v0.12.0.dev1

Publishing a new package to TestPyPi that has unit tests included.

v0.12.0

v0.12.0 Aug. 3, 2020

Enhancements

• Added string and categorical targets support for binary and multiclass pipelines and check for numeric targets for DetectLabelLeakage data check #932
• Added clear exception for regression pipelines if target datatype is string or categorical #960
• Added target column names and class labels in predict and predict_proba output for pipelines #951
• Added _compute_shap_values and normalize_values to pipelines/explanations module #958
• Added explain_prediction feature which explains single predictions with SHAP #974
• Added Imputer to allow different imputation strategies for numerical and categorical dtypes #991
• Added support for configuring logfile path using env var, and don't create logger if there are filesystem errors #975
• Updated catboost estimators' default parameters and automl hyperparameter ranges to speed up fit time #998

Fixes

• Fixed ReadtheDocs warning failure regarding embedded gif #943
• Removed incorrect parameter passed to pipeline classes in _add_baseline_pipelines #941
• Added universal error for calling predict, predict_proba, transform, and feature_importances before fitting #969, #994
• Made TextFeaturizer component and pip dependencies featuretools and nlp_primitives optional #976
• Updated imputation strategy in automl to no longer limit impute strategy to most_frequent for all features if there are any categorical columns #991
• Fixed UnboundLocalError forcv_pipeline when automl search errors #996
• Fixed Imputer to reset dataframe index to preserve behavior expected from SimpleImputer #1009

Changes

• Moved get_estimators to evalml.pipelines.components.utils #934
• Modified Pipelines to raise PipelineScoreError when they encounter an error during scoring #936
• Moved evalml.model_families.list_model_families to evalml.pipelines.components.allowed_model_families #959
• Renamed DateTimeFeaturization to DateTimeFeaturizer #977

Documentation Changes

• Update README.md #963
• Reworded message when errors are returned from data checks in search #982
• Added section on understanding model predictions with explain_prediction to User Guide #981
• Added a section to the user guide and api reference about how XGBoost and CatBoost are not fully supported. #992
• Added custom components section in user guide #993
• Update FAQ section formatting #997
• Update release process documentation #1003

Testing Changes

• Moved predict_proba and predict tests regarding string / categorical targets to test_pipelines.py #972
• Fix dependency update bot by updating python version to 3.7 to avoid frequent github version updates #1002

Breaking Changes

• get_estimators has been moved to evalml.pipelines.components.utils (previously was under evalml.pipelines.utils) #934
• Removed the raise_errors flag in AutoML search. All errors during pipeline evaluation will be caught and logged. #936
• evalml.model_families.list_model_families has been moved to evalml.pipelines.components.allowed_model_families #959
• TextFeaturizer: the featuretools and nlp_primitives packages must be installed after installing evalml in order to use this component #976
• Renamed DateTimeFeaturization to DateTimeFeaturizer #977

v0.11.2

v0.11.2 July 16, 2020

Enhancements

• Added NoVarianceDataCheck to DefaultDataChecks #893
• Added text processing and featurization component TextFeaturizer #913, #924
• Added additional checks to InvalidTargetDataCheck to handle invalid target data types #929

Fixes

• Makes automl results a read-only property #919

Changes

• Deleted static pipelines and refactored tests involving static pipelines, removed all_pipelines() and get_pipelines() #904
• Moved list_model_families to evalml.model_family.utils #903
• Updated all_pipelines, all_estimators, all_components to use the same mechanism for dynamically generating their elements #898
• Rename master branch to main #918
• Add pypi release github action #923
• Updated AutoMLSearch.search stdout output and logging and removed tqdm progress bar #921
• Moved automl config checks previously in search() to init #933

Documentation Changes

• Reorganized and rewrote documentation #937
• Updated to use pydata sphinx theme #937

Testing Changes

• Cleaned up fixture names and usages in tests #895

Breaking Changes

• list_model_families has been moved to evalml.model_family.utils (previously was under evalml.pipelines.utils) #903
• Static pipeline definitions have been removed, but similar pipelines can still be constructed via creating an instance of PipelineBase #904
• all_pipelines() and get_pipelines() utility methods have been removed #904

v0.11.dev1 July 10, 2020

A development release to check pypi github action deployment to test.pypi.org.

v0.11.0

v0.11.0 June 30, 2020

Enhancements

• Added multiclass support for ROC curve graphing #832
• Added preprocessing component to drop features whose percentage of NaN values exceeds a specified threshold #834
• Added data check to check for problematic target labels #814
• Added PerColumnImputer that allows imputation strategies per column #824
• Added transformer to drop specific columns #827
• Added support for categories, handle_error, and drop parameters in OneHotEncoder #830 #897
• Added preprocessing component to handle DateTime columns featurization #838
• Added ability to clone pipelines and components #842
• Define getter method for component parameters #847
• Added utility methods to calculate and graph permutation importances #860, #880
• Added new utility functions necessary for generating dynamic preprocessing pipelines #852
• Added kwargs to all components #863
• Updated AutoSearchBase to use dynamically generated preprocessing pipelines #870
• Added SelectColumns transformer #873
• Added ability to evaluate additional pipelines for automl search #874
• Added default_parameters class property to components and pipelines #879
• Added better support for disabling data checks in automl search #892
• Added ability to save and load AutoML objects to file #888
• Updated AutoSearchBase.get_pipelines to return an untrained pipeline instance #876
• Saved learned binary classification thresholds in automl results cv data dict #876

Fixes

• Fixed bug where SimpleImputer cannot handle dropped columns #846
• Fixed bug where PerColumnImputer cannot handle dropped columns #855
• Enforce requirement that builtin components save all inputted values in their parameters dict #847
• Don't list base classes in all_components output #847
• Standardize all components to output pandas data structures, and accept either pandas or numpy #853
• Fixed rankings and full_rankings error when search has not been run #894

Changes

• Update all_pipelines and all_components to try initializing pipelines/components, and on failure exclude them #849
• Refactor handle_components to handle_components_class, standardize to ComponentBase subclass instead of instance #850
• Refactor "blacklist"/"whitelist" to "allow"/"exclude" lists #854
• Replaced AutoClassificationSearch and AutoRegressionSearch with AutoMLSearch #871
• Renamed feature_importances and permutation_importances methods to use singular names (feature_importance and permutation_importance) #883
• Updated automl default data splitter to train/validation split for large datasets #877
• Added open source license, update some repo metadata #887

Documentation Changes

• Fix some typos and update the EvalML logo #872

Testing Changes

• Update the changelog check job to expect the new branching pattern for the deps update bot #836
• Check that all components output pandas datastructures, and can accept either pandas or numpy #853
• Replaced AutoClassificationSearch and AutoRegressionSearch with AutoMLSearch #871

Breaking Changes

• Pipelines' static component_graph field must contain either ComponentBase subclasses or str, instead of ComponentBase subclass instances #850
• Rename handle_component to handle_component_class. Now standardizes to ComponentBase subclasses instead of ComponentBase subclass instances #850
• Renamed automl's cv argument to data_split #877
• Pipelines' and classifiers' feature_importances is renamed feature_importance, graph_feature_importances is renamed graph_feature_importance #883
• Passing data_checks=None to automl search will not perform any data checks as opposed to default checks. #892
• Pipelines to search for in AutoML are now determined automatically, rather than using the statically - defined pipeline classes. #870
• Updated AutoSearchBase.get_pipelines to return an untrained pipeline instance, instead of one which happened to be trained on the final cross - validation fold #876

v0.10.0

v0.10.0 May 29, 2020

Enhancements

• Added baseline models for classification and regression, add functionality to calculate baseline models before searching in AutoML #746
• Port over highly-null guardrail as a data check and define DefaultDataChecks and DisableDataChecks classes #745
• Update Tuner classes to work directly with pipeline parameters dicts instead of flat parameter lists #779
• Add Elastic Net as a pipeline option #812
• Added new Pipeline option ExtraTrees #790
• Added precicion-recall curve metrics and plot for binary classification problems in evalml.pipeline.graph_utils #794

Fixes

• Update pipeline score to return nan score for any objective which throws an exception during scoring #787
• Fixed bug introduced in #787 where binary classification metrics requiring predicted probabilities error in scoring #798
• CatBoost and XGBoost classifiers and regressors can no longer have a learning rate of 0 #795

Changes

• Cleanup pipeline score code, and cleanup codecov #711
• Remove pass for abstract methods for codecov #730
• Added str for AutoSearch object #675
• Add util methods to graph ROC and confusion matrix #720
• Refactor AutoBase to AutoSearchBase #758
• Updated AutoBase with data_checks parameter, removed previous detect_label_leakage parameter, and added functionality to run data checks before search in AutoML #765
• Updated our logger to use Python's logging utils #763
• Refactor most of AutoSearchBase._do_iteration impl into AutoSearchBase._evaluate #762
• Port over all guardrails to use the new DataCheck API #789
• Expanded import_or_raise to catch all exceptions #759
• Adds RMSE, MSLE, RMSLE as standard metrics #788
• Don't allow Recall to be used as an objective for AutoML #784
• Removed feature selection from pipelines #819

Documentation Changes

• Add instructions to freeze master on release.md #726
• Update release instructions with more details #727 #733
• Add objective base classes to API reference #736
• Fix components API to match other modules #747

Testing Changes

• Delete codecov yml, use codecov.io's default #732
• Added unit tests for fraud cost, lead scoring, and standard metric objectives #741
• Update codecov client #782
• Updated AutoBase str test to include no parameters case #783
• Added unit tests for ExtraTrees pipeline #790
• If codecov fails to upload, fail build #810
• Updated Python version of dependency action #816
• Update the dependency update bot to use a suffix when creating branches #817

Breaking Changes

• The detect_label_leakage parameter for AutoML classes has been removed and replaced by a data_checks parameter #765
• Moved ROC and confusion matrix methods from evalml.pipeline.plot_utils to evalml.pipeline.graph_utils #720
• Tuner classes require a pipeline hyperparameter range dict as an init arg instead of a space definition #779
• Tuner.propose and Tuner.add work directly with pipeline parameters dicts instead of flat parameter lists #779
• PipelineBase.hyperparameters and custom_hyperparameters use pipeline parameters dict format instead of being represented as a flat list #779
• All guardrail functions previously under evalml.guardrails.utils will be removed and replaced by data checks #789
• Recall disallowed as an objective for AutoML #784

v0.9.0

v0.9.0 Apr. 27, 2020

Enhancements

• Added accuracy as an standard objective :pr:624
• Added verbose parameter to load_fraud :pr:560
• Added Balanced Accuracy metric for binary, multiclass :pr:612 :pr:661
• Added XGBoost regressor and XGBoost regression pipeline :pr:666
• Added Accuracy metric for multiclass :pr:672
• Added objective name in AutoBase.describe_pipeline :pr:686

Fixes

• Removed direct access to cls.component_graph :pr:595
• Add testing files to .gitignore :pr:625
• Remove circular dependencies from Makefile :pr:637
• Add error case for normalize_confusion_matrix() :pr:640
• Fixed XGBoostClassifier and XGBoostRegressor bug with feature names that contain [, ], or < :pr:659
• Update make_pipeline_graph to not accidentally create empty file when testing if path is valid :pr:649
• Fix pip installation warning about docsutils version, from boto dependency :pr:664
• Removed zero division warning for F1/precision/recall metrics :pr:671
• Fixed summary for pipelines without estimators :pr:707

Changes

• Updated default objective for binary/multiseries classification to log loss :pr:613
• Created classification and regression pipeline subclasses and removed objective as an attribute of pipeline classes :pr:405
• Changed the output of score to return one dictionary :pr:429
• Created binary and multiclass objective subclasses :pr:504
• Updated objectives API :pr:445
• Removed call to get_plot_data from AutoML :pr:615
• Set raise_error to default to True for AutoML classes :pr:638
• Remove unnecessary "u" prefixes on some unicode strings :pr:641
• Changed one-hot encoder to return uint8 dtypes instead of ints :pr:653
• Pipeline _name field changed to custom_name :pr:650
• Removed graphs.py and moved methods into PipelineBase :pr:657, :pr:665
• Remove s3fs as a dev dependency :pr:664
• Changed requirements-parser to be a core dependency :pr:673
• Replace supported_problem_types field on pipelines with problem_type attribute on base classes :pr:678
• Changed AutoML to only show best results for a given pipeline template in rankings, added full_rankings property to show all :pr:682
• Update ModelFamily values: don't list xgboost/catboost as classifiers now that we have regression pipelines for them :pr:677
• Changed AutoML's describe_pipeline to get problem type from pipeline instead :pr:685
• Standardize import_or_raise error messages :pr:683
• Updated argument order of objectives to align with sklearn's :pr:698
• Renamed pipeline.feature_importance_graph to pipeline.graph_feature_importances :pr:700
• Moved ROC and confusion matrix methods to evalml.pipelines.plot_utils :pr:704
• Renamed MultiClassificationObjective to MulticlassClassificationObjective, to align with pipeline naming scheme :pr:715

Documentation Changes

• Fixed some sphinx warnings :pr:593
• Fixed docstring for AutoClassificationSearch with correct command :pr:599
• Limit readthedocs formats to pdf, not htmlzip and epub :pr:594 :pr:600
• Clean up objectives API documentation :pr:605
• Fixed function on Exploring search results page :pr:604
• Update release process doc :pr:567
• AutoClassificationSearch and AutoRegressionSearch show inherited methods in API reference :pr:651
• Fixed improperly formatted code in breaking changes for changelog :pr:655
• Added configuration to treat Sphinx warnings as errors :pr:660
• Removed separate plotting section for pipelines in API reference :pr:657, :pr:665
• Have leads example notebook load S3 files using https, so we can delete s3fs dev dependency :pr:664
• Categorized components in API reference and added descriptions for each category :pr:663
• Fixed Sphinx warnings about BalancedAccuracy objective :pr:669
• Updated API reference to include missing components and clean up pipeline docstrings :pr:689
• Reorganize API ref, and clarify pipeline sub-titles :pr:688
• Add and update preprocessing utils in API reference :pr:687
• Added inheritance diagrams to API reference :pr:695
• Documented which default objective AutoML optimizes for :pr:699
• Create seperate install page :pr:701
• Include more utils in API ref, like import_or_raise :pr:704
• Add more color to pipeline documentation :pr:705

Testing Changes

• Matched install commands of check_latest_dependencies test and it's GitHub action :pr:578
• Added Github app to auto assign PR author as assignee :pr:477
• Removed unneeded conda installation of xgboost in windows checkin tests :pr:618
• Update graph tests to always use tmpfile dir :pr:649
• Changelog checkin test workaround for release PRs: If 'future release' section is empty of PR refs, pass check :pr:658

Breaking Changes

• Pipelines will now no longer take an objective parameter during instantiation, and will no longer have an objective attribute.
• fit() and predict() now use an optional objective parameter, which is only used in binary classification pipelines to fit for a specific objective.
• score() will now use a required objectives 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 and ConfusionMatrix plot methods via Auto(*).plot have been removed by :pr:615 and are replaced by roc_curve and confusion_matrix in evamlm.pipelines.plot_utils in :pr:704
• normalize_confusion_matrix has been moved to evalml.pipelines.plot_utils :pr:704
• Pipelines _name field changed to custom_name
• Pipelines supported_problem_types field is removed because it is no longer necessary :pr:678
• Updated argument order of objectives' objective_function to align with sklearn :pr:698
• pipeline.feature_importance_graph has been renamed to pipeline.graph_feature_importances in :pr:700
• Removed unsupported MSLE objective :pr:704

v0.8.0

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

Fixes

Changes

• Undo version cap in XGBoost placed in #402 and allowed all released of XGBoost #407
• 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
  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
  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

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

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

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

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

Breaking Changes

• Python 3.5 will not be actively supported.

v0.6.0

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

Fixes

• Lower botocore requirement #235
• Fixed decision_function calculation for FraudCost objective #254
• Fixed return value of Recall metrics #264

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

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

Testing Changes

• Added support for testing on Windows with CircleCI #226
• Added support for doctests #233

Breaking Changes

• The fit() method for AutoClassifier and AutoRegressor has been renamed to search().
• AutoClassifier has been renamed to AutoClassificationSearch
• AutoRegressor has been renamed to AutoRegressionSearch
• AutoClassificationSearch.results and AutoRegressionSearch.results now is a dictionary with pipeline_results and search_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.