load_fraud
560
- Added Balanced Accuracy metric for binary, multiclass
612
661
- Added XGBoost regressor and XGBoost regression pipeline
666
- Added Accuracy metric for multiclass
672
- Added objective name in AutoBase.describe_pipeline
686
- Added accuracy as an standard objective
- Fixes
- Removed direct access to cls.component_graph
595
- Add testing files to .gitignore
625
- Remove circular dependencies from Makefile
637
- Add error case for normalize_confusion_matrix()
640
- Fixed XGBoostClassifier and XGBoostRegressor bug with feature names that contain [, ], or <
659
- Update make_pipeline_graph to not accidentally create empty file when testing if path is valid
649
- Fix pip installation warning about docsutils version, from boto dependency
664
- Removed zero division warning for F1/precision/recall metrics
671
- Fixed summary for pipelines without estimators
707
- Removed direct access to cls.component_graph
- Changes
- Updated default objective for binary/multiseries classification to log loss
613
- Created classification and regression pipeline subclasses and removed objective as an attribute of pipeline classes
405
- Changed the output of score to return one dictionary
429
- Created binary and multiclass objective subclasses
504
- Updated objectives API
445
- Removed call to get_plot_data from AutoML
615
- Set raise_error to default to True for AutoML classes
638
- Remove unnecessary "u" prefixes on some unicode strings
641
- Changed one-hot encoder to return uint8 dtypes instead of ints
653
- Pipeline _name field changed to custom_name
650
- Removed graphs.py and moved methods into PipelineBase
657
,665
- Remove s3fs as a dev dependency
664
- Changed requirements-parser to be a core dependency
673
- Replace supported_problem_types field on pipelines with problem_type attribute on base classes
678
- Changed AutoML to only show best results for a given pipeline template in rankings, added full_rankings property to show all
682
- Update ModelFamily values: don't list xgboost/catboost as classifiers now that we have regression pipelines for them
677
- Changed AutoML's describe_pipeline to get problem type from pipeline instead
685
- Standardize import_or_raise error messages
683
- Updated argument order of objectives to align with sklearn's
698
- Renamed pipeline.feature_importance_graph to pipeline.graph_feature_importances
700
- Moved ROC and confusion matrix methods to evalml.pipelines.plot_utils
704
- Renamed MultiClassificationObjective to MulticlassClassificationObjective, to align with pipeline naming scheme
715
- Updated default objective for binary/multiseries classification to log loss
- Documentation Changes
- Fixed some sphinx warnings
593
- Fixed docstring for AutoClassificationSearch with correct command
599
- Limit readthedocs formats to pdf, not htmlzip and epub
594
600
- Clean up objectives API documentation
605
- Fixed function on Exploring search results page
604
- Update release process doc
567
- AutoClassificationSearch and AutoRegressionSearch show inherited methods in API reference
651
- Fixed improperly formatted code in breaking changes for changelog
655
- Added configuration to treat Sphinx warnings as errors
660
- Removed separate plotting section for pipelines in API reference
657
,665
- Have leads example notebook load S3 files using https, so we can delete s3fs dev dependency
664
- Categorized components in API reference and added descriptions for each category
663
- Fixed Sphinx warnings about BalancedAccuracy objective
669
- Updated API reference to include missing components and clean up pipeline docstrings
689
- Reorganize API ref, and clarify pipeline sub-titles
688
- Add and update preprocessing utils in API reference
687
- Added inheritance diagrams to API reference
695
- Documented which default objective AutoML optimizes for
699
- Create seperate install page
701
- Include more utils in API ref, like import_or_raise
704
- Add more color to pipeline documentation
705
- Fixed some sphinx warnings
- 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
- Removed unneeded conda installation of xgboost in windows checkin tests
618
- Update graph tests to always use tmpfile dir
649
- Changelog checkin test workaround for release PRs: If 'future release' section is empty of PR refs, pass check
658
- Matched install commands of check_latest_dependencies test and it's GitHub action
Warning
Breaking Changes
- Pipelines will now no longer take an objective parameter during instantiation, and will no longer have an objective attribute.
fit()
andpredict()
now use an optionalobjective
parameter, which is only used in binary classification pipelines to fit for a specific objective.score()
will now use a requiredobjectives
parameter that is used to determine all the objectives to score on. This differs from the previous behavior, where the pipeline's objective was scored on regardless.score()
will now return one dictionary of all objective scores.ROC
andConfusionMatrix
plot methods viaAuto(*).plot
have been removed by615
and are replaced byroc_curve
andconfusion_matrix
in evamlm.pipelines.plot_utils in :pr:`704normalize_confusion_matrix
has been moved toevalml.pipelines.plot_utils
704
- Pipelines
_name
field changed tocustom_name
- Pipelines
supported_problem_types
field is removed because it is no longer necessary678
- Updated argument order of objectives' objective_function to align with sklearn
698
- pipeline.feature_importance_graph has been renamed to pipeline.graph_feature_importances in
700
- Removed unsupported
MSLE
objective704
- 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 hyperparameters and made pipelines take hyperparemeters from components
516
- Allow numpy.random.RandomState for random_state parameters
556
- 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
- Unhid PR references in changelog
656
- 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
- Added simple test to make sure OneHotEncoder's top_n works with large number of categories
552
- Run windows unit tests on PRs
557
- Added automated dependency check PR
Warning
Breaking Changes
AutoClassificationSearch
andAutoRegressionSearch
'smodel_types
parameter has been refactored intoallowed_model_families
ModelTypes
enum has been changed toModelFamily
- Components and Pipelines now have a
model_family
field instead ofmodel_type
get_pipelines
utility function now acceptsmodel_families
as an argument instead ofmodel_types
PipelineBase.name
no longer returns structure of pipeline and has been replaced byPipelineBase.summary
PipelineBase.problem_types
andEstimator.problem_types
has been renamed tosupported_problem_types
pipelines/utils.save_pipeline
andpipelines/utils.load_pipeline
moved toPipelineBase.save
andPipelineBase.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 utility function to show system and environment information
300
- 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 ofpipeline_id
.- Pipelines now require an estimator as the last component in
component_list
. Slicing pipelines now throws anNotImplementedError
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