v0.24.0

v0.24.0 May. 4, 2021

Enhancements

• Added date_index as a required parameter for TimeSeries problems #2217
• Have the OneHotEncoder return the transformed columns as booleans rather than floats #2170
• Added Oversampler transformer component to EvalML #2079
• Added Undersampler to AutoMLSearch, as well as arguments _sampler_method and sampler_balanced_ratio #2128
• Updated prediction explanations functions to allow pipelines with XGBoost estimators #2162
• Added partial dependence for datetime columns #2180
• Update precision-recall curve with positive label index argument, and fix for 2d predicted probabilities #2090
• Add pct_null_rows to HighlyNullDataCheck #2211
• Added a standalone AutoML search method for convenience, which runs data checks and then runs automl #2152
• Make the first batch of AutoML have a predefined order, with linear models first and complex models last #2223

Fixes

• Fixed partial dependence not respecting grid resolution parameter for numerical features #2180
• Enable prediction explanations for catboost for multiclass problems #2224

Changes

• Deleted baseline pipeline classes #2202
• Reverting user specified date feature PR #2155 until pmdarima installation fix is found #2214
• Updated pipeline API to accept component graph and other class attributes as instance parameters. Old pipeline API still works but will not be supported long-term. #2091
• Removed all old datasplitters from EvalML #2193
• Deleted make_pipeline_from_components #2218

Documentation Changes

• Renamed dataset to clarify that its gzipped but not a tarball #2183
• Updated documentation to use pipeline instances instead of pipeline subclasses #2195
• Updated contributing guide with a note about GitHub Actions permissions #2090
• Updated automl and model understanding user guides #2090

Testing Changes

• Use machineFL user token for dependency update bot, and add more reviewers #2189

Breaking Changes

• All baseline pipeline classes (BaselineBinaryPipeline, BaselineMulticlassPipeline, BaselineRegressionPipeline, etc.) have been deleted #2202
• Updated pipeline API to accept component graph and other class attributes as instance parameters. Old pipeline API still works but will not be supported long-term. Pipelines can now be initialized by specifying the component graph as the first parameter, and then passing in optional arguments such as custom_name, parameters, etc. For example, BinaryClassificationPipeline(["Random Forest Classifier"], parameters={}). #2091
• Removed all old datasplitters from EvalML #2193
• Deleted utility method make_pipeline_from_components #2218

v0.23.0

v0.23.0 Apr. 21, 2021

Enhancements

• Refactored EngineBase and SequentialEngine api. Adding DaskEngine #1975.
• Added optional engine argument to AutoMLSearch #1975
• Added a warning about how time series support is still in beta when a user passes in a time series problem to AutoMLSearch #2118
• Added NaturalLanguageNaNDataCheck data check #2122
• Added ValueError to partial_dependence to prevent users from computing partial dependence on columns with all NaNs #2120
• Added standard deviation of cv scores to rankings table #2154

Fixes

• Fixed BalancedClassificationDataCVSplit, BalancedClassificationDataTVSplit, and BalancedClassificationSampler to use minority:majority ratio instead of majority:minority #2077
• Fixed bug where two-way partial dependence plots with categorical variables were not working correctly #2117
• Fixed bug where hyperparameters were not displaying properly for pipelines with a list component_graph and duplicate components #2133
• Fixed bug where pipeline_parameters argument in AutoMLSearch was not applied to pipelines passed in as allowed_pipelines #2133
• Fixed bug where AutoMLSearch was not applying custom hyperparameters to pipelines with a list component_graph and duplicate components #2133

Changes

• Removed hyperparameter_ranges from Undersampler and renamed balanced_ratio to sampling_ratio for samplers #2113
• Renamed TARGET_BINARY_NOT_TWO_EXAMPLES_PER_CLASS data check message code to TARGET_MULTICLASS_NOT_TWO_EXAMPLES_PER_CLASS #2126
• Modified one-way partial dependence plots of categorical features to display data with a bar plot #2117
• Renamed score column for automl.rankings as mean_cv_score #2135

Documentation Changes

• Fixed conf.py file #2112
• Added a sentence to the automl user guide stating that our support for time series problems is still in beta. #2118
• Fixed documentation demos #2139
• Update test badge in README to use GitHub Actions #2150

Testing Changes

• Fixed test_describe_pipeline for pandas v1.2.4 #2129
• Added a GitHub Action for building the conda package #1870 #2148

Breaking Changes

• Renamed balanced_ratio to sampling_ratio for the BalancedClassificationDataCVSplit, BalancedClassificationDataTVSplit, BalancedClassficationSampler, and Undersampler #2113
• Deleted the "errors" key from automl results #1975
• Deleted the raise_and_save_error_callback and the log_and_save_error_callback #1975
• Fixed BalancedClassificationDataCVSplit, BalancedClassificationDataTVSplit, and BalancedClassificationSampler to use minority:majority ratio instead of majority:minority #2077

v0.22.0

v0.22.0 Apr. 7, 2021

Enhancements

• Added a GitHub Action for linux_unit_tests#2013
• Added recommended actions for InvalidTargetDataCheck, updated _make_component_list_from_actions to address new action, and added TargetImputer component #1989
• Updated AutoMLSearch._check_for_high_variance to not emit RuntimeWarning #2024
• Added exception when pipeline passed to explain_predictions is a Stacked Ensemble pipeline #2033
• Added sensitivity at low alert rates as an objective #2001
• Added Undersampler transformer component #2030

Fixes

• Updated Engine's train_batch to apply undersampling #2038
• Fixed bug in where Time Series Classification pipelines were not encoding targets in predict and predict_proba #2040
• Fixed data splitting errors if target is float for classification problems #2050
• Pinned docutils to <0.17 to fix ReadtheDocs warning issues #2088

Changes

• Removed lists as acceptable hyperparameter ranges in AutoMLSearch #2028
• Renamed "details" to "metadata" for data check actions #2008

Documentation Changes

• Catch and suppress warnings in documentation #1991 #2097
• Change spacing in start.ipynb to provide clarity for AutoMLSearch #2078
• Fixed start code on README #2108

v0.21.0

v0.21.0 Mar. 24, 2021

Enhancements

• Changed AutoMLSearch to default optimize_thresholds to True #1943
• Added multiple oversampling and undersampling sampling methods as data splitters for imbalanced classification #1775
• Added params to balanced classification data splitters for visibility #1966
• Updated make_pipeline to not add Imputer if input data does not have numeric or categorical columns #1967
• Updated ClassImbalanceDataCheck to better handle multiclass imbalances #1986
• Added recommended actions for the output of data check's validate method #1968
• Added error message for partial_dependence when features are mostly the same value #1994
• Updated OneHotEncoder to drop one redundant feature by default for features with two categories #1997
• Added a PolynomialDetrender component #1992

Fixes

• Updated binary classification pipelines to use objective decision function during scoring of custom objectives #1934

Changes

• Removed data_checks parameter, data_check_results and data checks logic from AutoMLSearch #1935
• Deleted random_state argument #1985
• Updated Woodwork version requirement to v0.0.11 #1996

Documentation Changes

Testing Changes

• Removed build_docs CI job in favor of RTD GH builder #1974
• Added tests to confirm support for Python 3.9 #1724
• Changed build_conda_pkg job to use latest_release_changes branch in the feedstock. #1979

Breaking Changes

• Changed AutoMLSearch to default optimize_thresholds to True #1943
• Removed data_checks parameter, data_check_results and data checks logic from AutoMLSearch. To run the data checks which were previously run by default in AutoMLSearch, please call DefaultDataChecks().validate(X_train, y_train) or take a look at our documentation for more examples. #1935
• Deleted random_state argument #1985

v0.20.0

v0.20.0 Mar. 10, 2021

Enhancements

• Added a GitHub Action for Detecting dependency changes #1933
• Create a separate CV split to train stacked ensembler on for AutoMLSearch #1814
• Added a GitHub Action for Linux unit tests #1846
• Added DataCheckAction class and DataCheckActionCode enum #1896
• Updated Woodwork requirement to v0.0.10 #1900
• Added BalancedClassificationDataCVSplit and BalancedClassificationDataTVSplit to AutoMLSearch #1875
• Update default classification data splitter to use downsampling for highly imbalanced data #1875
• Updated describe_pipeline to return more information, including id of pipelines used for ensemble models #1909
• Added utility method to create list of components from a list of DataCheckAction #1907
• Updated validate method to include a action key in returned dictionary for all DataCheckand DataChecks #1916
• Aggregating the shap values for predictions that we know the provenance of, e.g. OHE, text, and date-time. #1901
• Improved error message when custom objective is passed as a string in pipeline.score #1941
• Added score_pipelines and train_pipelines methods to AutoMLSearch #1913
• Added score_batch and train_batch abstact methods to EngineBase and implementations in SequentialEngine #1913

Fixes

• Removed CI check for check_dependencies_updated_linux #1950
• Added metaclass for time series pipelines and fix binary classification pipeline predict not using objective if it is passed as a named argument #1874
• Fixed stack trace in prediction explanation functions caused by mixed string/numeric pandas column names #1871
• Fixed stack trace caused by passing pipelines with duplicate names to AutoMLSearch #1932
• Fixed AutoMLSearch.get_pipelines returning pipelines with the same attributes #1958

Changes

• Reversed GitHub Action for Linux unit tests until a fix for report generation is found #1920
• Updated add_results in AutoMLAlgorithm to take in entire pipeline results dictionary from AutoMLSearch #1891
• Updated ClassImbalanceDataCheck to look for severe class imbalance scenarios #1905
• Deleted the explain_prediction function #1915
• Removed HighVarianceCVDataCheck and convered it to an AutoMLSearch method instead #1928

Documentation Changes

• Updated model_understanding.ipynb to demo the two-way partial dependence capability #1919

Testing Changes

Breaking Changes

• Deleted the explain_prediction function #1915
• Removed HighVarianceCVDataCheck and convered it to an AutoMLSearch method instead #1928
• Added score_batch and train_batch abstact methods to EngineBase. These need to be implemented in Engine subclasses #1913

v0.19.0

v0.19.0 Feb. 24, 2021

Enhancements

• Added a GitHub Action for Python windows unit tests #1844
• Added a GitHub Action for checking updated release notes #1849
• Added a GitHub Action for Python lint checks #1837
• Adjusted explain_prediction, explain_predictions and explain_predictions_best_worst to handle timeseries problems. #1818
• Updated InvalidTargetDataCheck to check for mismatched indices in target and features #1816
• Updated Woodwork structures returned from components to support Woodwork logical type overrides set by the user #1784
• Updated estimators to keep track of input feature names during fit() #1794
• Updated visualize_decision_tree to include feature names in output #1813
• Added is_bounded_like_percentage property for objectives. If true, the calculate_percent_difference method will return the absolute difference rather than relative difference #1809
• Added full error traceback to AutoMLSearch logger file #1840
• Changed TargetEncoder to preserve custom indices in the data #1836
• Refactored explain_predictions and explain_predictions_best_worst to only compute features once for all rows that need to be explained #1843
• Added custom random undersampling sampler for classification #1857
• Updated OutliersDataCheck implementation to calculate the probability of having no outliers #1855
• Added Engines pipeline processing API #1838

Fixes

• Changed EngineBase random_state arg to random_seed and same for user guide docs #1889

Changes

• Modified calculate_percent_difference so that division by 0 is now inf rather than nan #1809
• Removed text_columns parameter from LSA and TextFeaturizer components #1652
• Added random_seed as an argument to our automl/pipeline/component API. Using random_state will raise a warning #1798
• Added DataCheckError message in InvalidTargetDataCheck if input target is None and removed exception raised #1866

Testing Changes

• Added back coverage for _get_feature_provenance in TextFeaturizer after text_columns was removed #1842
• Pin graphviz version for windows builds #1847
• Unpin graphviz version for windows builds #1851

Breaking Changes

• Added a deprecation warning to explain_prediction. It will be deleted in the next release. #1860

v0.18.2

v0.18.2 Feb. 10, 2021

Enhancements

• Added uniqueness score data check #1785
• Added "dataframe" output format for prediction explanations #1781
• Updated LightGBM estimators to handle pandas.MultiIndex #1770
• Sped up permutation importance for some pipelines #1762
• Added sparsity data check #1797
• Confirmed support for threshold tuning for binary time series classification problems #1803

Fixes

Changes

Documentation Changes

• Added section on conda to the contributing guide #1771
• Updated release process to reflect freezing main before perf tests #1787
• Moving some prs to the right section of the release notes #1789
• Tweak README.md. #1800
• Fixed back arrow on install page docs #1795

Testing Changes

v0.18.1

v0.18.1 Feb. 1, 2021

Enhancements

• Added graph_t_sne as a visualization tool for high dimensional data #1731
• Added the ability to see the linear coefficients of features in linear models terms #1738
• Added support for scikit-learn v0.24.0 #1733
• Added support for scipy v1.6.0 #1752
• Added SVM Classifier and Regressor to estimators #1714 #1761

Fixes

• Addressed bug with partial_dependence and categorical data with more categories than grid resolution #1748
• Removed random_state arg from get_pipelines in AutoMLSearch #1719
• Pinned pyzmq at less than 22.0.0 till we add support #1756
• Remove ProphetRegressor from main as windows tests were flaky #1764

Changes

• Updated components and pipelines to return Woodwork data structures #1668
• Updated clone() for pipelines and components to copy over random state automatically #1753
• Dropped support for Python version 3.6 #1751
• Removed deprecated verbose flag from AutoMLSearch parameters #1772

Documentation Changes

• Add Twitter and Github link to documentation toolbar #1754
• Added Open Graph info to documentation #1758

Testing Changes

Breaking Changes

• Components and pipelines return Woodwork data structures instead of pandas data structures #1668
• Python 3.6 will not be actively supported due to discontinued support from EvalML dependencies.
• Deprecated verbose flag is removed for AutoMLSearch #1772

v0.18.0

v0.18.0 Jan. 26, 2021

Enhancements

• Added RMSLE, MSLE, and MAPE to core objectives while checking for negative target values in invalid_targets_data_check #1574
• Added validation checks for binary problems with regression-like datasets and multiclass problems without true multiclass targets in invalid_targets_data_check #1665
• Added time series support for make_pipeline #1566
• Added target name for output of pipeline predict method #1578
• Added multiclass check to InvalidTargetDataCheck for two examples per class #1596
• Support graphviz 0.16 #1657
• Enhanced time series pipelines to accept empty features #1651
• Added KNN Classifier to estimators. #1650
• Added support for list inputs for objectives #1663
• Added support for AutoMLSearch to handle time series classification pipelines #1666
• Enhanced DelayedFeaturesTransformer to encode categorical features and targets before delaying them #1691
• Added 2-way dependence plots. #1690
• Added ability to directly iterate through components within Pipelines #1583

Fixes

• Fixed inconsistent attributes and added Exceptions to docs #1673
• Fixed TargetLeakageDataCheck to use Woodwork mutual_information rather than using Pandas' Pearson Correlation #1616
• Fixed thresholding for pipelines in AutoMLSearch to only threshold binary classification pipelines #1622 #1626
• Updated load_data to return Woodwork structures and update default parameter value for index to None #1610
• Pinned scipy at < 1.6.0 while we work on adding support #1629
• Fixed data check message formatting in AutoMLSearch #1633
• Addressed stacked ensemble component for scikit-learn v0.24 support by setting shuffle=True for default CV #1613
• Fixed bug where Imputer reset the index on X #1590
• Fixed AutoMLSearch stacktrace when a cutom objective was passed in as a primary objective or additional objective #1575
• Fixed custom index bug for MAPE objective #1641
• Fixed index bug for TextFeaturizer and LSA components #1644
• Limited load_fraud dataset loaded into automl.ipynb #1646
• add_to_rankings updates AutoMLSearch.best_pipeline when necessary #1647
• Fixed bug where time series baseline estimators were not receiving gap and max_delay in AutoMLSearch #1645
• Fixed jupyter notebooks to help the RTD buildtime #1654
• Added positive_only objectives to non_core_objectives #1661
• Fixed stacking argument n_jobs for IterativeAlgorithm #1706
• Updated CatBoost estimators to return self in .fit() rather than the underlying model for consistency #1701
• Added ability to initialize pipeline parameters in AutoMLSearch constructor #1676
• Make AutoMLSearch pipelines pickle-able #1721

Changes

• Added labeling to graph_confusion_matrix #1632
• Rerunning search for AutoMLSearch results in a message thrown rather than failing the search, and removed has_searched property #1647
• Changed tuner class to allow and ignore single parameter values as input #1686
• Capped LightGBM version limit to remove bug in docs #1711
• Removed support for np.random.RandomState in EvalML #1727

Documentation Changes

• Update Model Understanding in the user guide to include visualize_decision_tree #1678
• Updated docs to include information about AutoMLSearch callback parameters and methods #1577
• Updated docs to prompt users to install graphiz on Mac #1656
• Added infer_feature_types to the start.ipynb guide #1700
• Added multicollinearity data check to API reference and docs #1707

Testing Changes

Breaking Changes

• Removed has_searched property from AutoMLSearch #1647
• Removed support for np.random.RandomState in EvalML. Rather than passing np.random.RandomState as component and pipeline random_state values, we use int random_seed #1727

v0.17.0

v0.17.0 Dec. 29, 2020

Enhancements

• Added save_plot that allows for saving figures from different backends #1588
• Added LightGBM Regressor to regression components #1459
• Added visualize_decision_tree for tree visualization with decision_tree_data_from_estimator and decision_tree_data_from_pipeline to reformat tree structure output #1511
• Added DFS Transformer component into transformer components #1454
• Added MAPE to the standard metrics for time series problems and update objectives #1510
• Added graph_prediction_vs_actual_over_time and get_prediction_vs_actual_over_time_data to the model understanding module for time series problems #1483
• Added a ComponentGraph class that will support future pipelines as directed acyclic graphs #1415
• Updated data checks to accept Woodwork data structures #1481
• Added parameter to InvalidTargetDataCheck to show only top unique values rather than all unique values #1485
• Added multicollinearity data check #1515
• Added baseline pipeline and components for time series regression problems #1496
• Added more information to users about ensembling behavior in AutoMLSearch #1527
• Add woodwork support for more utility and graph methods #1544
• Changed DateTimeFeaturizer to encode features as int #1479
• Return trained pipelines from AutoMLSearch.best_pipeline #1547
• Added utility method so that users can set feature types without having to learn about Woodwork directly #1555
• Added Linear Discriminant Analysis transformer for dimensionality reduction #1331
• Added multiclass support for partial_dependence and graph_partial_dependence #1554
• Added TimeSeriesBinaryClassificationPipeline and TimeSeriesMulticlassClassificationPipeline classes #1528
• Added make_data_splitter method for easier automl data split customization #1568
• Integrated ComponentGraph class into Pipelines for full non-linear pipeline support #1543
• Update AutoMLSearch constructor to take training data instead of search and add_to_leaderboard #1597
• Update split_data helper args #1597
• Add problem type utils is_regression, is_classification, is_timeseries #1597
• Rename AutoMLSearch data_split arg to data_splitter #1569

Fixes

• Fix Windows CI jobs: install numba via conda, required for shap #1490
• Added custom-index support for reset-index-get_prediction_vs_actual_over_time_data #1494
• Fix generate_pipeline_code to account for boolean and None differences between Python and JSON #1524 #1531
• Set max value for plotly and xgboost versions while we debug CI failures with newer versions #1532
• Undo version pinning for plotly #1533
• Fix ReadTheDocs build by updating the version of setuptools #1561
• Set random_state of data splitter in AutoMLSearch to take int to keep consistency in the resulting splits #1579
• Pin sklearn version while we work on adding support #1594
• Pin pandas at <1.2.0 while we work on adding support #1609
• Pin graphviz at < 0.16 while we work on adding support #1609

Changes

• Reverting save_graph #1550 to resolve kaleido build issues #1585
• Update circleci badge to apply to main #1489
• Added script to generate github markdown for releases #1487
• Updated dependencies to fix ImportError: cannot import name 'MaskedArray' from 'sklearn.utils.fixes' error and to address Woodwork and Featuretool dependencies #1540
• Made get_prediction_vs_actual_data() a public method #1553
• Updated Woodwork version requirement to v0.0.7 #1560
• Move data splitters from evalml.automl.data_splitters to evalml.preprocessing.data_splitters #1597
• Rename "# Testing" in automl log output to "# Validation" #1597

Documentation Changes

• Added partial dependence methods to API reference #1537
• Updated documentation for confusion matrix methods #1611

Testing Changes

• Set n_jobs=1 in most unit tests to reduce memory #1505

Breaking Changes

• Updated minimal dependencies: numpy>=1.19.1, pandas>=1.1.0, scikit-learn>=0.23.1, scikit-optimize>=0.8.1
• Updated AutoMLSearch.best_pipeline to return a trained pipeline. Pass in train_best_pipeline=False to AutoMLSearch in order to return an untrained pipeline.
• Pipeline component instances can no longer be iterated through using Pipeline.component_graph #1543
• Update AutoMLSearch constructor to take training data instead of search and add_to_leaderboard #1597
• Update split_data helper args #1597
• Move data splitters from evalml.automl.data_splitters to evalml.preprocessing.data_splitters #1597
• Rename AutoMLSearch data_split arg to data_splitter #1569